high redshift quasar abundances and environments...
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HIGH REDSHIFT QUASAR ABUNDANCES AND ENVIRONMENTSCONNECTING BLACK HOLE AND HOST GALAXY EVOLUTION
By
LEAH EILEEN SIMON
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2011
c© 2011 Leah Eileen Simon
2
For my parents, Rich and Mary
3
ACKNOWLEDGMENTS
I would like to thank my fellow graduate students for their continued support and
commiseration, especially my officemates Audra Hernandez and Justin Schafer. The
friendships we forged will last a lifetime. My friends Dr. Joanna Levine and Dr. Paola
Rodrıguez Hidalgo remind me to take things one step at a time. I am thankful to my
family and friends for believing in me. I would especially like to acknowledge Dr. Kim
Venn for challenging me to pursue this degree and my advisor, Dr. Fred Hamann, for
seemingly endless comments that keep me thinking critically. My most sincere gratitude
and thanks go out to Jose, who keeps me honest and whose help and support have
been invaluable.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
CHAPTER
1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 METALLICITY AND FAR-INFRARED LUMINOSITY OF HIGH REDSHIFTQUASARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2.1 Composite Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.2.2 Emission Line Flux Ratios . . . . . . . . . . . . . . . . . . . . . . . 272.2.3 Metallicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.2.4 Absorption Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 THE ORIGINS OF A RICH ABSORPTION LINE COMPLEX IN A QUASARAT REDSHIFT 3.45 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.1 Observations and Data Reduction . . . . . . . . . . . . . . . . . . . . . . 433.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.1 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.2.2 Line Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463.2.3 Ionization and Abundances . . . . . . . . . . . . . . . . . . . . . . 52
3.3 Notes on Individual Systems . . . . . . . . . . . . . . . . . . . . . . . . . 543.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.1 Location of the Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.4.2 Outflow Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.4.3 Metallicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4 A CENSUS OF NARROW C IV ABSORPTION LINES IN 24 QUASARS ATREDSHIFTS 1.9 < z <4.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 884.1.1 Sample Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 884.1.2 Observations and Data Reduction . . . . . . . . . . . . . . . . . . 89
5
4.2 Line Identification and Fitting . . . . . . . . . . . . . . . . . . . . . . . . . 914.2.1 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.2.2 Covering Fraction and Gaussian Line Fitting . . . . . . . . . . . . . 93
4.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974.3.1 Absorption Line Classes . . . . . . . . . . . . . . . . . . . . . . . . 984.3.2 NALs per Quasar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014.3.3 Basic Parameter Distributions . . . . . . . . . . . . . . . . . . . . . 1024.3.4 Intrinsic Fractions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.3.4.1 Versus REW and logN . . . . . . . . . . . . . . . . . . . . 1034.3.4.2 Versus b value . . . . . . . . . . . . . . . . . . . . . . . . 1044.3.4.3 Versus velocity shift . . . . . . . . . . . . . . . . . . . . . 105
4.4 Notes on Individual Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1054.4.1 Rich NAL Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . 1054.4.2 High-Velocity Outflow NALs . . . . . . . . . . . . . . . . . . . . . . 1084.4.3 Broad Outflow Features . . . . . . . . . . . . . . . . . . . . . . . . 109
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1104.5.1 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1104.5.2 Selection Effects and Comparisons to Other Work . . . . . . . . . 1124.5.3 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5 METALLICITY OF NARROW ABSORPTION LINES IN 19 QUASARS AT REDSHIFTS2.7 < z < 4.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1485.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5.2.1 Continuum Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1485.2.2 Line Identification and Gaussian Fits . . . . . . . . . . . . . . . . . 1495.2.3 Ionization and Abundances . . . . . . . . . . . . . . . . . . . . . . 152
5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1565.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
6 SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . 170
APPENDIX A: C IV ABSORPTION LINE MEASUREMENTS . . . . . . . . . . . . . 174
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
BIOGRAPHICAL SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
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LIST OF TABLES
Table page
2-1 Quasar sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2-2 Metallicity from emission line flux ratios. . . . . . . . . . . . . . . . . . . . . . . 37
3-1 Individual absorption lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3-2 Metal abundance and total H column density. . . . . . . . . . . . . . . . . . . . 72
4-1 NAL quasar sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4-2 Percentage of quasars and numbers of NALs. . . . . . . . . . . . . . . . . . . . 130
4-3 Average values by class. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
4-4 Average component b-values by Cf class. . . . . . . . . . . . . . . . . . . . . . 132
4-5 Percentages and numbers of NALs per velocity range. . . . . . . . . . . . . . . 133
A-1 C IV NALs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
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LIST OF FIGURES
Figure page
2-1 Quasar FIR luminosities, absolute B magnitude and bolometric luminosities. . 35
2-2 Normalized composite spectra. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2-3 Gaussian fits for Lyα, N V and C IV for each normalized composite spectrum. . 38
3-1 Region of the spectrum of J1023+5142 with C IV absorption. . . . . . . . . . . 69
3-2 Region of Lyα forest spectrum with the continuum fit over-plotted. . . . . . . . 69
3-3 Line profiles in the normalized spectrum J1023+5142 for system 1. . . . . . . . 73
3-4 Line profiles in the normalized spectrum J1023+5142 for system 2. . . . . . . . 74
3-5 Line profiles in the normalized spectrum J1023+5142 for systems 3 and 4. . . 75
3-6 Line profiles in the normalized spectrum J1023+5142 for systems 5 and 6. . . 76
3-7 Line profiles in the normalized spectrum J1023+5142 for system 7. . . . . . . . 77
3-8 Line profiles in the normalized spectrum J1023+5142 for system 8. . . . . . . . 78
3-9 Line profiles in the normalized spectrum J1023+5142 for system 9. . . . . . . . 79
3-10 τ -predicted line profiles for systems 5 and 6. . . . . . . . . . . . . . . . . . . . 80
3-11 Point-by-point covering fractions for C IV and N V in system 6 and the centerof system 5 with step size of three resolution elements. . . . . . . . . . . . . . 81
3-12 Point-by-point covering fractions for N V in system 8 with step size of four resolutionelements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4-1 The region of C IV absorption in the Keck-HIRES spectrum of J1008+3623and Magellan+MIKE spectrum J1307+1230. . . . . . . . . . . . . . . . . . . . 121
4-2 The region of C IV absorption in the Magellan-MIKE spectra of J1020+1039and J1326+0743. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4-3 The region of C IV absorption in the VLT-UVES and Magellan-MIKE spectraof BR 1202-0725 and J1430+0149. . . . . . . . . . . . . . . . . . . . . . . . . 125
4-4 The region of C IV absorption in the Keck-HIRES and Magellan-MIKE spectraof J1633+1411 and J1326+0743. . . . . . . . . . . . . . . . . . . . . . . . . . . 126
4-5 The region of C IV absorption in the Keck-HIRES spectra of J0933+733, J0351-1034,J0351-1034 and J1008+3623. . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4-6 NALs per quasar. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
8
4-7 Total number of NALs versus velocity shift from the quasar systemic. . . . . . . 129
4-8 Measured parameters versus velocity shift for components and systems. . . . 131
4-9 Intrinsic fraction versus REW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
4-10 Intrinsic fraction versus component N. . . . . . . . . . . . . . . . . . . . . . . . 133
4-11 Intrinsic fraction based on Cf only versus b-value. . . . . . . . . . . . . . . . . 134
4-12 Intrinsic fraction versus velocity for components. . . . . . . . . . . . . . . . . . 135
4-13 Intrinsic fraction versus velocity for systems. . . . . . . . . . . . . . . . . . . . . 135
4-14 The region of C IV absorption in the VLT+UVES spectrum of Q 0249-222. . . . 136
4-15 The region of C IV absorption in the VLT+UVES spectrum of PKS 2044-168. . 137
4-16 Point-by-point analysis of covering fraction for PKS 2044-168 system near1800 km s−1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4-17 The region of C IV absorption in the Keck-HIRES spectrum of J1008+3623. . . 139
4-18 Point-by-point analysis of covering fraction for J1008+3623 systems in a regionof rich C IV absorption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4-19 The region of C IV absorption in the Keck-HIRES spectrum of J1633+1411. . . 141
4-20 Point-by-point analysis of covering fraction for J1633+1411 components at-441, -6650, -7007 and -8335 km s−1. . . . . . . . . . . . . . . . . . . . . . . . 142
4-21 Point-by-point analysis of covering fraction for BR 0714-6455, J1633+1411and J1225+4831 high velocity systems. . . . . . . . . . . . . . . . . . . . . . . 143
4-22 Point-by-point analysis of covering fraction for J1307+1230, Q0401-1711 andQ 0249-222 high velocity systems. . . . . . . . . . . . . . . . . . . . . . . . . . 144
4-23 Broad absorption in C IV for two quasars in the sample. . . . . . . . . . . . . . 145
5-1 Continuum fits for the quasar J0714-6455, emission redshift zem = 4.46. . . . . 161
5-2 Continuum fits for the quasar J0749+4152 emission redshift zem = 3.11. . . . . 162
5-3 Gaussian fits for the quasar J0714-6455 emission redshift zem = 4.46. . . . . . 163
5-4 Gaussian fits for the quasar J0749+4152 emission redshift zem = 3.11. . . . . . 164
5-5 Gaussian fits for the quasar J1341-0115 emission redshift zem = 2.70. . . . . . 165
5-6 NAL metallicity versus velocity shift. . . . . . . . . . . . . . . . . . . . . . . . . 166
5-7 NAL metallicity versus velocity shift for different redshifts. . . . . . . . . . . . . 167
9
5-8 C IV and H I column densities for class A and B NALs. . . . . . . . . . . . . . . 168
5-9 H I versus C IV/H I column density ratios for class A and B NALs. . . . . . . . . 169
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Abstract of Dissertation Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Doctor of Philosophy
HIGH REDSHIFT QUASAR ABUNDANCES AND ENVIRONMENTSCONNECTING BLACK HOLE AND HOST GALAXY EVOLUTION
By
Leah Eileen Simon
May 2011
Chair: Fred HamannMajor: Astronomy
I examine the evolutionary relationship between quasar host galaxies and
supermassive black holes (SMBHs). Current models predict an evolutionary sequence
where SMBHs become active as quasars some time after major star formation episodes
in their host galaxies . The quasars may in turn produce outflows that quench the host
galaxy star formation. However, concrete mechanisms that explain the interactions
between the host galaxy and the SMBH remain poorly understood. I constrain this host
galaxy-SMBH relationship through the examination of host galaxy star formation and
quasar outflow phenomena. Specifically, I measure the quasar gas-phase metallicity,
which indicates the past level of star formation in the host galaxies, using emission and
absorption lines in quasar spectra. I examine, in particular, the nature and origin of
narrow absorption lines (NALs), which sometimes form in quasar outflows, and which
provide valuable information on the gas kinematics, column densities and ionizations in
a variety of quasar environments.
Current quasar host galaxy-SMBH evolution scenarios suggest that host galaxy
star formation rates should typically decrease across quasar lifetimes. I examine
the relationship between past and ongoing star formation in quasar hosts by, for the
first time, comparing emission line metallicity in redshift 2–4 quasars to far-infrared
luminosity, which indicates the ongoing star formation rate in the host galaxy. I measure
super-solar metallicities, regardless of the ongoing star formation rates.
11
I measure covering fractions and profile widths to determine the origins of every
individual NAL in a comprehensive NAL survey, the first of its kind, covering the full
range of quasar environments for 24 quasars at redshifts 2–4.7. I estimate that 20%
of all these NALs are intrinsic to the quasar environment, and up to 77% of these
likely formed in quasar outflows. I measure metallicities for the NALs that I find to be
intrinsic, and find a surprising range of metallicities of 0.03 Z¯ ≤ Z ≤∼ 20 Z¯. The
high metallicities are similar to those derived for other quasars at similar redshifts and
luminosities and are consistent with current evolution scenarios. The wide range in
metallicities suggests that the data provide the first ever compilation of gas phase
metallicities across the full range of near-quasar environments.
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CHAPTER 1INTRODUCTION
Quasars are among the brightest objects in the Universe, with luminosities
L > 1045 erg s−1. They are understood to be powered by accretion onto supermassive
black holes (Mass M > 106 M¯) at the centers of massive galaxies. The brightest
quasars reside in the most massive galaxies.
The current quasar model consist of an accreting supermassive black hole and an
accretion disk surrounded by clouds of gas. The accretion disk produces continuum
emission at ultraviolet (UV) and X-ray wavelengths, while the surrounding clouds of
gas produce broad emission lines (BELs). Further out from this region are more gas
clouds that produce absorption lines. Broad absorption lines (BALs) are present in about
10% of quasar spectra and are evidence for massive outflows of gas from the accretion
disk, which help dissipate angular momentum from the gas accreted onto the black hole
(Turnshek, 1988). Narrow absorption lines (NALs) are also present in at least 30–40% of
quasar spectra, and may be ubiquitous (Weymann et al., 1979; Steidel, 1990).
Quasars are not found in the local Universe, with the closest known located at
redshifts z ∼ 0.1 and are known to be located as far away as redshift z ∼ 7 or higher.
The spectrum of every quasar is unique, with emission lines characteristic of several
elements, including C IV(λλ1548,1551) and H I(λ1215,1025), shifted in wavelength by
the high redshift of the quasar. In particular, ultraviolet (UV) wavelengths are shifted
to the optical (visible) region of the spectrum. The quasars we observe with large
ground-based optical telescopes existed when the Universe was a fraction of its current
age. They provide a laboratory to study the formation of the massive galaxies and
black holes that populate the Universe today. Redshifts z ∼ 2–4 represent a particularly
interesting era in the history of the Universe, when massive host galaxies are thought
to have grown rapidly and formed most of their stars, possibly through merger events
(Perez-Gonzalez et al., 2008; Hopkins et al., 2008).
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There is a well-known correlation between the mass of the black hole and the mass
of the spheroid of its host galaxy, first characterized by the black hole mass-spheroid
velocity dispersion relation (Gebhardt et al., 2000; Merritt & Ferrarese, 2001; Tremaine
et al., 2002). This correlation implies that there is an evolutionary relationship between
the black hole and the host galaxy (Marconi & Hunt, 2003; Haring & Rix, 2004; Shields
et al., 2006). Quasars represent episodes of rapid supermassive black hole (SMBH)
growth and probably a unique period in the early evolution of galaxies. They may directly
follow a major galaxy merger (Perez-Gonzalez et al., 2008; Hopkins et al., 2008) or a
big blowout of gas and dust. Feedback from quasar outflows may play an important role
in the way host galaxies evolve. The mechanisms for this evolutionary relationship have
been examined, but remain poorly understood.
The emission and absorption spectra of quasars are important tools for understanding
the interactions between host galaxies and quasars. The BELs form very close to
the quasar central engine (d < 0.1 pc), and therefore provide a direct connection
to the quasar properties. The temperatures, kinematics and especially chemical
compositions of the gas provide valuable information about the environment very
close to the quasar. The absorption lines, both BALs and NALs provide different
information about the quasar environment, as they tend to form further from the
central source. Although BALs are known to form in outflows, they provide only limited
information, due to their broad nature. NALs, on the other hand, can form in a variety of
environments. Some are directly related to quasars and form in quasar outflows. These
are particularly interesting for understanding the interactions between the quasar and
its surroundings because they provide a means to obtain information about kinematics,
chemical abundances and other physical characteristics of the gas in the near-quasar
environment. Furthermore, outflows could be partially responsible for the influence of
black holes on their host galaxies (Springel et al., 2005b,a). The location, mass and
kinematics of the outflows is needed to determine the extent of this influence.
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NALs can form in many environments, other than outflows. We refer to NALs in
quasar outflows and other environments within the host galaxy of the quasar, including
starburst outflows, merger remnants in the host galaxy halo, and other halo gas as
“intrinsic” to the quasar environment, whereas gas forming in other unrelated galaxies
coincidentally in the line of sight to the quasar are “intervening”. NALs contain several
characteristics that are used to distinguish their origin as either intrinsic (outflows or
other gas originating near the quasar) or intervening. Some common diagnostics of
intrinsic gas include 1) variability over timescales of months to years, 2) partial covering
of the continuum source, indicating proximity to the continuum source, 3) broad, smooth
profiles indicative of gas in an outflow, and secondarily, 4) high metallicities greater than
solar.
The chemical abundances of the quasar environment provide insight into the
relationship between the host galaxy and the black hole. Elements other than hydrogen
and helium form in stars, thus, measuring the abundance of other elements is akin to
measuring a fossil record of star formation in the region. The abundance of carbon
is relatively easy to determine, as C IV (three times ionized carbon) is very abundant
in both BELs and NALs, and is conveniently shifted from UV wavelengths to optical
wavelengths for quasars located at high redshifts during the era of galaxy formation. The
relative abundance of carbon to nitrogen is also a particularly useful measure of the star
formation histories of these host galaxies. Nitrogen is a secondary element, forming as
part of the CNO cycle in stars, and is therefore sensitive to the amount of previous star
formation in a gas. Therefore, carbon to nitrogen ratios are sensitive to the selective
enrichment of nitrogen as a secondary element and/or to the decreasing temperatures
and increasing metal-line saturations that occur in metal-rich gas (Hamann & Ferland,
1999; Dietrich et al., 2003; Warner et al., 2004; Nagao et al., 2006).
Chapters 2, 3 and 4 of this thesis are self-contained journal articles. Chapters 2
and 3 are published in the Monthly Notices of the Royal Astronomical Society refereed
15
journal (Simon & Hamann, 2010a,b). Chapter 4 has been prepared in collaboration
with M. Pettini, and has not yet been submitted for publication. The Chapters are
chronological.
Chapter 2 presents a stand-alone study of quasar BEL abundances compared to
on-going star formation rates in the host galaxies. The on-going star formation rate is
ascertained from the far-infrared (FIR) luminosities of the galaxies. We combine 34
medium resolution rest-frame UV spectra into three FIR luminosity bins and measure
abundance ratios of N V/C IV and Si IV+O IV]/C IV for the resulting three composite
spectra. One evolutionary scenario for host galaxies and quasars postulates that
the quasar epoch follows an epoch of global star formation in the host galaxy, and
possibly causes the end of the star formation (Hopkins et al., 2008). Thus, systems in an
earlier stage of evolution might exhibit higher on-going star formation rates, and lower
abundances (if the global star formation period is the main enricher of the near-quasar
gas) (Sanders et al., 1988; Kauffmann & Haehnelt, 2000; Granato et al., 2004).
The main body of this thesis, Chapters 3 through 5, is centered around an
observing campaign of 24 high resolution quasar spectra with redshifts of 1.9 < z < 4.6.
We include quasars in the sample that contain NALs with velocities close to the quasar
systematic velocity. This unique, high-redshift sample is well-suited for examining the
nature of gas in absorption near the quasar environment, specifically in NAL quasar
outflows. The statistical excess of NALs near the quasar emission line redshift indicates
that a significant fraction of these absorption lines have some physical relationship
to the quasars (Weymann et al., 1979; Foltz et al., 1986; Anderson et al., 1987;
Vestergaard, 2003; Nestor et al., 2008; Wild et al., 2008). The intrinsic absorption lines
are interesting because they form in quasar outflows or other gaseous environments
near the quasar during a unique stage of massive galaxy evolution. Specifically, NALs
provide information on the kinematics, column densities, and ionization state of the gas,
that is, the physical nature of the gas. NALs can also yield information on the chemical
16
abundances, which then constrain the star formation histories and chemical ’maturity’
of the host environments and, therefore, constrain the models of host galaxy-black hole
evolution.
In Chapter 3 we measure the kinematics and chemical abundances of the NALs
within 10,000 km s−1 of the quasar systemic velocity in an individual quasar from
this sample. Quasar J1023+5142 has a rich complex of narrow C IV absorption. We
determine the origin and abundances of each individual NAL and conclude that all or
most of the gas is in a related complex, likely in a quasar outflow.
In Chapter 4 we present the results of a survey of all the C IV absorption lines in
our full sample of 24 quasars. We examine the incidence of intrinsic gas, specifically
that which is likely formed in quasar outflows, as well as the physical characteristics
of the gas. We measure covering fractions, column densities, and line widths, which
we use to determine the origin of each absorption line. Our results are consistent with
recent surveys of C IV NAL absorption lines, e.g. Vestergaard (2003); Misawa et al.
(2007); Nestor et al. (2008); Wild et al. (2008). We find several examples of C IV NALs
forming in rich complexes, such as that discussed in Chapter 3. Furthermore, we find
that although NAL outflows tend to be within 12,000 km s−1 of the quasar redshift, they
can be found at much higher velocities as well. The intrinsic NALs tend to be broader
and stronger than the intervening NALs.
Finally, in Chapter 5, we measure [C/H] abundances for those intrinsic NALs with
ionization constraints and hydrogen column densities in the full quasar sample studied
in Chapter 4. We create a crude map of chemical abundance versus velocity shift from
the quasar systemic velocity. This sample of intrinsic NALs contains stronger NALs than
similar studies of intervening gas, but weaker NALs than most studies of intrinsic gas
(Petitjean et al., 1994; Simcoe, 2004; Arav et al., 2007; Schaye et al., 2007). We find
that NALs with nearly solar to super-solar abundances are located at all velocity shifts,
regardless of the redshift of the quasar. We also find a population of intrinsic NALs with
17
lower abundances than most intrinsic NAL studies have previously found (less than
solar), but still higher than abundances found by most intervening studies (Aguirre et al.,
2004; D’Odorico et al., 2004; Simcoe, 2004; Ganguly et al., 2006; Arav et al., 2007;
Schaye et al., 2007).
Chapter 6 summarizes our results and conclusions.
18
CHAPTER 2METALLICITY AND FAR-INFRARED LUMINOSITY OF HIGH REDSHIFT QUASARS
Numerous studies have shown a strong correlation between supermassive black
hole (SMBH) mass and host galaxy mass over broad mass ranges, implying that there
is a close evolutionary relationship between them (Gebhardt et al., 2000; Merritt &
Ferrarese, 2001; Tremaine et al., 2002; Marconi & Hunt, 2003; Haring & Rix, 2004;
Shields et al., 2006). Luminous quasars at high redshifts, which represent the most
massive SMBHs, are experiencing vigorous accretion of a significant portion of the final
SMBH mass. This accretion activity is believed to be triggered by global processes
(galaxy mergers, interactions or perhaps secular evolution) that also trigger major
episodes of star formation in the massive host galaxies, which are also rapidly being
assembled at high redshifts. Therefore, the processes of SMBH formation and growth
resulting in the quasar phenomenon are directly linked to the birth of massive galaxies
(Haehnelt et al., 1998; Richstone et al., 1998; Omont et al., 2001, 2003; Beelen et al.,
2006; Cox et al., 2006). However, the nature of the relationship between quasars and
galaxy formation is not well understood.
It is widely believed that strong interactions between gas rich galaxies can result
in ultra-luminous infrared galaxies (ULIRGs), defined by LIR >1012 L¯, which have star
formation rates (SFRs) of > 100 M¯ yr−1 (Houck et al., 1985; Omont et al., 2001, 2003;
Flores et al., 2004; Cox et al., 2005; Beelen et al., 2006; Daddi et al., 2007; Cao et al.,
2008). A fraction of ULIRGs have been found to contain dust-enshrouded quasars
(Sanders & Mirabel, 1996; Lonsdale et al., 2006). There is evidence at both low and
high redshift that these embedded quasars are precursors to optically luminous quasars.
For example, Cao et al. (2008) find that low redshift quasars with IR luminosities in the
Reprinted with permission from Simon L. E., Hamann F., 2010a, MNRAS, 407,1826.
19
ULIRG range fall between optically selected PG quasars and ULIRGs (starbursts) for
a variety of mid-IR spectroscopic indicators of starburst and active galactic nucleus
(AGN) contributions including: polycyclic aromatic hydrocarbon (PAH) luminosities,
fine structure emission line strengths, silicate absorption strengths, spectral slope and
mid-IR color indices. These findings suggest that IR luminous quasars could represent
an intermediate stage between a dominant starburst and a dominant AGN phase.
Quasars with high SFRs therefore may be at an intermediate stage between an
embedded quasar in a star forming galaxy and a visible quasar in a galaxy where
most star formation has ceased (Sanders et al. (1988)). At low redshifts, numerous
star formation indicators in quasar host galaxies are seen, e.g. high IR luminosities,
PAH emission, strong (sub)-mm emission and strong CO emission (Hao et al., 2005;
Schweitzer et al., 2006; Farrah et al., 2007a,b; Netzer et al., 2007). At high redshifts
(z ∼ 2–6), observations at sub-mm and mm wavelengths (rest-frame far-infrared (FIR)
to sub-mm, depending on wavelength and redshift range) of optically luminous quasars
suggest that up to 30% of quasars also fall within the ULIRG range, similar to the IR
quasars studied by Cao et al. (2008) (Carilli et al., 2001; Omont et al., 2001, 2003;
Cox et al., 2005; Beelen et al., 2006; Hao et al., 2008). Coppin et al. (2008) compare
dynamical, gas and SMBH masses of ten z ≈ 2 sub-mm detected quasars and z ≈ 2
sub-mm galaxies (SMGs), which are high redshift counterparts to ULIRGs, though less
extreme with more evenly distributed star formation instead of a localized starburst
(Menendez-Delmestre et al., 2009). They find that the fainter half of their quasar sample
could likely be ‘transition objects’ between SMGs and luminous quasars, based on
their SMG-like surface densities and their proximity to the local MBH/Msph relation, since
luminous quasars tend to lie above this relation and typical SMGs tend to lie below.
The highest redshift sub-mm–selected source currently known has also been observed
to posses similar stellar and gas masses to this z ≈ 2 sample of transition objects, as
well as a small AGN contribution (Coppin et al., 2009). These quasars eventually may
20
produce enough energy from accretion to effectively blow out the surrounding gas,
halting star formation and clearing the view to an optically bright quasar before halting
their own growth (Wyithe & Loeb, 2003; Di Matteo et al., 2005; Hopkins et al., 2008).
This regulation of star formation by the accreting SMBH could naturally produce the
observed black hole–galaxy mass correlation, and different stages of the process should
present different SFRs, with initially high SFRs declining as the AGN becomes more
dominant, culminating in a phase of strong SMBH accretion and little or no ongoing star
formation (Kauffmann & Haehnelt, 2000; Granato et al., 2004).
FIR luminosities can be used to determine the star formation rates in quasar host
galaxies. The FIR emission is due to dust heated either by star formation or quasar
emission (see Haas et al. (2003) for discussion). Lutz et al. (2007, 2008) find evidence
that FIR emission in quasar host galaxies at all redshifts is caused by dust heated by
star formation and not the quasar. The strength of PAH emission, which is found almost
exclusively in star forming regions, tightly corresponds to the strength of FIR emission
in the same hosts (Dale et al., 2001; Calzetti et al., 2007; Lutz et al., 2008). Beelen
et al. (2006) measure FIR emission from six high redshift quasars and derive FIR to
radio spectral indexes consistent with local star forming galaxies without AGN. The FIR
emission is seemingly uncontaminated by hotter dust potentially heated by the AGN.
These results along with others (see for example, Efstathiou & Rowan-Robinson (1995);
Serjeant & Hatziminaoglou (2009)) further substantiate the claim that the FIR luminosity
in quasars is dominated by star formation and not by the AGN.
FIR luminosity traces ongoing star formation, but past star formation also can
be observed indirectly by measuring chemical abundances. Several studies have
found that high-redshift quasars typically have metallicities greater than or equal to
solar metallicity in the broad emission line region (BLR), which requires significant
previous star formation in the host (Hamann & Ferland, 1999; Dietrich et al., 2003;
Warner et al., 2004; Nagao et al., 2006). These BLR studies rely on emission line ratios
21
such as N V/C IV and Si IV+O IV]/C IV that are sensitive to the selective enrichment of
nitrogen as a secondary element and/or to the decreasing temperatures and increasing
metal-line saturations that occur in metal-rich gas. The main result for typically solar
or higher metallicities near quasars has been corroborated by independent studies
of the narrow emission lines (Groves et al., 2006; Nagao et al., 2006) and narrow
absorption lines (Hamann & Ferland, 1999; D’Odorico et al., 2004; Gabel et al., 2006;
Simon & Hamann, 2010b) in quasar spectra. The metal-rich BLR result is true even for
the highest redshifts studied, e.g. Pentericci et al. (2002); Jiang et al. (2007); Juarez
et al. (2009), with redshifts out to z = 6.4. There is no known change in metallicity with
redshift (Matsuoka et al. (2009) and references above). Simple chemical evolution
models for quasars and elliptical galaxies find that galactic centers tend to be more
metal-rich than their halos, and a centralized starburst can enrich the galactic center to
super solar abundances in a short time (≤ 108 yr) (Friaca & Terlevich, 1998; Hamann
& Ferland, 1999; Granato et al., 2001; Hamann et al., 2002; Granato et al., 2004;
Hamann et al., 2007; Juarez et al., 2009). These models, combined with the consistently
super-solar gas abundances observed in quasar environments, imply that quasars tend
to emerge after or near the end of (potentially) short centralized star formation epochs.
If the quasar phase emerges when star formation is on the decline, less advanced
environments might have higher SFRs and lower abundances (Georgakakis et al.,
2009).
The absorption lines in quasar spectra provide additional information about outflows
that might be related to the blowout of gas from the host galaxies, and perhaps, about
the gaseous remnants of recent galaxy mergers. These absorption lines may be more
common in quasars with higher SFRs if mergers and interactions trigger the star
formation as in ULIRGs (Weymann et al., 1991; Becker et al., 2000; Richards, 2001;
Rupke et al., 2005a; Georgakakis et al., 2009). Quasar ‘associated’ C IV absorption
lines (AALs), near the emission redshifts with velocity widths less than 500 km s−1
22
and broad absorption lines (BALs), with velocity widths greater than a few thousand
km s−1, are examples of these potential merger, interaction and outflow signatures. In
systems with recent mergers, remnants from the interaction may manifest as a greater
incidence of low-velocity (< 2000 km s−1) AALs. About 25% of bright quasars contain
AALs with rest equivalent widths (REWs) of >0.3 A, and around 10% of quasars have
BALs (Ganguly et al., 2001; Vestergaard, 2003; Misawa et al., 2003; Trump et al., 2006;
Nestor et al., 2008; Wild et al., 2008; Gibson et al., 2008; Rodrıguez Hidalgo et al.,
2010a). A higher incidence of AALs or BALs may occur in the quasars with higher
SFRs if these quasars have more recently experienced an interaction and/or there is a
progression in quasar outflow characteristics with time.
In this chapter, we present an exploratory observational study examining whether
SFR in the host galaxies correlates with metallicity in the near-quasar environment. We
measure metallicity in the quasar BLR from the rest frame ultraviolet (UV) spectrum and
estimate galactic SFRs from the FIR luminosities.
The data and analysis are described in § 2.1 and § 2.2. The results and discussion
are presented in § 2.3, with a brief summary in § 2.4. We adopt cosmological parameters
H0 = 70 km s−1 Mpc−1, m = 0.3, and � = 0.7 throughout this work.
2.1 Data
We select quasars for this study from the sample observed with MAMBO at IRAM
at 1.2 mm by Carilli et al. (2001) and Omont et al. (2001, 2003) and SCUBA at JCMT
at 850 µm by Isaak et al. (2002), McMahon et al. (1999) and Priddey et al. (2003) and
all compiled by Hao et al. (2008). The quasars all were selected to be optically bright
with absolute B-band Magnitude MB < -26.1 for the Carilli et al. objects, MB < -27.0 for
the Omont et al. objects and MB < -27.51 for the McMahon et al. (1999); Isaak et al.
(2002) and Priddey et al. (2003) objects. Carilli et al. (2001) observed a representative
1 They use an Einstein deSitter cosmology, M = 1, � = 0, H0 = 50 km s−1.
23
sample of 41 out of the more than 100 quasars with z ≥ 3.6 found as part of the Sloan
Digital Sky Survey (SDSS) Galactic Cap and Southern Equatorial Stripe survey.
Omont et al. (2001, 2003) observed a random selection of 97 radio quiet, optically
luminous sources from the multicolor Palomar Digital Sky Survey available from G.
Djorgovski’s web page2 with 3.9 < z < 4.5 and the Veron-Cetty & Veron (2000) catalog
with 1.8 < z < 2.8. McMahon et al. (1999) selected a small sample of 6 bright z > 4
radio quiet quasars from the APM survey (Storrie-Lombardi et al., 1996). Isaak et al.
(2002) selected a larger sample of the 76 most UV luminous z ≥ 4 radio quiet quasars
known at the time of observation, and Priddey et al. (2003) selected a complimentary
sample of 57 z ≥ 2 quasars from various large surveys.
We cross-reference this sample with the optical quasar spectra in the SDSS
data-release 6, finding 116 objects with available spectra. The SDSS spectra from data
release 6 have resolution R = λ/δλ ∼ 2000 and wavelength coverage λ = 3800–9200 A
(Adelman-McCarthy et al., 2008). We consider only those objects with redshifts between
2.17 and 4.75, compatible with SDSS spectral coverage of the full 1200–1600 A
rest-frame wavelength range. Spectra missing regions within the specified wavelength
range are further excluded from the analysis. Five spectra with very low signal to noise
ratios (S/N) also are rejected. The final sample consists of 34 optical SDSS spectra with
a range of sub-mm brightnesses and a redshift range of 2.2 < z < 4.6.
The MAMBO 1.2 mm and SCUBA 850 µm observations correspond to 160–400 µm
rest wavelengths, depending on quasar redshift. We follow the prescription in Hao et al.
(2008) to convert from observed flux into FIR luminosity at 60 µm, L60 = λLλ(60 µm),
in which we assume that a gray-body spectrum describes the rest frame FIR spectral
energy distribution (SED) with a dust temperature of 41 K and dust emissivity index
2 http://astro.caltech.edu/˜george/z4.qsos
24
of 1.95, as detailed in Priddey & McMahon (2001). We find agreement with Hao et al.
(2008) to within 10%.
The L60, absolute B-band magnitude (MB) and bolometric luminosity (Lbol) for
each object are shown in Figure 2-1, and listed in Table 2-1. The FIR luminosity,
log(L60/L¯), ranges from ≤ 12.1 to 13.4, is dominated by star formation and falls roughly
within the ULIRG range. We estimate the star formation rate of each quasar host from
L60, corrected to exclude the small quasar contribution by assuming the hosts follow
the same regression line as typical quasars for Lbol vs. L60 as shown in Figure 1 of
Hao et al. (2008). This corrected L60 is then used in Hao et al.’s equation 2, which is
derived from the Kennicutt star formation rate law (Kennicutt, 1998), and the estimated
star formation rates for each object are also listed in Table 2-1 . We convert the MB
from the observation papers to our adopted cosmology, and obtain MB spanning the
range of -26.7 to -29.4, which corresponds to log(Lbol) of 47.2 to 48.3 log(erg s−1).
Lbol measures the quasar black hole accretion luminosity, and is estimated using a
bolometric correction factor of 9.74 applied to the monochromatic continuum luminosity
λLλ(4400 A) (∼ MB) following Vestergaard (2004).
We separate the sample into three bins based on L60 such that each bin contains a
similar number of objects: FIR bright quasars with log(L60/L¯) ≥ 13.17, FIR intermediate
quasars with 12.8 < log(L60/L¯) ≤ 13.1 and FIR faint quasars with log(L60/L¯) < 12.75,
in which the FIR faint luminosities are upper limits. Any objects with log(L60/L¯) upper
limits above 12.75 were not included in the study. The FIR-bright and FIR-faint bins each
span the redshift range from ∼ 2.2 to ∼ 4.4. The FIR bright bin also includes one object
with z ∼ 4.6, and the FIR intermediate bin spans a smaller redshift range from 3.7 to 4.4.
The divisions between the L60 bins are represented in Figure 2-1 by horizontal dashed
lines and average values in each bin for Lbol and L60 are denoted by filled triangles.
These average values are listed in Table 2-2 for each luminosity bin in columns 2 and 3,
25
along with the corresponding star formation rates in column 4, and several abundance
indicators, discussed in §’s 2.2.3 and 2.3.
2.2 Analysis
2.2.1 Composite Spectra
To make comparisons between different L60, we create one composite spectrum
from the SDSS spectra comprising each of the three FIR bins in the sample described
in § 2.1. To create the composites, we begin by shifting each spectrum to its rest
wavelength using the redshifts provided by SDSS (SubbaRao et al., 2002). We manually
inspect each individual spectrum and remove absorption features by interpolating across
the affected regions. The presence of narrow absorption lines and their relationship
to L60 is discussed in § 2.2.4 below. Then we average together the spectra in each
bin to create the final composites. To ensure that no single spectrum is dominating
the composite we also compute the median spectra for each bin, compare them to
the average spectra used throughout the rest of the analysis, and confirm that the two
composite types are well-matched.
Our analysis is limited to the spectral region between Lyα 1216 A and C IV 1550 A.
These limits are imposed by the incomplete spectral coverage at longer wavelengths
and by suppression in the Lyα forest starting in the blue wing of the Lyα emission line
and extending to shorter wavelengths. Before normalizing the composite spectra, we
visually compare the continuum slopes of the three composites. We find no significant
trend for reddening with FIR luminosity in the continuum slopes of the composite
spectra. Differences between the composite slopes are negligible compared to the
dispersion of slopes among the individual quasars making up each composite.
We fit a power-law continuum to the FIR-intermediate composite spectrum
using wavelength regions devoid of absorption or emission, 25 A wide and centered
at λ1460, and λ1770, following Warner et al. (2003). Unfortunately, the FIR-faint and
FIR-bright composites have incomplete spectral coverage past ∼ λ1700 A, so we fit
26
the continuum using the available wavelength region around λ1460 A, plus the region
around λ1335–1355 A, which is also used by Juarez et al. (2009), and which we
determine by visual inspection to be devoid of absorption or any detectable emission.
