SONG Targets of Opportunity: Searching for Pulses in Gamma-Ray Burst AfterglowsSONG Targets of Opportunity: Searching

for Pulses in Gamma-Ray Burst Afterglows

Jon HakkilaJon Hakkila

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SONG’s primary mission : asteroseismology and searching for extrasolar planets.

SONG’s primary mission : asteroseismology and searching for extrasolar planets.

TOO observations, coordinated by the SONG TAC, should take advantage of SONG’s 24-hour observations, but should not be disruptive over long time periods. The search for pulses in selected gamma-ray burst afterglows is one example of TOO science for which SONG is a preferred instrument.

TOO observations, coordinated by the SONG TAC, should take advantage of SONG’s 24-hour observations, but should not be disruptive over long time periods. The search for pulses in selected gamma-ray burst afterglows is one example of TOO science for which SONG is a preferred instrument.

SONG also has the potential to observe occasional, special secondary Targets of Opportunity (TOOs), provided these do not interfere with the primary mission.

SONG also has the potential to observe occasional, special secondary Targets of Opportunity (TOOs), provided these do not interfere with the primary mission.

Nonthermal spectra peak at ~270 keV

Durations 10-3 s to 103 s; highly variable Cosmological sources Spectra evolve, but no previously known

pattern of time history evolution Beamed radiation Indicative of relativistic shocks Afterglows observed for many GRBs;

afterglow physics is well-understood Prompt emission is still poorly-understood

Bright flashes of primarily -radiation

General GRB background:

GRB Prompt EmissionGRB Prompt Emission

25-50 keV25-50 keV, , 50-100 keV50-100 keV, , 100-300 keV100-300 keV, , 300 keV-1 MeV300 keV-1 MeV

SpectraSpectra

Epk

Light CurvesLight Curves

Standard synchrotron shock model (e.g. Rees & Meszaros, ApJL, 1994, 430, 94).

GRB ClassesGRB Classes

Short Long

Intermediate?

Hypernova Central Engine Model of Long GRBs

Hypernova Central Engine Model of Long GRBs

Merging Compact Objects Central Engine Model of Short GRBs

Merging Compact Objects Central Engine Model of Short GRBs

Intermediate class’s existence appears to be statistical, rather than representing a source population.

GRB bulk properties do not indicate simple behaviors, even though some (e.g. lag, Epk, variability) are luminosity indicators.

GRB bulk properties do not indicate simple behaviors, even though some (e.g. lag, Epk, variability) are luminosity indicators.

• GRB complexity results from overlapping pulses. • Long and Short GRBs appear inherently different.• Relativistic cosmology alters GRB observed properties:

GRB pulse properties appear to be simplerGRB pulse properties appear to be simpler

Energy shift: distant GRBs have their fluxes shifted to lower observed energies than similar nearby ones.

Energy shift: distant GRBs have their fluxes shifted to lower observed energies than similar nearby ones.

Inverse square law: distant GRBs appear fainter than similar nearby ones Inverse square law: distant GRBs appear fainter than similar nearby ones Time dilation: distant GRBs have durations and temporal structures

stretched more than nearby ones. Time dilation: distant GRBs have durations and temporal structures

stretched more than nearby ones. Energy shift: distant GRBs have their fluxes shifted to lower observed

energies than similar nearby ones. Energy shift: distant GRBs have their fluxes shifted to lower observed

energies than similar nearby ones.

• Inherent time asymmetry (longer decay than rise rates),• Hard-to-soft spectral evolution, and • Pulse lengthening at lower energies

• Inherent time asymmetry (longer decay than rise rates),• Hard-to-soft spectral evolution, and • Pulse lengthening at lower energies

Pulse extraction via semi-automated pulse-fitting algorithm.Uses the Bayesian Blocks methodology (Scargle 1998), a dual timescale threshold definition (Hakkila et al. 2003), and a 4-parameter pulse model (Norris et al. 2005).

Pulse extraction via semi-automated pulse-fitting algorithm.Uses the Bayesian Blocks methodology (Scargle 1998), a dual timescale threshold definition (Hakkila et al. 2003), and a 4-parameter pulse model (Norris et al. 2005).

• Pulse peak flux (p256) - peak flux of summed multichannel data (black) measured on 256 ms timescale.• Pulse duration - time interval between times when flux is e-3 of pulse peak flux.• Pulse peak lag- time interval between channel 3 peak (100-300 keV; green) and channel 1 peak (25-50 keV; red).• Fluence - time-integrated flux.• Hardness - ratio of channel 3 fluence to channel 1 fluence.• Asymmetry - pulse shape measure; 0 is symmetric and 1 is asymmetric.

