bayesian photometric redshifts (bpz) narciso benítez 1,2 (2000) narciso benítez 1,2 et al. (2004)...

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Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University 1 Instituto de Astrofísica de Andalucía 2 JPL/Caltech 3 Scienc e Team

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Page 1: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Bayesian Photometric Redshifts (BPZ)

Narciso Benítez1,2 (2000) Narciso Benítez1,2 et al. (2004)Dan Coe1,2,3 et al. (2006)

Johns Hopkins University1 Instituto de Astrofísica de Andalucía2

JPL/Caltech3

ScienceTeamScienceTeam

Page 2: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Photo-z Methods

Spectral Energy Distribution (SED) Template Fitting

Empirical Training Set (Neural Networks)

Page 3: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Coleman, Wu, Weedman ‘80

Kinney ‘96

Bruzual & Charlot ‘03

Spectral Energy Distribution (SED) templates

BPZ v1.99bBPZ v1.99bBenítez ‘00,

‘04Benítez ‘00,

‘04Coe ‘06Coe ‘06

recalibrated with real photometry

http://adcam.pha.jhu.edu/~txitxo/

Normally interpolate 2 between adjacent templates

Page 4: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Flu

x

Wavelength

SED template fit

SED template fit

Page 5: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Redshift

Pro

babi

lity

prior: I = 26

without prior

with prior

with prior

Bayesian use of priors

Benítez00

Output:

Page 6: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Benítez00

Redshift Inaccuracy (photo-z vs. spec-z)Redshift Inaccuracy (photo-z vs. spec-z)

Poo

rnes

s of

Fit

Poo

rnes

s of

Fit

Poorest fits yieldmost accurate redshifts!

Page 7: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

2 = 4.27

2 = 0.11

Wavelength

Flu

x

2mod = 0.03

2mod = 0.19

Page 8: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PHAT GOODS BPZ results (training set)Important to plot error bars and goodness-of-fit

Page 9: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PHAT GOODS BPZ results (training set)Single-peaked P(z) [ODDS 0.95]

no error bars plotted

Page 10: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Most GOODS objects have good photometry

ACSgroundIRAC

Page 11: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

…but some are bad

ACSgroundIRAC

Page 12: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

ACSgroundIRAC

…some are ugly

Page 13: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Robust photo-z’s require

Robust photometry

One of the best methods(even if Peter doesn’t like it ;)

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Page 14: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PSF-corrected aperture-matched photometry

What is the best method?

Page 15: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PHOTEST

Photometry TestingPSF Degradation vs. Model FittingMagnitude UncertaintiesZeropoint CalibrationObject Detection & Deblending…

Sounds like a job for a new group Let’s meet in Greece 2009

Page 16: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

UDF NICMOS fluxes too low

Page 17: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

NICMOS flux recalibration

Objects w/ spec-z

Page 18: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Comprehensive Segmentation MapForced into SExtractor

Page 19: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Wish List(Goals for PHAT?)

Improve SED librarymore galaxy typesbroader wavelength coverageSED uncertainties derived from population synthesis models??

Improve Priors using UDF, surveys

Page 20: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Optimal Filter Choice for a given amount of observing time

Benítez et al. (2008) A&A submitted

4 - 5 filters is sub-optimal ! addition of near-IR helps somewhat > 8 filters performs much better

Page 21: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Filters tested

= const

contiguous overlapping

Page 22: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Photo-z completenessBest is > 8 overlapping filters

Depth to which 80% of objects have ODDS ≥ 0.99

Page 23: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

Photo-z accuracy for ODDS ≥ 0.99 objectsBest is many non-overlapping (contiguous) filters

Page 24: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

lab

including CCD, atmosphere, mirror reflectivity

ALHAMBRA Survey (Moles08)

20 medium-band (310Å wide) filters3500 - 9700Å, supplemented by JHKs

Page 25: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

QuickTime™ and aTIFF (Uncompressed) decompressor

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ALHAMBRA

Survey

1.5’ x 1.5’

14-filter color image

to cover4+ sq deg

Page 26: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

8,000 - 10,000 sq deg z < 0.9 - 1.0 4 - 5 years 6 sq deg camera new 2-3m telescope to be built in

Aragon, Spain

8,000 - 10,000 sq deg z < 0.9 - 1.0 4 - 5 years 6 sq deg camera new 2-3m telescope to be built in

Aragon, Spain

Page 27: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

QuickTime™ and aTIFF (Uncompressed) decompressor

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PAU Survey: 40 100Å-wide filters (~4000-8000Å) + SDSS u & z

Page 28: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

QuickTime™ and aTIFF (Uncompressed) decompressor

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PAU Survey: z/(1+z) < 0.0015 for z < 0.4, L > L*, I < 23 LRGs

Page 29: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PAU Survey: BAO cosmological constraints

Page 30: Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University

PAU Survey: relative w constraints