The normalized average composite spectra are shown in Figure 2-2.
2.2.2 Emission Line Flux Ratios
We measure several quasar broad emission line fluxes to calculate line flux
ratios, which we then use to estimate the gas phase metallicity in the near-quasar
environment. Flux ratios are measured for the emission lines that have been shown
by e.g. Hamann et al. (2002); Juarez et al. (2009), to give good abundance estimates
within the limited wavelength range from λ1216 A to λ1600 A: N V λ1240/C IV λ1549
and Si IV λ1397+O IV] λ1402/C IV λ1549. These emission lines are labeled in
Figure 2-2. Other ratios such as N III] λ1750/O III] λ1664, N V λ1240/He II λ1640 or
N III λ991/C III λ977 are too weak given the S/N in the spectrum and/or they fall in a
poorly characterized region of the spectrum. The measured line flux ratios are listed in
columns 5 and 7 of Table 2-2.
Because the N V emission is strongly blended with the Lyα emission, which is
itself significantly degraded by absorption in the Lyα forest, C IV is the only strong,
non-blended emission line in the spectra. Thus, the C IV emission lines are fit with
Gaussians and used as templates for the other emission lines. The fits to C IV and the
scaled fits to Lyα and N V are shown for the three composites in Figure 2-3. The fits are
performed using GATORPLOT, an IDL program written by C. Warner3 , and are carried
out using the smallest number of Gaussians possible to provide a good match to the
data: one broad and one narrow component for each of the two doublet lines at λ1548
and λ1551 A. The two Gaussians comprising the λ1548 emission line fit are held at
fixed full width at half maximum (FWHM) and are scaled in total flux at the fixed central
3 http://www.astro.ufl.edu/˜warner/GatorPlot/
27
wavelength of the Lyα λ1216 A and N V λ1239, 1243 A doublet emission to match these
line strengths. Because C IV is known to be blue-shifted relative to Lyα and the nominal
quasar redshift by ≈ 310 km s−1 (Tytler & Fan, 1992), the locations of the N V and Lyα
centroids are shifted from their laboratory values by this amount relative to C IV.
To accurately determine the N V flux, the red Lyα wing beneath the N V emission
must be characterized. To do this precisely, we scale the C IV fit to align with the
non-absorbed region of Lyα, which in some cases is quite small (≤ 20 A). The scaled
fit may even rise above the data where heavy Lyα forest absorption has eaten into the
emission, as is the case for all of the three composites, or miss the peak of Lyα as is
the case in the FIR faint composite; both cases are clearly shown in Figure 2-3. In order
to better match the Lyα emission peak in the FIR faint composite, we allow the Lyα
Gaussian to be ≈ 15% narrower than the C IV Gaussian. Narrowing the Gaussian for
the Lyα emission line does not have a noticeable effect on the resulting N V strength.
Regardless, a precise match to the Lyα emission line is not required for this analysis,
and does not effect the final N V emission results (see e.g. Baldwin et al. (2003) for
further discussion).
The C IV and N V doublets are fit at their respective fixed separations and with a 3:2
intensity ratio (halfway between the allowed 2:1 and 1:1 intensity ratios) for the shorter
and longer wavelength respectively, as in Baldwin et al. (2003). Possible broadening
in N V is predicted if the N V emission forms at a larger distance from the quasar than
the C IV emission. Peterson et al. (2004) and Peterson (2008) find that N V forms twice
as close to the quasar as C IV, so if the line widths are controlled by virial motions, N V
could be up to ∼ √2 times broader than C IV. Lyα, on the other hand, forms at about
the same distance from the quasar as C IV and should be no broader (and probably
narrower) than C IV (Peterson et al., 2004; Peterson, 2008). Therefore, the N V profiles
we adopt, which have the same FWHM’s as C IV, may be narrower than the actual N V
FWHM’s, while the Lyα FWHM could be slightly broader. We estimate the uncertainties
28
in the N V line strengths due to uncertainty in the continuum placement by repeating
the line fits with different reasonable continuum heights. We also include estimates
of N V line strength uncertainties due to blending with Lyα and the poorly defined N V
FWHM by repeating the line fits with a range of different N V and Lyα FWHM’s. The
overall uncertainties, combining these effects, are less than a factor of 1.5, so that the
measurement errors in the N V/C IV ratios are also less than a factor of 1.5.
The Si IV+O IV] emission line is weaker than the C IV and N V emission lines in
the composite spectra, however it also is isolated from other emission. This isolation
means that measuring the equivalent width of the emission without fitting, using
IRAF4 , produces accurate fluxes. We confirm the accuracy of the equivalent width
measurements by comparing the previously measured fluxes from Gaussian fits to C IV
to the fluxes from C IV equivalent widths, and find compatible results. We measure
Si IV+O IV] and C IV equivalent widths to calculate the Si IV+O IV]/C IV line flux ratios
listed in Table 2-2. To estimate uncertainties in the Si IV+O IV] line strengths, we vary the
equivalent width measurements by using a range of continuum heights and wavelength
cutoffs for the edge of the emission lines. The overall uncertainties in the Si IV+O IV]
line strengths are less than a factor of 1.2, so that the measurement errors in the
Si IV+O IV]/C IV ratios are also less than a factor of 1.2.
2.2.3 Metallicity
We convert the N V/C IV flux ratios into metallicities using the theoretical relationship
in which nitrogen abundance increases relative to carbon as metallicity increases
because of secondary enrichment (CNO nucleosynthesis) processes, and determine
the average metallicity for each L60 composite (Shields, 1976; Hamann & Ferland, 1993,
1999). The correlation is characterized by Hamann et al. (2002) with recent updates to
4 IRAF is the Image Reduction and Analysis Facility, supported by NOAO and AURAInc.
29
the solar abundance ratios by Dhanda et al. (2007). We find supersolar metallicities for
all three composite spectra, as listed in column 6 of Table 2-2.
Similar characterizations for the Si IV+O IV]/C IV flux ratio-metallicity correlation
are performed by Nagao et al. (2006), using the older solar abundances also used by
Hamann et al. (2002). We apply the latest corrections for the solar abundance ratios for
this characterization and determine that the metallicities are also supersolar according
to this ratio, as shown in column 8 of Table 2-2.
There is no significant trend among the three composites in the line ratios, based
both on our measurements of these ratios and on a visual inspection of the stacked
composites in Figure 2-2, and correspondingly there is no significant trend in metallicity.
The average metallicities across the three composites are Z ∼ 9.5 Z¯ for the N V/C IV
ratio and 4.2 Z¯ for the Si IV+O IV]/C IV ratio.
We note that the metallicities inferred from the two line ratios can differ by as much
as a factor of ∼ 2 in the same spectrum. This is typical of the dispersion found between
different line ratios in other studies, and it might be a good indication of the theoretical
uncertainties (Dietrich et al., 2003; Nagao et al., 2006; Hamann & Simon, 2010). We
compare our metallicities to the metallicities in the Lbol range 1047 to 1048 erg s−1
sampled by a large emission line study by Warner et al. (2004), who find metallicities
of Z ∼ 3–6 Z¯, broadly consistent with the metallicities found in this sample. We also
note that the results based on N V/C IV (and N V/He II) tend to be higher than other line
ratios, and thus our best guess at the metallicity in the current sample overall would
be near the lower end of the range 4–9 Z¯ (Hamann et al., 2002; Baldwin et al., 2003;
Nagao et al., 2006). The relative metallicities between the three composites, which
smooth over object-to-object scatter, are robust for differences greater than a factor
of 2–3, based on the uncertainties in the line ratio measurements plus the theoretical
uncertainties, and are therefore useful for spotting strong metallicity trends among the
different L60 bins.
30
2.2.4 Absorption Lines
We inspect each SDSS spectrum used to make the composites and count the
number of C IV AALs with REW > 0.3 A per spectrum. We find that 17/34 quasars have
at least one AAL and 6/34 have absorption too broad to be classified as an AAL, yet
too narrow to be classified as a BAL, with widths more than twice the thermal width and
velocity dispersion expected for unrelated gas in the line of sight, which is indicative of
gas forming in an outflow (Bahcall et al., 1967; Young et al., 1982). These statistics are
broadly consistent with previous work, given the small numbers involved in this study
(Vestergaard, 2003; Trump et al., 2006; Nestor et al., 2008; Wild et al., 2008; Gibson
et al., 2008; Rodrıguez Hidalgo et al., 2010a). AALs and outflow lines appear in each of
the three L60 bins, with no significant differences in their occurrence fractions among the
bins that would indicate a trend with L60, particularly given the small number of objects in
each bin.
2.3 Discussion
The average line flux ratios N V/C IV ≈ 1.35 and Si IV+O IV]/C IV ≈ 0.33 across the
three composites, as shown in columns 5 and 7 of Table 2-2, correspond to average
metallicities of Z ∼ 9.5 and 4.2 Z¯ respectively, shown in columns 6 and 8 of Table 2-2.
There is no significant trend in metallicity with L60 in this sample. We note that the FIR
bright bin has the most luminous average Lbol, while the FIR faint and intermediate
bins have roughly equal, and less luminous average Lbol. This difference in Lbol could
appear in the relative metallicities among the bins because more luminous quasars tend
to be more metal-rich than less luminous quasars (Hamann & Ferland, 1999; Warner
et al., 2004; Shemmer et al., 2004). However, the absence of a corresponding gradient
in the metallicity is not surprising given the relatively high average of the total Lbol, the
range in average Lbol among the composites of less than 0.4 dex (Table 2-2), which
corresponds to a range in metallicity of no more than 0.2 dex in Warner et al. (2004),
31
and the sensitivity of the data to trends in metallicity greater than ∼ 0.3 dex between the
three L60 bins.
The fact that the data show no significant metallicity evolution with changing SFR
(L60) could be due to the small sample sizes being affected by object-to-object scatter
or by the small dynamic range in L60, with values that are all within the luminosity/SFR
range of powerful ULIRGs.
Absorption features such as AALs and BALs and reddened continua may be more
numerous among quasar environments that have undergone a recent merger or are
participating in the blowout of gas that is revealing the visible quasar (Becker et al.,
2000; Richards, 2001; D’Odorico et al., 2004; Georgakakis et al., 2009). Although
these are optically selected quasars chosen to be bright in the rest-frame UV and
strongly reddened sources would be excluded, there could still be a more subtle trend in
sub-samples by L60. We do not find a trend in the number of AALs per quasar or in the
continuum slope per L60 bin. Each bin appears to have roughly the same percentage of
quasars with AALs. The absence of a trend could be due simply to the small numbers
in our sample. Or, if the consistency across bins is real, it could indicate that there is no
progression in quasar outflow characteristics with changing SFRs. A larger sample size
is needed to resolve this question.
The overall conclusion from this and other studies is that quasars appear to be
metal-rich at all redshifts, with only small variations due to SMBH mass and luminosity
dependencies (Warner et al., 2004; Shemmer et al., 2004; Nagao et al., 2006; Jiang
et al., 2007; Juarez et al., 2009). The consistently high metallicities suggest that the star
formation producing these FIR-bright quasars is not the star formation that determines
the metallicity of the BLR. Instead, the BLR gas must have been enriched prior to
these FIR-producing star formation episodes, possibly shortly after a merger or some
other star formation trigger when the starburst first began. There are no significant
differences among the L60 bins in the outflow fraction or in the amount of debris leftover
32
from mergers in the form of absorption, which might correlate with L60 if this is indeed
an evolutionary sequence. This scenario is consistent with models of galaxy and quasar
evolution, which predict a lack of metallicity evolution. The models also find that quasar
activity, though caused by a disruption of some sort, is significantly delayed after the
disruption event and most of the star formation and enrichment in the host is complete
even as the quasar phase first begins to emerge (Di Matteo et al., 2005; Hopkins et al.,
2008; Li et al., 2008). The FIR-bright quasars may be in some sense less mature than
the FIR-faint quasars, however, this is not manifest in significant metallicity evolution
over the timescales probed by this sample, which are shorter than a quasar lifetime.
The reality of quasar-host galaxy formation could also be more complicated than
any simple monotonic sequence in which high SFRs correlate cleanly with lower
metallicities and an earlier stage of evolution. Veilleux et al. (2009) suggest a ‘softer’
ULIRG-quasar evolution paradigm with more scatter in the path of an individual galaxy’s
evolution from ULIRG to quasar. They measure neon abundances of Z ∼ 2.9 Z¯ in
the nuclear regions of ULIRGs. If these high metallicities are ubiquitous in ULIRGs,
we will never observe metal-poor quasars, regardless of their stage of evolution. Other
plausible scenarios suggest that AGN and star formation activity in galaxies may be
episodic in nature (Di Matteo et al., 2008). Thus, as a quasar emerges at the tail end
of a star-forming phase, there is no guarantee that its BLR metallicity is linked to this
recent and/or ongoing star formation phase, because several previous episodes of star
formation and AGN activity could already have occurred and enriched the BLR gas to
supersolar values.
An alternative interpretation is provided by the works of Dave & Oppenheimer
(2007) and Finlator & Dave (2008). They postulate a scenario where a star-forming
galaxy could quickly reach an equilibrium metallicity early in its formation history. Higher
mass galaxies would have higher abundances, but for a given galaxy, the abundance
would not change significantly after an initial balance of in-flowing metal-poor and
33
out-flowing metal-rich gas was reached. The lack of BLR metallicity evolution with star
formation rate found in this study is consistent with these objects having previously
reached an equilibrium metallicity, unaffected by the observed ongoing star formation.
2.4 Summary
We create composite rest-frame UV spectra for a sample of 34 quasars observed
in the FIR and measure N V/C IV and Si IV+O IV]/C IV flux ratios. We convert these
flux ratios into a metallicity for each of three L60 bins, which we interpret as SFR bins.
We find that all three bins have supersolar metallicities of the order 4 to 9 times solar,
and find no metallicity trend with L60. We investigate the amount of outflowing gas, as
manifest by both AALs and somewhat broader absorption lines, in each L60 bin and find
no difference in number between the L60 bins, neither do we find a change in continuum
slope with changing L60, as would be consistent with more dusty star formation in hosts
with higher SFRs. Together, these results suggest that the ongoing star formation in the
host is not responsible for the metal enrichment of the BLR gas. Instead, the BLR gas
must have been enriched before the visible quasar phase. We note that we require a
substantially larger sample size with a broader range in L60 to confirm that these results
are not affected by unusual individual objects or biased by the narrow L60 range.
34
Figure 2-1. Quasar FIR luminosities, absolute B magnitude and bolometric luminosities.Horizontal dashed lines represent the division between the three FIRluminosity bins, crosses are FIR-detected quasars and arrows are upperlimits. Filled triangles are average luminosities for each FIR luminosity bin.
35
Table 2-1. Quasar sample.z log Lbol MB log L60µm SFR
Name log(erg s−1) log L¯ M¯yr−1 ref.a
FIR BrightPSS J1057+4555 4.12 48.1 -29.0 13.2 3720 O01PSS J1347+4956 4.56 47.9 -28.5 13.3 4690 O01J144758.46-005055.4 3.80 47.2 -26.8 13.3 6350 C01J154359.3+535903 2.37 47.9 -28.4 13.3 5550 O03PSS J1248+3110 4.35 47.6 -27.8 13.3 6220 O01PSS J1418+4449 4.28 48.0 -28.8 13.3 5320 O01PSS J0808+5215 4.45 48.1 -28.9 13.3 5350 O01J110610.8+640008 2.19 48.3 -29.4 13.4 4720 O03HS B1140+2711 2.63 48.0 -28.6 13.2 3290 P03J142647.82+002740.4 3.69 47.3 -26.8 13.2 4580 C01FIR IntermediateJ023231.40-000010.7 3.81 47.2 -26.7 12.8 1870 C01PSS J1535+2943 3.99 47.4 -27.3 12.8 1710 O01J012403.78+004432.7 3.81 47.9 -28.4 12.9 1230 C01J025518.58+004847.6 3.97 47.6 -27.8 12.9 1670 C01J015048.83+004126.2 3.67 47.7 -27.9 12.9 1920 C01J141332.35-004909.7 4.14 47.4 -27.3 12.9 2330 C01J025112.44-005208.2 3.78 47.3 -26.9 13.0 2580 C01J111246.30+004957.5 3.92 47.6 -27.7 13.0 2530 C01PSS J1317+3531 4.36 47.6 -27.7 13.1 3330 O01FIR FaintJ015339.61-000910.3 4.20 47.5 -27.4 ≤12.5 ≤430 C01J140554.07-000037.0 3.55 47.5 -27.6 ≤12.7 ≤850 C01J225419.23-000155.0 3.68 47.3 -26.9 ≤12.1 ∼0 C01PSS J1443+5856 4.27 48.0 -28.7 ≤12.4 ∼0 O01J225759.67+001645.7 3.75 47.5 -27.4 ≤12.2 ∼0 C01HS B0808+1218 2.26 47.6 -27.8 ≤12.4 ∼0 P03HS B1111+4033 2.18 47.7 -27.9 ≤12.7 ≤550 P03LBQS B1210+1731 2.54 47.8 -28.1 ≤12.4 ∼0 P03LBQS B1334-0033 2.80 47.6 -27.8 ≤12.6 ≤610 P03BR 1600+0729 4.35 47.8 -28.3 ≤12.7 ≤280 M99PSS J0852+5045 4.20 47.7 -28.0 ≤12.4 ∼0 I02PSS J0957+3308 4.25 47.8 -28.1 ≤12.7 ≤650 I02PSSJ1618+4125 4.21 47.4 -27.2 ≤12.7 ≤990 O01PSS J1633+1411 4.35 47.6 -27.6 ≤12.6 ≤570 O01J142329.98+004138.4 3.76 47.2 -26.6 ≤12.2 ≤180 C01
a O01 Omont et al. (2001), O03 Omont et al. (2003), I02 Isaak et al. (2002), C01Carilli et al. (2001), P03 Priddey et al. (2003), M99 McMahon et al. (1999)
36
Table 2-2. Metallicity from emission line flux ratios.Composite < logLbol > < logL60 > < SFR > Flux Ratio Z/Z¯ Flux Ratio Z/Z¯spectra log(erg s−1) log(erg s−1) M¯yr−1 N V/C IV N V/C IV Si IV+O IV]/C IV Si IV+O IV]/C IV
FIR Faint 47.60 ≤46.07 ≤340 1.2 7.9 0.3 3.0FIR Int. 47.52 46.52 2130 1.4 9.6 0.3 3.0FIR Bright 47.89 46.87 4980 1.5 10.9 0.4 6.5
Figure 2-2. Normalized composite spectra. The FIR-faint (solid line), FIR-intermediate(dashed line) and FIR-bright (dotted line) spectra for the (rest) wavelengthrange from Lyα to C IV are shown. Prominent emission features are labeled.The continuum has been normalized to 1, as described in the text.
37
Figure 2-3. Gaussian fits for Lyα, N V and C IV for each normalized composite spectrum.The Lyα and N V emission lines are shown in the left panels, while the C IVemission lines are shown in the right panels. The vertical scale is normalizedintensity. The top panels shows the FIR faint spectrum, the middle panelsshows the FIR intermediate spectrum and the FIR bright spectrum is shownin the bottom panels. The solid curves are the data and total Gaussian fits,the dashed curves show the individual Gaussian fits for each emission line.The N V and C IV features each have two Gaussians, one for each doubletmember, which are each composed of a narrow and a wide Gaussian (notshown). The error spectra for each region are shown as dot-dashed lines.
38
CHAPTER 3THE ORIGINS OF A RICH ABSORPTION LINE COMPLEX IN A QUASAR AT
REDSHIFT 3.45
Quasars represent episodes of rapid supermassive black hole (SMBH) growth and
probably a unique period in the early evolution of galaxies. They may directly follow a
major merger (Perez-Gonzalez et al., 2008; Hopkins et al., 2008) or a big blowout of
gas and dust. However, the nature of the relationship between SMBH growth and galaxy
formation is not well understood. The tight relationship observed between SMBH mass
and host galaxy bulge mass suggests that the SMBH and host galaxy are interacting
during the growth phases of one or both (Gebhardt et al., 2000; Merritt & Ferrarese,
2001; Tremaine et al., 2002; Marconi & Hunt, 2003; Haring & Rix, 2004; Shields et al.,
2006). Feedback from quasar outflows may be one mechanism for interaction and may
influence the evolution of this relationship. We are using narrow absorption lines (NALs)
in quasar spectra to study quasar outflows and environments across a range of scales.
NALs have full widths at half minimum (FWHMs) less than several hundred km s−1,
and they appear in a variety of ultraviolet (UV) resonance transitions, including
C IV λ1548 and λ1551, N V λ1239 and λ1243, C III λ977, Si IV λ1394 and λ1402
and Lyα λ1216, Lyβ λ1026, Lyγ λ973, Lyδ λ950, and Lyε λ938 (Foltz et al., 1986;
Anderson et al., 1987; Hamann & Sabra, 2004). The first step in using these absorption
lines to study the quasar environment is to determine simply where the lines form. There
are several possibilities, including unrelated (intervening) clouds and galaxies which
happen to lie in the line-of-sight, nearby cluster galaxies and intrinsic clouds within the
quasar host galaxy and its extended environment (Ganguly et al., 2001; Vestergaard,
2003; Trump et al., 2006; Nestor et al., 2008; Wild et al., 2008; Gibson et al., 2008).
Reprinted with permission from Simon L. E., Hamann F., 2010b, MNRAS, 409, 269.
39
The statistical excess of C IV NALs at velocity shifts v > -12,000 km s−1, where
negative v indicates motion towards the observer, indicates that many of these
absorbers are directly related to quasar environments (Weymann et al., 1979; Nestor
et al., 2008; Wild et al., 2008). The excess is largest at v ≥ -1000 km s−1, where
roughly 80% of C IV systems with rest equivalent width REW(λ1548) ≥ 0.3 A have a
quasar-related origin (Nestor et al., 2008). This intrinsic gas can form in quasar-driven
outflows, starburst-driven outflows, merger remnants or ambient gas in the host halos,
or in other galaxy halos in the same galaxy cluster as the quasar. At higher velocities,
the excess can be attributed directly to quasar-driven outflows. In the velocity range
-1000 to -12,000 km s−1, Nestor et al. (2008) estimate that ≥ 43% of C IV NALs with
REW(λ1548) ≥ 0.3 A originate in a quasar outflow.
NALs encompass a wealth of information about the basic properties of quasar
outflows, which we use to gain insights into the outflow physics, acceleration mechanisms
and geometry of the near quasar environment. NALs represent a very different type of
quasar outflow compared to the well-studied much broader and higher velocity broad
absorption lines (BALs), but they might simply be different manifestations of a single
outflow phenomenon viewed at different angles (Ganguly et al., 1999, 2001; Elvis,
2000). The NALs that do not form in outflows probe the gaseous environments of
quasars more generally, and can be used to examine conditions in different host
galaxies environments such as starburst-driven outflows or larger-scale gas distributions
produced by messy mergers.
We are involved in a program to study the location, origin and abundance
information for absorbers in a sample of high redshift quasars. We are particularly
interested in high redshift quasars because z ∼ 2–4 is the cosmic era when host
galaxies are thought to grow rapidly and form most of their stars, possibly through
merger events (Perez-Gonzalez et al., 2008; Hopkins et al., 2008). Choosing redshifts
above z ∼ 2.7–3 also allows us to measure lines at shorter rest-frame wavelengths with
40
ground-based telescopes, including importantly, the H I Lyman series, in order to obtain
more and better constraints on the absorber ionizations, column densities and metal
abundance. We are interested in using abundances to discern rough star formation
histories of quasar host galaxies in order to make inferences about the relationship
between the quasar, the growth of the central black hole, and the evolution of the host
galaxy.
Broad emission lines (BELs) have been used most often to study quasar abundances.
The most reliable results suggest metallicities of at least solar, and up to a few times
solar, which requires significant previous star formation in the host (Hamann & Ferland,
1999; Hamann et al., 2002; Dietrich et al., 2003; Warner et al., 2004; Nagao et al.,
2006; Simon & Hamann, 2010a). The metal-rich BEL result is true even for the highest
redshifts studied, e.g. Pentericci et al. (2002); Jiang et al. (2007); Juarez et al. (2009),
with redshifts out to 6.4. The most reliable results based on BAL column densities
suggest metallicity ranges between solar and ten times solar (Arav et al., 2001).
Previous studies of NALs in low redshift samples have found super-solar metallicities
and highly ionized gas, and have successfully probed several other NAL outflow
characteristics (Hamann & Ferland, 1999; Ganguly et al., 2003; D’Odorico et al.,
2004; Hutsemekers et al., 2004; Ganguly et al., 2006; Gabel et al., 2006). These lower
redshift samples cover a wide range of luminosities, observed in the UV spectral range,
where many useful metal and Hydrogen lines occur.
NALs offer certain advantages in the study of metallicities and other gas characteristics
in the near-quasar environment. Their narrow widths mean the C IV doublets, separated
by 500 km s−1, are resolved. We use resolved absorption line doublets to disentangle
saturation effects, and to obtain accurate line optical depth and column density
measurements. NALs also form in a range of physical locations, providing a more
complete picture of the regions near quasars. Because the NAL methods are completely
41
independent from the BEL methods, requiring only column densities and ionization
corrections, the NAL metallicities provide an independent test of the BEL results.
Here we present results for the luminous quasar J102325.31+514251.0 (hereafter
J1023+5142) at a redshift of zem = 3.447, which is during the peak in quasar activity
and the epoch of rapid galaxy formation. This quasar was chosen for in-depth analysis
here because it was found to contain a rich complex of nine distinct C IV NAL systems at
velocities from -1400 to -6200 km s−1. The density and diversity of lines in this complex
merit special attention. We will argue below that most (or all) of these systems form in a
highly structured quasar-driven outflow.
To interpret the metallicities and other data provided by these NALs, we examine
several diagnostics that can identify intrinsic NALs that form in quasar-driven outflows
(Hamann et al., 1997; Barlow et al., 1997). In particular, 1) variability studies have
found intrinsic absorbers varying on relatively short timescales of months to years,
providing strong evidence for these absorbers belonging to outflows either crossing the
line of sight to the quasar or experiencing changing ionization with the variations in the
continuum emission (Hamann et al., 1997; Barlow et al., 1997; Aldcroft et al., 1997;
Narayanan et al., 2004; Misawa et al., 2007). 2) Detection of partial coverage of the
background light source along the line of sight strongly implies gas forming very near
the source. This phenomenon occurs when the absorbing ’clouds’ are smaller than
the background source, allowing part of the light from the source to reach the observer
unabsorbed. This partial covering is easily detected in multiplets like the C IV doublet
where the optical depth ratio between the two lines is fixed by the oscillator strengths.
When the source is partially covered, some light fills in the bottom of the absorption
line, and makes the apparent optical depth ratio appear different than the real optical
depth ratio. 3) Outflow lines tend to have profiles that are broad and smooth compared
to thermal widths (Hamann & Ferland, 1999; Srianand & Petitjean, 2000; Ganguly et al.,
2006; Schaye et al., 2007). In well studied NALs, these three indicators (variability,
42
partial covering and broad profiles) tend to appear together, which further increases the
probability that the occurrence of an individual indicator accurately predicts an outflow
origin very near the quasar for a given absorption line, see also Hamann & Simon (in
preparation) and references therein. We also note that super-solar metallicities are
consistent with an intrinsic origin for the gas. There are examples of high-metallicity gas
in intervening systems, but not of low-metallicity intrinsic gas (Prochaska et al., 2006;
Schaye et al., 2007).
We describe the data acquisition and reduction in § 3.1, the identification and fitting
of the absorption lines in § 3.2.1 and 3.2.2 and the abundance and ionization analysis
in § 3.2.3. We briefly describe individual absorption line systems in § 3.3. We discuss
the arguments for the locations, probable intrinsic origins and quasar-driven outflow
properties of the gas in § 3.4 and conclude with a summary in § 3.5.
3.1 Observations and Data Reduction
We observed the quasar J1023+5142 on March 29, 2007 with the Keck I HIRESr
Echelle spectrograph as part of an observing campaign to measure spectra of high
redshift quasars with known narrow associated absorption lines. We used an 0”.86 wide
slit for a spectral resolution of R ∼ 40,000 or velocity resolution of ∼ 7 km s−1. Our data
span the wavelength range from 3700 to 8100 A corresponding to 830 to 1820 A in the
quasar rest frame. This spectral range covers a variety of interesting lines, including
rest-frame Ly γ 970 A, C IV 1548, 1551 A and several other lines in the H I Lyman
series down to the Lyman limit at 912 A. We use four exposures totaling 2 hours on the
source. The spectral region from ∼ 3700 to ∼ 4980 A is well-covered with considerable
overlap between Echelle orders at some wavelengths, but above 6540 A there are small
15–40 A gaps between orders (one such gap is apparent in Figure 3-1), and two larger
gaps at 4980–5070 A and 6575–6670 A where the spectrum falls into a physical gap
between detectors.
43
We reduce all the data using the MAKEE HIRES data reduction package. The
spectra are sky background-subtracted and extracted from the 2D frame with a low order
polynomial trace using a white dwarf standard observed the previous night with similar
seeing conditions. We use a Thorium Argon (ThAr) lamp spectrum for wavelength
calibration. The resulting spectra are on a vacuum and heliocentric wavelength scale.
The spectra are not absolute flux calibrated.
We normalize the spectra to unity by fitting a pseudo-continuum to all of the quasar
emission, including the emission lines. The pseudo-continuum is defined as follows:
for regions with few absorption lines, we apply a polynomial fit to the local continuum
in each Echelle order. We accomplish the continuum normalization in crowded regions
where the continuum is affected by significant absorption, e.g. the Lyα forest, by first
averaging together several adjacent spectral orders into a single spectrum. Then, we
visually inspect the region for small sections of continuum not affected by absorption or
obvious noise spikes, and interpolate between these sections, fitting the entire region
with a low order polynomial. Our continuum fit for a region of the Lyα forest containing
the Lyβ and O VI NALs is shown in Figure 3-2. The continuum placement has an
uncertainty of ∼ 10% in the forest and 2–3% at other wavelengths.
3.2 Analysis
3.2.1 Identification
The broad, flat shape of the emission features in the spectrum of J1023+5142
make an accurate emission redshift difficult to determine. The redshift provided by the
SDSS spectrum is zem = 3.447. We estimate the reliability of this value by measuring
the redshifts of the C IV λ1549, C III] λ1909 and Si IV+O IV] λ1398 emission lines using
measurements of their centroids. We shift each centroid respectively by -824, -730 and
+36 km s−1 to correct for known offsets from the nominal quasar redshift ([O III] λ5007
emission), based on measurements by Tytler & Fan (1992) and Shen et al. (2007) for
average quasars, where negative values are blueshifts. The average redshift obtained
44
from these emission lines is zem = 3.429, which is offset from the SDSS value by
�z = 0.018 or ∼ -1200 km s−1. These results and our efforts to measure the inherently
uncertain emission line centroids suggest that the uncertainty of the redshift measured
by SDSS is not more than �zem ≤ 0.02, corresponding to �v ≤ -1350 km s−1. We adopt
the SDSS value throughout the remainder of this chapter.
We identify nine distinct C IV absorption line systems within 6200 km s−1 of the
quasar redshift. We will refer to these as systems 1–9, as indicated in Figure 3-1 and
Table 3-1 below. Other C IV systems are present at -16,800 and -33,800 km s−1 in the
spectrum, but they have narrow widths, complete covering, and blending problems in
the Lyα forest, which, along with their high velocities, make them likely candidates for
intervening gas and exclude them from further analysis in this work.
After identifying the C IV doublets, we search the spectrum for other common NALs
such as Si IV, N V, C III, O VI, and H I Lyman series lines at the same redshift. We
also search for lower ionization species, such as C II and Si II, but find none. All of the
systems, except possibly system 1, appear to have relatively high ionizations based on
the presence and absence of high and low ionization species respectively. Each set of
absorption lines at one redshift is considered a system, as labeled in Figure 3-1. Several
of these systems are blends of two or three components, which are not individually
labeled in the figure.
Systems 1 and 2 (Figures 3-3 and 3-4) appear to be line-locked in C IV. The velocity
offset between the λ1548 line in system 1 and the λ1551 line in system 2 is remarkably
small (< 2 km s−1) compared to the FWHMs of these lines (∼ 30 km s−1) and the
velocity shifts from the quasar systemic, ∼ -1440 and ∼ -1940 km s−1. If this overlap
between the C IV lines in systems 1 and 2 represents a physical line-lock, where the
velocities of the two systems are actually separated by exactly their doublet separation,
and not a chance alignment in the spectrum (see Ganguly et al. (2003) and Braun
& Milgrom (1989) for full discussions of the possible physical nature of line-locking),
45
then it provides evidence for these lines forming in a quasar outflow driven by radiation
pressure (§ 3.4.1 below).
3.2.2 Line Fitting
We fit each NAL system with a Gaussian optical depth profile. The narrowest
absorption lines are at least 1.5 times broader than the spectral resolution and the
other lines are significantly broader than this. The absorption lines are, therefore,
fully resolved, and such Gaussian optical depth profile fits are sufficient to determine
accurate optical depths and covering fractions. The optical depths and covering fractions
are held constant across the width of each line profile. Gaussian fits are actually
essential to distinguish individual absorption features in the crowded Lyα forest, and also
useful to disentangle blended absorption in other areas of the spectrum. Furthermore,
Gaussian fits smooth over noise spikes and large optical depth and covering fraction
uncertainties in the wings of the lines. We also use Gaussian fits to simultaneously fit
and lock together various parameters including redshift, Doppler b parameter, covering
fraction and a 2:1 optical depth ratio based on oscillator strength ratios for doublets such
as the C IV, Si IV, N V, and O VI.
To measure accurate optical depths, we must consider the possible effects of partial
coverage of the emission source by the absorbing gas. The line of sight covering fraction
affects the observed line intensity as follows:
Iv = (1− Cf )I0 + Cf I0e−τv (3–1)
where 0 ≤ Cf ≤ 1 is the velocity dependent line of sight covering fraction, I0 is the
emitted (unabsorbed) intensity and Iv and τv are the observed intensity and line optical
depth at each velocity shift v. This equation assumes that the background light source
has a uniform brightness given by I0 and the foreground absorber is homogeneous with
a single value of τv. The viability of this assumption is discussed by Hamann & Sabra
(2004) and Arav et al. (2005). We assume that all lines in a given multiplet have the
46
same Cf at a given velocity. We do not explicitly attempt to distinguish between partial
covering of the continuum source and of the BEL region as discussed by Ganguly et al.
(1999). However, we estimate from the SDSS spectrum that the C IV BEL peaks 20%
above the continuum, which implies that the BEL can only account for partial covering of
0.8 or higher.
We attempt to fit each system with the smallest possible number of Gaussian
components. This minimizes the number of free parameters and provides a more
robust characterization of column densities, ionizations and abundances in absorbing
regions whose internal velocities might be more complex than simple Gaussians
(e.g., in outflows). We fit each absorption line with a single Gaussian unless 1) the
system clearly has multiple components distinguished by inflection points that stand
out significantly above the noise fluctuations in the spectrum (e.g. system 7), or 2) a
single Gaussian would miss a significant fraction, ≥ 25%, of the absorption line strength
(e.g. system 4). The ∼ 25% threshold is somewhat arbitrary, but it ensures that we
achieve a good fit to the observed line and that significant portions of absorption (i.e.,
large enough to change the column density measurements) are not missed. For these
exceptional cases we use the minimum number of Gaussians possible to achieve an
accurate fit to the data. If a system is fit with two or more Gaussians, each Gaussian
is labeled as a component. We assume that the covering fraction is the same for all
components in a given system, such that the optical depths in Equation 3–1 simply
add together in regions of component overlap (see Hamann et al. (in preparation) for
further discussion). This simplifying assumption is well justified by the excellent fits to all
the systems, with the possible exception of system 6, which we discuss in more detail
in § 3.3. All ions with Gaussian fits are shown along with their Gaussian optical depth
profiles in Figures 3-3 through 3-9. Badly blended members of, e.g., the Lyman-series
lines are not used to constrain ionization or abundance in § 3.2.3 and are not shown. We
check the Gaussian results using direct integration and point-by-point measurements of
47
τv and Cf across the line profiles for several systems with either non-Gaussian profiles
(system 8) or Cf < 1 (systems 5 and 6) as discussed later in this section.
The free parameters in the Gaussian fits are Cf , central optical depth, τ , the
Doppler b parameter, and redshift for each component in each system. We fit the
C IV absorption lines first, then base the fit for other absorption lines on the C IV fit
parameters. To ensure we are analyzing the same gas in different ions, we fix the
redshift for all absorption lines in a system to the C IV redshift. We further exclude
unwanted contributions from blends or complex multi-phase gas by fixing the b-value
of all ions with ionizations lower than C IV to that of C IV. Higher ionization lines, such
as N V and O VI, are allowed to be broader. However, we cap the b-values of the
H I profiles at 140% of C IV. This cap is important for the abundance analysis below
(§ 3.2.3) because it ensures that the derived H I column densities do not include gas
with dramatically different kinematics than C IV. We choose to cap the H I b-values at
140% of the C IV b-values instead of the much higher percentage expected for purely
thermal broadening because the widths of the C IV lines exceed the thermal widths
expected for a gas photoionized by either the quasar or the inter-galactic UV spectrum.
Therefore, we assume the b-values are dominated by non-thermal broadening effects.
On the other hand, setting the cap at 140% instead of something smaller, such as 100%,
allows for some contribution of thermal broadening to b in the narrower systems (which
would affect H I more than C IV). Overall, our fits to the Lyman lines should lead to
reasonable but generously large estimates of the amount of H I gas that coexists with
C IV, and therefore, to conservatively low estimates of the C/H abundance.
