Bayesian Photometric Redshifts (BPZ)
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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
description
Bayesian Photometric Redshifts (BPZ). Narciso Ben ítez 1,2 (2000) Narciso Ben ítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006). Johns Hopkins University 1 Instituto de Astrof ísica de Andalucía 2 JPL/Caltech 3. Science Team. Photo-z Methods. - PowerPoint PPT Presentation
Transcript of Bayesian Photometric Redshifts (BPZ)
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
Photo-z Methods
Spectral Energy Distribution (SED) Template Fitting
Empirical Training Set (Neural Networks)
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
Flu
x
Wavelength
SED template fit
SED template fit
Redshift
Pro
babi
lity
prior: I = 26
without prior
with prior
with prior
Bayesian use of priors
Benítez00
Output:
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!
2 = 4.27
2 = 0.11
Wavelength
Flu
x
2mod = 0.03
2mod = 0.19
PHAT GOODS BPZ results (training set)Important to plot error bars and goodness-of-fit
PHAT GOODS BPZ results (training set)Single-peaked P(z) [ODDS 0.95]
no error bars plotted
Most GOODS objects have good photometry
ACSgroundIRAC
…but some are bad
ACSgroundIRAC
ACSgroundIRAC
…some are ugly
Robust photo-z’s require
Robust photometry
One of the best methods(even if Peter doesn’t like it ;)
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PSF-corrected aperture-matched photometry
What is the best method?
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
UDF NICMOS fluxes too low
NICMOS flux recalibration
Objects w/ spec-z
Comprehensive Segmentation MapForced into SExtractor
Wish List(Goals for PHAT?)
Improve SED librarymore galaxy typesbroader wavelength coverageSED uncertainties derived from population synthesis models??
Improve Priors using UDF, surveys
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
Filters tested
= const
contiguous overlapping
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Photo-z completenessBest is > 8 overlapping filters
Depth to which 80% of objects have ODDS ≥ 0.99
Photo-z accuracy for ODDS ≥ 0.99 objectsBest is many non-overlapping (contiguous) filters
lab
including CCD, atmosphere, mirror reflectivity
ALHAMBRA Survey (Moles08)
20 medium-band (310Å wide) filters3500 - 9700Å, supplemented by JHKs
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ALHAMBRA
Survey
1.5’ x 1.5’
14-filter color image
to cover4+ sq deg
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
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PAU Survey: 40 100Å-wide filters (~4000-8000Å) + SDSS u & z
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PAU Survey: z/(1+z) < 0.0015 for z < 0.4, L > L*, I < 23 LRGs
PAU Survey: BAO cosmological constraints
PAU Survey: relative w constraints