Pulse-fitting example: GRB 950325a (BATSE 3480)

Pulse-fitting example: GRB 950325a (BATSE 3480)

25 keV - 1 MeV

1 2

3

100 keV - 300 keV

50 keV - 100 keV

25 keV - 50 keV

300 keV - 1 MeV

Pulse 1

Pulse 2

Pulse 3

lag

0.01 s ± 0.01

0.11s ± 0.05

0.85 s ± 0.15

duration 1.00 s ± 0.08 3.06 s ± 0.10 9.45 s ± 0.52

peak flux (256 ms)

19.47 c/s ± 0.21 6.35 c/s ± 0.14 1.35 c/s ± 0.03

100 keV - 300 keV

50 keV - 100 keV

25 keV - 50 keV

Pulse-fitting example: GRB 910930 (BATSE 0840)

Pulse-fitting example: GRB 910930 (BATSE 0840)

Pulse 1

Pulse 2

Pulse 3

Pulse 4

lag

0.00 s ± 0. 0.02 s ± 0. 0.00 s ± 0. 0.06 s ± 0.

duration 4.84 s ± 12.54 1.97 s ± 0.47 2.22 s ± 6.29 1.6 s ± 0.35

peak flux (256 ms)

1.260 c/s ± 927. 0.818 c/s ± 0.05 0.492 c/s ± 12.15 0.647 c/s ± 0.1

25 keV - 1 MeV

1 2 3 4

300 keV - 1 MeV

100 keV - 300 keV

50 keV - 100 keV

25 keV - 50 keV

25 keV - 1 MeV

1 2

Pulse-fitting example: GRB 930123 (BATSE 2600)

Pulse-fitting example: GRB 930123 (BATSE 2600)

Pulse 1

Pulse 2

lag

6.71 s ± 0.31

0.70s ± 0.30

duration 45.8 s ± 4.6 6.4 s ± 0.3

peak flux (256 ms)

0.39 c/s ± 0.01 0.80 c/s ± 0.02

25 keV - 1 MeV

1 2

The CCF lag is dominated by large amplitude, narrow pulses with short lags. Longer-lag pulses can smear out this behavior. The GRB peak flux is not generally the pulse peak flux of the brightest pulse, due to pulse overlap.

original burst CCF;short lag

CCF of reconstructed pulse 1: long lag

CCF of reconstructed pulse 2: short lag

GRB 930123 (BATSE 2600)GRB 930123 (BATSE 2600)

CCF of reconstructedpulses 1+2: short lag

What does GRB lag measure (as obtained from the CCF)?What does GRB lag measure (as obtained from the CCF)?

Pulse Property Correlations(1390 pulses in 646 BATSE

GRBs)

Peak luminosity vs. duration (w) and peak luminosity vs. lag for BATSE GRBs: Pulse relations replace bulk prompt emission relations (Hakkila et al. 2008, ApJ 677, L81).

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-5 -4 -3 -2 -1 0 1 2

log(pulse peak lag)

log(pulse duration)

The GRB lag vs. luminosity relation (Norris et al. 2000) is actually a pulse lag vs. pulse luminosity relation. Furthermore, pulse duration is highly correlated with pulse lag, so pulse duration also indicates luminosity.

More Pulse Property Correlations

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

log(pulse duration)

log(pulse peak flux)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

log(pulse duration)

log(pulse fluence)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

pulse asymmetry

log(pulse duration)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

log(pulse duration)

log(pulse hardness)

Long GRB Pulse CorrelationsLong GRB Pulse Correlations

Short GRB Pulse CorrelationsShort GRB Pulse Correlations

Probability of uncorrelated attributes

Lag

p256

Hardness Asymmetry Fluence

Duration

<10-38 2.3 x10-36 7.6 x10-26 3.8 x10-33 <10-38

Lag --- 8.8 x10-7 4.2 x10-7 1.3 x10-11 <10-38

p256 --- --- 3.8 x10-22 2.7 x10-3 9.5 x10-21

Hardness --- --- --- 8.6 x10-10 1.3 x10-4

Asymmetry --- --- --- --- 1.6 x10-13

Probability of uncorrelated attributes

Lag

p256

Hardness Asymmetry Fluence

Duration

7.7 x10-8 1.7 x10-8 1.4 x10-1 6.9 x10-1 6.5 x10-2

Lag --- 1.2 x10-5 1.8 x10-1 6.6 x10-3 1.6 x10-1

p256 --- --- 6.9 x10-6 6.2 x10-1 9.2 x10-21

Hardness --- --- --- 7.2 x10-1 2.3 x10-2

Asymmetry --- --- --- --- 4.9 x10-1

Correlations among GRB pulse properties are unmistakable!

Some low-z BATSE burstsSome low-z BATSE bursts

BATSE 0332; z ≈ 0.9

BATSE 0563; z ≈ 0.8 BATSE 1406; z ≈ 0.8

BATSE 0111; z ≈ 0.9

BATSE 0214; z ≈ 4.3 BATSE 0237; z ≈ 5.3

BATSE 0803; z ≈ 4.6BATSE 0594; z ≈ 5.4

Some high-z BATSE burstsSome high-z BATSE bursts

Correlated pulse properties can be used to estimate GRB redshifts (Hakkila, Fragile, & Giblin, 2009, AIP Conf. 1133, 479) .