As stated above, the covering fraction is a free parameter in the Gaussian optical
depth fits of each doublet. In cases where the best fit profile has Cf < 1, we repeat the
fit with Cf = 1 to test the robustness of the Cf < 1 result. We then compare how well
each of the two fits with different values of Cf match the data. In cases where the fits
are comparable, we assume Cf = 1, otherwise, the best fit is chosen. For example, we
48
confirm that the Cf < 1 fit follows the data in systems 5 and 6, as shown in Figure 3-6,
whereas the Cf = 1 fit does not match the observed doublet ratios in C IV and N V. We
assume Cf = 1 for all singlet lines. The covering fraction for the Lyman lines is fixed at
the C IV doublet covering fraction. This is necessary because the observed line ratios
within the Lyman series are too severely affected by blending in the Lyα forest to yield
their own independent measures of Cf .
Table 3-1 lists fit parameters for all of the absorption lines that yield useful
constraints for the ionization and abundance analysis described in § 3.2.3. Absorption
lines that are badly blended are not used in subsequent analysis, and are not listed in
the table. Each system is listed separately by redshift and velocity shift, where negative
velocities denote gas moving towards the observer. Only the stronger member of the
O VI doublet is listed, as O VI is never used in the abundance analysis because of
either line saturation or strong blending in these lines in all systems. However, the
strength of O VI is still useful as an indicator of the ionization of the gas. In systems
where N V is not present, we list upper limits for the stronger member of the N V
doublet for completeness. Table 3-1 lists the central wavelength (λ) and Doppler b
parameter values along with column densities and rest equivalent widths (REW) derived
from the Gaussian optical depth profile fits. Systems 4 and 7 each have two blended
components. The values of λ, b, and logN are listed separately for these components in
Table 3-1, but the REWs, listed only with the first component data, apply to the entire
blend. We measure upper limits on H I column densities in all cases where all the
Lyman series absorption lines are blended with intervening absorption lines in the Lyα
forest. The same is true for singlet ions with upper limits on the column densities.
We estimate uncertainties for the column densities by placing the continuum at
the top and bottom of the noise around the fitted continuum, corresponding to the
reasonable maximum/minimum values (∼ 3σ uncertainties) for continuum placement.
We measure 3σ uncertainties for the H I column densities of 0.18 dex on average.
49
The covering fraction is Cf = 1 for all the systems, unless otherwise noted in the
footnotes of Table 3-1, e.g. systems 5 and 6. We estimate the uncertainty in the
covering fraction derived from the Gaussian profile fits both formally and informally.
The formal uncertainties are estimated by propagating the error spectrum through the
calculation of Cf . However, these uncertainties are much smaller than the informal
uncertainties, which are dominated by uncertainty in continuum placement. We
estimate covering fraction uncertainties due to continuum placement uncertainties
by first shifting the continuum near each red doublet member up and down by the
3σ continuum uncertainty, and then fitting the doublets with this new continuum. The
actual uncertainties are probably smaller than the uncertainties we derive in this way,
because a similar shift in the continuum around both doublet members (a more likely
occurrence) produces smaller changes in Cf . We find Cf = 0.7 ± 0.15 for system 5
and Cf = 0.7 ± 0.20 for system 6. If we fix the covering fraction in systems 5 and 6 at
Cf = 1 instead of at the measured values, the column densities in all ions decrease by
an average of 0.25 dex.
We perform a simple test to determine the reliability of the Cf < 1 result from the
Gaussian profile fits for systems 5 and 6. We predict the shape of the longer wavelength
C IV and N V doublet members based on the intensity of the shorter wavelength
member, combined with the 2:1 τ -ratio derived from the oscillator strengths of each
line. The predicted shorter wavelength member will only match the data if Cf = 1. These
predictions are shown in Figure 3-10. The observed data for the shorter and longer
wavelength doublet members are plotted with bold and thin solid curves respectively and
the predicted longer wavelength doublet member is plotted with a dot-dashed curve. The
predicted shape of the longer wavelength member of C IV and especially of N V is much
weaker than the observed shape for system 5. In system 6, the line centers of C IV, and
more clearly N V, are stronger in the observed data for the longer wavelength members
50
than in the predictions. We conclude that Cf < 1 for at least some portion of each of
these lines in systems 5 and 6.
The Gaussian fitting technique described above assumes a single covering fraction
across the entire line. However, we demonstrate with the above τ -ratio analysis that
covering fractions are not always constant across a single line profile. To better account
for this, and to determine if the Gaussian fits find reasonable average values for Cf ,
particularly for lines with very non-Gaussian shapes, we use a point-by-point method in
addition to the Gaussian fitting method to determine τv and Cf across the line profiles
in several systems. We fit systems 5 and 6, the two systems with Cf < 1, along with
system 8, which has a very non-Gaussian profile. We step across the absorption line,
calculating average intensity in each small (a few times the resolution) regularly spaced
sections of the spectrum, using the ratio of the intensities in the doublet in Equation 3–1
to measure Cf and τv at each step. The point-by-point fits are shown in Figures 3-11
and 3-12. The solid curve shows the shorter wavelength doublet member, while the
dot-dashed curve shows the longer wavelength doublet member. The covering fraction
at each point is represented by the filled circles. The steps used for system 6 are three
resolution elements wide, which is wide enough to smooth over the noise but narrow
enough to avoid blending the wings and core of the line. However, system 5 is narrow
enough that using bins three resolution elements wide, or wider, across the wings of the
line would blend too much information from the core and the continuum. Furthermore,
the spectrograph resolution could be blending the covering fraction in the wings of
the line with the continuum. Thus, for the narrow system 5, we measure only the 3
resolution element bin at the line core. The step size for system 8 is four resolution
elements. This larger step size further smooths over noise, and can be used because
the line is much broader than the other systems, lessening the impact of blending of
the core and wings of the line. We derive formal covering fraction uncertainties (σCf ),
represented as error bars at each point in Figures 3-11 and 3-12. The average C IV and
51
N V central covering fraction from the point-by-point method matches the C IV and N V
covering fraction derived from the Gaussian fitting method to within 10% in systems 5
and 6.
Based on this result, the results of the τ -derived doublet ratio analysis, and
uncertainties derived from the Gaussian fitting method, we are confident of the accuracy
of the Cf < 1 measurements for systems 5 and 6, although the exact value of the
covering fraction remains unknown beyond the fact that it is below 1. The C IV and N V
column densities found in system 6 using the point-by-point method match the C IV and
N V column densities derived from the Gaussian fitting method to within 0.14 dex and
0.06 dex respectively. The same comparison for N V in system 8 yields a difference
of 0.18 dex between the two methods. We conclude that the Gaussian technique is
sufficient for comparing different systems and generally provides accurate column
density and covering fraction results for the purposes of this work.
3.2.3 Ionization and Abundances
The abundance ratios can be derived from the ratio of measured column densities
corrected for the degree of ionization in the gas. For example, the relative carbon to
hydrogen abundance normalized to solar is given by:
[CH
]= log
(N(C IV)N(HI )
)+ log
(f (HI )
f (C IV)
)+
[HC
]
¯(3–2)
where f is the ionization fraction of a given ion, N is the column density and the final
term on the right-hand side is the logarithmic solar abundance ratio of hydrogen to
carbon listed in Grevesse et al. (2007). The second term on the right is the ionization
correction (IC). These correction factors can be large when comparing a highly
ionized metal like C IV to H I. The exact values depend on the ionization mechanism.
Photoionization by the quasar spectrum is by far the most likely scenario based on
the arguments in § 3.4.1 that all of the systems are likely to be intrinsic to the quasar
environment.
52
We derive values of IC using the photoionization calculations shown in Hamann
et al. (2011). Their calculations adopt a nominal quasar spectrum consistent with
recent observational estimates at the critical ionizing (far-UV) photon energies1 .
The calculations also assume that the absorbing gas is optically thin in the Lyman
continuum, which is appropriate for the column densities we measure in the absorption
lines of J1023+5142 (Table 3-1).
Ideally, we would constrain the absorber ionizations by comparing the ratios of
observed column densities in different ions of the same element, such as N(C III)/N(C IV)
or N(N III)/N(N V), to the theoretical results in Hamann et al. (2011). However, these
constraints are only marginally usable in our data because N(C III) and N(N III) are
always blended in the Lyα forest and are therefore only ever constrained as upper limits.
Therefore we estimate the IC from ratios such as N(N V)/N(C IV) or N(Si IV)/N(C IV),
with the additional assumption that the relative metal abundances are approximately
solar. The specific ionization constraints used for each system sometimes lead to upper
limits, lower limits or specific values for the abundance ratios, and are described in more
detail for individual systems in § 3.3 below. Our best estimates for the C/H abundances
based on these constraints are all super-solar, except in system 1. Table 3-2, which
contains several different abundance indicators for each NAL system, lists these
estimated ’best’ abundances in column 3, titled [C/H]best , for the nine systems.
1 The calculations in Hamann et al. (2011) apply to gas that is photoionized bya typical quasar spectrum. We perform additional CLOUDY (Ferland et al., 1998)calculations using the inter-galactic background spectrum in CLOUDY, which isbased on Haardt & Madau (2005, private communication). We find that the ionizationfractions of interest in the present work have only negligible differences between thetwo calculations, e.g., compared to uncertainties in the measured quantities or derivedionization constraints. Therefore, our analysis of the ionization and abundances inJ1023+5142 should apply whether the absorbers are located near the quasar or outsidethe quasar’s radiative sphere of influence.
53
We also calculate robust lower limits on the metal to hydrogen abundance ratios by
applying minimum values of the ionization correction (ICmin, Hamann et al. (1997)) to the
measured C IV, Si IV and N V column densities, when available. Each metal ion has a
unique global ICmin that occurs near the peak of its own ionization fraction. For example,
f (H I)/f (C IV) peaks approximately where f (C IV) is largest. We use the values of
ICmin listed in Hamann et al. (2011). Applying these minimum correction factors to the
observed column density ratios (Equation 3–2) leads to the firm lower limits listed for
[C/H]min, [Si/H]min and [N/H]min abundances in columns 4–6 of Table 3-2. The minimum
ionization corrections provide firm lower limits on the abundances that do not depend on
the ionization uncertainties or the possibility of a multi-phase gas. In particular, any gas
components not at an ionization corresponding to ICmin would have the effect of raising
the actual value of IC and thus also the actual abundance.
We derive total H column densities for each NAL system from the H I column
densities listed in column 7 of Table 3-1 and the best ionization correction described
above. We use
log N(H) = log N(H I)− log f (H I), (3–3)
where log N(H) is the total H column density, log N(H I) is the column density of H I
and log f (H I) is the H I fraction used to obtain IC. log N(H) for each system is listed in
column 7 of Table 3-2.
The uncertainties in these results are dominated by uncertainties in the IC. In
addition to the limited constraints provided by the data, a few well-studied cases have
shown that individual absorbers can span a range of ionizations and have a range of
IC values (e.g. Hamann et al. (1997)). We assume a single ionization state for each
absorption line system. We discuss the individual systems briefly in § 3.3.
3.3 Notes on Individual Systems
System 1, v = -1441 km s−1: The C IV in system 1 appears line-locked with the
C IV in system 2, as discussed further in § 3.4.1. O VI is not present, or is very weak,
54
implying that the ionization is low. Further evidence for low ionization is the weak C IV
combined with strong H I measured in Lyα and Lyγ, as seen in Figure 3-3. Our best
ionization constraint comes from an upper limit on C III, which means the best C/H
abundance is an upper limit as well. This gas is a likely candidate for host galaxy halo
gas based on the weakness of the metal lines and the low abundances.
System 2, v = -1938 km s−1: The C IV in system 2 appears to be line-locked
with system 1, as mentioned above and discussed further in § 3.4.1. The H I could be
shifted to a lower velocity by as much as 30 km s−1 from the metal lines in this system,
indicating a multi-phase gas, but heavy blending obscures the precise shift of the
lines as can be seen in Figure 3-4. The Lyα absorption line is poorly constrained. The
resulting H I optical depth and Doppler b parameter are upper limits, resulting in lower
limits for the best estimate of C/H abundance. We constrain the ionization by the relative
strengths of C IV and N V, assuming solar abundance ratios.
System 3, v = -2120 km s−1: The H I lines in system 3 are blended with those
from system 4, but appear consistent with the metal lines, shown in Figure 3-5. Because
of the relatively poor constraints on the H I absorption lines, the H I optical depth and
Doppler b parameter are upper limits, resulting in lower limits for the best estimate of
C/H abundance. We constrain the ionization by the relative strengths of C IV and Si IV,
assuming solar abundance ratios.
System 4, v = -2182, -2200 km s−1: The C IV and Si IV doublets in system 4 are
fit with two blended Gaussian components to accommodate the asymmetric profile. We
use the central velocity of each component to identify the system. The H I absorption
lines are poorly constrained due to blending with system 3. The resulting H I optical
depth and Doppler b parameter are upper limits, resulting in lower limits for the best
estimate of C/H abundance. We constrain the ionization by the relative strengths of
C IV and Si IV, assuming solar abundance ratios. This system is broad and asymmetric,
which is indicative of a wind or outflow feature (§ 3.4.1).
55
System 5, v = -3121 km s−1: The H I in system 5 is well constrained by Lyα. The
O VI is strongly blended with that of system 6 as shown in Figure 3-6. The covering
fraction in the doublet is ∼ 0.7. The Cf = 0.7 Gaussian fits are shown as solid curves
in Figure 3-6. System 5 appears to have 2 components; a narrow, optically thick
component sitting directly on top of a broader, optically thin one. This is most clearly
seen in Figure 3-6 in the longer wavelength members of the C IV and N V doublets,
which have a much sharper central feature than their shorter wavelength counterparts.
We constrain the ionization by the relative strengths of C IV and N V, assuming solar
abundance ratios. The partial coverage in this system indicates that it is intrinsic
to the quasar. The partial coverage in this system and in system 6 are examined
qualitatively with the τ -ratio predicted doublets, shown in Figure 3-10, and further with
the point-by-point analysis, illustrated in Figure 3-11. Both analysis’s confirm similar
Cf < 1 results in both systems (See § 3.2.2 for details).
System 6, v = -3254 km s−1: H I is well-constrained in system 6, with three mostly
blend-free Lyman lines. The covering fraction in the doublets is Cf ∼ 0.7, similar to
system 5. The solid curves in Figure 3-6 represent the Cf < 1 Gaussian fits, as for
system 5. The O VI lines are blended with the O VI lines in system 5. The covering
fraction in H I appears to be Cf = 1 because Lyα reaches zero intensity. We constrain
the ionization by the relative strengths of C IV and N V, assuming solar abundance
ratios. The broad smooth shape, along with the partial coverage indicate that this
system is part of an outflow.
This system appears somewhat asymmetric and the τ -ratio analysis in Figure 3-10
suggests further that there may be two components, one with partial covering near
the line-center, and a second broader component with complete covering in the blue
wing. Although one component does not provide the best possible fit to all the lines
in system 6, it is not clear that adding a second distinct component would provide
a better characterization of the actual conditions in the absorber. We test this by
56
fitting the system with one and two Gaussian components, where the two component
fit still assumes the same covering fraction in both components. Both fits produce
similar column densities in all ions, �N(C IV) = 0.15 dex, �N(N V) = 0.1 dex and
�N(H I) = 0.1 dex in the same direction, therefore we prefer the single Gaussian fit
in keeping with our prescription to minimize free parameters in the fits. Also, by using
the single Gaussian fit, we ignore parts of the Lyα absorption which do not correspond
directly to C IV absorbing gas, and therefore retain the ability to directly compare H I and
C IV column densities for the abundance analysis.
System 7, v = -3430, 3496 km s−1: The H I column density is constrained as an
upper limit in system 7 because of blending in the Lyman lines, shown in Figure 3-7,
resulting in lower limits for the best estimate of C/H abundance. We fit this broad system
with two Gaussian components to better match the absorber shapes, and identify the
system by the central velocities of the two components. The ionization is constrained by
the relative strengths of C IV and N V, assuming solar abundance ratios.
System 8, v = -4763 km s−1: The longer wavelength member of the C IV doublet in
system 8 falls on a gap between orders of the spectrograph between 6785 and 6795 A,
but the N V doublet is present in the spectrum, as is the shorter wavelength member
of the C IV doublet. The N V doublet is used to determine the Cf and the Doppler b
parameter for both doublets. This system is almost broad enough to be a mini-BAL, and
is likely an outflow system based on the shape and strength of the line profile, shown in
Figure 3-8. The H I appears to be relatively weak in this system compared to the metal
lines, although there is severe blending in the Lyα forest. This blending means the H I
absorption is poorly constrained with an upper limit, and therefore the best estimate for
C/H abundance is a lower limit. We constrain the ionization by the relative strengths of
C IV and N V, assuming solar abundance ratios.
We use the Gaussian fit to compare system 8 to other systems, but the profile
of system 8 is distinctly non-Gaussian. Therefore we also fit the central trough of the
57
line with a point-by-point analysis, shown in Figure 3-12. The C/H abundance found
by the Gaussian fit is consistent within 10% of the C/H abundance found using the
point-by-point method.
System 9, v = -6083, -6186, -6298 km s−1: System 9 has three components,
but we chose to analyze only the central component for abundances, as the two outer
components are very poorly constrained, as shown in Figure 3-9. This system has
the highest velocity shift out of the group of narrow absorption lines, and lies just
nominally outside of the velocity shift region for associated lines (v ≥ -5000 km s−1), at
∼ -6200 km s−1. The Lyman lines could be shifted up to 20 km s−1 from the metal lines,
indicating a possible multiphase gas, but the line are too weak to determine their precise
centroids. The weakness of the Lyman lines, along with blending in the Lyα forest mean
the H I column densities are upper limits, so the best estimate of the C/H abundance
is a lower limit. We constrain the ionization by the relative strengths of C IV and N V,
assuming solar abundance ratios.
3.4 Discussion
J1023+5142 has nine NAL systems with a range of column densities from
N(H) ≤ 1017.2 to 1019.1 cm−2, velocities from -1400 to -6200 km s−1, C IV Doppler b
values from 7 to 150 km s−1, C IV REW(1548A) from 0.02 to 0.81 A and two systems
with partial covering of either the continuum source or the broad emission line region
(BLR), Cf ≈ 0.7, which imply absorber diameters of ≤ 0.03 pc or ≤ 0.8 pc (discussed
below in § 3.4.2). These systems are generally much weaker than those studied in larger
statistical surveys of NALs, such as Vestergaard (2003), which use lower resolution
data, and measure C IV REW integrated across the doublet, with completeness limits
of 0.3–0.5 A. The NAL systems all appear to be highly ionized; none of the systems
exhibit low ionization species such as Si II, C II or Si III, whereas all contain C IV and
some contain higher ionization species such as O VI and N V. Systems 5, 6, 8 and 9
exhibit high ionization (O VI) absorption, and others may also have absorption at these
58
wavelengths that is not observable due to blending in the Lyα forest. Systems 2–9
exhibit super-solar metallicities ranging from Z ≥ 1 to ≥ 8 Z¯. System 1 has a slightly
lower metallicity of Z ≤ 0.3 Z¯. We examine several diagnostics to estimate directly the
location of each system.
3.4.1 Location of the Gas
The tight grouping and similar high metallicities (§ 3.4.3 for further discussion) for all
but one (systems 2–9) of the nine C IV absorption line systems in J1023+5142 suggest
a possible physical connection between the absorbers. The proximity of this NAL
complex to the quasar redshift suggests further that the physical relationship includes
the quasar itself. The velocity span across the group is too large to be explained by
a single galaxy or even a large cluster of galaxies. It might be consistent with some
larger cosmic structure connected to the quasar, but then we would expect the velocity
distribution to include the red side of the quasar systemic. A more likely explanation is
that the NAL complex formed in a multi-component outflow from the quasar.
There are several indirect arguments for an intrinsic origin for the gas in this NAL
complex. i) 8 of the 9 systems have super-solar metallicities, discussed in detail in
§ 3.4.3 below. ii) Some authors have argued that strong O VI absorption may indicate
intrinsic gas near the quasar. Fox et al. (2008) carry out a detailed study of O VI
absorption in 2 < z < 3 quasars and argue that logN(O VI) ≥ 15.0 log(cm−2) indicates
an intrinsic origin, supported by evidence for partial covering in most of these systems.
We measure N(O VI) in four systems in J1023+5142. Two of them (6 and 8) are above
the intrinsic threshold defined by Fox et al., while the other two (5 and 9) are very near
this threshold at logN(O VI) ≥ 14.5. iii) All systems with O VI, that is systems 5, 6, 8
and 9, also have strong N V compared to C IV. Strong N V, especially compared to
C IV, is often (though not always) present in intrinsic gas (e.g. Weymann et al. (1981);
Hartquist & Snijders (1982); Hamann et al. (1997); Kuraszkiewicz & Green (2002); Fox
et al. (2008)). iv) The presence of strong O VI and N V, especially with the absence of
59
low ionization species such as C II in these absorption systems is consistent with gas
exposed to the intense ionizing radiation field near a quasar.
If the NALs in J1023+5142 are intrinsic to the quasar environment, the most
likely origin is in a quasar-driven outflow. Other possible intrinsic origins all have
lower velocities: i) starburst-driven outflows typically have 100 < v < 1000 km s−1
(Heckman et al., 2000), and in Seyfert galaxies have maximum outflow speeds of
600 km s−1, and more typical speeds of 100–200 km s−1 (Rupke et al., 2005b), ii) other
galactic/halo gas should have velocities near the typical velocity dispersion for such
galaxies (σ ∼ 300 km s−1), iii) gas in the narrow line region of the quasar has typical
velocities of v ≤ 1000 km s−1, and maximum velocities of v ≤ 2000 km s−1 (Ruiz et al.,
2001, 2005; Veilleux et al., 2005), and iv) intra-cluster galaxy motions are shown by
Popesso & Biviano (2006) to generally have velocity dispersions σv < 1000 km s−1 or
less for clusters with higher numbers of active galactic nuclei (Richards et al., 1999;
Heckman et al., 2000; Vestergaard, 2003; Nestor et al., 2008).
Statistical studies, Nestor et al. (2008) (see also Wild et al. (2008)), have shown
that > 43% of NALs at -750 ≥ v ≥ -12000 km s−1 with REW(1548A) > 0.3 A form in
high-velocity quasar outflows. This percentage increases to ∼ 57% for the narrower
range of -1250 ≥ v ≥ -6750 km s−1, spanned by the NALs in J1023+5142. The
percentage reaches ∼ 72% for the narrow range of -1250 ≥ v ≥ -3000 km s−1, which
encompasses systems 1 through 4 in J1023+5142. These percentages are probably
lower for weaker lines (Nestor et al. (2008) and private communication). Misawa
et al. (2007) also find that for C IV NALs with REW(1548A) > 0.056 A at velocities
v < 5000 km s−1, the intrinsic (outflow) fraction is ≥ 33% and at higher velocities,
5000 < v < 70000 km s−1, the intrinsic fraction is ≥ 10–17%. Nonetheless, these
outflow fractions support the idea that most or all of the systems in this group of nine
NALs in J1023+5142 form in a quasar outflow.
60
We search for direct signatures of quasar outflow origin via 1) line variability, 2)
partial covering and 3) broad profiles (see Hamann et al. (2011) and Hamann & Simon
(in preparation) and references therein for more discussion).
1) We have only very poor constraints on the variability. We compare C IV and
N V REW results measured from the Gaussian fits to the SDSS and Keck spectra
(�trest ≈ 11 months) in search of variability in the absorption lines. System 8 is the only
individual C IV and N V system resolved in the SDSS spectrum, while the weaker lines
are not detected in the SDSS spectrum. System 8 is the strongest of the nine systems,
and did not vary in REW by more than 15% in C IV and N V between the SDSS and
Keck observations. For the eight weaker systems, we conclude only that variability
greater than a factor of 2 to 3 did not occur.
2) There is partial covering in two (systems 5 and 6, Figures 3-6 and 3-11) out of
the nine systems. Absorption lines with partial covering of the luminosity source are
attributed to gas near the quasar because partial covering is not expected to occur in
intervening clouds or galaxies (Hamann et al., 2011). The presence of partial covering
in these lines strongly suggests that the gas is intrinsic and located in the near quasar
environment.
3) The profiles of systems 4, 6, 7, 8, and 9 shown in Figures 3-5, 3-6, 3-7, 3-8
and 3-9 have C IV and N V b values between 33 and 155 km s−1 and O VI b values
between 42 and 150 km s−1. These b values are broad and smooth compared to the
thermal widths for gas at the highest expected temperature near T = 105 K (Arnaud
& Rothenflug, 1985; Hamann et al., 1995) for a photoionized gas near a quasar
(33 km s−1 for H and less than 10 km s−1 for C and N). They are also broader than
typical non-damped Lyα intervening C IV, N V and O VI absorption lines, which have on
average b < 12–14 km s−1 for O VI, and b < 10–12 km s−1 for C IV and N V (Tzanavaris
& Carswell, 2003; Bergeron & Herbert-Fort, 2005; Schaye et al., 2007; Fox et al., 2008).
These profiles, therefore, exhibit morphologies consistent with formation in an outflow.
61
These three characteristics, variability, partial covering, and broad profiles are
often found together in a single object, further supporting the idea that each individual
characteristic likely indicates an outflow. One well studied NAL outflow in J2123-0050
(Hamann et al., 2011) is a prime example of all three, exhibiting variability, partial
covering, and broad profiles that still have FWHMs that are as narrow or narrower than
many of the systems in J1023+5142.
There is more tentative evidence for a quasar outflow origin in the apparent line-lock
between the C IV doublets in systems 1 and 2. Line-locking, where the difference in
outflow velocities of two systems is exactly the velocity separation of the doublet, means
that the lines are being radiatively accelerated directly towards the observer. The reality
of the line-locking in this case is unclear, due to the difference in derived metallicities
between the two systems. Nevertheless, the incredibly small velocity offset (§ 3.2.1),
along with the very small probability for chance alignments (Ganguly et al., 2003)
suggest that the phenomenon may be real. The possible line-lock in C IV in systems
1 and 2 suggests that they are both part of an outflow and that these weak C IV lines
play a significant role in radiatively driving the flow. If this is really the case here, the gas
probably originated near the source of radiative acceleration, i.e. the quasar.
Finally, we note a trend in line width with velocity shift away from the quasar. The
narrowest lines, with FWHM ∼ 20 km s−1 are closest to the quasar redshift. The lines
appear progressively broader as the velocity shift increases, with the broadest system
described as a (narrow) mini-BAL with FWHM = 270 km s−1, shown in Figure 3-1. A
similar phenomenon has been observed before in other quasars with multiple C IV
absorption lines clearly forming in outflows (Hamann et al., 1997; Steidel, 1990;
Hamann et al., 2011). Although it provides no direct information on the absorber
locations, the appearance of this pattern in J1023+5142 supports the idea that at least
some of the systems form in a quasar outflow. The tight grouping of all nine of the
systems also suggests a relationship between them. Ganguly et al. (2003) determine
62
that the probability of six similarly grouped NALs in the quasar RX J1230.8+0115 all
forming in intervening (uncorrelated) gas at similar velocity shifts is extremely small.
The similarities between those NALs and the NAL complex in J1023+1542 implies a
similarly small probability for all nine NALs in this complex forming independently in
intervening gas. Although there could be up to several interlopers in the NAL complex of
J1023+5142 that might form in nearby galaxies in the line of sight, the density of these
galaxies required to form all of the absorbers in the complex is beyond any expectations
of cluster density at this redshift.
Overall, we conclude that at least six out of the nine systems originate in a highly
structured outflow driven by the quasar, because they exhibit one or more of the
following properties: partial covering, broad profile shapes, large line strengths, tight
grouping with other systems, and proximity to the quasar redshift. Systems 5 and 6
are the most likely outflow candidates because they exhibit partial covering as well
as several of the other properties listed above. Systems 4 and 8 are likely outflows
because of their strong, broad, asymmetric and smooth profiles and systems 7 and 9
are probably outflows because of their broad and smooth shapes. Systems 1 through
3 are more ambiguous in origin, with narrow widths, complete covering, lower velocity
shifts and smaller strengths. However systems 1 and 2 exhibit line-locking, which could
be evidence of an outflow. Ultimately, we find strong evidence that systems 4, 5, 6,
7, 8 and 9 are part of a quasar outflow, whereas systems 1, 2, and 3 could consist of
intervening gas from the intergalactic medium (IGM) or other galaxies in the line of sight.
3.4.2 Outflow Properties
As described in § 3.4.1, the evidence suggests that the majority of absorption
lines in this grouping are part of a complex quasar outflow. This flow must be highly
structured, with at least 6 and as many as 9 distinct absorbing structures along the
line of sight. The velocities in the 6 most secure outflow systems range from -2120 to
-4760 km s−1. Several of these systems (4, 6, 7 and 8) also have super-thermal line
63
widths, indicative of large turbulence or strong radial velocity sheer across the outflow
structure.
Some of the outflow structures, represented by systems 5 and 6, must be spatially
small to produce partial covering of the background emission source. These lines lie on
top of the very weak C IV broad emission line. Therefore, nearly all (> 80%) of the flux
beneath these lines is continuum emission and any partial covering below Cf = 0.80
can be ascribed to the continuum source and not the much larger BLR. We estimate
the diameter of the accretion disk continuum source at 1550 A to be d ≈ 0.03 pc
and the diameter of the C IV broad line region to be d ≈ 0.8 pc, based on the scaling
relations2 in Hamann & Simon, in preparation. To partially cover the emission source,
the absorbing clouds should have characteristic sizes similar to or less than the BLR
diameter, and possibly even less than the accretion disk diameter.
If the absorbers are discrete clouds, their small sizes and substantial velocity
dispersions should lead to fairly rapid dissipation in the absence of an external pressure
(Hamann & Simon, in preparation). In particular, the characteristic size of d ∼ 0.03 pc
or d ∼ 0.8 pc combined with b = 45 km s−1 in the partial covering system 6 indicate a
dissipation time of roughly tdis ∼ d/b ∼ 660 yrs or tdis ∼ 17,400 yrs. At the measured
velocity of v = -3254 km s−1, this gas component would travel just ∼ 2 pc or ∼ 60 pc
before dissipating. A thorough discussion of the creation and survival of these absorbing
structures is beyond the scope of this thesis. However, these simple arguments suggest
2 We estimate the luminosity from the rest-frame flux at 1450 A measured in theSDSS spectrum, which, combined with the luminosity distance and the bolometriccorrection factor L = 3.4νLν(1450), gives λLλ(1450A). We measure the C IV emissionline FHWM in the SDSS spectrum, and using Equation 7 in Vestergaard & Peterson(2006), derive a black hole mass of logMBH = 9.8 M¯. Based on these values, wecalculate an Eddington luminosity fraction of 0.8. The black hole mass and Eddingtonluminosity fraction are then used in the scaling relations in Hamann & Simon, inpreparation, to calculate the size of the continuum and broad line emission regions.
64
that at least some of the outflow components we measure are very near their point of
creation.
It is useful to compare the basic properties of this NAL outflow to BALs. The
ionizations in both types of outflows are similar, with very little low ionization gas (e.g.
C III). The outflow velocities of the NALs in J1023+5142 are lower, ≤ 6200 km s−1,
than the typical outflow velocities in BALs, which can reach up to ≥ 20,000 km s−1
(Korista et al., 1993), but they do overlap. The NALs have total H column densities
(N(H) ≤ 1017.2 to 1019.1 cm−2, individual values listed in Table 3-2), more than 1000
times lower than typical BAL H column densities (N(H) ≥ 1020-1022 cm−2, and probably
higher). By definition, these NALs also have FWHMs around 1000 times narrower than
typical BALs, and much smaller REWs as well. Nonetheless, NALs like this might be
part of the same general outflow phenomenon as BALs, viewed at different angles
(Elvis, 2000; Ganguly et al., 2001).
This complex of weak NAL outflows appears to be dramatically different from
typical BAL outflows, and constitutes a nearly unexplored part of the quasar outflow
phenomenon, with a range of physical parameters and kinematics more complex and
varied than previously thought. It is well-known that NALs are a common feature of
quasar spectra. Previous surveys have found that 40% of quasars have C IV NALs, in
particular 25% have strong C IV NALs within v > -5000 km s−1 (Vestergaard, 2003),
60% have quasar-driven outflows in some form, either BALs, NALs, or something
in-between (Ganguly & Brotherton, 2008; Rodrıguez Hidalgo et al., 2010a), and
including high-velocity outflows raises the percentage to 70% (Misawa et al., 2007).
If the coverage fraction of these outflows is less than 100%, which is likely, they could be
ubiquitous in the near quasar environment, and could potentially play an influential role
in the physical processes occurring therein.
Finally, we would like to understand what role the NAL outflow in J1023+5142 might
have in feedback to galaxy evolution. The low speeds and small column densities, e.g.,
65
compared to BAL flows, suggest that its feedback contribution is negligible. However,
there are large uncertainties relating to the outflow location and geometry. For one
particular NAL outflow at a derived radial distance of ∼ 5 pc, Hamann et al. (2011)
estimate that the kinetic energy yield is several orders of magnitude smaller than that
necessary to influence feedback. At the opposite extreme, Moe et al. (2009) argue that
the feedback contribution is significant for another NAL outflow at a derived distance of
∼ 2–5 kpc. The location of the NAL outflow in J1023+5142 is not known well enough
to make these estimates. A more sensitive search for variability in these NALs could
be very helpful for refining both the location and the total energy yield (Hamann et al.
(2011) and references therein).
3.4.3 Metallicity
We find greater than or consistent with solar abundances in all of the absorption
systems in J1023+5142 except in system 1, in agreement with previous studies of
narrow associated absorption at lower redshifts (Petitjean & Srianand, 1999; Hamann
et al., 2001; D’Odorico et al., 2004; Gabel et al., 2006). These high metallicities are
consistent with the results of other studies of intrinsic gas as well, including BEL gas
(Hamann et al., 2002) and therefore consistent with our interpretation that the gas is
intrinsic to the quasar. Intervening absorbers generally have very low metallicities, with
Z no more than a few hundredths solar, although there are cases where high metallicity
intervening gas has been observed (Prochaska et al., 2006; Schaye et al., 2007). We
argue that the high metallicities found in 8 of the 9 systems in this quasar are consistent
with locations near the quasar, however, we do not rely solely on this argument to
determine the gas location. Instead, we consider that high metallicities could be a
general phenomenon found in all gas in the quasar host environment (Prochaska &
Hennawi, 2009).
The high metallicities of the NAL systems in J1023+5142 require that its host
galaxy had vigorous star formation in the epoch before the quasar was observable,
66
leading to metal-rich gas in the quasar outflows (Falomo et al., 2008). This evidence,
along with previous studies of BELs lead us to conclude that the generally accepted
paradigm of quasar-host galaxy evolution is correct, where a major merger leads
to a vigorous burst of star formation, which then funnels gas to the center of the
galaxy and ignites a quasar that eventually blows out obscuring gas and dust to
become visibly luminous (Perez-Gonzalez et al., 2008; Hopkins et al., 2008; Ramos
Almeida et al., 2009). However, larger samples are needed to examine the full range
of NAL properties and study their relationships to quasar outflows and host galaxy
environments. Measurements at high redshifts are particularly valuable because this is
the main epoch of host/massive galaxy formation when the NAL gas might have a close
relationship to ongoing or recent star formation in the hosts.
3.5 Summary
We use NALs to improve our understanding of the evolutionary relationship
between the central black hole and its host galaxy through the study of their location,
origin and abundance information in high redshift quasars. Here, we examine the
properties of nine NAL systems in the quasar J1023+5142 and find N(H) from ≤ 1017.2
to 1019.1 cm−2, velocities from -1400 to -6200 km s−1, C IV Doppler b values from 7
to 150 km s−1, C IV REW from 0.02 to 0.81 A and two systems with partial covering
of either the continuum or the BLR, at the level of Cf ≈ 0.7, which imply absorber
diameters of ≤ 0.03 pc or ≤ 0.8 pc.
The NAL systems all appear to be highly ionized; none of the systems exhibit low
ionization species such as Si II, C II or Si III, whereas all contain C IV and some contain
higher ionization species such as O VI and N V.
The C IV absorption NALs are tightly grouped, suggesting that they have a physical
relationship to one-another, and the proximity of the NAL complex to the quasar redshift
suggests that the physical relationship includes the quasar itself. The range in velocity
across the complex is larger than can be easily explained by a single galaxy or even by
67
a large cluster of galaxies. A more likely explanation is that the NAL complex formed in a
multi-component quasar-driven outflow.
We estimate directly the location of each system in J1023+5142 through the use
of several diagnostics and find strong evidence (partial covering, broad and smooth
profiles compared to thermal widths, velocities greater than galaxy dispersion velocities,
super-solar metallicities) that systems 4, 5, 6, 7, 8 and 9 are part of a quasar outflow.
Systems 1, 2, and 3 have more ambiguous origins because they exhibit narrow widths,
lower velocity shifts, and system 1 has a lower metallicity, so these systems could
consist of intervening gas from the IGM or other galaxies in the line of sight.
Systems 2–9 in J1023+5142 exhibit super-solar metallicities ranging from Z ≥ 1 to
≥ 8Z¯. Systems 1 has low metallicity of Z ≤ 0.3Z¯. The high metallicities are consistent
with scenarios of galaxy and black hole formation and evolution.
The NALs in outflows appear to be part of a related outflow complex, which is very
different than other known outflow regions such as BAL outflows, and constitutes
a relatively unexplored part of the quasar outflow phenomenon. The outflows in
J1023+5142 could be important for feedback between the black hole and the host
galaxy, depending on the radial distance of the gas from the quasar.
The narrow widths of NALs mean that detailed studies of individual objects like
this are the only way to make progress in understanding this type of outflow. Variability
studies could be useful to add more examples of NAL outflows to the current sample
available for similar detailed analysis.
We will add significantly to this sample in future work, including detailed studies of
the full range of NAL properties in 24 quasars at high redshift, during the main epoch of
host/massive galaxy formation.
