Transcript 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
Science
Team
Photo-z Methods
 Spectral Energy Distribution (SED)
Template Fitting
 Empirical Training Set
(Neural Networks)
BPZ v1.99b
Benítez ‘00, ‘04
Coe ‘06
Bruzual &
Charlot ‘03
Kinney ‘96
Coleman, Wu,
Weedman ‘80
Normally interpolate 2
between adjacent templates
http://adcam.pha.jhu.edu/~txitxo/
Spectral
Energy
Distribution
(SED)
templates
recalibrated
with real
photometry
Flux
SED template fit
Wavelength
Bayesian
use of
priors
without
prior
Probability
prior:
I = 26
with
prior
with
prior
Output:
Redshift
Benítez00
Poorness of Fit
Poorest fits yield
most accurate
redshifts!
Benítez00
Redshift Inaccuracy (photo-z vs. spec-z)
2 = 4.27
2mod = 0.03
2 = 0.11
Flux
2mod = 0.19
Wavelength
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
ACS
ground
IRAC
…but some are bad
ACS
ground
IRAC
…some are ugly
ACS
ground
IRAC
Robust photo-z’s require
Robust photometry
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One of the best methods
(even if Peter doesn’t like it ;)
PSF-corrected aperture-matched photometry
What is the best method?
PHOTEST
 Photometry Testing
 PSF Degradation vs. Model Fitting
 Magnitude Uncertainties
 Zeropoint Calibration
 Object Detection & Deblending
…
 Sounds like a job for a new group
 Let’s meet in Greece 2009
UDF
NICMOS
fluxes
too low
Objects
w/ spec-z
NICMOS
flux
recalibration
Comprehensive Segmentation Map
Forced into SExtractor
Wish List
(Goals for PHAT?)
 Improve SED library
 more galaxy types
 broader wavelength coverage
 SED 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
Photo-z completeness
Best is > 8 overlapping filters
Depth to
which
80% of
objects
have
ODDS ≥
0.99
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Photo-z accuracy for ODDS ≥ 0.99 objects
Best is many non-overlapping (contiguous) filters
ALHAMBRA Survey (Moles08)
20 medium-band (310Å wide) filters
3500 - 9700Å, supplemented by JHKs
lab
including
CCD,
atmosphere,
mirror reflectivity
ALHAMBRA
Survey
1.5’ x 1.5’
14-filter
color
image
to cover
4+ sq deg
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 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
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
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PAU Survey: BAO cosmological constraints
PAU Survey: relative w constraints