€

DL =L /dΩ

f256

= 1+ z( )c

H0

dz

Ωm 1+ z( )3

+ ΩΛ0

z

∫

Arimoto et al. (2010, PASJ, in press) verify the pulse lag and pulse duration vs. pulse luminosity relations using HETE-2. They find that pulse curvature can explain the observed energy dependence (see also Lu, Qin, and Zhang 2006, MNRAS, 367, 275).

Supportive ObservationsSupportive Observations

Pulse PhysicsPulse Physics

Pulses appear to start simultaneously at all energies (Hakkila and Nemiroff 2009, ApJ 705, 372).

Pulse Epk values decay from the moment the pulse begins.

Pulses appear to start simultaneously at all energies (Hakkila and Nemiroff 2009, ApJ 705, 372).

Pulse Epk values decay from the moment the pulse begins.

Model: Kinematic energy injection into a medium via relativistic shock; the medium cools. Standard spectral model has been either a synchrotron spectrum or a thermal plus power law spectrum.

Peng et al. (ApJ in press)

Boci, Hafizi, & Mochkovitch (2010, A&A, submitted) predict the pulse lag and pulse duration vs. pulse luminosity relations from pulse properties near peak flux. They find, however, that these correlative relations cannot be obtained as a direct and simple consequence of the standard synchrotron shock model (Rees & Meszaros, ApJL, 1994, 430, 94).

Pulse PhysicsPulse

Physics

Connecting Prompt Emission to the AfterglowConnecting Prompt Emission to the Afterglow

X-ray pulses typicallystart seconds to minutes after the prompt emission and end within minutes to hours.

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GRB 070311

Connecting Prompt Emission to the AfterglowConnecting Prompt Emission to the Afterglow

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Optical flares typicallystart minutes to hours after the prompt emission and end within hours to days, with 10 ≤ R ≤ 20.

Hypothesis: afterglow flares are late, low energy GRB pulses.

Hypothesis: afterglow flares are late, low energy GRB pulses.

If so, then the properties of these late pulses must be correlated as they are for the prompt pulses, (lags roughly minutes long) --> a testable hypothesis.

If so, then the properties of these late pulses must be correlated as they are for the prompt pulses, (lags roughly minutes long) --> a testable hypothesis.

Many follow-up questions can be addressed if this hypothesis is validated:

• Are late pulses limited to GRBs with specific morphologies (e.g. Long vs. Short, many pulses vs. few pulses)?

• What constraints can late pulses place on the GRB energy mechanism?

• What are the limiting amplitudes of late pulses?

• How common are late pulses? How often can they happen within a single GRB afterglow?

Standard TOO observing:

1. Schedule a TOO to break away from current SONG target upon receipt of an appropriate GCN notice.

Alternate TOO strategies:1. Have a standing TOO request to respond to a GCN

notice with an offline SONG node.

2. Near the end of an observing campaign on the current target, have an increasing prioritization to divert SONG nodes in response to a GCN notice.

Standard TOO observing:

1. Schedule a TOO to break away from current SONG target upon receipt of an appropriate GCN notice.

Alternate TOO strategies:1. Have a standing TOO request to respond to a GCN

notice with an offline SONG node.

2. Near the end of an observing campaign on the current target, have an increasing prioritization to divert SONG nodes in response to a GCN notice.

SONG’s temporal resolution and sampling completeness are excellent for making multiwavelength observations of GRB afterglow flares.

ConclusionsConclusions Pulses are the basic building blocks of GRB prompt emission. Pulse properties correlate with one another. GRB bulk properties are constructed by combining and smearing out pulse characteristics in ways that potentially lose valuable information. Pulse physics models indicate simple pulse geometries but may not support the standard synchrotron shock model. SONG observations can ascertain whether afterglow flares are part of the afterglow, or instead late shocks having the correlated properties of GRB pulses. JH acknowledges support by SC Space Grant, NASA Swift, NASA AISR, NASA ADAP, and SC Space Grant Palmetto Scholars, and graciously thanks the SONG conference organizers.

Pulses are the basic building blocks of GRB prompt emission. Pulse properties correlate with one another. GRB bulk properties are constructed by combining and smearing out pulse characteristics in ways that potentially lose valuable information. Pulse physics models indicate simple pulse geometries but may not support the standard synchrotron shock model. SONG observations can ascertain whether afterglow flares are part of the afterglow, or instead late shocks having the correlated properties of GRB pulses. JH acknowledges support by SC Space Grant, NASA Swift, NASA AISR, NASA ADAP, and SC Space Grant Palmetto Scholars, and graciously thanks the SONG conference organizers.