68
Figure 3-1. Region of the spectrum of J1023+5142 with C IV absorption. Individual C IVdoublets are labeled by number. The lower x-axis is observed wavelength inangstroms, while the upper x-axis is velocity shift of the shorter wavelengthdoublet line at 1548.20 A from the quasar rest frame in kilometers persecond. The flux units are normalized so that the continuum has a value ofone. The gap between 6788 and 6797 A is a gap between Echelle orders inthe spectrograph. The longer wavelength line of system 8 falls in this gap.Strongly blended lines are considered components of a single system, e.g.systems 7 and 9.
Figure 3-2. Region of Lyα forest spectrum with the continuum fit over-plotted. Theregions spanning the Ly β and O VI NALs are labeled above the spectrum.
69
Table 3-1. Individual absorption lines.# zabs ID λrest λ REW b logN
v (km s−1) A A km s−1 cm−2
1 3.42865 Ly γ 973 4307.03 0.162 27.8 15.05-1442 C III 977 4326.88 0.076 10.8 ≤13.37
Ly α 1216 5383.78 0.445 27.8 15.05N V 1239 5486.30 ≤0.002 10.8 ≤12.57C IV 1548 6856.43 0.034 10.8 12.99C IV 1551 6867.81 0.018 10.8 12.99
2 3.42133 C III 977 4319.73 0.015 12.0 ≤12.39-1938 Ly β 1026 4535.05 0.011 14.0 ≤13.23
Ly α 1216 5374.88 0.069 14.0 ≤13.23N V 1239 5477.24 0.034 20.5 13.24N V 1243 5494.83 0.018 20.5 13.24C IV 1548 6845.11 0.017 10.0 12.66C IV 1551 6856.47 0.009 10.0 12.66
3 3.41864 Ly ε 938 4143.82 0.011 13.9 ≤14.26-2120 Ly γ 973 4297.29 0.035 13.9 ≤ 14.26
Ly α 1216 5371.61 0.186 13.9 ≤14.26N V 1239 5473.90 ≤0.006 9.5 ≤12.45
Si IV 1394 6158.52 0.023 7.2 12.48Si IV 1403 6198.30 0.013 7.2 12.48C IV 1548 6840.94 0.096 9.5 13.62C IV 1551 6852.29 0.062 9.5 13.62
4 3.41775 Ly ε 938 4142.98 0.059 15.4 ≤14.86-2182 Ly γ 973 4296.43 0.164 15.4 ≤14.86
Ly α 1216 5370.53 0.589 15.4 ≤14.86N V 1239 5472.79 ≤0.055 11.0 ≤12.28
Si IV 1394 6157.28 0.081 9.1 12.76Si IV 1403 6197.09 0.046 9.1 12.76C IV 1548 6839.56 0.215 11.0 13.56C IV 1551 6850.91 0.132 11.0 13.56
3.41747 Ly ε 938 4142.72 * 46.8 ≤14.64-2200 Ly γ 973 4296.15 * 46.8 ≤14.64
Ly α 1216 5370.19 * 46.8 ≤14.64N V 1239 5472.44 * 33.4 ≤13.45
Si IV 1394 6156.89 * 14.0 12.75Si IV 1403 6196.66 * 14.0 12.75C IV 1548 6839.12 * 33.4 13.67C IV 1551 6850.47 * 33.4 13.67
70
Table 3-1. Continued5a 3.40391 C III 977 4302.71 0.059 19.7 ≤13.05
-3121 O VI 1032 4544.53 0.275 25.3 ≤14.99Ly α 1216 5353.70 0.176 32.2 13.91N V 1239 5455.65 0.098 13.0 14.30N V 1243 5473.18 0.075 13.0 14.30C IV 1548 6818.13 0.141 15.0 14.12C IV 1551 6829.45 0.105 15.0 14.12
6b 3.40196 Ly γ 973 4281.07 0.055 31.7 14.41-3254 Ly β 1026 4515.18 0.137 31.7 14.41
O VI 1032 4542.51 0.739 63.5 ≤15.54Ly α 1216 5351.33 0.382 31.7 14.41N V 1239 5453.24 0.284 50.0 14.57N V 1243 5470.76 0.188 50.0 14.57C IV 1548 6815.11 0.220 45.0 14.02C IV 1551 6826.42 0.128 45.0 14.02
7 3.39936 Ly δ 950 4178.26 0.008 27.3 ≤13.19-3430 C III 977 4298.26 0.102 19.8 ≤12.36
N III 990 4354.49 0.023 19.8 ≤13.04Ly α 1216 5348.17 0.295 27.3 ≤13.19N V 1239 5450.02 0.310 18.0 13.50N V 1243 5467.53 0.180 18.0 13.50C IV 1548 6811.09 0.187 19.9 13.09C IV 1551 6822.40 0.101 19.9 13.09
3.39835 Ly δ 950 4177.31 * 60.0 ≤13.74-3496 C III 977 4297.28 * 43.7 ≤13.19
N III 990 4353.49 * 43.7 ≤13.04Ly β 1026 4511.48 * 60.0 ≤13.74Ly α 1216 5346.95 * 60.0 ≤13.74N V 1239 5448.77 * 52.4 14.22N V 1243 5466.27 * 52.4 14.22C IV 1548 6809.53 * 43.7 13.62C IV 1551 6820.83 * 43.7 13.61
8 3.37983 Ly β 1026 4492.48 0.135 149.0 ≤14.29-4763 O VI 1032 4519.68 1.705 149.8 15.86
Ly α 1216 5324.43 0.766 149.0 ≤14.29N V 1239 5425.82 1.045 155.5 14.88N V1243 5443.26 0.643 155.5 14.88
C IV 1548 6780.85 0.807 155.5 14.41
a System 5 has covering fractions Cf (H I) = 0.70, Cf (N V) = 0.67, and Cf (C IV) = 0.70.b System 6 has covering fractions Cf (H I) = 1.0, Cf (N V) = 0.67, and Cf (C IV) = 0.72.
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Table 3-1. Continued9 3.35922 Ly γ 973 4239.50 0.039 58.4 ≤14.02
-6186 C III 977 4258.94 ≤0.158 50.0 ≤13.35Ly β 1026 4471.34 0.112 58.4 ≤14.02O VI 1032 4498.41 0.413 42.2 14.51Ly α 1216 5299.37 0.513 58.4 ≤14.02N V 1239 5400.29 0.212 40.0 13.84N V 1243 5417.64 0.115 40.0 13.84C IV 1548 6748.94 0.156 40.4 13.35C IV 1551 6760.14 0.083 40.4 13.35
Table 3-2. Metal abundance and total H column density.# zabs [C/H]best [C/H]min [N/H]min [Si/H]min logN(H) cm−2
1 3.42865 ≤-0.47 ≥-2.25 – – 17.622 3.42133 ≥+0.32 ≥-0.76 ≥-0.47 – ≤17.953 3.41864 ≥+0.38 ≥-0.82 – ≥-0.34 ≤17.164 3.41775 ≥+0.79 ≥-1.32 – ≥-0.66 ≤17.245 3.40391 +0.72 ≥+0.02 ≥-0.10 – 18.216 3.40196 +0.50 ≥-0.58 ≥-0.32 – 19.137 3.39889 ≥+0.40 ≥-0.30 ≥+0.01 – ≤18.198 3.37985 ≥+0.94 ≥-0.07 ≥-0.17 – ≤18.909 3.35906 ≥+0.14 ≥-0.86 ≥ +0.11 – ≤18.60
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Figure 3-3. Line profiles in the normalized spectrum J1023+5142 for system 1. Thecentral velocity for the Gaussian profile fit is v = -1441 km s−1. The velocityscale is with respect to the rest frame of the quasar based on zem = 3.45,where negative velocities denote motion towards the observer and awayfrom the quasar. The velocity range is 400 km s−1 for this and figures 3-4through 3-7 and 3-9. The solid curve in each panel is the Gaussian opticaldepth fit to individual lines. The dashed vertical line is the central velocity ofthe system. All of the lines used to derive or constrain column densities withGaussian fits are shown in the figure.
73
Figure 3-4. Line profiles in the normalized spectrum J1023+5142 for system 2. Thecentral velocity for the Gaussian profile fit is v = -1938 km s−1. The symbolsand ranges are the same as in Figure 3-3.
74
Figure 3-5. Line profiles in the normalized spectrum J1023+5142 for systems 3 and 4.The central velocities of the Gaussian profile fits are v = -2120 km s−1 forsystem 3 and v = -2200 km s−1 for system 4. The symbols and ranges arethe same as in Figure 3-3.
75
Figure 3-6. Line profiles in the normalized spectrum J1023+5142 for systems 5 and 6.The central velocities of the Gaussian profile fits are v = -3121 km s−1 forsystem 5 and v = -3254 km s−1 for system 6. The solid curve is the Cf < 1Gaussian fit for the C IV, N V and H I absorption lines. System 5 is thenarrower system. The ranges are the same as in Figure 3-3.
76
Figure 3-7. Line profiles in the normalized spectrum J1023+5142 for system 7. Thissystem has two blended components with central velocities of v = -3430 and-3496 km s−1. The symbols and ranges are the same as in Figure 3-3.
77
Figure 3-8. Line profiles in the normalized spectrum J1023+5142 for system 8. TheGaussian profile fit has a central velocity of v = -4763 km s−1. The velocityrange is 1100 km s−1. The symbols and ranges are the same as inFigure 3-3.
78
Figure 3-9. Line profiles in the normalized spectrum J1023+5142 for system 9. Thissystem is a blend of three components with central velocities, v = -6083,-6186 and -6298 km s−1. Although all three components are fit withGaussian profiles, only the central component is considered in theabundance analysis. The symbols and ranges are the same as inFigure 3-3.
79
Figure 3-10. τ -predicted line profiles for systems 5 and 6. System 5 is the narrowersystem. The dot-dashed curve shows the smoothed C IV and N V predictedlong wavelength doublet member, based on doublet optical depth ratio fromshort wavelength member, assuming Cf = 1. The actual smoothed data forthe shorter and longer wavelength doublets are shown as the bold and thincurves, respectively. The longer wavelength data are stronger than thepredictions, indicating partial covering, especially for the N V doublet insystem 5 and in the center of the N V doublet in system 6.
80
Figure 3-11. Point-by-point covering fractions for C IV and N V in system 6 and thecenter of system 5 with step size of three resolution elements. System 5 isthe narrower system. The solid curve is the smoothed shorter wavelengthline, the dashed curve is the smoothed longer wavelength line, with theirrespective error spectra below. The circles represent 1 − Cf at each stepso that a point at zero flux has complete coverage, and a point at thecontinuum flux of one has no coverage. The circles are located at thecenter of the average velocity steps.
81
Figure 3-12. Point-by-point covering fractions for N V in system 8 with step size of fourresolution elements. The symbols are the same as in Figure 3-11.
82
CHAPTER 4A CENSUS OF NARROW C IV ABSORPTION LINES IN 24 QUASARS AT REDSHIFTS
1.9 < Z <4.6
Spectroscopic surveys have shown that narrow absorption lines (NALs) in C IV
λλ1548, 1551 and other metal ions are common in quasars spectra (Weymann et al.,
1979; Steidel, 1990). They can have a variety of origins, including quasar or starburst
outflows, merger remnants in the host galaxy halo and intervening galaxies along the
line of sight to the quasar. The statistical excess of NALs near the quasar emission line
redshift indicates that a significant fraction of these absorption lines have some physical
relationship to the quasars. The class of narrow associated absorption lines (AALs), with
velocity shifts v < 5000 km s−1 from the quasar systematic, was defined to encompass
most of this excess (Weymann et al., 1979; Foltz et al., 1986; Anderson et al., 1987;
Vestergaard, 2003). However, more recent studies have shown that the excess in C IV
extends out to v ∼ 12000 km s−1 (Nestor et al., 2008; Wild et al., 2008).
We will use the term “intrinsic” to describe lines that are physically related to the
quasars or their host galaxies, irrespective of their velocity shifts. Intrinsic lines can have
a range of origins, as noted above, and a range of locations, from very near the central
black hole to the outer halo of the host galaxies. These features are overlooked in the
majority of quasar absorption line studies that focus on intervening and/or intergalactic
material. However, intrinsic lines of all varieties are extremely valuable as probes of the
gaseous environments of quasars during an important stage of quasar-galaxy evolution.
The period when a quasar is active in a massive galaxy is a time of rapid growth of the
black hole and probably the host galaxy as well. Intrinsic NALs provide information on
the kinematics, column densities, and ionization state of the gas, that is, the physical
nature of the gas, as well as on the chemical abundances, which yield constraints on the
star formation histories and chemical ’maturity’ of the host environments. The chemical
abundance of a gas indicates the historical level of star formation in that environment,
which can constrain galaxy formation models. It is also important to quantify the nature
83
and incidence of narrow intrinsic lines in quasar spectra, e.g., at large velocity shifts,
to understand how they might contaminate studies of the intervening/intergalactic gas
(Richards et al., 1999).
Quasars at high redshifts, above z ∼ 2, are important objects for studies of intrinsic
gas because this is the epoch of major mergers and galaxy assembly for the massive
galaxies that are host to quasars. The quasar is thought to influence the host galaxy
evolution, but concrete mechanisms for this interaction are not well understood (Di
Matteo et al., 2005, 2008; Hopkins et al., 2008). Quasar outflows are one possible
medium of interaction, possibly influencing the star formation in the surrounding host as
they are ejected from the central quasar engine. The incidence of quasar outflows, as
well as their physical characteristics determines the degree to which they influence the
course of host galaxy evolution.
In this chapter, we describe results from a spectroscopic survey of C IV NALs in
24 quasars known from previous observations to contain NALs. Our main goals are
to determine the fraction of intrinsic NALs, specifically quasar outflows, in this quasar
sample and to determine the physical characteristics of the intrinsic gas. In Chapter 5,
we will include measurements of lines of various elements and ions other than C IV to
place constraints on the ionization, total column densities and metal abundances of the
intrinsic gas.
Intrinsic NAL systems can be identified through several techniques: 1) variability,
where changes in the location or ionization of the gas near the quasar may cause
variability of the line strength, 2) partial covering, where the absorbing gas region is
physically small and probably near the emission source and therefore only partially
covers the emission source, allowing part of the light from the source to reach the
observer unabsorbed, and 3) broad, smooth profiles compared to thermal widths,
presumably caused by outflow motions in the gas rather than thermal broadening
(Hamann et al., 1997; Barlow et al., 1997; Aldcroft et al., 1997; Hamann & Ferland,
84
1999; Srianand & Petitjean, 2000; Narayanan et al., 2004; Ganguly et al., 2006; Misawa
et al., 2007; Schaye et al., 2007; Hamann et al., 2011). All of these properties are most
readily attributed to the dense and dynamic environments near quasars. Furthermore,
these three properties tend to appear together in many well-studied NALs, suggesting
that each individual property may be a good indicator of intrinsic gas, even when it
appears on its own.
Variability requires multiple epochs of comparable data, which we do not currently
have for the quasars in the survey discussed in this chapter. Partial covering is easily
detected in multiplets like the C IV doublet where the optical depth ratio between the
two lines is fixed by the oscillator strengths (Verner et al., 1994). When the source
is partially covered, some unabsorbed light artificially decreases the depth of the
absorption line so that the apparent optical depth ratio appears different than the
actual optical depth ratio. The most likely origin of gas exhibiting partial covering is in
a quasar-driven outflow, e.g. very near the quasar source, however there are several
cases of low-density AAL regions that exhibit partial covering at kpc distances from
the quasars (Hamann et al., 2001), see also Hamann & Simon (in preparation). We
interpret partial covering as an indicator of intrinsic origin, i.e., line formation somewhere
in the quasar environment, keeping in mind that intrinsic lines at large velocity shifts
can only be due to quasar-driven outflows. Other possible intrinsic origins can be
discounted because they have lower velocities, such as: i) starburst-driven outflows,
which generally are found with velocities 100 < v < 1000 km s−1 (Heckman et al., 2000;
Veilleux et al., 2005), ii) other galactic/halo gas, which is expected to follow the typical
velocity dispersion for such galaxies (σv ∼ 300 km s−1), iii) gas in the narrow line region
of the quasar, which has typically been found to have velocities of v ≤ 1000 km s−1,
and maximum velocities of v ≤ 2000 km s−1 (Ruiz et al., 2001, 2005), and finally iv)
Popesso & Biviano (2006) have found that intra-cluster galaxy motions generally have
velocity dispersions σv < 1000 km s−1 or smaller in clusters with more active galactic
85
nuclei. A conservative velocity threshold is ∼ 1500 km s−1, above which, any intrinsic
gas must form in a quasar outflow. Finally, broad, smooth profiles are relatively easily
measured in high resolution spectra through simple inspection. In the present study,
we consider only partial covering and broad smooth profiles as indicators of intrinsic
absorption. In subsequent papers we will also examine other secondary indicators,
including NAL abundances and the presence of strong N V and O VI absorption.
There are several NAL surveys that have been carried out in recent years. Misawa
et al. (2007) examine high resolution spectra of 37 quasars at high redshifts. They
conduct a statistical analysis of the fraction of NALs that exhibit partial covering. Their
survey differs from the survey presented in this work primarily in that the quasar sample
is not selected to contain quasars with NALs, and does not have uniform velocity
coverage of all the quasar spectra, with many spectra missing the ‘associated’ region
near the quasar systemic redshift. Nestor et al. (2008) examine the incidence of
C IV NALs in medium resolution Sloan Digital Sky Survey (SDSS) spectra of a large
sample of quasars also not specifically selected to contain NALs. Their study focuses
on the statistical excess of C IV NALs at velocity shifts from the quasar redshift of
0–12,000 km s−1. Wild et al. (2008) produce a C IV NAL study with SDSS spectra
similar to Nestor et al. (2008), and also consider Mg II NALs, which exhibit a statistical
excess that extends to lower velocities than the C IV excess. They find comparable
excesses in NAL occurrence both in C IV and in Mg II at lower velocities. Vestergaard
(2003) also studies NALs in lower-resolution spectra, comparing NAL properties with
the radio properties of the observed quasars and finding no significant correlations
between radio properties and C IV NAL occurrence. Finally, Richards et al. (1999)
investigate the correlations between C IV NAL occurrence at large velocity shifts and
radio properties of the host galaxy, finding an apparent connection between the two,
which suggests that up to one third of these high velocity NALs are actually intrinsic
to the host galaxy. These low-resolution surveys must rely on secondary indicators to
86
determine NAL origins, because partial covering cannot be measured in low resolution
spectra, nor are the broad smooth profile shapes discernible. Furthermore, these
studies are only sensitive to relatively strong lines and cannot measure column densities
for ionization and abundance analysis. In this work we use high resolution spectra like
Misawa et al. (2007) along with broad velocity coverage (including low velocities near
the quasar systematic velocity) like Nestor et al. (2008) and Wild et al. (2008) to create a
comprehensive study of high redshift quasar NALs.
We present a unique quasar sample containing high redshift quasars previously
known to contain NALs near the quasar systematic redshift. This sample is well-suited
to a study of C IV AALs in particular, as we obtain coverage down to, and even redward
of the quasar systematic redshift. In the previous chapter (Chapter 3), we provided an
in-depth study of an interesting individual quasar in this sample. In this chapter, we
expand the study to present our full sample of 24 quasars with high resolution spectra
of the rest-frame ultra-violet (UV) absorption line region from the Very Large Telescope
with the Ultra-Violet Echelle Spectrograph (VLT+UVES), the Magellan Clay telescope
with the Magellan Inamori Kyocera Echelle (Mag.+MIKE) and the Keck I telescope with
the High Resolution Echelle Spectrograph (Keck+HIRES). We study this sample of
NALs to learn more about the quasar environment during the epoch of massive galaxy
formation, from the incidence of outflows to the average strength of the absorption lines
in both intervening and intrinsic gas. In the subsequent chapter we will present chemical
abundances for the intrinsic NALs in this sample. In the present work we measure
C IV NALs out to 40000 km s−1 in our sample of 24 quasars. We measure covering
fractions, line widths and column densities, which we use to determine the origin of each
absorption line.
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4.1 Data
4.1.1 Sample Selection
We construct a unique sample of mostly high redshift quasars with C IV NALs.
This observing campaign was designed to determine metal abundances for as many
AALs and NALs as possible in quasars at the peak of the massive galaxy formation
epoch in the Universe (z ∼ 2–4). We chose objects with redshifts z ≥ 2.7 (with 5 lower
redshift exceptions observed when no higher redshift targets were available), which
provides spectral coverage by the detectors in the observed wavelength range of at least
two members of the H I Lyman series. This minimum coverage requirement is crucial
to the measurement of hydrogen column densities in the common cases where Lyα
absorption lines are saturated. We measure hydrogen column densities for ionization
and metallicity constraints, presented in Chapter 5.
We chose objects with known NALs within |v| < 8000 km s−1 in lower resolution
data, e.g. SDSS spectra. In order to be detected in the lower resolution spectra, these
NALs always have rest-equivalent widths (REWS) REW(C IV1548A) ≥ 0.1 A. For the
Magellan and Keck observations, we particularly selected NALs with apparent C IV
doublet REW ratios ≥ 1.5, in order to exclude strongly saturated systems, for which
accurate optical depths and abundances are difficult or impossible to determine. For
the VLT observations, we chose objects more loosely, selecting quasars with known
strong AALs by inspecting published spectra (Storrie-Lombardi et al., 1996; Veron-Cetty
& Veron, 2000; Peroux et al., 2001), which resulted in several saturated C IV systems. In
all cases, the individual doublet members are identifiable in the lower resolution spectra
(i.e. velocity widths < 700 km s−1).
Finally, we limit the apparent Magnitude (R or r, where available), which generally
encompasses the region of the spectrum with C IV emission and absorption, to r < 18.8
(except for quasar J1633+1411, zem > 4, which has r = 19.25), in order to achieve
acceptable signal to noise ratios (S/N) for reasonable observing times of tobs ≤ 3 hrs.
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Overall, the sample includes 24 quasars with 19 at z > 2.7, 8 of which are at z > 4.
We have full coverage of the velocity range of +5000 to -40,000 km s−1 from the quasar
systematic, where negative velocities denote motion towards the observer. The quasars
in this sample are not in any way biased regarding the intrinsic versus intervening nature
of the NALs in their spectra.
4.1.2 Observations and Data Reduction
We obtained data from several observing campaigns at the VLT+UVES, the
Magellan+MIKE and Keck+HIRES telescopes between the years of 2003–2008.
The VLT+UVES data were reduced by A. Narayanan using the VLT pipeline, the
Magellan+MIKE data were reduced using the Mike-Redux IDL routines package,
and the Keck+HIRES data were reduced using the MAKEE data reduction pipeline
written by T. Barlow. The spectra are sky background-subtracted and extracted from the
2D frame with a low order polynomial trace using a white dwarf standard observed under
similar seeing conditions as the target. We use a Thorium Argon (ThAr) lamp spectrum
for wavelength calibration. The spectra are not absolute flux calibrated.
We normalize the spectra to unity by fitting a pseudo-continuum to all of the quasar
emission, including the emission lines. We define the pseudo-continuum by applying
a polynomial fit to the local continuum in each Echelle order using IRAF1 . In cases
where significant absorption in a single order disguises the continuum, we combine
several adjacent Echelle orders, inspect the region for small unabsorbed sections of the
spectrum, and interpolate between these sections, dividing the entire region by a low
order polynomial fit. The continuum placement has an uncertainty of 2–3%, except in
heavily absorbed regions, where it increases to ∼ 10%.
1 The Image Reduction and Analysis Facility (IRAF) is supported by the NationalOptical Astronomy Observatories (NOAO) in Tucson, Arizona.
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The 24 quasars observed in this sample are listed in Table 4-1 along with their
emission line redshifts (zem), magnitudes (Mag.) and some observing details, including
observation dates, (rest) wavelength coverage (λ), spectral resolution (R), and
instruments used in the observations. The magnitudes are taken from the SDSS
where available. Others are from the NED database or the Veron-Cetty & Veron (2006)
catalog. The magnitude filter ( r , R or V ) is indicated with each entry in the table. The
last two columns of Table 4-1 list the number of NAL components (column 9) and
systems (column 10) that are intrinsic to the quasar environment, out of the total number
of components and systems found in each quasar (§ 4.3.2 below).
The emission redshifts are taken from the SDSS when available, or from recent
literature where redshifts are measured from Lyα, C IV, and possibly other lower
ionization lines (Peroux et al., 2001; Veron-Cetty & Veron, 2006; Hewett & Wild, 2010).
The SDSS redshifts are corrected for shifts in various emission lines with respect to the
quasar redshift. The reliability of the redshifts listed in Table 4-1 depends on the quasar,
but are generally accurate to within ∼ 1000 km s−1 of the quasar systemic velocity. The
uncertainty in redshift mainly affects the velocity shifts of AALs near the quasar redshift.
Thus, NALs with positive velocities (possibly indicating quasar infall) are considered
highly marginal candidates for infall, and likewise, only intrinsic NALs with velocities
above ∼ 2500 km s−1 from the quasar redshift are considered to be quasar outflow
candidates.
Above 6000 A, eight of the quasar spectra observed with HIRES and UVES are
affected by small gaps, generally 20–60 A, between Echelle orders or larger gaps,
∼ 100 A, where the spectrum falls into a physical gap between detectors. These
gaps affect about 6.5% of the total spectral region in the velocity range +5000 <
v < -40,000 km s−1 examined in this sample.
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4.2 Line Identification and Fitting
4.2.1 Identification
We identify C IV absorption lines by the appearance of doublet features in the 24
quasar spectra in our sample by visual inspection. We mark all absorption features
between +5000 and -40,000 km s−1 of the C IV emission line as defined by the quasar
emission redshift. We identify C IV doublets by the 498 km s−1 velocity separation
between the stronger (λ1548) and weaker (λ1551) members. The equivalent width is
measured by integrating across the Gaussian profile of each absorption line or blend
of absorption lines (§ 4.2.2). For an unblended line, we are complete down to rest
equivalent width, REW(1548)min = 0.02 A for the stronger C IV doublet member, except
for four spectra, BR 0351-1034, PSS 0134+3307, J1341-0115 and J1020-0020, which
have poor signal to noise and are complete to REW(1548)min = 0.03 A.
Each C IV absorption line is made up of individual components and is part of
a system of (probably related) absorption lines. Column densities and b-values are
evaluated individually for each component. Components may appear as: a) isolated
absorption lines, b) absorption features blended together to form an asymmetric line
profile, c) distinct absorption lines partially blended together but separated enough
to display a rise between two or more minima. In the last case, a component is only
counted if it contains at least ∼ 25% of the total REW of the absorber. The 25%
threshold was chosen to limit the number of individual components to the fewest
possible required to adequately describe the data (See Chapter 3). This conservative
approach ensures we are not over-interpreting the complexity of each absorption
feature. A system is comprised of a group of components, where each component
has a velocity separation of ≤ 200 km s−1 from the next component. By combining
absorption lines into systems, we assume that lines in clusters are physically related
(Sargent et al., 1988). The separation velocity we use was chosen based on our
observance of more common occurrences of clustering in our data at separations
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below this threshold, and also to aid the comparison of our results to that of Misawa
et al. (2007), who also use this separation velocity to define ’Poisson systems’. In
total, we identify 136 C IV systems, comprised of 271 individual components. 57 of
those systems, and 120 components are within the nominal associated velocity shift
of v < 5000 km s−1. Throughout this chapter we use the terms ‘lines’ and ‘NALs’ more
generally, for convenience, refering to components, systems or both depending on the
context.
The comparisons in Figure 4-1 illustrate the clear advantages of higher resolution
spectra for detecting weak lines and resolving multi-component blends. The figure
shows Keck+HIRES and Magellan+MIKE spectra of two quasars in our sample in
black, with the SDSS spectra of the same quasars over-plotted in red. C IV doublets
are labeled above the spectra. Consider, for example, the case of J1008+3625, whose
SDSS spectrum was included in the quasar absorption line surveys by Nestor et al.
(2008); Rodrıguez Hidalgo et al. (2010b). These studies did not detect most of the
discrete NALs shown in Figure 4-1, including the doublets at v = -5686 km s−1 and
-989 km s−1 that we identify as intrinsic lines based on the broad profiles (v = -5686 km s−1
line only) and evidence for partial covering (§ 4.3 and § 4.4 below). They also obtain a
good fit to the complex blend at -2450 km s−1 using just one C IV absorption doublet
with velocity v = -2451 km s−1, full width at half minimum FWHM = 474 km s−1 and a
1:1 doublet ratio (indicating saturation). Their derived FWHM is below the conservative
threshold they adopt for likely outflow systems (FWHM > 700 km s−1). It is therefore
identified as a single NAL, with an ambiguous origin. However, Figure 4-1 shows that
this feature is much more complex and our analysis (§ 4.3 below) indicates that it is
also part of a quasar outflow. The second quasar in the bottom panel of Figure 4-1,
J1307+1230, shows similar examples of several narrow NALs in the high resolution
spectrum that appear as single broad NALs in the SDSS spectrum. Furthermore,
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blending with Mg II λλ2796,2804 absorption is resolved in the high resolution spectrum,
but not in the SDSS spectrum.
4.2.2 Covering Fraction and Gaussian Line Fitting
The covering fraction (Cf ) of an absorption line system is a measure of the fraction
of the emission source which is covered by the absorbing gas. Measuring accurate
optical depths depends on correctly determining this factor. The line of sight covering
fraction affects the observed line intensity as follows:
Iv = (1− Cf )I0 + Cf I0e−τv (4–1)
where 0 ≤ Cf ≤ 1 is the velocity dependent line of sight covering fraction, I0 is the
emitted (unabsorbed) intensity and Iv and τv are the observed intensity and line optical
depth at each velocity shift, v. Our priority in measuring covering fractions of the NALs
in this sample is to use partial covering as a diagnostic of the intrinsic origin of the
gas. The background light source is assumed to have a uniform brightness (I0) and the
foreground absorber is assumed to be homogeneous with a single value of τv and Cf .
If the actual covering fraction does vary across the line profiles, the values of covering
fraction we derive from the fits are most relevant to the line cores (See Chapter 3,
Hamann et al. (2011)). These simplifying assumptions, implicit in equation 4–1, do not
impact the determination of the origin of the gas. For example, if the absorber is actually
inhomogeneous, the covering fractions we derive indicate the amount of coverage by
absorbing material with τ ≥ 1 (Hamann & Sabra, 2004; Arav et al., 2005). This still
requires small non-uniformities on a scale comparable to the emission source, and so
our conclusion about the location of the absorber is the same. We also assume that
both members of the C IV doublet have the same Cf at a given velocity, a necessity of
our Gaussian fitting technique.
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We fit each C IV absorption line component with a single Gaussian optical depth
profile using GATORPLOT2 in IDL. We use the Gaussian fits as a uniform, simple
method to measure line width, covering fraction and redshift for the full sample of NALs.
The Gaussian fits are useful and important for i) disentangling blends, ii) providing a
template for fitting or placing limits on other lines that can be badly contaminated by
unrelated absorption in e.g. the Lyα forest (useful in our abundance analysis presented
in Chapter 5) and iii) providing more reliable results than the point-by-point or τ -ratio
analysis in noisy data by fitting the ensemble profile (below) (Simon & Hamann, 2010b;
Hamann et al., 2011). Free parameters in the Gaussian fits are line-center optical depth
(τ0), redshift, Doppler width (b-value), and covering fraction (Cf ). The redshift, b-value,
covering fraction and the 2:1 τ ratio expected from the oscillator strength ratios for the
C IV doublet are locked and fit together for each doublet in the Gaussian fits.
We use these parameters to calculate the column density of ion i (Ni ) as follows,
Ni = 1.33× 1016(
τo
f λo
)(b
20 km s−1
)cm−2 (4–2)
where f is the line oscillator strength and λo is the rest wavelength in A. N is an
important parameter for determining the ionization and abundance of the gas; quantities
that we address for these NALs in Chapter 5.
Our Gaussian line profile fits measure the covering fraction of each component. The
covering fraction is fixed for all components in a blend. Even in simple blends it is not
possible to set up a formalism that applies to Cf < 1 in blends without making arbitrary
assumptions about how the absorbing regions overlap. Furthermore, in complex blends,
where, for example, C IV(1548) in one component blends with C IV(1551) of another, it is
not possible to derive Cf and the whole region must be fit together.
2 www.astro.ufl.edu/~warner/GatorPlot/
94
We are careful not to overestimate the number of systems or components with
partial covering. For all cases where the best fit Gaussian profile has Cf < 1, we repeat
the fit with Cf = 1 and compare the two fits with the data. We choose the Cf = 1 fit
unless the Cf < 1 fit is a visibly better match to the data. Individual components within
a complex blend that is otherwise well-fit by Cf = 1 may have partial covering, which is
indicated by poor fits with Cf = 1 to these individual components (See § 4.4 for specific
examples).
For all complex blends with signs of partial covering in individual components,
as well as for all other components with best-fit Gaussians with Cf < 1, we perform a
simple test using the 2:1 τ -ratio derived from the oscillator strengths of each doublet
member. We use this ratio along with the shape of the blue doublet member to predict
the shape of the red doublet member. The prediction should match the data if Cf = 1,
but should be weaker than the data if Cf < 1.
Finally, as is evident from the above τ -ratio analysis, the covering fraction is not
necessarily constant across an entire absorption line or blended system. To measure
this effect, and as a further check for NALs that appear to have partial covering, we
use a point-by-point method to measure τv and Cf across each profile suspected to
have partial covering based on the Gaussian fits. We step across the absorption line,
calculating average intensity in each small (a few times the resolution) regularly spaced
sections of the spectrum, using the ratio of the intensities in the doublet to measure Cf
and τv at each step.
All three of these methods for determining covering fraction (Gaussian fits, τ -ratio
analysis and point-by point analysis) are necessary to obtain reliable results, along
with some trial and error using different fits and our personal judgment. The actual
uncertainties in the Cf of an individual absorption line are not well characterized by the
photon statistics, which are measured in formal errors derived from the point-by-point
method. Furthermore, as in Hamann et al. (2011) and Chapter 3, the estimates for Cf
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and the formal errors break down for weak lines because the data are consistent with a
range of covering fractions, even with infinite signal to noise ratios. We therefore always
take the conservative approach where, if the range of acceptable covering fraction
values includes Cf = 1, we adopt Cf = 1.
The narrowest absorption lines have FWHM at least 1.5 times broader than the
spectral resolution although the vast majority of lines (93%) are more than 3 times
broader than the resolution, and 89% are significantly broader than this. The absorption
lines are, therefore, generally fully resolved, and such Gaussian optical depth profile
fits are sufficient to determine accurate optical depths and covering fractions. For the
small subset (29 or ∼ 11%) of components with FWHM less than 4 times the resolution,
we convolve the b-value with the instrument resolution to determine a more accurate
value. The covering fractions of these unresolved lines are highly uncertain. Narrow,
saturated doublets could be mistaken for doublets with partial covering in unresolved
lines. Therefore, all components with FWHM < 4 times the resolution automatically have
Cf = 1.
If both the point-by-point method and the τ -ratio method produce Cf < 1, and
the absorption line in question is resolved by at least FWHM = 4 times the spectral
resolution, and the noise in the spectrum is smaller than the formal errors in Cf , we
choose the Cf < 1 result. If a component appears to have Cf < 1 after consideration
from all three techniques, but is narrow (marginally unresolved), is in a noisy region
of the spectrum, or is in a region with uncertain continuum placement, we take a
conservative approach to avoid over-counting lines with partial covering and choose
the Cf < 1 result, but label it as ‘probable’. These ‘probable’ cases make up 3±1% of
all components in the sample. Similarly, we label all absorption lines with marginal or
unconvincing evidence for Cf < 1 as Cf = 1.
Two examples of absorption lines with robust Cf < 1 results are shown in Figure 4-2.
For the rightmost absorption line in J1326+0743 in the right panels of this figure, we
96
assume Cf = 1, because the red member of the C IV doublet appears to be affected
by noise spikes. The two other C IV doublets in this figure are well resolved and show
obvious signatures of partial covering. An example of two absorption lines with robust
Cf = 1 results are shown in Figure 4-3. The covering fraction in these examples is
generally Cf = 1 across the full velocity range, although one or two ‘dips’ in covering
fraction appear where the components are very narrow and unresolved, as in the
rightmost absorption feature in the right panel of Figure 4-3.
We show in Figure 4-4, two examples of absorptions lines with uncertain covering
fractions because they are unresolved (left panels) or have large uncertainties in
continuum placement (right panels). For the possibly unresolved absorption line (left
panel), which has FWHM ∼ 3 times the spectral resolution, we adopt the Cf < 1 result
and list the covering fraction as probably Cf < 1. There are only two cases where partial
covering is suspected for an unresolved or marginally resolved line. One case is shown
in Figure 4-4 and a second high-velocity absorption line has a resolved companion
nearby with partial covering, and is discussed below in § 4.4.2. For the absorption line
with uncertain continuum placement (right panel in Figure 4-4), we label the Cf < 1
result suspect, and include this absorption line in the sample with Cf = 1. We include
further examples of the τ -ratio analysis and point-by-point procedure in the discussion of
individual NALs in § 4.4.
4.3 Analysis
A table listing measured parameters for all 271 C IV NAL components can be
found in Appendix A. We divide the NALs into 3 classes using criteria based on Cf and
Doppler width (b-value) to separate intrinsic and probable intrinsic lines from the others.
We then compare the velocity distributions and other statistical properties of these line
classes to learn more about the nature and origins of intrinsic NALs. Every quasar in
the sample is covered from +5000 to -40,000 km s−1, and has (by design) at least one
NAL of moderate strength at v < 8000 km s−1, which ensures that we have sufficient
97
numbers of NALs in the interesting low-velocity regime for our comparisons between
different classes. We look for differences across emission redshift within the different
classes, but our sample is not large and we see no significant trends. Therefore,
throughout the rest of this chapter, we consider all the quasars together.
4.3.1 Absorption Line Classes
The 3 classes are defined as follows: class A contains only absorption we are
confident forms in intrinsic gas near the quasar, class B contains absorption that
probably forms in intrinsic gas, and class C contains all remaining absorption not
in class A or B, including unidentified intrinsic gas as well as intervening gas. We
determine if a NAL is in class A (intrinsic, outflow if v > 2500 km s−1) if i) it shows
unambiguous signatures of partial covering or ii) it has an ‘outflow-like’ profile and
b > 80 km s−1, our chosen b-value threshold, as described below. A NAL is classified
as class B (probably intrinsic) if i) it shows signatures of partial covering, but the partial
covering cannot be rigorously confirmed, or ii) it has 60 < b < 80 km s−1, and an
‘outflow-like’ profile, as described below. All other NAL components and systems are
classified as class C (intervening gas).
The intrinsic gas (class A or B) can form either in near-quasar environments such
as merger remnants or starburst outflows, or in quasar outflows. The gas with velocities
v > 1500 km s−1, a conservative threshold, must form in a quasar outflow, as gas in all
other near-quasar environments has lower velocities (§ 4). However, the uncertainty in
emission redshift for these quasars increases the velocity threshold for quasar outflows
up to v > 2500 km s−1.
Partial covering of the emission source implies that the absorbing gas is small and
probably near the emission source, so that the area of the absorbing gas is smaller than
the area of the emitting source. Although, see Hamann & Simon (in preparation) for
possible exceptions and further discussion of this topic. We assume that absorbing gas
exhibiting partial covering is intrinsic to the quasar environment, specifically in quasar
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outflows if the velocity is above 2500 km s−1 throughout this analysis. Our approach to
determine the covering fractions of NALs conservatively assumes Cf = 1 unless there is
strong evidence (§ 4.2.2) for partial covering (Cf < 1), possibly resulting in some intrinsic
NALs being classified as class C.
We use b-values to determine if certain NALs are formed in outflows based on
the b-values for intervening C IV NALs found in recent literature. One unambiguous
intervening absorption line is the Damped Lyman Alpha absorption (DLA), which forms
from gas in galaxies along the line of sight to the quasar. One study of Proximate DLAs,
like DLAs but in the same ‘over-density’ as the quasar, fit high ionization species with
Voigt profiles having 5 < b < 30 km s−1 (Ellison et al., 2010). Other studies of DLAs find
component b < 20 km s−1 in most cases, with no occurrences of b > 40 km s−1 (Lehner
et al., 2008; Peroux et al., 2002; Fox et al., 2007). Sub-DLAs are similar to DLAs, with
smaller H I column densities. The median C IV b-value for a sample of both DLAs and
sub-DLAs studied by Fox et al. (2009) is b ∼ 16 km s−1. High-ionization gas in Lyman
Limit Systems, another type of intervening gas, are measured by several authors to
have C IV b-values consistent with DLA studies (Prochter et al., 2010; Fox et al., 2008;
Schaye et al., 2007). Tzanavaris & Carswell (2003) fit non-DLA C IV absorption in higher
redshift quasars and find b < 16.2 km s−1, except for two cases with b = 32 km s−1.
From this brief review we conclude that the Doppler b-values for C IV NALs in gas
known to form in intervening systems rarely rises above b = 30 km s−1. Furthermore,
C IV absorption line gas forming in DLA and sub-DLA systems is generally comprised of
numerous narrow components.
One precaution when comparing the analysis of this current work with other studies
in the literature, is that the requirements for distinguishing a blended component in this
work are more strict than what is generally required in DLA literature. Consequently, this
work would more likely use fewer, broader components to fit the same C IV absorption
presented in the literature.
99
Bearing this precaution in mind, we use b-values to determine if NALs not
already considered intrinsic because of partial covering are formed in outflows. We
conservatively consider only absorption lines with b > 60 km s−1, twice as broad
as most of the broadest intervening gas, as class B outflows. NALs with widths
b > 80 km s−1, twice as broad as the broadest intervening NAL components, we
consider as class A outflows. With these conservative b thresholds, we miss narrow
outflow systems like the one in Hamann et al. (2011). Nonetheless, there are a small
number of absorption lines that meet these criteria in our sample. However, we do not
rely on b-values alone to determine the class of these broad lines. We also consider the
profile shape.
To illustrate which of these absorption lines fall into our class A or B outflow lines,
we depict several examples in Figure 4-5. The top left panel shows a C IV NAL with
b = 62 km s−1 in the quasar J0933+733. This NAL has a very square profile, consistent
with DLA or other intervening gas and is designated as class C. The top right panel
shows a C IV NAL with b = 196 km s−1 in the quasar J0351-1034. This NAL is also
very square, and not well-fit by our Gaussian optical depth fits, and is also designated
as class C. Further evidence that this NAL may be part of a DLA system, and not
an outflow is that this NAL doublet appears to be a blend of several components
with smaller b-values, evidenced by the square sides and spikes in the bottom of
the feature, which are not taken into account by our fitting algorithm that limits the
number of components to the fewest possible. The bottom left panel shows two NALs
with b ∼ 115 km s−1, also in the quasar J0351-1034. These NALs are well-fit by the
Gaussian fits and show broad and smooth profiles and strong evidence of partial
covering, and so are designated as class A. The bottom right panel shows a NAL with
b = 96 km s−1 in the quasar J1008+3623. It is well-fit by the Gaussian fit, and likely has
Cf < 1. It is also in class A.
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4.3.2 NALs per Quasar
The last two columns of Table 4-1 list the number of components and systems in
each individual quasar found in class A, in class A+B and the total number of NALS
found in each quasar. Figure 4-6 shows the number of components and systems per
quasar for the associated region (v < 5000 km s−1) and for the full range of velocity
coverage out to v < 40,000 km s−1. Black histograms show classes A+B+C (all NALs),
gray filled histograms show class A NALs and blue hashed histograms show classes
A+B NALs. Our selection of quasars with at least one NAL forming within 8000 km s−1
is evident, in that none of our quasars have less than one NAL within 5000 km s−1.
Our sample is not biased in the number of intrinsic NALs at v < 5000 km s−1. We find
percentages of intrinsic NALs roughly consistent with other recent NAL surveys in that
29+8−9% of our quasars have unambiguous (class A) intrinsic gas within 5000 km s−1,
and 46±10% have unambiguous intrinsic absorption within the full velocity range out to
40,000 km s−1. Intrinsic absorption occurs more frequently at lower velocities, with the
majority (64%) of class A absorption lines occuring within 5000 km s−1 of the quasar
systemic. Errors on fractions and percentages, here and elsewhere in this chapter, are
calculated using the Wilson score interval (Wilson, 1927; Agresti & Coull, 1998), which
takes into account errors from counting statistics, especially with small numbers. We use
a 66% confidence interval.
Table 4-2 lists percentages and average numbers (〈n〉) of NALs for each class
of NALs per quasar for different velocity ranges. The listed uncertainties for the
percentages and averages are defined by the above mentioned counting statistic
uncertainties. The percentage of quasars with one or more NALs in class A or
B is between 20 and 33% in the velocity ranges v < 5000, 0 < v < 12,000 and
5000 < v < 40,000 km s−1, and increases to 46% for class A NALs in the velocity range,
v < 40,000 km s−1. It is almost as common to have one intrinsic NAL component or
system as it is to have two or more in one spectrum or velocity bin. Intrinsic absorption
101
is commonly found in clusters of NALs, in that 64% of quasars with intrinsic class A
absorption lines have more than one intrinsic line. NALs can, however, be isolated or
in rich clusters. Individual intrinsic (class A or B) NALs are more common at velocities
below 12,000 km s−1, whereas those forming in quasars with more than one intrinsic
system more often appear at velocities above 12,000 km s−1. The few cases with more
than 6 systems are dominated by a few rich complexes at low velocity. There are two
quasars with 6 systems within 5000 km s−1 of the quasar redshift. J1225+4821 and
J1008+3623 both contain complicated C IV absorption complexes, similar in appearance
to that found in J1023+5142 (Chapter 3). We discuss these individual systems in some
detail in § 4.4.
4.3.3 Basic Parameter Distributions
Figure 4-7 shows the total number of NAL components (top) and systems (bottom)
versus velocity shift from the quasar. There is a clear excess of NALs (components
and systems) below 8000 km s−1. This excess is very similar to the excess found by
Nestor et al. (2008), even though we measure mostly much weaker lines in this sample
(below, also § 4.5.2) and our quasar sample was selected to have at least one C IV NAL
of moderate strength at velocities < 8000 km s−1 (§ 4.1.1). Evidently, the requirement in
our sample selection for at least one line at v < 8000 km s−1 does not introduce a strong
bias toward an excess of lines at these velocities. Moreover, there is no bias whatsoever
regarding the intrinsic versus intervening nature of these lines, i.e., the fractions that
fall in classes A, B and C. We also see a slight excess at velocities up to +2500 km s−1
towards the quasar in the components, but not the systems. This excess could be
evidence for quasar infall, although quasar emission redshift uncertainties render these
velocity shifts uncertain by up to ∼ 1000 km s−1 (§ 4.1.2).
Figure 4-8 presents various parameters for each NAL feature (component or
system) versus velocity shift. The large majority of intrinsic NALs appear below
8000 km s−1. The systems with large b-values tend to be intrinsic. Intrinsic (class A)
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NALs have slightly higher average column densities than those that are not intrinsic
(class C), a trend also seen in Table 4-3, which lists mean and median logN(C IV) for
each class of components. The errors on the means here and elsewhere in this chapter
are dispersion errors. Many narrow components, and some systems, with small REW
form in intrinsic gas. Table 4-3 also lists mean and median REWs for each class of
components and systems. It is interesting to note that most of the NALs have REWs
below the thresholds of previous medium-resolution NAL surveys. For example, 92+1−2%
of all components and 68% of all systems have REW < 0.3 A.
4.3.4 Intrinsic Fractions
4.3.4.1 Versus REW and logN
Figure 4-9 shows numbers (top) and fractions (bottom) of systems (left) and
components (right) versus REW(C IV1548A). There are very few NALs with REW > 0.6 A,
and above this limit, ∼ 60% form in intrinsic gas. The figure shows a general rise in
intrinsic fraction toward increasing REW. Also, as noted in Table 4-3, the intrinsic NALs
tend to be stronger and more often above the threshold of REW = 0.3 A of previous
medium-resolution NAL surveys. For example, 18+5−6% of components in Class A
compared to only 5±1% in Class C have REW > 0.3 A, while 57+11−10% of systems in
Class A compared to 27+3−4% in Class C lie above this threshold.
Generally, the stronger components are more likely to be intrinsic. In systems the
trend is the same, with 35% of NALs at 0.5 < REW < 0.6 A forming in intrinsic gas, and
the percentage increases with increasing REW. Overall, intrinsic lines are stronger than
intervening. For comparison, the mean REW for components in class A is 0.20±0.18 A
and the median is 0.14 A, compared to mean and median REWs of 0.12±0.22 A and
0.08 A for class C components. For systems the trend is the same: in class A, the mean
REW is 0.50±0.41 A and the median is 0.36 A, while for class C systems, the mean is
0.26±0.50 A and the median is 0.14 A. The very strong systems ( REW > 1.2 A) are all
in class A.
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Figure 4-10 shows number and percentages of C IV NAL components versus
column density. The NALs with the highest column densities are more likely to be
in class A or B than in class C. This trend is present in the the mean and median
column densities in Table 4-3, as well as in Figure 4-10. The mean column density
for components in class A is logN = 13.92±0.43 cm−2 and the median is 13.92 cm−2
and these values are roughly the same for classes A+B, while for class C, the mean
is logN = 13.42±0.49 cm−2 and the median is 13.39 cm−2. This suggests that the
tendency seen in Figure 4-9 for intrinsic lines to have larger REWs is not simply due to
these lines having larger b-values. Instead, intrinsic lines really do have larger column
densities. We cannot distinguish between whether this is caused by larger total columns,
N(H), or higher ionization and thus more C IV in the intrinsic systems. It could be a
combination of both, and we will address this question by comparing N(H I)/N(C IV) in
these different classes in Chapter 5.
4.3.4.2 Versus b value
We divide the components into subclasses based just on covering fractions. The
subclasses correspond to components with robust partial covering (A′), probable
partial covering (B′) and complete covering (C′). Figure 4-11 shows numbers (top) and
fractions (bottom) of components with partial and complete covering versus b-value.
The left side shows only case A′, while the right side shows cases A′+ B′ in the colored
histograms. The components in this sample are generally narrow. There are very
few NALs with b-values above 60 km s−1, and the majority have 10 < b < 20 km s−1.
The components with larger b-values are more likely to have partial covering. For
example, ∼ 30% of components with 30 < b < 60 km s−1 exhibit partial covering, while
below b < 30 km s−1, only ∼ 10% of NALs show partial covering. The tendency for
components with partial covering to be broader is further illustrated in Table 4-4, which
lists mean and median b-values for each class of components. Considering all the NALs
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together, 16–19% (class A only or class A+B) of components and 15–17% of systems
have Cf < 1.
4.3.4.3 Versus velocity shift
Figures 4-12 and 4-13 show numbers and percentages of components and
systems versus velocity shift from the quasar rest frame. Overall, roughly 20% of both
components and systems are probable intrinsic (classes A+B). The intrinsic components
and systems have lower velocities on average than the class C components and
systems, with a mean velocity shift of -5496±7715 km s−1 and median of -3254 km s−1
for the class A components and a mean of -7588±9613 km s−1 and median of
-4683 km s−1 for class A systems, as opposed to the class C components, which have a
mean of -23217±20428 km s−1, and median of -19121 km s−1 and the systems, which
have a mean of -23443±20239 km s−1, and a median of -22702 km s−1. There are
roughly 5 times more intrinsic systems in the velocity range of AALs, v < 5000 km s−1,
compared to high velocities, v > 8000 km s−1. The percentages of outflows in
components and systems is highest, up to 55%, at velocities below 10,000 km s−1.
We list the key percentages in Table 4-5. The excess in class C at low velocities, below
8000 km s−1, compared to higher velocities is due to unidentified intrinsic systems. This
is direct evidence that our counting algorithm is missing a significant number of intrinsic
lines. Therefore, there is a population of intrinsic NALs with Cf = 1 and small b-value.
Above v = 2500 km s−1, these intrinsic NALs are formed in outflows, while at lower
velocities they could also be environmental lines.
4.4 Notes on Individual Systems
4.4.1 Rich NAL Complexes
In this section we describe four rich absorption line complexes, similar in appearance
to the complex we discuss in Chapter 3. These rich complexes have more than ∼ 5
components spread over a small velocity range, generally ∼ 1000 km s−1 wide. They
are not found at velocities above ∼ 8000 km s−1. This fact suggests that most if not
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all such complexes are intrinsic to the quasar. Here we discuss the three complexes
in this study that are good candidates for being intrinsic to the quasar, along with one
complex that has an uncertain origin and may be a DLA or other intervening candidate.
For all the rich complexes in this study, we examined the profile of the corresponding
Lyα absorption and looked for other low ionization lines to rule out any probable DLA
candidates.
The only other complex cases like these with probable outflow origins in the
literature are Simon & Hamann (2010b) (J1023+5142) and Misawa et al. (2007)
(Hs1603+3802), the second of which also shows variability. A follow up study looking for
variability in the complex cases in this study may produce interesting results.
Q 0249-222: This complex is comprised of eight components, all in one system,
around 6800 km s−1 from the quasar emission velocity, and is shown in Figure 4-14.
There are two other absorption features, not shown in the figure, closer to the emission
velocity and several further out, all of which are in class C. This system and all of its
components have Cf = 1, and b-values below the cutoff for intrinsic components,
therefore they are in class C. This complex could be part of a DLA complex, or other
intervening gas. However, the b ∼ 40 km s−1 feature at v ∼ 6855 km s−1, along with the
relatively large velocity spread of this complex (�v ∼ 590 km s−1) could be indications
that the system is actually part of a quasar outflow, even though it is not counted as
such in this study.
PKS 2044-168: This complex has 10 components, all in 1 system, near 1500 km s−1
from the quasar emission velocity. The emission redshift is measured from the peak of
strong emission lines (probably C IV, possibly Si IV or Lyα) (Osmer et al., 1994). The
complex is shown in Figure 4-15. There are several NALs at higher velocities in this
quasar in class C. The component b-values are all small, less than 20 km s−1. The
REW for the system is greater than 0.3 A, but the individual components all have
REW < 0.2 A. This complex has partial covering in the components near 1700 km s−1,
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but not in those near 1550 km s−1. We show the partial covering analysis for these lines
in Figure 4-16. Based on the partial covering in the components at 1700 km s−1, this
system is in class A. The velocity shift near v∼ 2000 km s−1 is near (but below) the
threshold for intrinsic gas definitely forming in an outflow.
J1008+3623: This quasar has a rich absorption spectrum in C IV, with a total of
19 components making up 5 systems, around 1000 km s−1 from the quasar emission
velocity. The emission redshift for J1008+3623 comes from the SDSS spectrum, and is
uncertain up to ∼ 1200 km s−1 (zem = 3.1255). The C IV region is shown in Figure 4-17.
The b-values in this complex range from 5–56 km s−1. The REW is 1.573 A for the
system near 2500 km s−1. The components near 600, 0, 990 and 2500 km s−1 in
particular exhibit partial covering (Figure 4-18). The partial covering occurs not only in
the complex blend around 2500 km s−1, but interestingly, also in the relatively isolated
systems at lower velocities. Based on the partial covering, these systems are all in class
A. The high velocities of e.g. the system near 2500 km s−1 indicate a quasar outflow,
but two of the Cf < 1 systems appear to have v ≥ 0 km s−1. These intrinsic lines near or
above v = 0 km s−1 could be infall, although the emission line redshift uncertainty could
easily preclude this possibility.
This quasar also has another broad intrinsic line at v = 5686 km s−1 (Figures 4-1
and 4-5) that appears to have Cf < 1 and most likely forms in a quasar outflow, based
on the covering fraction, its large b-value and its high (> 2000 km s−1) velocity.
J1633+1411: This quasar has a rich C IV absorption line complex comprised of 16
components in 7 systems between 0 and 8000 km s−1, shown in Figure 4-19. Hewett &
Wild (2010) recalculate emission redshifts for SDSS spectra and measure zem = 4.375
for this quasar. We adopt this redshift, but note that the original redshift from SDSS is
4.334±0.001, and would shift all velocities toward the red by ∼ 2290 km s−1. The b
values in this complex range from 10 to 100 km s−1. This complex has partial covering
in several components, including the component/system near 400 km s−1, which is
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near the emission redshift (Figure 4-20). This particular NAL is nominally unresolved,
with FWHM ∼ 3.5 times the resolution. Based on its proximity to other NALs with
partial covering and because it could be considered resolved under less conservative
resolution limits, this system is in class B. The system near 5100 km s−1 is also in class
B, because it has b = 74 km s−1 and a smooth profile. The systems near 1540 and
5300 km s−1 are in class C, while the remainder of the systems in this complex are in
class A, due to partial covering in the components. This quasar also has a high velocity
narrow outflow system, near 19,840 km s−1 (discussed in § 4.4.2).
4.4.2 High-Velocity Outflow NALs
We highlight 6 of the 7 high velocity NALs above 10,000 km s−1 with partial covering
in this study in Figures 4-21 and 4-22. There are very few currently known cases of
partial covering at high velocities, and these could substantially increase their numbers
(Misawa et al., 2007; Rodrıguez Hidalgo et al., 2010b; Hamann et al., 2011). The
systems in Figure 4-21 have robust partial covering results. The line in BR 0714-6455
is narrow, but resolved, the line in J1633+1411 is resolved and has a good solid
continuum, and both are in class A. J1225+4831 has one strong, resolved NAL,
plus a second NAL with a more questionable covering fraction at 39,100 km s−1 that
is unresolved. This marginally unresolved NAL gains credibility next to its companion
NAL that clearly exhibits partial covering because the proximity of the two lines suggests
that they are related. The marginally unresolved NAL is in class B while its companion
is in class A. The b-values for these four lines are 17, 23, 6 and 15 km s−1. The highest
velocity line in this group is in the quasar J1225+4831, at v = 39,315 km s−1.
The systems shown in Figure 4-22 all have large uncertainties associated with the
covering fraction result; the line in J1307+1230 is in a noisy region of the spectrum, and
the second component of the system at 27,500 km s−1 appears to have Cf = 1, the line
in Q 0401-1711 is also in a very noisy part of the spectrum, and the line in Q 0249-222
is unresolved in the quasar spectrum, and furthermore is next to a component at
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22,800 km s−1 that appears to have Cf = 1. Although these lines possibly have partial
covering, our conservative classification scheme groups them in class C because of the
uncertainties listed above. Additional observations are needed to test the outflow versus
intervening nature of these NALs. The b-values for these three lines are 20, 30 and
7 km s−1. The highest velocity line in this group is at v = 55,620 km s−1, which is outside
of the range considered for the full quasar sample. The quasar sample is not complete
beyond 40,000 km s−1, and thus, any NALs above 40,000 km s−1 are not included in the
statistical aspects of this study.
The seventh high velocity NAL with partial covering, not shown in the figures, is in
class A at v = 26,200 km s−1, and in the quasar J0749+4152. Six of the high velocity
systems with partial covering occur as the only intrinsic lines in the quasar, while the
seventh is in quasar J1633+411, which has a rich C IV absorption complex and several
intrinsic systems at lower velocities (Figures 4-19 and 4-20) described above in § 4.4.1.
To confirm that these lines are in outflows, they should be checked for variability. We will
present metallicity measurements for the subset of these lines with sufficient ionization
data in Chapter 5.
4.4.3 Broad Outflow Features
We measured two broad outflow systems that are not included in our sample of
C IV NALs. We did not conduct a thorough search for broad outflow lines because our
normalizations and other processing of the segmented Echelle spectra might omit them.
These two cases stood out by visual inspection and we took special care with their
normalizations. Figure 4-23 compares our measurements of these features to their
appearance in SDSS. Our observations show clearly that the broad profiles are smooth
even at ∼ 6 km s−1 resolution, and they are not composed of many blended NALs.
The feature in J1341-0115 is a BAL with FWHM ∼ 4545 km s−1 and REW ∼ 9.9 A.
The feature in J1020+1039 has FHWM ∼ 1495 km s−1 and REW ∼ 1.4 A. It is better
described as a “mini-BAL” because it is slightly too weak and narrow to be a BAL
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(according to the definition in Weymann et al. (1991)). It is interesting to note that
there are several C IV NALs near this mini-BAL in the J1020+1039 spectrum, including
one pair with b ∼ 72 km s−1 (v = 12,450 km s−1) that we conservatively classify as
a possible outflow system (class B). Variability is common in broad outflow lines
(Rodrıguez Hidalgo et al., 2010b; Capellupo et al., 2010; Gibson et al., 2008, 2010),
and there is evidence for variability in both of these cases (Figure 4-23).
4.5 Discussion
4.5.1 Summary of Results
We study a sample of 24 quasars with 136 NAL systems comprised of 271
components to examine the nature of intrinsic gas, specifically in quasar outflows.
We select quasars with at least one relatively strong NAL at velocities < 8000 km s−1
within the redshift range 1.94 < z < 4.69 and measure all C IV NALs within the velocity
range of +5000 > v > -40,000 km s−1. We divide the NALs into three classes based
on the presence of partial covering or broad profiles to separate intrinsic lines from the
others, with secure intrinsic lines in class A, probable intrinsic lines in class B and all
other lines in class C. Our main results follow.
1) The fraction of quasars in our sample with at least one intrinsic NAL in the full
measured velocity range, v < 40,000 km s−1, is 46% for both intrinsic classes A and
A+B. The fraction is smaller if we consider only limited velocity intervals. For example,
29% of the quasars have at least one intrinsic NAL at velocities v < 5000 km s−1, while
the same percentage, 29%, have at least one intrinsic NAL at 5000 < v < 40,000 km s−1
(Table 4-2). These percentages might be skewed upward slightly, e.g., at velocities
v < 8000 km s−1, because i) we selected quasars to have at least one NAL in this
velocity range, and ii) the fraction of NALs that are intrinsic is higher at low velocities
(Figures 4-12 and 4-13).
2) Our quasar sample is not biased whatsoever regarding the intrinsic versus
intervening nature of the NALs at any velocity. We note a weak trend for higher intrinsic
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fraction at lower velocities in Figures 4-12 and 4-13. The highest class A and A+B
fractions occur around 2500 < v < 5000 km s−1, where nearly 40% of components, and
35% of systems are intrinsic.
3) There is a strong trend, clearly visible in Figure 4-10 (also Table 4-3), for
increasing intrinsic fraction with increasing C IV column density. Although we measure
just 10 NAL components with logN > 14.5 cm−2, our results suggest that roughly
half of these NALs are intrinsic. In contrast, the intrinsic fraction at column densities
12.5 < log N < 13.5 cm−2 is less than 5%, based on ∼ 180 measured components.
4) There is also a strong trend for increasing intrinsic fraction with increasing
REW (Figure 4-9). The mean and median REWs of the intrinsic (A+B) and other (C)
distributions are similar (Table 4-3), but large REW values clearly favor intrinsic lines.
5) A count of intrinsic lines based only on partial covering (classes A′ and B′)
produces a strong trend for increasing intrinsic fraction with increasing line width, i.e.,
b value (Figure 4-11). NALs with and without (class C′) partial covering both span wide
ranges in b, but the Cf < 1 features are clearly skewed toward higher b values. The
mean b value among class A′ NALs is 34±26 km s−1 compared to 21±18 km s−1 for
class C′. (The large dispersions quoted for these mean b values reflect the wide ranges
of measured values.) Across the b range from 30 to 60 km s−1, roughly 25–30% of the
NALs we measure are intrinsic based on Cf < 1.
6) We detect six rich complexes of NALs containing at least five components and
spanning velocity ranges from �v ∼ 600 km s−1 to at least �v ∼ 3000 km s−1. (One
of these complexes was described previously in Chapter 3.) All of these complexes
appear near the quasar redshifts. However, their maximum velocity extents range
from vmax ∼ 2000 km s−1 to vmax ∼ 7500 km s−1. Many of the C IV doublets in these
complexes are blended together and their proximity to each other in the spectrum
suggests that most or all of them have a common physical origin. Four of these
six complexes exhibit partial covering, while a fifth (shown in Figure 4-14) has one
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component with b ≈ 40 km s−1 and a wide velocity spread, �v ≈ 690 km s−1. These
highly structured, multi-component NAL complexes appear to indicate highly structured,
multi-component outflows from the quasars (Chapter 3 and § 4.5.3 below).
7) We identify three NALs at v > 10,000 km s−1 that are strong candidates for
quasar outflow lines based on definite partial covering (Class A). Their velocity shifts
range from 14,900 to 39,300 km s−1 with b values of only 15 to 23 km s−1 (Figure 4-21).
Three other high-velocity NALs at v ∼ 22,750 to ∼ 55,600 km s−1 present weak but
inconclusive (Class C) evidence for partial covering (Figures 4-22).
8) We find two incidences of BAL or mini-BAL spectra with profiles that remain
broad and smooth even at the high resolution of our study (Figure 4-23). These lines
also appear to be variable based on comparisons to previous lower resolution data.
4.5.2 Selection Effects and Comparisons to Other Work
A variety of techniques are commonly used to identify intrinsic NALs in quasar
spectra. They can be divided into two general categories. The first is a statistical
approach that uses large quasar samples to look for correlations between the incidence
of NALs and properties of the quasars. The best known example of this is the large
excess of NALs near the quasar redshifts (e.g., Weymann et al. (1979); Nestor et al.
(2008); Wild et al. (2008) and references therein). The excess at low velocities implies
that a large fraction of low velocity NALs have some physical relationship to quasar
environments. Nestor et al. (2008) and Weymann et al. (1979) decompose this excess
into a high velocity quasar outflow component and a low velocity ‘environmental’
component that could have either a quasar outflow or galactic origin. The second
approach examines the NALs individually for signatures of an intrinsic origin, such
as line variability, partial covering of the background line source, and broad smooth
absorption profiles (Hamann et al., 1997; Barlow & Sargent, 1997). In the present study,
we have relied on partial covering and (secondarily) broad smooth profiles to identify
individual intrinsic NALs.
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The extent to which these different techniques are redundant or complementary is
difficult to determine. Each has its own unique limitations and biases, and all of them
rely on assumptions that tend to underestimate the true numbers of intrinsic NALs in
quasar spectra. For example, the excess of NALs at small velocity shifts mentioned
above is measured relative to a baseline incidence of NALs at large velocities that are
assumed to be entirely intervening. These studies are therefore only sensitive to intrinsic
fractions of the relatively low velocity systems. However, other studies (Misawa et al.
(2007); Richards et al. (1999); Richards (2001), this work) have shown that significant
fractions (10% to 20%) of high-velocity NALs are intrinsic, and therefore the intrinsic
fractions inferred from the excess at low velocities are underestimated.
Similarly, studies that rely on measurement of some particular property of intrinsic
absorption will obviously miss intrinsic NALs that do not have this property. One
example of this is the variability studies by Narayanan et al. (2004) and Wise et al.
(2004), which both found that ∼ 25% of strong AALs (v < 5000 km s−1) in their samples
varied between just two observations, and therefore at least ∼ 25% of the AALs in
their samples are intrinsic. Our study relies on Cf < 1 and large b, but it is not known
what fractions of intrinsic lines have neither of these properties. It is also worth noting
that all of these properties, variability, Cf < 1 and large b, are probably much better
suited to detecting quasar outflow lines than they are at finding NALs that form in the
extended regions of quasar environments. Moreover, we have applied conservative,
strict thresholds on both Cf and b to avoid contaminating our intrinsic NAL sample with
false positives, such as unusually broad intervening lines.
More work is needed to estimate the extent to which our study and others like
it under-count intrinsic NALs. We note that there are known cases of intrinsic NALs
that do not satisfy our Cf or b criteria. For example, Hamann et al. (2011) measured
b ≈ 30 km s−1 in several C IV NALs that clearly have an intrinsic origin based on their
variability, partial covering and (secondarily) smooth rounded profiles. These features
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appear at velocity shifts of v ≈ 10,000 km s−1 and therefore, in spite of their narrow
widths, clearly form in a high-speed quasar-driven outflow. In another quasar, Hamann
et al. (2001) found Cf ≈ 0.96 in very well-measured box-shaped C IV profiles. The
partial covering is more obvious in other lines, see also Misawa et al. (2007). Low
densities derived for these particular NALs indicate that they form roughly 25 kpc from
the quasar, see also Hamann & Simon (in preparation). NALs like these would not be
identified as intrinsic in our current study.
Another important bias affecting all of these studies is the REW detection threshold.
We have shown here that NALs with larger REWs, as well as larger column densities
and larger b values, are significantly more likely to be intrinsic. This conclusion is
supported by the statistical study by Nestor et al. (2008), who showed that the excess
of NALs at low velocities is larger when considering only strong lines with large REWs.
This trend will affect any comparisons between the low resolution surveys (e.g., Nestor
et al. (2008); Wild et al. (2008); Vestergaard (2003)), which have REW thresholds
around 0.3 to 0.5 A, and high-resolution surveys like our own that are roughly ten times
more sensitive (also Misawa et al. (2007)). Roughly 70% of the NAL systems in our
survey have REWs below the medium resolution survey 0.3 A threshold.
With these caveats in mind, we now discuss briefly the results from previous work.
The quasar sample in this study was selected to contain quasars known to have NALs
within v < 8000 km s−1 of the quasar emission velocity, ensuring that a relatively large
number of NALs are present in the sample. We study individual NALs for signs of
intrinsic origins (partial covering and large b). The quasars are observed with high
resolution, resulting in a sensitivity to weaker NALs than are typically observable in
medium resolution surveys (Nestor et al., 2008; Wild et al., 2008; Weymann et al.,
1979). We also have complete velocity coverage from at least 0 to 40,000 km s−1 in our
entire sample and our selection requirement for at least one NAL at v < 8000 km s−1
ensures that these important velocities near the quasar redshift are well represented
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in our NAL analysis. We also emphasize that our quasar selection requiring a NAL at
v < 8000 km s−1 does not in any way bias our results regarding the nature and origins of
these lines, e.g., the fractions of lines that are intrinsic versus intervening.
Ganguly et al. (2001) use a statistical approach, assuming the excess below
v< 10000 km s−1 is composed of intrinsic systems in their study of z < 1 quasars. This
approach suggests that 66% of NALs are intrinsic at v < 10,000 km s−1. Wild et al.
(2008), in a large study of NAL quasars, find that 45% of strong (REW > 0.3 A) NALs
at velocities 3000 < v < 12000 km s−1 are intrinsic to the quasar. We find much lower
intrinsic fractions, 21% of NAL systems, for the velocity range 0 < v < 12000 km s−1
(Table 4-5).
Richards et al. (1999) and Richards (2001) take a different approach using medium
resolution spectra. They determine the fractions of NALs that are intrinsic to quasars by
looking for correlations with the quasar radio properties. They estimate that up to 36%
of strong C IV NALs (REW > 0.3 A) at 5000 < v < 25,000 km s−1, and possibly up to
∼ 71,000 km s−1 are intrinsic quasar outflow lines.
Wild et al. (2008) and Nestor et al. (2008) find very similar NAL excesses at low
velocities (v ≤ 12,000 km s−1), which they attribute to intrinsic gas. There is also an
excess of intrinsic NALs below 8,000 km s−1 in our sample (Figures 4-12 and 4-13),
although our excess is smaller. This smaller excess and the lower outflow fractions
obtained in our analysis could be caused by i) our much greater emphasis on weak
lines along with the trend in this sample for higher intrinsic fractions with larger REW,
ii) our conservatively high b threshold, which would miss at least one securely identified
outflow NAL system with b ∼ 30 km s−1 (Hamann et al., 2011), iii) our conservative
threshold for securely identify NALs with Cf < 1 (Class A), and iv) we miss intrinsic lines
with Cf = 1.
Vestergaard (2003) surveyed ∼ 100 quasars at z ∼ 2, finding that NALs with larger
REWs are generally closer to the quasar rest velocity. Figure 4-8 shows a similar trend
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in our study as well for both systems and components. Vestergaard (2003) also sees
‘high velocity enhancements’ of strong NALs (REW > 0.5 A) near the BAL termination
velocity of ∼ 20,000 km s−1, which are considered to be likely candidates for quasar
outflows. This enhancement is not present in our survey.
For the small velocity range from 1800 < v < 4400 km s−1, in a survey of 1400
quasars with 2560 NALs, Nestor et al. (2008) find that ≥ 61% of NALs form specifically
in quasar outflows, as estimated from fits to the distributions. We find a smaller
intrinsic fraction of nearly 35% for NAL systems in a similar velocity range around
2500 < v < 5000 km s−1 in this study. Above 2500 km s−1, intrinsic gas must form
in quasar outflows, because other intrinsic environments do not produce such high
velocities.
The majority of the intrinsic gas found in our quasar sample is formed in outflows.
Of the class A intrinsic NALs in our sample, 60% of components and 77% of systems
are formed above v > 2500 km s−1 in quasar outflows.
In a study of 37 quasars with high resolution spectra at z ∼ 2, Misawa et al.
(2007) find 150 NALs, including 124 C IV and 18 AALs. They use the detection of
partial covering to identify intrinsic and outflow NALs and have similar detection limits
(REW ∼ 0.03–0.05 A) to our study. Misawa et al. (2007) find that ∼ 19% of all NALs
are reliably classified as intrinsic, while when the less reliable cases are included, this
fraction raises to 26% for their full sample of NALs. These intrinsic fractions are roughly
consistent with our sample. We find, for our full velocity range, v < 40,000 km s−1, that
17±2% of components and 15±3% of systems are reliably intrinsic (class A), and when
the less reliable cases (class B) are included, the percentages increase to ∼ 20% for
both components and systems. Misawa et al. (2007) find that 10–20% of NALs with
velocity shifts above 5000 km s−1 could be intrinsic based on partial covering. For our
NALs with 5000 < v < 40,000 km s−1, the percent of components and systems in class
A is 8–11%, which is somewhat lower than their fractions. We find six possible cases of
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NALs with partial covering at high velocities, v > 10,000 km s−1, three of them class A
NALs (§ 4.4.2). The high velocity outflow systems in this sample are much weaker than
e.g. the high velocity mini-BALs found by Rodrıguez Hidalgo et al. (2010b). They are
also mostly much narrower than the variable outflow lines in Hamann et al. (2011), and
do not appear in groups like those lines.
Misawa et al. (2007) also find that 32% of quasars contain at least one intrinsic
C IV NAL. When they also include N V and Si IV NALs, up to 50% of quasars contain
at least one intrinsic NAL, even though these lines are less common than C IV and are
measureable in their data across more limited velocity ranges than C IV. We find that up
to 50±10% of our quasars contain at least one intrinsic NAL. For the low velocity range
0 < v < 12,000 km s−1, that percentage decreases to 33±10%, whereas Nestor et al.
(2008) find a percentage of ≥ 14% for the same velocity range. However, because every
one of our quasars has at least one NAL with velocity shift v < 8000 km s−1, and low
velocity NALs and AALs more generally have higher intrinsic fractions than high velocity
systems, our quasar sample might overestimate the fraction of quasars with one or more
intrinsic NAL. Misawa et al. (2007), on the other hand, should underestimate the fraction
of quasars with an intrinsic NAL because the low velocity range is not covered for ∼ 50%
of their quasar sample.
Misawa et al. (2007) do not find any trends in line width with the intrinsic nature of
NALs, with average intrinsic σv = 53.4 km s−1 and average intervening σv = 44.5 km s−1.
While we do not find any clear threshold in b-value that indicates intrinsic gas, we do
notice a trend for increasing b-value with increasing intrinsic fraction.
Tripp et al. (2008) observe 16 z < 0.5 quasars at high enough resolutions to
measure column densities and b-values. They find an excess of O VI NALs near the
emission redshift, similar to the studies based on C IV. These likely intrinsic NALs with
velocity shifts v < 2500 km s−1 posses few similarities to the population of randomly
distributed lines we would expect if some significant fraction were ejected in quasar
117
outflows. In a similar study of 16 quasars at z ∼ 2–3, Fox et al. (2008) argue that
the NALs with the largest O VI column densities, logN(O VI) > 15.0, probably form in
near-quasar environments. Some of these systems also exhibit partial covering. Our
analysis based on C IV does not indicate a clear threshold in N(C IV), above which all
NALs are intrinsic based on partial covering. However, we do find a clear trend for a
greater incidence of intrinsic C IV lines (based on Cf < 1) in components with larger
N(C IV) (Figures 4-8 and 4-10). This trend is somewhat weaker but still also evident in
the REWs (Figure 4-9).
4.5.3 Implications
Intrinsic narrow absorption lines are valuable probes of quasar outflows and host
galaxy environments during an important stage of massive galaxy evolution at high
redshifts (§ 4). It has become increasingly clear that significant fractions of the NALs in
quasar spectra are intrinsic to quasar environments. These lines might form in blowouts
of gas driven by starbursts or the quasar, which may contribute to feedback between the
black hole and host galaxy, ambient interstellar gas or halo material, or in gas left over
from messy mergers. This survey takes an important step to identify and characterize
some of the basic physical properties of these intrinsic lines.
Approximately 20% of all NALs with velocities v < 40000 km s−1 measured in this
quasar sample are intrinsic (class A and B), roughly consistent with similar studies (See
§ 4.5.2). Interestingly, the majority of this intrinsic gas (60% of components and 77% of
systems) is found above v > 2500 km s−1. Starburst winds typically produce outflows
with lower velocities (Heckman et al., 2000), suggesting that the majority of this intrinsic
gas forms in quasar outflows.
In the full measured velocity range, v < 40,000 km s−1, of this quasar sample, 46%
of the quasars contain at least one intrinsic class A or B NAL. In smaller velocity ranges,
for example v < 5000 km s−1, the fraction of quasars with class A or B intrinsic NALs
is 29%. Of all the quasars in the sample, 37% have at least one quasar outflow based
118
on the intrinsic NAL velocities and 21% have multiple outflow systems. The apparent
abundance of NAL outflows suggests that these previously uncounted outflows may
play an important role in quasar-host galaxy interactions. We do not yet have a clear
understanding of the amount of gas in the NAL outflows, which will be essential for
determining their significance in these processes.
The excess of NALs near v ≈ 0 km s−1 in our sample implies we are missing up to
40% of intrinsic NALs. The similar excesses and intrinsic fractions in Wild et al. (2008);
Nestor et al. (2008) and in other surveys (§ 4.5.2) further support this interpretation.
Additionaly, this implies that intrinsic gas often forms narrow lines, which do not exhibit
partial covering. The above outflow fractions we derive are therefore lower limits.
Other methods of detecting intrinsic gas, such as variability and abundances should be
employed to reduce the number of intrinsic NALs presently excluded from this and other
similar studies. In a subsequent paper, we will consider metallicity, ionization and total
column density, which constrain the prior star formation, evolutionary stage and physical
conditions of the gas, and may therefore provide useful methods to detect the previously
undetected intrinsic NALs.
The most extreme velocity NALs are interesting because they present a unique
challenge to our theoretical understanding of the structure and acceleration of quasar
outflows (Rodrıguez Hidalgo et al., 2010b; Hamann et al., 2011). We find three definite
and three additional possible Cf < 1 NALs at velocities v > 10,000 km s−1. The highest
velocity case has v ∼55,600 km s−1, but inconclusive evidence for an outflow origin.
Care should be taken in future studies of NAL outflows to consider the high-velocity
regime, so as not to miss this potentially intriguing population of high-velocity narrow
quasar outflows. As they are not commonly considered in NAL studies, their occurrence
rate is currently unknown. They could make up a significant fraction of NALs at high
velocities.
119
We also find evidence for highly structured quasar outflows in measurements
of several rich multi-component NAL complexes with Cf < 1 (Chapter 3). Some of
the velocity extents for the rich complexes are substantially larger than expected for
gas in individual galaxies or galaxy clusters (Richards et al., 1999; Ruiz et al., 2001,
2005; Popesso & Biviano, 2006). Therefore, a likely origin for these complexes is a
quasar-driven outflow. Four out of six of these complexes exhibit partial covering, and
one has at least one broad component with b > 80 km s−1, indicating more directly that
they are intrinsic to the quasar environments (Class A). This evidence for an intrinsic
origin combined with the large velocity shifts again point toward absorption in quasar
outflows. A fifth complex (Figure 4-14) with vmax ∼ 7270 km s−1 and �v ∼ 690 km s−1
does not exhibit partial covering and we classify it as Class C. Nonetheless, it has at
least one fairly broad component with b ∼ 40 km s−1 (at v ∼ 6855 km s−1) and its similar
appearance to the other rich complexes suggests that it is also an outflow candidate.
If the outflow interpretation is correct, these highly structured, multi-component NAL
complexes are indicative of highly structured, multi-component quasar outflows.
120
Figure 4-1. The region of C IV absorption in the Keck-HIRES spectrum of J1008+3623and Magellan+MIKE spectrum J1307+1230. The spectra of J1008+3623(top) and J1307+1230 (bottom) are shown in black. The SDSS spectra areshown in red. C IV doublets are labeled. The horizontal axis is velocity withrespect to the quasar emission velocity in kilometers per second and thevertical axis is flux as observed by SDSS.
121
Figure 4-2. The region of C IV absorption in the Magellan-MIKE spectra of J1020+1039and J1326+0743. The spectra of J1020+1039 (left) and J1326+0743 (right)are shown in black. These are clear cases of partial covering, except for theindividual absorption line component in J1326+0743 at -6115 km s−1, whichhas Cf = 1. The top panels show point by point analysis, while the bottompanels show the τ -ratio analysis. The horizontal axis is velocity shift and thevertical axis is normalized flux. In the upper panels, the solid curve is theblue member of the C IV doublet, while the dot-dashed curve is the redmember of the C IV doublet. The solid circles represent 1-Cf for the regioncentered on each circle. In the lower panels, the solid curve is the blue(stronger) component and the dot-dashed curve is the red (weaker)component of the C IV doublet. The dotted curve represents the predictedshape of the red (weaker) member of the doublet based on the optical depthof the blue member and the fixed optical depth ratio of the doublet. Theactual red member is stronger than the prediction, which can only occur ifthe pair exhibit partial covering.
122
Tabl
e4-
1.N
AL
quas
arsa
mpl
e.Q
SO
z em
Mag
.O
bs.
Dat
eλ
Ran
ge(A
)λ
Ran
ge(r
est)
RIn
st.
f com
pf s
ysQ
0105
+061
1.96
17.2
(V)†
21/9
/03
6000
-100
0020
30-3
380
1100
00U
VE
S0-
0/5
0-0/
421
/9/0
335
10-4
720
1185
-159
580
000
UV
ES
PS
SJ0
134+
3307
4.53
218
.8(R
)†26
/12/
0355
75-7
950
1010
-144
045
000
HIR
ES
0-2/
20-
2/2
6235
-857
511
30-1
550
4500
0H
IRE
SB
R02
45-0
608
4.23
818
.6(R
)†22
/9/0
360
80-7
940
1160
-151
511
0000
UV
ES
0-0/
20-
0/2
8055
-990
015
40-1
890
1100
00U
VE
S60
00-1
0000
1145
-191
011
0000
UV
ES
Q02
49-2
223.
205
17.7
(V)‡
22/9
/03
3260
-451
577
5-10
7580
000
UV
ES
0-0/
210-
0/8
4760
-684
011
30-1
625
1100
00U
VE
SQ
0334
-204
3.13
218
.2(V
)‡22
/9/0
332
60-4
450
790-
1075
8000
0U
VE
S0-
0/11
0-0/
347
60-6
840
1150
-165
511
0000
UV
ES
BR
0351
-103
44.
351
18.6
(R)†
26/1
2/03
6035
-839
011
30-1
570
4500
0H
IRE
S2-
0/4
2-0/
455
20-7
615
1030
-142
545
000
HIR
ES
BR
0401
-171
14.
236
18.7
(R)†
21/9
/03
6075
-989
011
60-1
890
1100
00U
VE
S0-
0/3
0-0/
3B
R07
14-6
455
4.46
18.3
5(R
)‡13
/2/0
833
40-5
140
610-
940
5760
0M
IKE
1-1/
91-
0/5
4840
-942
088
5-17
2545
150
MIK
EJ0
749+
4152
3.11
17.6
7(r)
26-2
7/3/
0736
45-8
100
885-
1970
4500
0H
IRE
S2-
0/12
1-0/
7Q
0933
+733
2.52
817
.3(V
)‡26
/12/
0340
75-6
505
1155
-184
545
000
HIR
ES
0-0/
110-
0/6
J100
8+36
233.
125
17.6
2(r
)26
-27/
3/07
3645
-810
088
5-19
6545
000
HIR
ES
14-0
/25
5-0/
9J1
020+
1039
3.16
817
.77
(r)
26/1
2/03
4075
-650
597
5-15
6045
000
MIK
E1-
1/11
1-1/
83/
21/0
733
40-5
140
800-
1230
5760
0M
IKE
4840
-942
011
60-2
260
4515
0M
IKE
J102
0-00
202.
5996
17.7
6(r
)20
/3/0
733
40-5
140
930-
1430
5760
0M
IKE
0-0/
20-
0/1
4840
-942
013
45-2
615
4515
0M
IKE
J102
3+51
423.
4518
.47
(r)
28/3
/07
3680
-810
082
5-18
2045
000
HIR
ES
3-0/
202-
0/8
1201
+011
63.
233
17.8
1(r
)21
/3/0
733
40-5
140
790-
1215
5760
0M
IKE
0-0/
70-
0/5
4840
-942
011
45-2
225
4515
0M
IKE
BR
1202
-072
54.
6918
.7(R
)†21
,28-
29/7
/03
6700
-850
011
75-1
495
1100
00U
VE
S0-
0/16
0-0/
75/
6/03
6700
-850
011
75-1
495
1100
00U
VE
S0-
0/16
0-0/
766
00-1
0600
1160
-186
011
0000
UV
ES
123
Tabl
e4-
1.C
ontin
ued
J122
5+48
313.
0917
.67
(r)
27/3
/07
3680
-810
090
0-19
8045
000
HIR
ES
1-0/
301-
0/16
J130
7+12
303.
217
.69
(r)
20/3
/07
3340
-514
079
5-12
2557
600
MIK
E0-
0/11
0-0/
648
40-9
420
1150
-224
045
150
MIK
E13
26+0
743
4.17
17.7
4(r
)13
/2/0
833
50-5
140
650-
995
5760
0M
IKE
2-0/
62-
0/4
4840
-942
093
5-18
2045
150
MIK
EJ1
341-
0115
2.76
618
.18
(r)
21/3
/07
3340
-514
088
5-13
6557
600
MIK
E1-
2/6
1-1/
548
40-9
420
1285
-250
045
150
MIK
EJ1
430+
0149
2.11
17.7
3(r
)20
/3/0
733
40-5
140
1075
-165
057
600
MIK
E0-
0/11
0-0/
448
40-9
420
1555
-303
045
150
MIK
EJ1
633+
1411
4.37
5††
19.2
5(r
)27
/6/0
847
22-8
780
880-
1640
4500
0H
IRE
S11
-4/2
04-
2/10
PK
S20
44-1
681.
937
17.3
6(V
)†21
-22/
9/03
6000
-100
0020
40-3
405
1100
00U
VE
S7-
0/10
1-0/
19/
22/0
335
10-4
720
1195
-161
080
000
UV
ES
Q22
04-4
083.
155
17.5
7(V
)†21
-22/
9/03
4760
-684
011
45-1
645
1100
00U
VE
S0-
0/16
0-0/
89/
22/0
332
60-4
450
785-
1070
8000
0U
VE
STo
tal
......
......
......
...46
-54/
271
20-2
7/13
6N
OTE
S.–
Col
s.(2
)and
(3):
Red
shift
san
dr-
mag
nitu
des
from
SD
SS
obse
rvat
ions
,unl
ess
othe
rwis
eno
ted,
Col
.(4
):O
bser
vatio
nda
te(d
d/m
m/y
r),C
ols.
(5)a
nd(6
):O
bser
ved
and
rest
wav
elen
gth
rang
es,C
ols.
(7)a
nd(8
):R
esol
utio
nan
din
stru
men
tuse
din
obse
rvat
ions
,Col
s.(9
)and
(10)
:Fr
actio
nof
com
pone
nts
and
syst
ems
incl
ass
(A)-
(B)/(
All)
inth
equ
asar
.†
mag
nitu
dean
dre
dshi
ftfro
mN
ED
data
base
.‡
mag
nitu
dean
dre
dshi
ftfro
mV
eron
-Cet
ty&
Ver
on(2
006)
.††
reds
hift
from
Hew
ett&
Wild
(201
0)
124
Figure 4-3. The region of C IV absorption in the VLT-UVES and Magellan-MIKE spectraof BR 1202-0725 and J1430+0149. The spectra of BR 1202-0725 (left) andJ1430+0149 (right) are shown in black. These are clear cases of completecovering. The top panels show point by point analysis, while the bottompanels show the τ -ratios. The symbols and line-styles are the same as inFigure 4-2.
125
Figure 4-4. The region of C IV absorption in the Keck-HIRES and Magellan-MIKEspectra of J1633+1411 and J1326+0743. The spectra of J1633+1411 (left)and J1326+0743 (right) are shown in black. These are marginal cases ofpartial covering. The case shown on the left is categorized as probablyCf < 1 in the sample, while the case shown on the right is categorized asCf = 1 in the sample. The top panels show point by point analysis, while thebottom panels show the τ -ratio analysis. The symbols and line-styles are thesame as in Figure 4-2.
126
Figure 4-5. The region of C IV absorption in the Keck-HIRES spectra of J0933+733,J0351-1034, J0351-1034 and J1008+3623. The spectra of J0933+733 (left)and J0351-1034 (right) in the top panels and J0351-1034 (left) andJ1008+3623 (right) in the bottom panels are shown in black. The top twopanels show examples of C IV doublets that have b-values 62 and196 km s−1, but are not considered outflow candidates (class C), because oftheir square profiles. The bottom two panels show C IV doublets that haveb-values 120, 109 and 96 km s−1 and are considered outflow candidates(class A), because of their rounded profiles, large b and partial covering.Gaussian fits are shown in blue, illustrating the poor fit to the top two panels,and the good fit to the bottom two panels. The upper horizontal axis isvelocity with respect to the quasar emission velocity in kilometers persecond, the lower horizontal axis is observed wavelength in angstroms andthe vertical axis is normalized flux.
127
Figure 4-6. NALs per quasar. Black hashed histograms show all NALs (A+B+C), grayfilled histograms show only class A NALs and blue hashed histograms showclasses A+B NALs. The top panels show systems (left) and components(right) for associated absorption lines within v < 5000 km s−1. The bottompanels show systems (left) and components (right) for all NALs withinv < 40,000 km s−1.
128
Figure 4-7. Total number of NALs versus velocity shift from the quasar systemic.Components are shown in the top panel and systems in the bottom panel.The velocity shifts are shown in bins of 2500 km s−1 atv < -10,000 km s−1and 5000 km s−1 at v > -10,000 km s−1.
129
Tabl
e4-
2.Pe
rcen
tage
ofqu
asar
san
dnu
mbe
rsof
NA
Ls.
Velo
city
rang
e—
——
-Cla
ssA
——
—-
——
-Cla
sses
A+B
——
-—
–A
llC
lass
es(A
+B+C
)—–
(km
s−1 )
1+2+
〈n〉
1+2+
〈n〉
1+2+
〈n〉
Com
pone
nts:
v<
5000
29+
10 −821
+9 −7
1.38±
3.12
29.1
0−8
21+
9 −71.
38±
3.12
100+
0 −475
+8 −1
05.
0±
4.83
0<
v<
1200
033
+10 −9
25+
10 −81.
50±
3.05
33+
10 −925
+10 −8
1.50±
3.05
96+
3 −679
+7 −9
6.42±
4.75
5000
<v
<40
000
29+
10 −88+
7 −40.
50±
1.10
29+
10 −88+
7 −40.
50±
1.10
92+
4 −779
+7 −9
6.29±
5.38
v<
4000
046±
1029
+10 −8
1.88±
3.65
46±
1029
+10 −8
1.88±
3.65
100+
0 −410
0+0 −4
11.2
9±
7.61
Sys
tem
s:v
<50
0029
+10 −8
13+
8 −50.
50±
0.98
33+
10 −921
+9 −7
0.67±
1.13
100+
0 −458
+9 −1
02.
38±
1.58
0<
v<
1200
037
+10 −9
21+
9 −70.
63±
1.01
38+
10 −925
+10 −8
0.75±
1.15
96+
3 −675
+8 −1
02.
96±
1.85
5000
<v
<40
000
29+
10 −84+
6 −30.
38±
0.71
33+
10 −98+
7 −40.
46±
0.78
92+
4 −775
+8 −1
03.
29±
2.39
v<
4000
046±
1021
+9 −7
0.88±
1.33
50±
1033
+10 −9
1.13±
1.60
100+
0 −492
+4 −7
5.67±
3.36
NO
TES
–Pe
rcen
tage
sof
quas
ars
with
1+or
2+N
ALs
inea
chcl
ass,
and
aver
age
num
bero
fNA
Lspe
rqua
sar(〈n〉).
130
Figure 4-8. Measured parameters versus velocity shift for components and systems.The filled squares show class A NAL components or systems. The opendiamonds show class B+C components or systems. The horizontaldot-dashed line in the top left panel shows the b-value cutoff for inclusion inclass B based on width. The horizontal dashed line shows the b-value cutofffor inclusion in class A based on width, if the absorption shows signs ofoutflow signatures (See § 4.3). The horizontal dashed lines in the rightpanels mark the completeness limit for Nestor et al. (2008) and other similarNAL studies of REW(1548) > 0.3 A.
Table 4-3. Average values by class.Class REW A logN cm−2
Mean Median Mean MedianComponents:A 0.20± 0.18 0.14 13.92± 0.43 13.92A+B 0.21± 0.19 0.141 13.92± 0.44 13.92C 0.12± 0.22 0.08 13.42± 0.49 13.39All 0.14± 0.22 0.09 13.49± 0.52 13.44Systems:A 0.50± 0.41 0.36 – –A+B 0.47± 0.38 0.37 – –C 0.26± 0.50 0.14 – –All 0.30± 0.49 0.15 – –
131
Figure 4-9. Intrinsic fraction versus REW. The gray filled histograms show class Acomponents (right panels) and systems (left panels), the blue hashedhistograms show class A+B components (right panels) and systems (leftpanels), and the black histograms show class C components (right panels)and systems (left panels).
Table 4-4. Average component b-values by Cf class.Class Mean b km s−1 Median b km s−1
A′ 33.57± 26.13 26.0A′+B′ 32.13± 24.94 24.33C′ 21.16± 18.17 16.70All 22.67± 19.59 17.42
132
Figure 4-10. Intrinsic fraction versus component N. The gray filled histograms showclass A components, the blue hashed histograms show class A+B outflowcomponents, and the black histograms show class C components.
Table 4-5. Percentages and numbers of NALs per velocity range.Velocity (km s−1) % Class A % Classes A+B Total All Classes (A+B+C)Components:v < 5000 28± 4 33± 4 120v < 7500 25± 3 29± 3 1600 < v < 12000 23± 3 27± 3 1545000 < v < 40000 8± 2 10+3
−2 1517500 < v < 40000 5± 2 7+3
−2 111v < 40000 17± 2 20± 2 271Systems:v < 5000 21+6
−5 28+6−5 57
v < 7500 23+5−4 28± 5 74
0 < v < 12000 21+5−4 25± 5 71
5000 < v < 40000 11+4−3 14+4
−3 797500 < v < 40000 6+4
−2 10+4−3 62
v < 40000 15± 3 20± 3 136
133
Figure 4-11. Intrinsic fraction based on Cf only versus b-value. The filled histogramsshow the sure (class A′) and sure + probable (classes A′+B′) partialcovering cases (green and blue), the red hashed histograms show thecomplete covering cases (class C′), and the black histograms show allcomponents (classes A+B+C). The top panels show numbers, the bottompanels show percentages.
134
Figure 4-12. Intrinsic fraction versus velocity for components. The gray filled histogramsshow class A components, the blue hashed histograms show class A+Bcomponents, and the black histograms show class C components. Thenumbers and percentages are per 2500 km s−1, and the bins are2500 km s−1 wide up to 10000 km s−1, and 5000 km s−1 wide from 10000to 40000 km s−1.
Figure 4-13. Intrinsic fraction versus velocity for systems. The gray filled histogramsshow class A systems, the blue hashed histograms show class A+Bsystems, and the black histograms show class C systems. The numbersand percentages are per 2500 km s−1, and the bins are 2500 km s−1 wideup to 10000 km s−1, and 5000 km s−1 wide from 10000 to 40000 km s−1.
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Figure 4-14. The region of C IV absorption in the VLT+UVES spectrum of Q 0249-222.The spectrum is shown in black. The Gaussian fit is shown in blue and C IVdoublets are labeled. The lower horizontal axis is observed wavelength inangstroms, the upper horizontal axis is velocity with respect to the quasaremission velocity in kilometers per second and the vertical axis isnormalized flux.
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Figure 4-15. The region of C IV absorption in the VLT+UVES spectrum ofPKS 2044-168. The spectrum is shown in black. See Figure 4-14 fordescriptions of other lines, symbols and axes.
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Figure 4-16. Point-by-point analysis of covering fraction for PKS 2044-168 system near1800 km s−1. In the upper panel, the solid curve is the blue member of theC IV doublet, while the dot-dashed curve is the red member of the C IVdoublet. The solid circles represent 1-Cf for the region centered on eachcircle. In the lower panel, the solid curve is the blue (stronger) componentand the dot-dashed curve is the red (weaker) component of the C IVdoublet. The dotted curve represents the predicted shape of the red(weaker) member of the doublet based on the optical depth of the bluemember and the fixed optical depth ratio of the doublet. The actual redmember is stronger than the prediction, which can only occur if the pairexhibit partial covering.
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Figure 4-17. The region of C IV absorption in the Keck-HIRES spectrum of J1008+3623.The spectrum is shown in black. See Figure 4-14 for descriptions of otherlines, symbols and axes. We note that the redshift of the quasar isuncertain by up to ∼ 1200 km s−1 due to blueshifts in the BELs relative tothe systematic redshift of the quasar (Espey, 1993), and thus v > 0 km s−1
velocities does not necessarily imply infall towards the quasar.
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Figure 4-18. Point-by-point analysis of covering fraction for J1008+3623 systems in aregion of rich C IV absorption. The symbols, line styles and axes are thesame as in Figure 4-16.
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Figure 4-19. The region of C IV absorption in the Keck-HIRES spectrum of J1633+1411.The spectrum is shown in black. See Figure 4-14 for descriptions of otherlines, symbols and axes. The same redshift uncertainties apply to thisquasar as to J1008+3623, as described in Figure 4-17.
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Figure 4-20. Point-by-point analysis of covering fraction for J1633+1411 components at-441, -6650, -7007 and -8335 km s−1. The symbols, line styles and axesare the same as in Figure 4-16.
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Figure 4-21. Point-by-point analysis of covering fraction for BR 0714-6455, J1633+1411and J1225+4831 high velocity systems. These three systems have robustpartial covering results and are grouped in class A. The symbols, line stylesand axes are the same as in Figure 4-16.
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Figure 4-22. Point-by-point analysis of covering fraction for J1307+1230, Q0401-1711and Q 0249-222 high velocity systems. These three systems all have largeuncertainties, and are grouped in class C. The symbols, line styles andaxes are the same as in Figure 4-16.
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Figure 4-23. Broad absorption in C IV for two quasars in the sample. Regions containingBAL or mini-BAL C IV absorption in the spectra of J1341-0115 andJ1020+1039 in the top and bottom panels are shown in black. The upperhorizontal axis is velocity with respect to the quasar emission velocity inkilometers per second, the lower horizontal axis is observed wavelength inangstroms and the vertical axis is normalized flux. The SDSS spectra areshown in red.
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CHAPTER 5METALLICITY OF NARROW ABSORPTION LINES IN 19 QUASARS AT REDSHIFTS
2.7 < Z < 4.6
Narrow absorption lines (NALs) in C IV λλ1548, 1551 and other metal ions in
quasar spectra can be found across a wide range of velocity shifts from the quasar
redshift. Recent medium resolution surveys, including Richards et al. (1999); Vestergaard
(2003); Nestor et al. (2008); Wild et al. (2008), have discovered that a large fraction of
NALs at all velocity shifts may actually be physically related to the quasar environment.
These NALs, called “intrinsic” NALs, can form in the host galaxy halo, in starburst
outflows or in quasar outflows. The intrinsic NALs may be located near the central black
hole or far out in the halo of the host galaxy and are therefore extremely valuable tools
for understanding the gaseous environments near the quasar. Intervening NALs form
outside of the immediate environment of the quasar, most often in intervening galaxies
along our line of sight to the quasar.
Quasar gaseous environments are regions that can potentially influence the
evolution of the quasar-host galaxy system. These environments are formed as part of
an evolutionary process involving interaction between quasars and their host galaxies,
and provide clues as to how this process develops and progresses. High redshift
quasars are particularly interesting in this regard, in that they occur during the peak
of massive galaxy formation, between redshifts 2 to 4. The appearance of a quasar in
one of these massive host galaxies signifies the rapid growth of the central black hole,
and probably also the host galaxy. Studying the gaseous environment using intrinsic
NALs produces information about gas kinematics, column densities, ionizations and
abundances. The ionizations and abundances in particular constrain the star formation
histories and chemical ‘maturity’ of the gas in the near-quasar environment of the
host galaxy. Most models of quasar-host galaxy evolution predict that the host galaxy
experiences a massive burst of star formation before the quasar becomes active (Di
Matteo et al., 2005, 2008; Hopkins et al., 2008). The quasar may even serve to quench
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the star formation in the near-quasar environment. The chemical maturity of the gas
in the near-quasar environment indicates the extent of the star formation before the
quasar activity dominated the system, and thus provides useful constraints for these
quasar-host galaxy evolution models.
Most surveys of intrinsic NALs are not of high enough resolution to measure column
densities, ionizations and metallicities. Those studies that do measure metallicity
typically do so for only the strongest NALs present in quasar spectra (Hamann
et al., 1997; Ganguly et al., 2006; Arav et al., 2007). Abundance measurements
of intrinsic NALs generally reveal metal-rich gas, from solar to a few times solar
metallicity (Hamann et al., 1997; D’Odorico et al., 2004; Ganguly et al., 2006; Arav
et al., 2007). These results are consistent with other studies using different methods
to determine abundances in quasar environments, e.g. studies that measure emission
line abundance ratios (Dietrich et al., 2003; Warner et al., 2003; Nagao et al., 2006;
Matsuoka et al., 2009; Simon & Hamann, 2010a)
In this study, we measure column densities, ionizations and metal abundances
for the sample of intrinsic C IV NALs in high redshift quasars analyzed in Chapter 4.
We build on the analysis and results of Chapter 4 by including the NALs of ions other
than C IV, deriving additional constraints on the ionizations and metal abundances
of the intrinsic gas. The NALs in this study are on average much weaker than those
measured in previous abundance studies, and have a range of velocity shifts, which
probably indicates a range of origins, from quasar outflows to merger remnants in the
host galaxy halo. We present results of the first large-scale abundance survey of these
weak, intrinsic NALs. The numerous possible origins of these NALs allow us to probe
the metallicities of a wide range of high redshift quasar environments for the first time.
These metallicities constrain the star formation histories in the different near-quasar
environments, which in turn will lead to better constraints on the evolution of the host
galaxy, in particular with respect to the nature of host galaxy-quasar interactions.
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5.1 Data
The data consist of an unique sample of 19 quasars with emission redshifts
2.7 < z < 4.6, 8 of which are at z > 4. Each quasar was observed with one of three high
resolution spectrographs: the High Resolution Echelle Spectrograph (HIRES) on the
Keck I telescope, the Magellan Inamori Kyocera Echelle (MIKE) on the Magellan Clay
Telescope (Mag.) and the Ultra-Violet Echelle Spectrograph (UVES) on the Very Large
Telescope (VLT). The observing campaign was designed to obtain column densities and
metallicities for a large sample of C IV NALs in high-redshift quasars. We observe only
quasars with at least one C IV NAL above redshift z > 2.7 in order to ensure adequate
coverage of the Lyman series hydrogen wavelengths by the instruments. The result
of this limit is that we are able to measure at least Lyα and Lyβ at each NAL redshift,
so that hydrogen column densities can be determined, in principle, even where Lyα is
saturated.
The observations and data reduction are described in detail in Chapter 4.
5.2 Analysis
5.2.1 Continuum Fitting
Before we proceed with our analysis of individual NALs, we carefully normalize
each quasar spectrum to unity. We apply a pseudo-continuum to each spectrum, which
includes the quasar continuum as well as the emission lines. For relatively smooth
regions of the spectrum, e.g. near the C IV emission line, the pseudo-continuum is
defined by a polynomial fit to the local continuum using IRAF1 . For regions of the
spectrum with significant absorption that compromise the continuum, such as the
Lyα forest located at wavelengths below λrest < 1215 A and comprised of numerous
intervening Lyα absorption lines, we select large regions of the spectrum and apply a
1 The Image Reduction and Analysis Facility (IRAF) is supported by the NationalOptical Astronomy Observatories (NOAO) in Tucson, Arizona.
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polynomial fit to any small sections of the spectrum with no absorption, interpolating
between these sections.
Examples of our continuum fits for the regions around NALs used in the ionization
and abundance analysis (§ 5.2.3) for two NAL systems in two quasars are shown in
Figures 5-1 and 5-2. The spectra are shown in black, with the polynomial continuum
fits overplotted in red. Each panel shows the region around a different ion. The vertical
dashed lines indicate the location of the absorption line in each region. The continuum
placement is uncertain by only 2–3%, except in regions of heavy absorption, e.g. the
Lyα forest, where the uncertainty increases to ∼ 10%.
5.2.2 Line Identification and Gaussian Fits
We begin with the intrinsic C IV NALs measured in Chapter 4. In Chapter 4,
we determine the origins of 271 C IV NAL ‘components’ using measurements of
covering fraction (Cf ) and Doppler width (b). We define a NAL component in one of
three ways: 1) isolated absorption lines, 2) absorption features blended together to
form an asymmetric line profile, 3) distinct absorption lines partially blended together
but separated enough to display a rise between two or more minima. Further details
regarding these distinctions may be found in Chapter 4. In this chapter we refer to NAL
components simply as NALs. We divide the NALs into three classes based on origin:
class A NALs are securely intrinsic, class B NALs are probably intrinsic, and class C
NALs are the remainder of the NALs of undetermined origin. For the ionization and
abundance analysis presented here, we consider only the 54 class A and B intrinsic
NALs.
We search the quasar spectra manually, first for Lyα and other Lyman-series NALs
at the same redshifts as the C IV NALs. We refer to each set of NALs at a given redshift
as a system (not to be confused with the systems of related C IV NAL components
defined in Chapter 4). In cases where a C IV NAL is part of a complex blend of many
NALs, we select one or two components of the blend with the most separation from
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the other components. By selecting these more isolated components, we minimize
the number of Gaussian optical depth profile fits required to match the data, reducing
uncertainties inherent in the fitting procedure, without completely discarding all the NAL
gas in blends (which may have a greater likelihood of forming in a quasar outflow).
The Lyman lines are in the Lyα forest, and therefore are often severely blended
with intervening absorption lines. We measure NALs only in those systems with at
least two uncontaminated Lyman series lines or upper limits. By requiring at least two
Lyman lines, we are able to more accurately determine H I optical depths (and therefore
column densities) by considering the lines together as a multiplet and producing fits
to the data that accurately reflect the optical depth ratios expected from the respective
oscillator strengths of each line in the multiplet. We consider a Lyman line sufficiently
uncontaminated if the shape and centroid are consistent with other lines in the system,
or if the Lyman line is clearly absent (no absorption is present at the expected centroid
of the line). In an effort to remove possible biases in our sample of NAL systems related
H I strength, we do not exclude very strong, saturated Lyman lines, however, these lines
produce only lower limits on the H I column density. The Lyman lines are crucial for
determining H I column densities, used to measure the ionization and abundances of
the NALS. If sufficient Lyman lines are found at the C IV NAL redshift, we search the
spectrum for other common NALs such as N V, N III, Si IV, Si III, Si II, O VI, C III and
C II. These other NALs are useful for ionization constraints.
We fit each NAL with a Gaussian optical depth profile. The centroid of each
Gaussian is fixed at the redshift of the C IV NAL in the system. The free parameters
of the Gaussian fits are line center optical depth (τ0), Doppler width (b), and covering
fraction (Cf ). The covering fraction, Equation 4–1, measures the fraction of the emission
source that is covered by the absorbing gas along the line of sight. The covering fraction
is constant across each line profile, and all lines in multiplets such as the Lyman series,
Si IV and N V, have the same covering fraction.
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The fitting procedure for the lines other than C IV is largely similar to that of C IV,
as discussed in detail in Chapter 4.2. For multiplet lines, the optical depths are locked
to their appropriate ratios as dictated by their oscillator strength ratios. The centroids
are fixed to the redshift of C IV to ensure we consider only the physically related gas in
each system. As a further precaution, the b-values for H I are limited to ∼ 1.4 times the
b-values of C IV for NALs with C IV b-values above 30 km s−1. This limit is much lower
than the expected H I b-values assuming purely thermal broadening because these C IV
b-values greatly exceed the thermal widths expected for a gas photoionized by a quasar
or the inter-galactic ultraviolet spectrum (b < 15 km s−1). This indicates that the widths
are dominated by non-thermal broadening effects. However, we allow the H I b-values
to be up to ∼ 1.4 times the C IV b-values for the broader C IV NALs, and up to ∼ 3.6
times the C IV b-value for the narrowest C IV NALs instead of forcing the H I b-values
to be equal to those of C IV. This allows for some contribution of thermal broadening
to b in the narrower systems, which would affect H I more than C IV. Overall, our fits
to the Lyman lines should lead to reasonable but generously large estimates of the
amount of H I gas that coexists with C IV, and therefore, to conservatively low estimates
of the C/H abundance. The b-values for lines with lower ionization than C IV are fixed
to the C IV b-values. Higher ionization lines are allowed to be broader. The single lines
are also fit with Cf = 1 only, regardless of the covering fraction in C IV, because the
covering fraction for single lines generally cannot be uniquely determined, and does not
necessarily correspond to the covering fraction of C IV or other doublets at the same
redshift.
Examples of Gaussian fits for three systems in three quasars are shown in
Figures 5-3, 5-4 and 5-5. We measure ionizations and abundances for the higher
velocity NAL system shown in Figure 5-3, which has better H I constraints than the lower
velocity system, and for both systems shown in Figure 5-4, which have saturated Lyα
lines, but we measure upper limits for the H I column density from the Lyβ lines. We
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also measure ionizations and abundances for both systems shown in Figure 5-5, which
have good H I constraints but are not well-fit in H I by the single Gaussians used to fit
the C IV. The Gaussian fits to H I shown in the figure are the best fits to the data using
the parameters of the C IV fits. The ions used in the ionization and abundance analysis
(§ 5.2.3) are shown in the figures. More examples of Gaussian fits to C IV are shown
in Chapter 4, and further examples of Gaussian fits to intrinsic NALs in the quasar
J1023+5142 are shown in Chapter 3 in Figures 3-6 and 3-8.
5.2.3 Ionization and Abundances
We determine logarithmic [C/H] metallicities relative to solar metallicity for the class
A and B NALs with constraints on N(H I) using Equation 3–2 and N(C IV)/N(H I) column
density ratios. We use lines of other ions such as Si IV and N V only for ionization
corrections, using C IV, which is present in every NAL system in the study, and therefore
a consistent measurement for the whole study, to measure abundances.
We calculate column densities for all the NALs with Gaussian fitted optical depths
using Equation 4–2. The ionization correction (IC) is then determined by comparing
the ratios of column densities of different ions, preferably of the same element, e.g.
N(C III)/N(C IV), to the theoretical results of photoionization calculations presented in
Hamann et al. (2011). These correction factors can be large when comparing a highly
ionized metal like C IV to H I. The exact values depend on the ionization mechanism.
For their calculations, Hamann et al. (2011) adopt a nominal quasar spectrum consistent
with recent observational estimates at the critical ionizing (far-UV) photon energies. We
perform additional CLOUDY (Ferland et al., 1998) calculations using the inter-galactic
background spectrum in CLOUDY, which is based on Haardt & Madau (2005, private
communication). We find that the ionization fractions of interest in the present work
have only negligible differences between the two calculations, e.g., compared to
uncertainties in the measured quantities or derived ionization constraints. Therefore, our
ionization and abundance analysis should provide accurate results, regardless of the
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location of gas e.g. near the quasar or outside the quasar’s ionizing sphere of influence
(Chapter 4).
We prefer to derive ionization constraints from the column density ratios of two ions
of the same element. However, lower ionization ions such as C III and Si III are often
blended in the Lyα forest and are not formed in doublets, and so can only be treated
as upper limits. Therefore, we also estimate the IC from ratios such as N(N V)/N(C IV)
or N(Si IV)/N(C IV), with the additional assumption that the relative metal abundances
are approximately solar (Asplund et al., 2009). We also calculate robust lower limits on
the metal to hydrogen abundance ratios by applying minimum values of the ionization
correction (ICmin, Hamann et al. (1997)) to the measured C IV. Each metal ion has a
unique global ICmin that occurs near the peak of its own ionization fraction. For example,
f (H I)/f (C IV) peaks approximately where f (C IV) is largest. We use the values of
ICmin given in Hamann et al. (2011). Applying these minimum correction factors to the
observed column density ratios (Equation 3–2) leads to firm lower limits for [C/H]min.
The minimum ionization corrections provide firm lower limits on the abundances that
do not depend on the ionization uncertainties or the possibility of a multi-phase gas. In
particular, any gas components not at an ionization corresponding to ICmin would have
the effect of increasing the IC and thus also the measured abundance. See Hamann
et al. (2002) for further discussion.
The logarithmic [C/H] abundance ratio relative to the solar ratio can then be derived
from the ratio of measured column densities corrected for the degree of ionization in the
gas using Equation 3–2.
There are several sources of uncertainty folded into these abundance calculations.
These sources include errors in the continuum fits, in the Gaussian optical depth
line profile fits, uncertainties in the ionization corrections and the assumption of solar
metal/metal ratios. The formal statistical errors derived from the Gaussian fits are not
representative of the actual uncertainties, which are dominated by uncertainty in the
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continuum placement and in the inherent ambiguity in the choice of the ‘best’ Gaussian
fits. For isolated NALs, fitting uncertainties are dominated by uncertainties in the
continuum fits (∼ 2–3%), particularly in the Lyα forest (∼ 10%). For NALs in blends, the
unavoidable subjective decisions regarding the quality of a given Gaussian fit contributes
significantly to the uncertainty, especially for NALs in e.g. the Lyα forest.
Combining the uncertainties from both continuum and Gaussian fits yields
∼ 10–15% uncertainty overall in column density for an average, well-measured
absorption line (e.g. unblended C IV or other lines not found in the Lyα forest).
However, the column density uncertainty for a particular absorption line can vary
greatly, depending on the location of the line in the spectrum. For example, in the NAL
system shown in Figure 5-5, the Lyman lines are not well-fit by the single Gaussian used
to fit C IV. The continuum fits for the Lyman lines yield 8% uncertainty in the H I column
density, but the column density changes by a further 50% between the best-fit Gaussian
and the Gaussian that matches the C IV profile shape. In this particular system of lines,
as well as in the majority of other systems, the N(H I) has a higher uncertainty than
the N(C IV). The continuum is almost always better defined near the C IV NAL and we
use the C IV profile shape to define the profile shape used to fit all other NALs in the
system. In most systems, C IV is more often isolated than H I, which also produces
larger uncertainties in the H I fits than in the C IV. When propagating column density
uncertainties through to [C/H] abundances, the N(H I) uncertanties dominate N(C IV)
uncertainties. The higher velocity system shown in Figure 5-3 is an example of a system
that has fits with average uncertainties. This system also has the highest measured
metallicity, [C/H] =+1.3, of all the systems shown in Figure 5-6, while the system shown
in Figure 5-4 has larger than average uncertainties, especially in the H I fits, because
the H I NALs appear saturated, and the lowest measured metallicity, [C/H] =-1.8, of
all the systems. In systems where the Lyman lines are contaminated by blends, we
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only include upper limits on N(H I) in the abundance analysis. The systems with severe
blending in the Lyman lines are excluded from the abundance analysis altogether.
Different line strengths also contribute to the range of uncertainties, even for well-fit
lines. Weaker NALs are more affected by small uncertainties in the continuum fits. The
column density for a weak NAL can change by 25 to 50% depending on the continuum
fit uncertainty. On the other hand, saturated (strong) lines have larger uncertainties in
the Gaussian fits. The column density for a saturated NAL can change by 20 to 60%
depending on the parameters of the Gaussian fit.
The ionization correction uncertainties are different for each individual system.
For cases where the column density uncertainties are small for all ions involved in the
IC determination, the IC uncertainty is dominated by errors in the ionizing continuum
models used (below). In cases where the column density uncertainties are large,
the IC we measure is still largely unaffected. For example, a 50% uncertainty in the
N(Si IV)/N(C IV) column density ratio yields an uncertainty of 0.1 dex in the metallicity.
Regadless, the abundances determined using minimum ionization corrections for C IV
are independent of the IC uncertainty.
There is also an added uncertainty regarding the relative abundances of e.g. silicon
to carbon. In this study we assume [Si/C] = 0. For the generally large (approximately
solar) metallicities detected in this NAL sample, this is a reasonable assumption.
However, at lower metallicities in particular, Aguirre et al. (2004) make a strong case for
a non-solar [Si/C] ratio of [Si/C] = 0.77±0.05. Adopting this abundance ratio would lower
our metallicities by on average ∼ 0.15 dex.
There are further uncertainties related to the ionizing background used to determine
ionization fractions. We use the gas ionization parameters calculated by Hamann et al.
(2011) assuming a typical quasar spectrum. This spectrum is similar to the Haardt
& Madau (2001) spectrum, which, according to Aguirre et al. (2008), may produce
inaccurate results for gas located in the intergalactic medium (IGM). However, we
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compare ionization parameters obtained for a sample of NALs assuming both an
IGM background and our quasar background and find only small differences, where
metallicities measured assuming an IGM background would be lower by ∼ 0.2 dex.
Furthermore, the intrinsic (class A and B) NALs, for which we measure metallicities, are
likely located within the ionizing influence of the quasar background, except in the most
extreme cases, e.g. the NALs with velocity shifts above v ∼ 10,000 km s−1.
Combining all the sources of uncertainty, we estimate that our measured metallicities
have uncertainties of ≤ 0.25 dex in most cases, although the uncertainty could be as
high as 0.4 dex in the most poorly measured systems.
5.3 Discussion
We measure abundances for 28 C IV NALs in 19 quasars with emission redshifts
2.7 < z < 4.6. Figure 5-6 shows the [C/H] abundance versus velocity shift for all class
A and B (intrinsic) NAL systems with good ionization constraints. The green symbols
are abundances based on the minimum ionization correction for C IV, and are all lower
limits. The NALs with lower limits on abundances from assuming ICmin also have good
ionization constraints in many cases. The NALs with good ionization constraints have
ionizations measured from N(C II), N(C III), N(N V), or N(Si IV) versus N(C IV), or
from N(Si III)/N(Si IV) column density ratios. Abundances based on these measured
ionization corrections are shown in the figure in black, connected to the corresponding
lower limits based on ICmin (green symbols) with vertical lines. The class A NAL symbols
are diamonds, while the class B NAL symbols are triangles. The sizes of the symbols
correspond to the b values of the C IV NALs. The smallest have b < 27 km s−1, the
largest have b > 80 km s−1, and the medium sized symbols have b values in-between.
The metallicities we find are generally around solar, or slightly below solar. The
median metallicity lower limit we detect is [C/H] ∼ -0.46 (average is -0.63), while for
the detections with good ionization constraints the median metallicity is [C/H] = -0.08
(average is -0.2). There is no clear trend in metallicity with velocity shift for either NAL
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class (A or B). The NALs with broader C IV b values are more concentrated at the higher
metallicities, around solar and up to a few times solar. The NALs with lower metallicities
and broad b values only have lower limits on their metallicities.
Petitjean et al. (1994) create a similar figure to our Figure 5-6, and find a trend of
lower metallicity with larger velocity shifts. This trend appears to be in good agreement
with the findings of intrinsic NAL studies, as well as with those of intervening gas
studies, assuming intervening gas is predominantly found at larger velocity shifts.
Intrinsic gas is generally found to be metal-rich, with abundances equal to or above
solar, while intervening studies find much lower metallicities, often less than one
hundredth solar (Aguirre et al., 2004; D’Odorico et al., 2004; Simcoe, 2004; Ganguly
et al., 2006; Arav et al., 2007; Schaye et al., 2007). Our data do not clearly show this
trend for decreasing metallicity with increasing velocity shifts, although we have very few
points at the larger velocity shifts. Furthermore, all of the NALs in this study are intrinsic
based on partial covering in the C IV NALs or very broad C IV NALs (See Chapter 4).
The common intrinsic origin of all the NALs in this study likely contributes to the lack
of any obvious trend in metallicity with velocity. We do, however, see a wide range of
metallicities at low velocities. This is a surprising result, suggesting that not all the gas
in the near quasar environment is metal-rich, as is generally assumed. The metal-poor
NALs in our sample could be examples of gas in the host galaxy halo, far from the
center of the galaxy and the black hole.
One of the original goals of this study was to determine if the abundances or
physical properties of the NAL gas depend on redshift. For example, the NALs at
higher redshifts might have lower metallicities if the host galaxies are typically younger
and/or have experienced less prior star formation. We specifically targeted a sample of
z > 4 quasars to examine this relatively unknown region of redshift space. Figure 5-7
separates the NAL abundances by absorption line redshift. The blue diamonds are the
z > 4 NALs, the red triangles are the 3 < z < 4 NALs and the green crosses are the
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z < 3 NALs. We do not see a trend in metallicity with absorption line redshift. This is
consistent with the results from the emission line abundances studies (Dietrich et al.,
2003; Nagao et al., 2006; Matsuoka et al., 2009), which also find no trends with redshift.
However, our results contrast with the emission line studies, and with most prior
work on intrinsic NALs, in that we see a wide range in metallicities at all redshifts. Most
of the earlier work indicates that the metallicities in quasar environments are near solar
or several times higher (Hamann & Ferland, 1999; D’Odorico et al., 2004; Ganguly et al.,
2006; Arav et al., 2007). For example, Arav et al. (2007) measure a carbon abundance
of twice the solar abundance in Mrk 279 and D’Odorico et al. (2004) measure [C/H] for
six z > 2 NAL systems between +0.0 < [C/H] < +1.4 and two others with nearly solar
abundances ([C/H] = -0.04, -0.8). Our measured values range from [C/H] > -1.8 to +1.3.
There are several factors that might contribute to these metallicity differences.
First, the broad emission lines form within a parsec of the central black hole and are,
therefore, likely to sample the most metal-rich gas in the galaxy cores. Second, other
studies of intrinsic NALs have tended to focus on systems that are stronger than
ours (D’Odorico et al. (2004) is an exception) and that often have other indications
of formation very near the quasars such as variability and/or broad smooth profiles
indicative of quasar-driven outflows. Figure 5-8 shows N(H I) versus N(C IV) for our
class A and B NALs. Our N(C IV) are similar to or lower than most intrinsic studies, but
higher than studies of intervening gas (D’Odorico et al., 2004; Arav et al., 2007; Simcoe,
2004; Schaye et al., 2007). In Chapter 4 we argued that strong NALs are more likely
to be intrinsic and, at least at high velocities, more likely to form in a quasar outflow.
Thus, NALs with large N(C IV) are also more likely to be metal-rich. However, in our
sample we find that the NALs with stronger N(H I) generally do not have correspondingly
strong N(C IV). Figure 5-9 shows N(H I) versus N(C IV)/N(H I) for the class A and B
NALs in this study, and exhibits a trend for weaker N(C IV) compared to N(H I) as N(H I)
increases.
158
The high sensitivity of our data allows us to measure many weak (and very narrow)
NALs that might form characteristically in different gas components compared to the
stronger (and broader) NALs. For example, instead of being directly part of an outflow
ejected from the quasar, weak NALs might include filaments of ambient gas (e.g., farther
from the quasars) that are swept along by the quasar outflow gas. At low velocities
(conservatively below a few thousand km s−1 given the redshift uncertainties), we
expect NALs to form in a wider range of environments around the quasars, including
starburst-driven winds or ambient halo/interstellar gas, where the metallicities could be
considerably lower.
We tentatively conclude that our results are, for the first time, providing a representative
sampling of the metallicities across the full range of quasar environments. We have
discovered an unexpectedly large range in metallicity for intrinsic gas located in the
near-quasar environment. As expected from previous studies of quasar abundances,
NAL metallicity does not appear to evolve with quasar redshift. These results suggest
that quasar environments are not uniformly metal-rich, as has generally been assumed
based on the limited metallicity information available for a few specific quasar environments
(mostly emission line gas). These results remain consistent with the simplified model of
quasar-host galaxy evolution, wherein major episodes of star formation always precede
quasar activity. However, the interactions of the quasar with its host galaxy may be more
complex and varied than generally thought. In future work we will test these results
by i) including more NALs from Chapter 4 in our metallicity analysis to improve the
statistics and compare results between NALs that appear to have a common origin
based on their relationship in the spectra, ii) looking for metallicity trends with line
strength and N(C IV) to see if weaker NALs really do tend to sample lower metallicity
gas, iii) observing more quasars (and accessing archive data) to increase the sample
size, and iv) obtaining repeat observations of some quasars to check for NAL variability
159
(e.g., in the high-velocity systems) and thereby test the validity of partial covering alone
as an indicator of an intrinsic/outflow origin.
5.4 Summary
We present [C/H] abundance analysis results for 28 intrinsic NALs in a sample of 19
quasars with redshifts 2.7 < z < 4.6. We build on the analysis of C IV NALs presented
in Chapter 4. We measure C IV, H I, Si IV, N V, and other column densities, which we
use to determine the ionization of the intrinsic gas. We find a range of metallicities, from
[C/H] > -1.8 to [C/H] = +1.3. There is no appreciable trend in metallicity with velocity
shift for the intrinsic NALs studied, and similarly no trend in metallicity with redshift. The
broader NALs tend to have slightly higher metallicity than the narrower NALs. We are
likely sampling metallicities for a broad range of quasar environments, from metal-rich
outflows to ambient halo gas in the host galaxy.
160
Figure 5-1. Continuum fits for the quasar J0714-6455, emission redshift zem = 4.46.Each panel shows a region of the spectrum around a different NAL. TheNALs are labeled in the panels. The horizontal axis is velocity shift relative tothe quasar systemic. The vertical axis is flux in arbitrary units. The spectrumis shown in black, while the continuum fit is overplotted in red. The verticaldashed lines delineate the central velocity of the NAL being measured.
161
Figure 5-2. Continuum fits for the quasar J0749+4152, emission redshift zem = 3.11.The axes and symbols are the same as in Figure 5-1.
162
Figure 5-3. Gaussian fits for the quasar J0714-6455, emission redshift zem = 4.46. Thehorizontal axis is velocity shift of each NAL relative to the quasar and thevertical axis is flux in normalized units. Each panel shows the quasarspectrum around a NAL. The ions and rest wavelengths in angstroms arelabeled in the panels. The Gaussian fits are overplotted on top of the quasarspectrum in black. The vertical dashed lines show the central velocity ofeach NAL.
163
Figure 5-4. Gaussian fits for the quasar J0749+4152, emission redshift zem = 3.11. Theaxes and symbols are the same as in Figure 5-3.
164
Figure 5-5. Gaussian fits for the quasar J1341-0115, emission redshift zem = 2.70. Theaxes and symbols are the same as in Figure 5-3.
165
Figure 5-6. NAL metallicity versus velocity shift. The diamond symbols are class ANALs, the triangle symbols are class B NALs. Green symbols are lowerlimits for metallicities determined using the minimum ionization correction forC IV, while black symbols are metallicities for NALs with good ionizationconstraints based on the relative strengths of at least two ions. Vertical linesconnect metallicities of the same NALs measured using minimum ionizationcorrections and using other ionization constraints. Small symbols representNALs with b values b < 27 km s−1, medium symbols represent NALs with bvalues 27 < b < 53 km s−1, large symbols represent NALs with b values53 < b < 80 km s−1 and the largest symbols represent NALs with b valuesb > 80 km s−1.
166
Figure 5-7. NAL metallicity versus velocity shift for different redshifts. Symbols representmetallicities of NALs with good ionization constraints. Blue diamonds arehigher redshift NALs with zabs > 4, red triangles are medium redshift NALswith 3 < zabs < 4 and green crosses are lower redshift NALs with zabs < 3.
167
Figure 5-8. C IV and H I column densities for class A and B NALs. Column densities aremeasured in units of cm−2.
168
Figure 5-9. H I versus C IV/H I column density ratios for class A and B NALs. Columndensities are measured in units of cm−2.
169
CHAPTER 6SUMMARY AND CONCLUSIONS
In Chapter 2, we present the results of an exploratory study of broad line region
(BLR) metallicity in 34 quasars with redshifts between 2.2 ≤ z ≤ 4.6 and far-infrared
(FIR) luminosities (LFIR) from 1013.4 to ≤ 1012.1 L¯. LFIR appears to be a good indicator
of the star formation rate (SFR) in the host galaxy, and therefore our sample of quasars
with a range of LFIR’s might represent an evolutionary sequence if the SFRs in quasar
hosts generally diminish across quasar lifetimes. We construct three composite spectra
sorted by LFIR, corresponding to average SFRs of 4980, 2130 and ≤ 340 M¯ yr−1
after correcting for a nominal quasar FIR contribution, using rest-frame ultraviolet
spectra from the Sloan Digital Sky Survey. The measured N V λ1240/C IV λ1550 and
Si IV λ1397+O IV] λ1402/C IV λ1550 emission line ratios indicate super-solar BLR
metallicities in all three composites, with no evidence for a trend with the star formation
rate. The formal derived metallicities, Z ∼ 5–9 Z¯, are similar to those derived for the
BLRs of other quasars at similar redshifts and luminosities. These results suggest that
the ongoing star formation in the host is not responsible for the metal enrichment of
the BLR gas. Instead, the BLR gas must have been enriched before the visible quasar
phase. These results for high quasar metallicities, regardless of LFIR, are consistent
with evolution scenarios wherein visibly bright quasars appear after the main episode(s)
of star formation and metal enrichment in the host galaxies. Finally, young quasars,
those more closely associated with a recent merger or a blowout of gas and dust, may
exhibit tracers of these events, such as redder continuum slopes and higher incidence
of narrow absorption lines. With the caveat of small sample sizes, We find no relation
between LFIR and the reddening or the incidence of absorption lines.
In Chapter 3, we examine the nature and origin of a rich complex of narrow
absorption lines in the quasar J1023+5142 at redshift 3.447. We measure nine C IV
λλ1548,1551 absorption line systems with velocities from -1400 to -6200 km s−1,
170
and full widths at half minimum ranging from 16 to 350 km s−1. We also detect other
absorption lines in these systems, including H I, C III, N V, O VI, and Si IV. Lower
ionization lines are not present, indicating a generally high degree of ionization in all
nine systems. The total hydrogen column densities range from ≤ 1017.2 to 1019.1 cm−2.
The tight grouping of these lines in the quasar spectrum suggests that most or all
of the absorbing regions are physically related. We examine several diagnostics to
estimate more directly the location and origin of each absorber. Four of the systems
can be attributed to a quasar-driven outflow based on line profiles that are smooth
and broad compared to thermal line widths and to the typical absorption lines formed
in intergalactic gas or galaxy halos. Several systems also have other indicators of a
quasar outflow origin, including partial covering of the quasar emission source (e.g.,
in systems with velocities too high for a starburst-driven flow), O VI column densities
above 1015 cm−2 and an apparent line-lock in C IV (in two of the narrow profile systems).
A search for line variability yielded null results, although with very poor constraints
because the comparison spectra have much lower resolution. Altogether (but not
including the tentative line-lock) there is direct evidence for 6 of the 9 systems forming
in a quasar outflow. Consistent with a near-quasar origin, eight of the systems have
metallicity values or lower limits in the range Z ≥ 1–8 Z¯. The lowest velocity system,
which has an ambiguous location based on the diagnostics mentioned above, also
has the lowest metallicity, Z ≤ 0.3 Z¯, and might form in a non-outflow environment
farther from the quasar. Overall, however, this complex of narrow absorption lines can
be identified with a highly structured, multi-component outflow from the quasar. The
high metallicities are similar to those derived for other quasars at similar redshifts and
luminosities, and are consistent with evolution scenarios wherein quasars appear after
the main episodes of star formation and metal enrichment in the host galaxies.
In Chapter 4, we present the results of a comprehensive survey of 271 C IV
narrow absorption line (NAL) components in 136 C IV NAL systems in 24 quasars at
171
redshifts 1.94 < z < 4.69 between the velocity range of +5000 < v < -40000 km s−1.
We determine the likely origin of each of these components and systems as either
intrinsic to the quasar environment or in intervening gas clouds and galaxies. We
determine individual NAL origins by measuring covering fractions in the C IV NALs,
and (secondarily) NAL profile widths. 20% of all NAL components and systems are
probably intrinsic to the quasar environment. 60% of these intrinsic components and
77% of intrinsic systems have high velocities that limit their origin to quasar outflows.
We find a strong trend for increasing equivalent width and increasing column density
with increasing intrinsic fraction. We also find an excess of all NALs at velocities below
v ∼ 7500 km s−1 from the qausar systematic. Several high-velocity, narrow outflows
also appear in our sample. Up to 46% of the quasars in our sample contain at least
one intrinsic NAL, although only ∼ 30% contain an intrinsic NAL within 5000 km s−1
of the quasar redshift. Six rich and complex C IV NAL systems are present in the
quasar spectra, and may represent highly structured, multi-component quasar outflows.
Altogether, these results suggest that intrinsic NALs make up a significant fraction of all
NALs in quasar spectra. Furthermore, quasar outflows appear to form much narrower
NALs than previously considered likely. The total mass of gas likely to originate in these
quasar outflows is not yet known, and will be essential for determining the potential
effect of this gas on the quasar-host galaxy evolution.
Finally, in Chapter 5, we present abundances for 25 intrinsic NAL components
in the quasar sample. We find a range of logarithmic [C/H] abundances relative to
solar abundance from -1.8 to +1.3. This is a much broader range of metallicities than
what has previously been found for intrinsic NALs in similar quasars. We find a range
of metallicities at all velocity shifts, from 0 to 30,000 km s−1. The metallicities do not
correlate with redshift. This sample of intrinsic NALs may be the first to contain a
sampling of metallicities from all types of gas in the near quasar environment, from
quasar outflows to ambient host galaxy halo gas.
172
In conclusion, we have shown that quasar gaseous environments are metal-rich
at all redshifts and at all locations within the environment, although there is also a
population of lower metallicity gas, possibly representative of halo gas in the host galaxy.
BEL gas is enriched in all quasars, regardless of the star formation rates of the host
galaxy, requiring that the main phase of star formation responsible for enriching the gas
occurs long before the quasar becomes active. NAL gas may contribute significantly
to the interactions of the quasar with its host galaxy, with more than 50% of intrinsic
NALs likely forming in quasar outflows. The extent of the actual influence of these
NAL outflows on the evolution of the quasar-host galaxy system is still unknown.
The metallicity of these intrinsic NALs is more varied than previous NAL abundance
studies would predict. With our unique sample of intrinsic NALs, we may be probing
the metallicity of not just the strong outflow NALs, but also of other ambient gas in the
quasar environment, such as host galaxy halo gas swept along by stronger quasar
outflows. Our results are consistent with models predicting quasar formation in a
massive host galaxy after an intense burst of star formation, possibly caused by a
merger in the host galaxy.
In the future, we will expand our analysis of NAL metallicities through improvement
of the statistics presented in this thesis. We will accomplish this improvement by
measuring abundances of more NALs in this sample, as well by as by adding more
quasars to the sample through observations and by making use of public archives. We
will also measure BEL metallicities for the quasars in this sample with measured NAL
metallicities, which will allow us to measure any correlations between the metallicities of
gas found at different locations around individual quasar environments.
173
APPENDIX A: C IV ABSORPTION LINE MEASUREMENTS
This appendix contains the table of measured parameters for all 271 C IV NAL
components in the quasar sample introduced in Chapter 4. Column 1 lists the C IV
absorption line redshift, column 2 lists the corresponding velocity shift relative to the
quasar systemic, column 3 lists the C IV b-value, column 4 lists the C IV λ1548 FWHM,
column 5 lists the covering fraction, column 6 list the C IV λ1548 system REW, where
dashes indicate the listed component is part of the last system listed above with a
numeric value, column 7 lists the C IV λ1548 component REW, and column 8 lists the
NAL component class. Systems with one or more class A component are counted as
class A systems. Systems are counted in class B if they contain one or more class B
components.
174
Table A-1. C IV NALsz v b FWHM Cf REWs REWc Classc
(km s−1) (km s−1) (km s−1) A AQ0105+0601:
1.93186 -2863 30.0 70.7 1.0 0.473 0.473 C1.93529 -2513 35.0 82.4 1.0 0.625 0.572 C1.93453 -2591 7.0 16.5 1.0 - 0.012 C1.67336 -30430 10.0 23.5 1.0 0.086 0.086 C1.66446 -31419 17.9 42.2 1.0 0.054 0.055 C
PSS J0134+3307:4.51811 -754 38.7 91.1 0.98 0.569 0.569 B4.23554 -16496 68.0 160.3 1.0 0.599 0.600 B3.77590 -43745 10.2 24.0 1.0 0.402 0.037 C3.77603 -43737 5.3 12.5 1.0 - 0.050 C3.77640 -43714 11.1 26.1 1.0 - 0.105 C3.77694 -43681 9.0 21.2 1.0 - 0.075 C3.68745 -49214 42.2 99.4 1.0 0.512 0.286 C3.68845 -49152 8.8 20.7 1.0 - 0.059 C3.68971 -49074 11.9 28.1 1.0 - 0.078 C3.69036 -49033 9.6 22.7 1.0 - 0.064 C
BR 0245-0608:4.22995 -461 30.0 70.6 1.0 0.051 0.051 C3.65313 -35330 42.5 100.0 1.0 0.438 0.443 C3.57724 -40182 19.9 47.0 1.0 0.596 0.173 C3.57848 -40102 30.6 72.0 1.0 - 0.310 C3.57949 -40037 15.9 37.5 1.0 - 0.132 C3.22280 -63607 23.8 56.0 1.0 0.055 0.058 C3.15871 -67971 10.0 23.5 1.0 0.454 0.052 C3.15752 -68052 11.8 27.7 1.0 - 0.088 C3.15530 -68204 30.7 72.2 1.0 - 0.338 C
Q 0249-2223.17589 -1726 17.5 41.1 1.0 0.182 0.169 C3.17667 -1670 6.5 15.3 1.0 - 0.012 C3.12897 -5113 21.2 50.0 1.0 0.122 0.104 C3.12949 -5075 6.7 15.8 1.0 - 0.019 C3.09943 -7264 18.3 43.0 1.0 1.035 0.059 C3.10123 -7133 30.2 71.0 1.0 - 0.200 C3.10221 -7061 16.4 38.6 1.0 - 0.113 C3.10294 -7008 29.6 69.8 1.0 - 0.234 C3.10501 -6857 39.7 93.5 1.0 - 0.129 C3.10614 -6774 24.7 58.2 1.0 - 0.138 C
175
Table A-1. Continued3.10710 -6704 11.1 26.2 1.0 - 0.038 C3.10752 -6674 10.1 23.8 1.0 - 0.021 C2.89313 -22702 7.4 17.3 0.91 0.092 0.026 C2.89161 -22818 14.0 33.0 1.0 - 0.034 C2.89200 -22789 10.0 23.5 1.0 - 0.032 C2.86084 -25183 28.5 67.1 1.0 0.085 0.086 C2.77360 -31972 14.9 35.1 1.0 0.148 0.064 C2.77484 -31875 10.6 24.9 1.0 - 0.085 C2.70360 -37510 21.2 49.8 1.0 0.036 0.036 C2.67289 -39965 10.0 23.5 1.0 0.104 0.052 C2.67323 -39937 20.0 47.1 1.0 - 0.057 C2.54830 -50076 17.9 42.2 1.0 0.057 0.037 C2.54919 -50003 18.5 43.6 1.0 - 0.021 C2.48031 -55696 10.0 23.5 1.0 0.142 0.025 C2.48098 -55640 10.0 23.5 1.0 - 0.071 C2.48144 -55602 15.0 35.3 1.0 - 0.013 C2.48244 -55518 15.0 35.3 1.0 - 0.037 C
Q 0334-2043.09014 -3052 10.0 23.6 1.0 0.419 0.114 C3.09066 -3014 30.0 70.7 1.0 - 0.297 C3.09149 -2954 15.0 35.3 1.0 - 0.048 C3.09241 -2886 10.4 24.4 1.0 0.061 0.061 C3.04508 -6373 11.2 26.5 1.0 0.576 0.069 C3.04347 -6492 17.4 41.0 1.0 - 0.198 C3.04108 -6669 19.4 45.7 1.0 - 0.116 C3.04167 -6625 13.9 32.7 1.0 - 0.077 C3.03975 -6768 15.7 37.1 1.0 - 0.085 C3.03943 -6792 8.8 20.7 1.0 - 0.036 C2.89184 -17930 14.1 33.1 1.0 0.070 0.070 C
BR 0351-1034:4.35404 170 196.8 463.5 1.0 5.54 3.33 C4.27095 -4518 119.9 282.3 0.70 0.738 0.733 A4.28197 -3992 108.7 256.0 0.70 1.039 0.785 A4.15404 -11238 19.3 45.4 1.0 0.153 0.153 C3.51500 -50444 15.6 36.8 1.0 0.815 0.097 C3.51544 -50416 20.8 49.1 1.0 - 0.217 C3.51597 -50381 11.3 26.6 1.0 - 0.080 C3.51673 -50332 27.1 63.8 1.0 - 0.318 C3.51724 -50299 9.1 21.4 1.0 - 0.081 C3.51880 -50199 28.4 66.8 1.0 - 0.136 C
176
Table A-1. ContinuedBR 0401-1711:
4.22940 -378 29.9 70.3 1.0 0.551 0.552 C3.81395 -25135 20.0 47.1 1.0 0.077 0.077 C3.64130 -35970 39.2 92.3 1.0 0.242 0.245 C3.33990 -55621 29.1 68.5 0.6 0.071 0.073 C3.20568 -64660 13.0 30.6 1.0 0.338 0.069 C3.20652 -64602 23.4 55.1 1.0 - 0.229 C3.20398 -64775 10.6 25.0 1.0 - 0.043 C
BR 0714-6455:4.45919 -154 41.6 97.9 1.0 0.093 0.093 C4.42365 -2112 18.8 44.3 1.0 0.048 0.048 C4.19685 -14906 17.0 40.0 0.9 0.277 0.205 A4.19753 -14867 10.9 25.7 0.9 - 0.055 B4.19804 -14838 9.0 21.2 0.9 - 0.021 C3.96996 -28218 11.9 28.0 1.0 0.668 0.110 C3.97049 -28186 43.8 103.2 1.0 - 0.257 C3.96939 -28252 23.8 56.0 1.0 - 0.112 C3.80085 -38467 23.2 54.6 1.0 0.042 0.043 C3.75033 -41582 20.9 49.2 1.0 0.232 0.095 C3.75134 -41519 20.8 49.0 1.0 - 0.142 C3.74516 -41902 20.0 47.1 1.0 0.151 0.089 C3.74652 -41817 20.0 47.2 1.0 - 0.064 C3.42246 -62368 27.7 65.2 1.0 0.184 0.192 C
BR 0749+4152:3.09726 -931 15.6 36.8 1.0 0.009 0.009 C3.01603 -6933 17.0 40.1 1.0 0.314 0.032 C3.01686 -6871 15.8 37.1 1.0 - 0.032 C3.01772 -6807 24.5 57.7 1.0 - 0.165 C3.01851 -6748 17.9 42.2 1.0 - 0.087 C2.91809 -14325 12.0 28.3 1.0 0.019 0.019 C2.91894 -14260 10.6 24.9 1.0 0.018 0.018 C2.76480 -26233 12.2 28.0 0.64 0.150 0.078 A2.76567 -26164 15.4 36.3 0.39 - 0.062 A2.75447 -27050 15.0 35.3 1.0 0.130 0.096 C2.70614 -30898 628.3 1479.6 1.0 1.79 0.89 -2.64509 -35816 20.1 47.3 1.0 0.14 0.11 C2.61424 -38325 15.1 35.7 1.0 0.10 0.07 C2.58426 -40778 17.0 40.0 1.0 0.11 0.06 C2.58471 -40742 10.9 25.7 1.0 - 0.02 C
177
Table A-1. ContinuedBR 0933+733:
2.52690 -93 13.5 31.8 1.0 0.107 0.073 C2.52786 -12 13.6 32.1 1.0 - 0.034 C2.51105 -1444 8.1 19.0 1.0 0.027 0.027 C2.44944 -6750 8.1 19.1 1.0 0.043 0.022 C2.45017 -6686 8.0 18.8 1.0 - 0.021 C2.33228 -17092 62.7 147.7 1.0 1.146 1.097 C2.33429 -16912 6.3 14.9 1.0 - 0.052 C2.21444 -27824 9.5 22.5 1.0 0.032 0.032 C2.11075 -37536 30.4 71.7 1.0 0.701 0.145 C2.11195 -37422 27.5 64.7 1.0 - 0.302 C2.11313 -37310 31.6 74.4 1.0 - 0.323 C
J1008+3623:3.13660 800 10.8 25.4 1.0 0.645 0.031 C3.13726 848 8.0 18.8 1.0 - 0.042 C3.13773 882 5.1 12.0 1.0 - 0.031 C3.13599 756 6.3 14.9 1.0 - 0.051 C3.13522 700 32.3 76.1 1.0 - 0.078 C3.13383 600 35.0 83.3 0.88 - 0.287 A3.13446 645 20.1 43.9 0.88 - 0.153 A3.12763 150 19.9 52.8 0.88 0.507 0.151 A3.12557 -0 14.0 33.0 0.88 - 0.195 A3.12608 37 15.0 35.3 0.88 - 0.192 A3.11198 -989 17.6 41.5 0.63 0.141 0.141 A3.10820 -1265 32.0 75.4 1.0 0.064 0.064 C3.08616 -2877 33.5 78.8 0.73 1.573 0.094 A3.08821 -2727 56.5 133.2 0.73 - 0.154 A3.08968 -2619 28.2 66.4 0.73 - 0.139 A3.09043 -2564 30.5 71.8 0.73 - 0.302 A3.09168 -2473 44.3 104.2 0.73 - 0.283 A3.09277 -2393 38.8 91.4 0.73 - 0.180 A3.09443 -2271 47.9 112.8 0.73 - 0.439 A3.07141 -3961 18.4 43.2 1.0 0.036 0.036 C3.04805 -5686 97.6 229.9 0.30 0.131 0.139 A2.94786 -13192 17.3 40.9 1.0 0.292 0.082 C2.94865 -13132 17.3 40.8 1.0 - 0.067 C2.94917 -13092 17.6 41.5 1.0 - 0.128 C2.87032 -19121 19.7 46.5 1.0 0.127 0.127 C
178
Table A-1. ContinuedJ1020+1039:
3.11159 -4085 21.6 50.9 1.0 0.450 0.262 C3.11249 -4019 15.8 37.1 1.0 - 0.188 C3.10823 -4330 38.4 90.3 1.0 0.238 0.144 C3.10955 -4234 27.2 64.0 1.0 - 0.096 C3.11643 -3732 20.9 49.1 1.0 0.091 0.043 C3.09162 -5544 10.5 24.6 1.0 0.031 0.031 C3.07901 -6469 32.1 75.7 0.85 0.137 0.137 A3.00977 -11597 15.9 37.4 1.0 0.135 0.135 C2.99835 -12451 72.5 170.7 1.0 0.126 0.126 B2.96938 -14626 634.8 1494.8 1.0 1.372 1.374 -2.94012 -16838 16.2 38.2 1.0 0.081 0.043 C2.94074 -16791 21.5 50.7 1.0 - 0.039 C2.56497 -46474 18.0 42.5 1.0 0.400 0.120 C2.56562 -46421 15.8 37.2 1.0 - 0.136 C
J1020-0020:2.60300 283 10.6 24.9 1.0 0.281 0.102 C2.60342 318 14.9 35.2 1.0 - 0.185 C
J1023+5142:3.42865 -1442 10.4 24.4 1.0 0.034 0.034 C3.42133 -1938 9.6 22.5 1. 0.328 0.017 C3.41864 -2120 9.0 21.2 1. - 0.095 C3.41775 -2182 10.6 24.9 1. - 0.094 C3.41747 -2200 33.4 78.7 1. - 0.154 C3.40391 -3121 15.0 35.3 0.7 0.548 0.141 A3.40196 -3254 45.0 106.0 0.7 - 0.212 A3.39936 -3430 19.8 46.6 1. - 0.045 C3.39835 -3496 43.7 102.9 1. - 0.144 C3.37983 -4763 155.5 366.2 1. 0.807 0.808 A3.35752 -6295 45.0 105.9 1. 0.174 0.057 C3.35922 -6178 40.4 95.1 1. - 0.083 C3.36057 -6085 55.3 130.1 1. - 0.054 C3.20955 -16636 23.8 56.1 1. 0.277 0.215 C3.20785 -16756 11.9 28.1 1.0 - 0.043 C3.10282 -24298 21.6 50.9 1.0 0.141 0.056 C3.10456 -24172 19.3 45.5 1.0 - 0.030 C3.10537 -24113 19.0 44.8 1.0 - 0.030 C2.97752 -33509 9.9 23.4 1.0 0.176 0.079 C2.97522 -33681 18.7 44.0 1.0 - 0.099 C2.87560 -41170 8.8 20.6 1.0 0.065 0.026 C
179
Table A-1. Continued2.87748 -41028 14.7 34.7 1.0 - 0.041 C2.64367 -59146 11.4 27.0 1.0 0.393 0.107 C2.64425 -59100 18.6 43.7 1.0 - 0.222 C2.64572 -58984 12.0 28.3 1.0 - 0.082 C
1201+0116:3.18379 -3505 26.3 61.9 1.0 0.047 0.047 C3.15998 -5216 8.9 21.0 1.0 0.025 0.025 C3.13294 -7170 50.0 117.7 1.0 0.142 0.142 C3.07892 -11111 11.0 25.9 1.0 0.169 0.073 C3.08013 -11022 9.7 22.8 1.0 - 0.096 C2.79010 -32998 20.0 47.1 1.0 0.113 0.089 C2.79188 -32859 9.7 22.8 1.0 - 0.025 C
BR 1202-0725:4.68468 -280 18.3 43.1 1.0 0.470 0.230 C4.68594 -214 14.8 34.8 1.0 - 0.088 C4.68658 -180 13.7 32.2 1.0 - 0.159 C4.67025 -1042 16.9 39.8 1.0 0.148 0.148 C4.62414 -3490 21.8 51.3 1.0 0.067 0.067 C4.47720 -11421 27.3 64.3 1.0 0.505 0.128 C4.47808 -11373 11.5 27.0 1.0 - 0.082 C4.47853 -11349 9.3 21.9 1.0 - 0.042 C4.47879 -11334 16.3 38.3 1.0 - 0.053 C4.47998 -11270 15.2 35.8 1.0 - 0.090 C4.48104 -11212 21.6 50.9 1.0 - 0.066 C4.48195 -11162 25.0 58.8 1.0 - 0.060 C4.19044 -27471 25.0 58.9 1.0 0.126 0.126 C4.07164 -34339 22.2 52.3 1.0 0.288 0.291 C4.04695 -35782 18.6 43.9 1.0 0.105 0.074 C4.04629 -35820 18.9 44.4 1.0 - 0.036 C3.82499 -48993 35.6 83.9 1.0 0.153 0.157 C3.81151 -49809 19.3 45.6 1.0 0.082 0.023 C3.81214 -49771 24.5 57.6 1.0 - 0.062 C3.75307 -53364 19.4 45.7 1.0 0.120 0.124 C
J1225+4831:3.09555 407 9.4 22.2 1.0 1.144 0.073 C3.09592 434 16.7 39.3 1.0 - 0.156 C3.09414 303 48.0 113.0 1.0 - 0.836 C3.08262 -541 20.1 47.4 1.0 0.165 0.165 C3.08948 -38 39.1 92.0 1.0 0.300 0.256 C3.08476 -384 16.9 39.7 1.0 0.012 0.012 C
180
Table A-1. Continued3.08011 -726 12.5 29.3 1.0 0.176 0.024 C3.08066 -685 46.7 110.1 1.0 - 0.091 C3.08174 -606 41.7 98.3 1.0 - 0.066 C3.02519 -4788 10.7 25.2 1.0 0.072 0.033 C3.02362 -4905 7.3 17.1 1.0 - 0.009 C3.02405 -4873 13.9 32.8 1.0 - 0.031 C2.99753 -6855 22.0 51.9 1.0 0.110 0.077 C2.99798 -6821 12.4 29.1 1.0 - 0.036 C2.98960 -7450 15.1 35.4 1.0 0.052 0.052 C2.91821 -12856 16.1 37.9 1.0 0.041 0.041 C2.91726 -12929 19.5 45.9 1.0 0.069 0.069 C2.77843 -23705 20.7 48.7 1.0 0.036 0.036 C2.76121 -25065 11.9 28.0 1.0 0.264 0.112 C2.76187 -25013 12.9 30.5 1.0 - 0.154 C2.68719 -30972 39.6 93.3 1.0 0.338 0.278 C2.68790 -30915 17.5 41.3 1.0 - 0.072 C2.68332 -31283 15.5 36.4 1.0 0.149 0.065 C2.68368 -31254 8.9 21.0 1.0 - 0.034 C2.68396 -31232 15.7 36.9 1.0 - 0.055 C2.62642 -35797 9.3 21.8 1.0 0.032 0.033 C2.60129 -37945 15.2 35.7 1.0 0.182 0.057 C2.60057 -38004 31.6 74.4 1.0 - 0.130 C2.58622 -39184 6.4 15.0 0.71 0.185 0.048 C2.58463 -39314 15.0 35.3 0.90 - 0.141 A2.55415 -41825 14.9 35.1 1.0 0.057 0.058 C2.35594 -58541 27.5 64.7 1.0 0.218 0.089 C2.35689 -58460 27.3 64.4 1.0 - 0.083 C2.35089 -58975 17.1 40.3 1.0 0.139 0.144 C
J1307+1230:3.20298 213 14.8 34.9 1.0 0.554 0.095 C3.20395 282 14.9 35.2 1.0 - 0.139 C3.20489 349 30.2 71.1 1.0 - 0.324 C3.17970 -1453 30.3 71.3 1.0 0.173 0.095 C3.18070 -1381 18.0 42.4 1.0 - 0.078 C3.17714 -1636 16.2 38.2 1.0 0.047 0.047 C2.98124 -16021 14.9 35.2 1.0 0.119 0.067 C2.98170 -15986 10.7 25.2 1.0 - 0.053 C2.83020 -27553 19.4 45.7 0.5 0.204 0.103 C2.83086 -27502 34.0 80.1 1.0 - 0.123 C2.73064 -35361 35.3 83.1 1.0 0.09 0.09 C
181
Table A-1. ContinuedJ1326+0743:
4.10215 -3960 37.9 89.1 0.82 0.358 0.355 A4.06218 -6317 37.6 88.6 0.85 0.457 0.452 A4.06561 -6114 23.7 55.7 1.0 0.089 0.089 C4.06040 -6423 12.3 28.9 1.0 0.017 0.017 C4.04001 -7632 45.2 106.5 1.0 0.181 0.151 C4.04045 -7606 10.3 24.1 1.0 - 0.039 C3.47744 -42822 24.5 57.7 1. 0.658 0.276 C3.47805 -42782 9.7 22.8 1. - 0.045 C3.47874 -42736 33.8 79.7 1. - 0.297 C
J1341-0115:2.74482 -1691 36.0 84.7 0.95 0.542 0.453 B2.74581 -1612 36.6 86.2 0.95 - 0.104 B2.74280 -1853 16.8 39.6 0.68 0.093 0.093 A2.70762 -4683 49.9 117.4 1.0 0.121 0.121 C2.53184 -19219 1930.5 4546.1 1.0 9.91419 9.933 -2.41809 -28969 20.2 47.5 1.0 0.112 0.113 C2.40417 -30186 13.5 31.8 1.0 0.058 0.058 C
J1430+0149:2.08930 -2002 12.2 28.7 1.0 0.594 0.154 C2.08967 -1966 13.1 30.8 1.0 - 0.113 C2.09008 -1926 11.1 26.2 1.0 - 0.145 C2.08379 -2537 15.0 35.3 1.0 0.342 0.209 C2.08461 -2457 13.2 31.2 1.0 - 0.133 C2.06972 -3896 16.7 39.3 1.0 0.924 0.242 C2.07084 -3792 51.6 121.5 1.0 - 0.556 C2.07153 -3722 13.0 30.6 1.0 - 0.102 C2.06939 -3940 7.6 17.9 1.0 - 0.082 C1.78906 -32524 16.3 38.4 1.0 0.534 0.239 C1.78940 -32488 19.8 46.6 1.0 - 0.247 C
J1633+1411:4.36743 -440 10.4 24.6 0.85 0.089 0.085 B4.34777 -1541 65.3 153.8 1.0 0.061 0.061 C4.28081 -5317 106.4 250.5 1.0 0.131 0.131 C4.28469 -5098 74.0 174.2 1.0 0.372 0.372 B4.25113 -7007 26.0 61.3 0.73 1.204 0.214 A4.25486 -6794 14.2 33.4 0.73 - 0.009 A4.25555 -6755 24.3 57.3 0.73 - 0.039 A4.25672 -6688 21.2 50.0 0.73 - 0.059 A4.25724 -6658 9.5 22.3 0.73 - 0.041 B
182
Table A-1. Continued4.25795 -6618 19.7 46.4 0.73 - 0.170 B4.26194 -6391 94.0 221.4 0.73 - 0.346 A4.26303 -6329 45.6 107.3 0.73 - 0.115 A4.22645 -8418 41.8 98.3 0.72 0.212 0.076 A4.22790 -8335 31.3 73.7 0.72 - 0.140 A4.23461 -7951 49.8 117.3 0.72 0.338 0.170 A4.23817 -7747 35.6 83.9 0.72 - 0.063 A4.15138 -12750 25.5 60.1 1.0 0.136 0.059 C4.15268 -12675 30.4 71.6 1.0 - 0.078 C4.03067 -19837 23.4 55.0 0.31 0.072 0.072 A3.78144 -34940 11.4 26.7 1.0 0.025 0.026 C3.60119 -46247 12.2 28.8 1.0 0.019 0.019 C3.58094 -47537 10.1 23.8 1.0 0.106 0.024 C3.58193 -47474 18.6 43.8 1.0 - 0.085 C3.50387 -52482 19.0 44.6 1.0 0.557 0.234 C3.50493 -52414 14.5 34.1 1.0 - 0.126 C3.50528 -52391 13.1 30.8 1.0 - 0.128 C3.50577 -52359 13.5 31.8 1.0 - 0.121 C3.34716 -62706 10.2 24.0 1.0 0.106 0.020 C3.34655 -62746 9.5 22.5 1.0 - 0.051 C3.34445 -62884 13.7 32.3 1.0 - 0.024 C3.34307 -62976 13.2 31.1 1.0 - 0.016 C
PKS 2044+168:1.92221 -1513 7.7 18.1 1.0 0.817 0.008 C1.92153 -1583 10.1 23.9 1.0 - 0.018 C1.92199 -1536 9.4 22.0 1.0 - 0.036 C1.92059 -1680 12.1 28.4 0.78 - 0.101 A1.92035 -1704 6.7 15.7 0.78 - 0.031 A1.92009 -1731 13.7 32.3 0.78 - 0.130 A1.91831 -1914 18.1 42.7 0.78 - 0.187 A1.91898 -1845 16.3 38.3 0.78 - 0.184 A1.91796 -1950 18.1 42.7 0.78 - 0.089 A1.91977 -1764 13.0 30.6 0.78 - 0.063 A1.55753 -41213 28.4 66.9 1.0 0.459 0.395 C1.55874 -41074 15.0 35.3 1.0 - 0.075 C1.34410 -66476 15.0 35.3 1.0 0.095 0.099 C1.34202 -66730 10.0 23.5 1.0 0.105 0.079 C1.34222 -66705 10.0 23.6 1.0 - 0.032 C1.32811 -68425 35.1 82.7 1.0 0.350 0.368 C
183
Table A-1. ContinuedQ 2204-408:
3.15725 162 12.6 29.7 1.0 0.055 0.055 C3.02823 -9286 29.0 68.3 1.0 0.157 0.157 C3.17492 1434 24.7 58.1 1.0 0.026 0.026 C2.86389 -21738 11.8 27.7 1.0 0.291 0.133 C2.86459 -21684 22.7 53.6 1.0 - 0.069 C2.86548 -21615 24.0 56.6 1.0 - 0.053 C2.86708 -21492 26.1 61.5 1.0 - 0.039 C2.87264 -21063 16.6 39.0 1.0 0.161 0.065 C2.87304 -21033 14.2 33.4 1.0 - 0.082 C2.84864 -22917 28.4 67.0 1.0 0.313 0.053 C2.84920 -22874 12.5 29.3 1.0 - 0.049 C2.85017 -22799 21.4 50.5 1.0 - 0.163 C2.85059 -22766 7.3 17.1 1.0 - 0.056 C2.83671 -23842 13.0 30.7 1.0 0.298 0.144 C2.83730 -23796 18.0 42.4 1.0 - 0.159 C2.66578 -37360 11.7 27.5 1.0 0.022 0.022 C2.62870 -40357 24.9 58.6 1.0 0.317 0.102 C2.62724 -40475 20.0 47.1 1.0 - 0.204 C2.62551 -40616 10.0 23.6 1.0 - 0.017 C2.59359 -43215 13.1 30.9 1.0 0.021 0.022 C
184
REFERENCES
Adelman-McCarthy J. K., Agueros M. A., Allam S. S., Allende Prieto C., et al. 2008,ApJS, 175, 297
Agresti A., Coull B. A., 1998, The American Statistician, 52, 119
Aguirre A., Dow-Hygelund C., Schaye J., Theuns T., 2008, ApJ, 689, 851
Aguirre A., Schaye J., Kim T., Theuns T., Rauch M., Sargent W. L. W., 2004, ApJ, 602,38
Aldcroft T., Bechtold J., Foltz C., 1997, in Arav N., Shlosman I., Weymann R. J., eds,Mass Ejection from Active Galactic Nuclei Vol. 128 of Astronomical Society of thePacific Conference Series, Variability in Radio-Loud Quasar Associated AbsorptionLines. pp 25–+
Anderson S. F., Weymann R. J., Foltz C. B., Chaffee Jr. F. H., 1987, AJ, 94, 278
Arav N., de Kool M., Korista K. T., Crenshaw D. M., van Breugel W., Brotherton M.,Green R. F., Pettini M., et al. 2001, ApJ, 561, 118
Arav N., Gabel J. R., Korista K. T., Kaastra J. S., Kriss G. A., Behar E., CostantiniE., Gaskell C. M., Laor A., Kodituwakku C. N., Proga D., Sako M., Scott J. E.,Steenbrugge K. C., 2007, ApJ, 658, 829
Arav N., Kaastra J., Kriss G. A., Korista K. T., Gabel J., Proga D., 2005, ApJ, 620, 665
Arnaud M., Rothenflug R., 1985, A&AS, 60, 425
Asplund M., Grevesse N., Sauval A. J., Scott P., 2009, ARA&A, 47, 481
Bahcall J. N., Sargent W. L. W., Schmidt M., 1967, ApJ, 149, L11+
Baldwin J. A., Hamann F., Korista K. T., Ferland G. J., Dietrich M., Warner C., 2003, ApJ,583, 649
Barlow T. A., Hamann F., Sargent W. L. W., 1997, in Arav N., Shlosman I., WeymannR. J., eds, Mass Ejection from Active Galactic Nuclei Vol. 128 of Astronomical Societyof the Pacific Conference Series, Partial Coverage and Time Variability of Narrow-LineIntrinsic QSO Absorption Systems. pp 13–+
Barlow T. A., Sargent W. L. W., 1997, AJ, 113, 136
Becker R. H., White R. L., Gregg M. D., Brotherton M. S., Laurent-Muehleisen S. A.,Arav N., 2000, ApJ, 538, 72
Beelen A., Cox P., Benford D. J., Dowell C. D., Kovacs A., Bertoldi F., Omont A., CarilliC. L., 2006, ApJ, 642, 694
185
Bergeron J., Herbert-Fort S., 2005, in P. Williams, C.-G. Shu, & B. Menard ed., IAUColloq. 199: Probing Galaxies through Quasar Absorption Lines Highly ionisedabsorbers at high redshift. pp 265–280
Braun E., Milgrom M., 1989, ApJ, 342, 100
Calzetti D., Kennicutt R. C., Engelbracht C. W., Leitherer C., Draine B. T., Kewley L.,Moustakas J., Sosey M., Dale D. A., Gordon K. D., Helou G. X., Hollenbach D. J.,Armus L., Bendo G., Bot C., Buckalew B., et al. 2007, ApJ, 666, 870
Cao C., Xia X. Y., Wu H., Mao S., Hao C. N., Deng Z. G., 2008, MNRAS, 390, 336
Capellupo D., Hamann F. Rodrıguez Hidalgo P., Shields J., 2010, in prep.
Carilli C. L., Bertoldi F., Rupen M. P., Fan X., Strauss M. A., Menten K. M., Kreysa E.,Schneider D. P., Bertarini A., Yun M. S., Zylka R., 2001, ApJ, 555, 625
Coppin K. E. K., Smail I., Alexander D. M., Weiss A., Walter F., Swinbank A. M., GreveT. R., Kovacs A., De Breuck C., Dickinson M., Ibar E., Ivison R. J., Reddy N., SpinradH., Stern D., Brandt W. N., Chapman S. C. e. a., 2009, MNRAS, 395, 1905
Coppin K. E. K., Swinbank A. M., Neri R., Cox P., Alexander D. M., Smail I., Page M. J.,Stevens J. A., Knudsen K. K., Ivison R. J., Beelen A., Bertoldi F., Omont A., 2008,MNRAS, 389, 45
Cox P., Beelen A., Bertoldi F., Omont A., Carilli C. L., Walter F., 2005, in Wilson A., ed.,ESA Special Publication Vol. 577 of ESA Special Publication, Gas and dust in highredshift quasars. pp 115–120
Cox T. J., Jonsson P., Primack J. R., Somerville R. S., 2006, MNRAS, 373, 1013
Daddi E., Dickinson M., Morrison G., Chary R., Cimatti A., Elbaz D., Frayer D., RenziniA., Pope A., Alexander D. M., Bauer F. E., Giavalisco M., Huynh M., Kurk J., MignoliM., 2007, ApJ, 670, 156
Dale D. A., Helou G., Contursi A., Silbermann N. A., Kolhatkar S., 2001, ApJ, 549, 215
Dave R., Oppenheimer B. D., 2007, MNRAS, 374, 427
Dhanda N., Baldwin J. A., Bentz M. C., Osmer P. S., 2007, ApJ, 658, 804
Di Matteo T., Colberg J., Springel V., Hernquist L., Sijacki D., 2008, ApJ, 676, 33
Di Matteo T., Springel V., Hernquist L., 2005, Nature, 433, 604
Dietrich M., Hamann F., Shields J. C., Constantin A., Heidt J., Jager K., Vestergaard M.,Wagner S. J., 2003, ApJ, 589, 722
D’Odorico V., Cristiani S., Romano D., Granato G. L., Danese L., 2004, MNRAS, 351,976
186
Efstathiou A., Rowan-Robinson M., 1995, MNRAS, 273, 649
Ellison S. L., Prochaska J. X., Hennawi J., Lopez S., Usher C., Wolfe A. M., RussellD. M., Benn C. R., 2010, MNRAS, pp 814–+
Elvis M., 2000, ApJ, 545, 63
Espey B. R., 1993, ApJ, 411, L59
Falomo R., Treves A., Kotilainen J. K., Scarpa R., Uslenghi M., 2008, ApJ, 673, 694
Farrah D., Bernard-Salas J., Spoon H. W. W., Soifer B. T., Armus L., Brandl B.,Charmandaris V., Desai V., Higdon S., Devost D., Houck J., 2207a, ApJ, 667, 149
Farrah D., Lacy M., Priddey R., Borys C., Afonso J., 2007b, ApJ, 662, L59
Ferland G. J., Korista K. T., Verner D. A., Ferguson J. W., Kingdon J. B., Verner E. M.,1998, PASP, 110, 761
Finlator K., Dave R., 2008, MNRAS, 385, 2181
Flores H., Hammer F., Elbaz D., Cesarsky C. J., Liang Y. C., Fadda D., Gruel N., 2004,A&A, 415, 885
Foltz C. B., Weymann R. J., Peterson B. M., Sun L., Malkan M. A., Chaffee Jr. F. H.,1986, ApJ, 307, 504
Fox A. J., Bergeron J., Petitjean P., 2008, MNRAS, 388, 1557
Fox A. J., Petitjean P., Ledoux C., Srianand R., 2007, A&A, 465, 171
Fox A. J., Prochaska J. X., Ledoux C., Petitjean P., Wolfe A. M., Srianand R., 2009, A&A,503, 731
Friaca A. C. S., Terlevich R. J., 1998, MNRAS, 298, 399
Gabel J. R., Arav N., Kim T.-S., 2006, ApJ, 646, 742
Ganguly R., Bond N. A., Charlton J. C., Eracleous M., Brandt W. N., Churchill C. W.,2001, ApJ, 549, 133
Ganguly R., Brotherton M. S., 2008, ApJ, 672, 102
Ganguly R., Eracleous M., Charlton J. C., Churchill C. W., 1999, AJ, 117, 2594
Ganguly R., Masiero J., Charlton J. C., Sembach K. R., 2003, ApJ, 598, 922
Ganguly R., Sembach K. R., Tripp T. M., Savage B. D., Wakker B. P., 2006, ApJ, 645,868
187
Gebhardt K., Bender R., Bower G., Dressler A., Faber S. M., Filippenko A. V., Green R.,Grillmair C., Ho L. C., Kormendy J., Lauer T. R., Magorrian J., Pinkney J., RichstoneD., Tremaine S., 2000, ApJ, 539, L13
Georgakakis A., Clements D. L., Bendo G., Rowan-Robinson M., Nandra K., BrothertonM. S., 2009, MNRAS, 394, 533
Gibson R. R., Brandt W. N., Gallagher S. C., Hewett P. C., Schneider D. P., 2010, ApJ,713, 220
Gibson R. R., Brandt W. N., Schneider D. P., Gallagher S. C., 2008, ApJ, 675, 985
Granato G. L., De Zotti G., Silva L., Bressan A., Danese L., 2004, ApJ, 600, 580
Granato G. L., Silva L., Monaco P., Panuzzo P., Salucci P., De Zotti G., Danese L., 2001,MNRAS, 324, 757
Grevesse N., Asplund M., Sauval A. J., 2007, Space Science Reviews, 130, 105
Groves B. A., Heckman T. M., Kauffmann G., 2006, MNRAS, 371, 1559
Haardt F., Madau P., 2001, in D. M. Neumann & J. T. V. Tran ed., Clusters of Galaxiesand the High Redshift Universe Observed in X-rays Modelling the UV/X-ray cosmicbackground with CUBA
Haas M., Klaas U., Muller S. A. H., Bertoldi F., Camenzind M., Chini R., Krause O.,Lemke D., Meisenheimer K., Richards P. J., Wilkes B. J., 2003, A&A, 402, 87
Haehnelt M. G., Natarajan P., Rees M. J., 1998, MNRAS, 300, 817
Hamann F., Barlow T. A., Beaver E. A., Burbidge E. M., Cohen R. D., Junkkarinen V.,Lyons R., 1995, ApJ, 443, 606
Hamann F., Barlow T. A., Junkkarinen V., Burbidge E. M., 1997, ApJ, 478, 80
Hamann F., Beaver E. A., Cohen R. D., Junkkarinen V., Lyons R. W., Burbidge E. M.,1997, ApJ, 488, 155
Hamann F., Ferland G., 1993, ApJ, 418, 11
Hamann F., Ferland G., 1999, ARA&A, 37, 487
Hamann F., Kanekar N., Prochaska J. X., Murphy M. T., Ellison S., Malec A. L.,Milutinovic N., Ubachs W., 2011, MNRAS, 410, 1957
Hamann F., Korista K. T., Ferland G. J., Warner C., Baldwin J., 2002, ApJ, 564, 592
Hamann F., Sabra B., 2004, in Richards G. T., Hall P. B., eds, AGN Physics with theSloan Digital Sky Survey Vol. 311 of Astronomical Society of the Pacific ConferenceSeries, The Diverse Nature of Intrinsic Absorbers in AGNs. pp 203–+
188
Hamann F., Simon L. E., 2010, in K. Cunha, M. Spite, & B. Barbuy ed., IAU SymposiumVol. 265 of IAU Symposium, Quasar Metal Abundances and Host Galaxy Evolution.pp 171–178
Hamann F., Warner C., Dietrich M., Ferland G., 2007, in Ho L. C., Wang J.-W., eds,The Central Engine of Active Galactic Nuclei Vol. 373 of Astronomical Society of thePacific Conference Series, Quasar Metal Abundances and Host Galaxy Evolution. pp653–+
Hamann F. W., Barlow T. A., Chaffee F. C., Foltz C. B., Weymann R. J., 2001, ApJ, 550,142
Hao C. N., Xia X. Y., Mao S., Wu H., Deng Z. G., 2005, ApJ, 625, 78
Hao C.-N., Xia X.-Y., Shu-DeMao Deng Z.-G., Wu H., 2008, Chinese Journal ofAstronomy and Astrophysics, 8, 12
Haring N., Rix H.-W., 2004, ApJ, 604, L89
Hartquist T. W., Snijders M. A. J., 1982, Nature, 299, 783
Heckman T. M., Lehnert M. D., Strickland D. K., Armus L., 2000, ApJS, 129, 493
Hewett P. C., Wild V., 2010, MNRAS, 405, 2302
Hopkins P. F., Hernquist L., Cox T. J., Keres D., 2008, ApJS, 175, 356
Houck J. R., Schneider D. P., Danielson G. E., Neugebauer G., Soifer B. T., BeichmanC. A., Lonsdale C. J., 1985, ApJ, 290, L5
Hutsemekers D., Hall P. B., Brinkmann J., 2004, A&A, 415, 77
Isaak K. G., Priddey R. S., McMahon R. G., Omont A., Peroux C., Sharp R. G.,Withington S., 2002, MNRAS, 329, 149
Jiang L., Fan X., Vestergaard M., Kurk J. D., Walter F., Kelly B. C., Strauss M. A., 2007,AJ, 134, 1150
Juarez Y., Maiolino R., Mujica R., Pedani M., Marinoni S., Nagao T., Marconi A., OlivaE., 2009, A&A, 494, L25
Kauffmann G., Haehnelt M., 2000, MNRAS, 311, 576
Kennicutt Jr. R. C., 1998, ARA&A, 36, 189
Korista K. T., Voit G. M., Morris S. L., Weymann R. J., 1993, ApJS, 88, 357
Kuraszkiewicz J. K., Green P. J., 2002, ApJ, 581, L77
Lehner N., Howk J. C., Prochaska J. X., Wolfe A. M., 2008, MNRAS, 390, 2
189
Li C., Kauffmann G., Heckman T. M., White S. D. M., Jing Y. P., 2008, MNRAS, 385,1915
Lonsdale C. J., Farrah D., Smith H. E., 2006, Ultraluminous Infrared Galaxies. SpringerVerlag, pp 285–+
Lutz D., Sturm E., Tacconi L. J., Valiante E., Schweitzer M., Netzer H., Maiolino R.,Andreani P., Shemmer O., Veilleux S., 2007, ApJ, 661, L25
Lutz D., Sturm E., Tacconi L. J., Valiante E., Schweitzer M., Netzer H., Maiolino R.,Andreani P., Shemmer O., Veilleux S., 2008, ApJ, 684, 853
Marconi A., Hunt L. K., 2003, ApJ, 589, L21
Matsuoka K., Nagao T., Maiolino R., Marconi A., Taniguchi Y., 2009, A&A, 503, 721
McMahon R. G., Priddey R. S., Omont A., Snellen I., Withington S., 1999, MNRAS, 309,L1
Menendez-Delmestre K., Blain A. W., Smail I., Alexander D. M., Chapman S. C., ArmusL., Frayer D., Ivison R. J., Teplitz H., 2009, ApJ, 699, 667
Merritt D., Ferrarese L., 2001, ApJ, 547, 140
Misawa T., Charlton J. C., Eracleous M., Ganguly R., Tytler D., Kirkman D., Suzuki N.,Lubin D., 2007, ApJS, 171, 1
Misawa T., Eracleous M., Charlton J., Ganguly R., Tytler D., Kirkman D., O’Meara J.,Suzuki N., Lubin D., 2003, in Bulletin of the American Astronomical Society Vol. 35of Bulletin of the American Astronomical Society, A Census of Intrinsic NarrowAbsorption Lines at z ≈3.0. pp 1329–+
Misawa T., Eracleous M., Charlton J. C., Kashikawa N., 2007, ApJ, 660, 152
Moe M., Arav N., Bautista M. A., Korista K. T., 2009, ApJ, 706, 525
Nagao T., Maiolino R., Marconi A., 2006, A&A, 447, 863
Nagao T., Marconi A., Maiolino R., 2006, A&A, 447, 157
Narayanan D., Hamann F., Barlow T., Burbidge E. M., Cohen R. D., Junkkarinen V.,Lyons R., 2004, ApJ, 601, 715
Nestor D., Hamann F., Rodriguez Hidalgo P., 2008, MNRAS, 386, 2055
Netzer H., Lutz D., Schweitzer M., Contursi A., Sturm E., Tacconi L. J., Veilleux S., KimD.-C., Rupke D., Baker A. J., Dasyra K., Mazzarella J., Lord S., 2007, ApJ, 666, 806
Omont A., Beelen A., Bertoldi F., Cox P., Carilli C. L., Priddey R. S., McMahon R. G.,Isaak K. G., 2003, A&A, 398, 857
190
Omont A., Cox P., Bertoldi F., McMahon R. G., Carilli C., Isaak K. G., 2001, A&A, 374,371
Osmer P. S., Porter A. C., Green R. F., 1994, ApJ, 436, 678
Pentericci L., Fan X., Rix H.-W., Strauss M. A., Narayanan V. K., Richards G. T.,Schneider D. P., Krolik J., Heckman T., Brinkmann J., Lamb D. Q., Szokoly G. P.,2002, AJ, 123, 2151
Perez-Gonzalez P. G., Rieke G. H., Villar V., Barro G., Blaylock M., Egami E., Gallego J.,Gil de Paz A., Pascual S., Zamorano J., Donley J. L., 2008, ApJ, 675, 234
Peroux C., Petitjean P., Aracil B., Srianand R., 2002, New Astronomy, 7, 577
Peroux C., Storrie-Lombardi L. J., McMahon R. G., Irwin M., Hook I. M., 2001, AJ, 121,1799
Peterson B. M., 2008, New Astronomy Review, 52, 240
Peterson B. M., Ferrarese L., Gilbert K. M., Kaspi S., Malkan M. A., Maoz D., Merritt D.,Netzer H., Onken C. A., Pogge R. W., Vestergaard M., Wandel A., 2004, ApJ, 613,682
Petitjean P., Rauch M., Carswell R. F., 1994, A&A, 291, 29
Petitjean P., Srianand R., 1999, A&A, 345, 73
Popesso P., Biviano A., 2006, A&A, 460, L23
Priddey R. S., Isaak K. G., McMahon R. G., Omont A., 2003, MNRAS, 339, 1183
Priddey R. S., McMahon R. G., 2001, MNRAS, 324, L17
Prochaska J. X., Hennawi J. F., 2009, ApJ, 690, 1558
Prochaska J. X., O’Meara J. M., Herbert-Fort S., Burles S., Prochter G. E., BernsteinR. A., 2006, ApJ, 648, L97
Prochter G. E., Prochaska J. X., O’Meara J. M., Burles S., Bernstein R. A., 2010, ApJ,708, 1221
Ramos Almeida C., Rodrıguez Espinosa J. M., Barro G., Gallego J., Perez-GonzalezP. G., 2009, AJ, 137, 179
Richards G. T., 2001, ApJS, 133, 53
Richards G. T., York D. G., Yanny B., Kollgaard R. I., Laurent-Muehleisen S. A., vandenBerk D. E., 1999, ApJ, 513, 576
191
Richstone D., Ajhar E. A., Bender R., Bower G., Dressler A., Faber S. M., FilippenkoA. V., Gebhardt K., Green R., Ho L. C., Kormendy J., Lauer T. R., Magorrian J.,Tremaine S., 1998, Nature, 395, A14+
Rodrıguez Hidalgo P., Hamann F., Nestor D. B., Shields J., 2010a, in prep.
Rodrıguez Hidalgo P., Hamann F., Nestor D. B., Shields J., 2010b, in prep.
Ruiz J. R., Crenshaw D. M., Kraemer S. B., Bower G. A., Gull T. R., Hutchings J. B.,Kaiser M. E., Weistrop D., 2001, AJ, 122, 2961
Ruiz J. R., Crenshaw D. M., Kraemer S. B., Bower G. A., Gull T. R., Hutchings J. B.,Kaiser M. E., Weistrop D., 2005, AJ, 129, 73
Rupke D. S., Veilleux S., Sanders D. B., 2005a, ApJ, 632, 751
Rupke D. S., Veilleux S., Sanders D. B., 2005b, ApJS, 160, 115
Sanders D. B., Mirabel I. F., 1996, ARA&A, 34, 749
Sanders D. B., Soifer B. T., Elias J. H., Neugebauer G., Matthews K., 1988, ApJ, 328,L35
Sargent W. L. W., Boksenberg A., Steidel C. C., 1988, ApJS, 68, 539
Schaye J., Carswell R. F., Kim T.-S., 2007, MNRAS, 379, 1169
Schweitzer M., Lutz D., Sturm E., Contursi A., Tacconi L. J., Lehnert M. D., Dasyra K. M.,Genzel R., Veilleux S., Rupke D., Kim D.-C., Baker A. J., Netzer H., Sternberg A.,Mazzarella J., Lord S., 2006, ApJ, 649, 79
Serjeant S., Hatziminaoglou E., 2009, MNRAS, 397, 265
Shemmer O., Netzer H., Maiolino R., Oliva E., Croom S., Corbett E., di Fabrizio L., 2004,ApJ, 614, 547
Shen Y., Strauss M. A., Oguri M., Hennawi J. F., Fan X., Richards G. T., Hall P. B., GunnJ. E., Schneider D. P., Szalay A. S., Thakar A. R., Vanden Berk D. E., Anderson S. F.,Bahcall N. A., Connolly A. J., Knapp G. R., 2007, AJ, 133, 2222
Shields G. A., 1976, ApJ, 204, 330
Shields G. A., Salviander S., Bonning E. W., 2006, New Astronomy Review, 50, 809
Simcoe R. A., 2004, in Bulletin of the American Astronomical Society Vol. 36 of Bulletinof the American Astronomical Society, Measuring the Metallicity of the High RedshiftIGM: Traces of Early Stellar Feedback. pp 704–+
Simon L. E., Hamann F., 2010a, MNRAS, 407, 1826
Simon L. E., Hamann F., 2010b, MNRAS, 409, 269
192
Springel V., Di Matteo T., Hernquist L., 2005a, ApJ, 620, L79
Springel V., Di Matteo T., Hernquist L., 2005b, MNRAS, 361, 776
Srianand R., Petitjean P., 2000, A&A, 357, 414
Steidel C. C., 1990, ApJS, 72, 1
Storrie-Lombardi L. J., McMahon R. G., Irwin M. J., Hazard C., 1996, ApJ, 468, 121
SubbaRao M., Frieman J., Bernardi M., Loveday J., Nichol B., Castander F., Meiksin A.,2002, in Starck J.-L., Murtagh F. D., eds, Society of Photo-Optical InstrumentationEngineers (SPIE) Conference Series Vol. 4847 of Society of Photo-OpticalInstrumentation Engineers (SPIE) Conference Series, The Sloan Digital Sky Survey1-Dimensional Spectroscopic Pipeline. pp 452–460
Tremaine S., Gebhardt K., Bender R., Bower G., Dressler A., Faber S. M., FilippenkoA. V., Green R., Grillmair C., Ho L. C., Kormendy J., Lauer T. R., Magorrian J., PinkneyJ., Richstone D., 2002, ApJ, 574, 740
Tripp T. M., Sembach K. R., Bowen D. V., Savage B. D., Jenkins E. B., Lehner N., RichterP., 2008, ApJS, 177, 39
Trump J. R., Hall P. B., Reichard T. A., Richards G. T., Schneider D. P., Vanden BerkD. E., Knapp G. R., Anderson S. F., Fan X., Brinkman J., Kleinman S. J., Nitta A.,2006, ApJS, 165, 1
Turnshek D. A., 1988, in Blades J. C., Turnshek D. A., Norman C. A., eds, Proceedingsof the QSO Absorption Line Meeting BAL QSOs - Observations, models andimplications for narrow absorption line systems. pp 17–46
Tytler D., Fan X.-M., 1992, ApJS, 79, 1
Tzanavaris P., Carswell R. F., 2003, MNRAS, 340, 937
Veilleux S., Cecil G., Bland-Hawthorn J., 2005, ARA&A, 43, 769
Veilleux S., Rupke D. S. N., Kim D., Genzel R., Sturm E., Lutz D., Contursi A.,Schweitzer M., Tacconi L. J., Netzer H., Sternberg A., Mihos J. C., Baker A. J.,Mazzarella J. M., Lord S., Sanders D. B., Stockton A., Joseph R. D., Barnes J. E.,2009, ApJS, 182, 628
Verner D. A., Barthel P. D., Tytler D., 1994, A&AS, 108, 287
Veron-Cetty M., Veron P., 2006, A&A, 455, 773
Veron-Cetty M. P., Veron P., 2000, VizieR Online Data Catalog, 7215, 0
Vestergaard M., 2003, ApJ, 599, 116
Vestergaard M., 2004, ApJ, 601, 676
193
Vestergaard M., Peterson B. M., 2006, ApJ, 641, 689
Warner C., Hamann F., Dietrich M., 2003, ApJ, 596, 72
Warner C., Hamann F., Dietrich M., 2004, ApJ, 608, 136
Weymann R. J., Carswell R. F., Smith M. G., 1981, ARA&A, 19, 41
Weymann R. J., Morris S. L., Foltz C. B., Hewett P. C., 1991, ApJ, 373, 23
Weymann R. J., Williams R. E., Peterson B. M., Turnshek D. A., 1979, ApJ, 234, 33
Wild V., Kauffmann G., White S., York D., Lehnert M., Heckman T., Hall P. B., Khare P.,Lundgren B., Schneider D. P., vanden Berk D., 2008, MNRAS, 388, 227
Wilson E. B., 1927, Journal of the American Statistical Association, 22, 209
Wise J. H., Eracleous M., Charlton J. C., Ganguly R., 2004, ApJ, 613, 129
Wyithe J. S. B., Loeb A., 2003, ApJ, 595, 614
Young P., Sargent W. L. W., Boksenberg A., 1982, ApJS, 48, 455
194
BIOGRAPHICAL SKETCH
Leah Simon was born in Olympia, WA, USA. She attended Capital High School
in Olympia and was enrolled in the inaugural year of the International Baccalaureate
(IB) program. She attended Macalester College in St. Paul, MN, USA and graduated
Magna cum Laude with a Bachelor of Arts in physics with an Emphasis in astronomy,
and in German studies in May 2000. She received her Ph.D. in astronomy in the spring
of 2011, and went on to teach astronomy and physics at Austin Peay State University in
Clarksville, TN, USA.
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