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Photometric Redshifts. PHAT Meeting Pasadena 3-5 Dec 2008. Christian Wolf. data. model. Farb- bibliothek. estimator. Schätzer/ Klassifikator. Frequentist precision statistics: = “Using what IS there: N(z)!”. result. Bayesian frontier exploration: = “What do we (not) know: p(z)=?”. - PowerPoint PPT Presentation
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Christian Wolf
Photometric Redshifts PHAT Meeting
Pasadena3-5 Dec 2008
Photo-z Ingredients & Application
Farb-bibliothek
Schätzer/Klassifikator
result
model data
estimator
spectral energy
distribution
PDF: p(z)
empirical dataor
external template
2-fittingartificial neural netlearning algorithms
Frequentist precision statistics:= “Using what IS there: N(z)!”
Bayesian frontier exploration:= “What do we (not) know: p(z)=?”
Model + Estimator Combinations
Code 2 NN
Model
Template Empirical
2
+ PDF Ambiguity warning
• NN– No PDF, no warning
• Template model+ Can be extrapolated in z,mag– Calibration issues– Priors’ issues
• Empirical model+ Good priors+ No calibration issues– Can not be extrapolated
Class Decision and z-EstimationClass Decision and z-Estimation
PStar
PGalaxy
PQuasar
type
redshift
redshift
ML
MEV
<z> ± z
∫∫∫ >> QuasarStarGalaxy ppp
Can include morphology etc.in statistics or neural net
Examples from COMBO-17
Classification ~98% complete at R<23Stars (~3,000)
White Dwarfs (~30)
Ultra-cool WD (1)
Galaxies (~30,000)
QSOs (~300)
Redshift Error Regimes
• Three regimes in photo-z quality
• Saturation– Model-data calibration offsets;
intrinsic dimensionality of class
• Transition– Locally linear colour(z) grid
• Breakdown– Globally nonlinear
colour(z) grid€
z ∝σ colour ∝N
S∝
1
100.4m⇒ logσ z ∝m
stars galaxies quasars
21 22 23 24 25 21 22 23 24 25 21 22 23 24 25
100%
0%
CA+B A+B
A+BCC
21 23 25 21 23 25 21 23 25 21 23 25
1
0.1
0.01
quasars @ z<2.2 quasars @ z>2.2quiescent galaxies starburst galaxies
Δz
R-mag
mag
SbSb
EE
z=0.52z=0.56
colour
colo
ur
Galaxies: Saturation & Transition
R=20 R=22 R=23.7
Galaxies at z~0.45
QSOs: Saturation at R<24
QSOs at z~2.8
R=23.8R=22.9R=21.5
rms 0.0087%-20%
outlier
Photo-Z Trouble
• Catastrophic failures & misclassifications
• Large z errors
• Local z bias
• Unrealistic z errors
• Model ambiguities in colour space (spotted?)
• PDF too unconstrained
• PDF wrong (calib, prior)
• Mismatch between data and model
Common Fixes
• Catastrophic failures & misclassifications
• Large z errors
• Local z bias
• Unrealistic z errors
• Model ambiguities in colour space
• PDF too unconstrained
• PDF wrong (calib, prior)
• Mismatch between data and model
Repair modelsor data
Add more data
Add model priors
Realistic data errors
Add More Data: Wider Range
• Wider coverage – Covers spectral features
across wider z range
• Add NIR data– For z > 1 galaxies
– Only weak & high-variance features in rest-frame UV
– Red z>1 galaxies with noisy optical data
• Add UV data (e.g. GALEX)– For QSOs (Ball et al. 2007)
– Lyman break at z < 2-3Abdalla et al. 2007
Add More Data: Narrow Filters
• Improve localization and contrast of features– QSO line detection avoids
catastrophic failures at z < 3
– Galaxies+QSOs: improve z
– Galaxy, star, QSO, WD,… ?
λ / nm
QE (
%)
Wolf 2001
Wolf, Gray & Meisenheimer 2005
z/(1+z) ~ 0.006
Add More Data: Narrow Filters
• Improve localization and contrast of features– QSO line detection avoids
catastrophic failures at z < 3
– Galaxies+QSOs: improve z
– Galaxy, star, QSO, WD,… ?
λ / nm
QE (
%)
z/(1+z) ~ 0.015
Wolf et al. 2004
Wolf 2001
Add Priors…
• Impact: ptot = pprior pcolour
– Reduce rate of bimodal PDFs
– Reduce larger z (up to √2)
• Explicit for template models– Luminosity function, range– Mag / z extrapolation ~”ok”
• Implicit for empirical models– Restricted in mag & z– Mag extrapolation wrong– Z extrapolation impossible– Representative sample?
p
p
p
S/N medium
S/N low
S/N high
prior
Repair (or Make) Templates
Budavari et al. 2000,2001
Repair Models: Uncommon Objects
• COMBO-17 field Abell 901/2– Super-cluster at zspec~ 0.16
– 800 members with R < 21
• Photo-z 2002: using SEDs by Kinney et al. (1996)
– 25% of S/N~100 members outliers with zphot~ 0.06
Δz ~ 0.1 SHOCK!
– Red spirals!
• Photo-z 2003: include dust-reddened old SEDs – ~1% outliers
Age
Dust
Wolf, Gray & Meisenheimer 2005
Kinney et al. templates 25% z outliers
Abell 901 z~0.16
Photometry & SEDs
• Photometry:– PSF-matched
– Calibration (obs. frame)
– Artefacts: instrumental, data reduction
– Error distribution, non-Gaussian systematics in Gaussian error floor
• Source variability– Stars: RR Lyr, long-term
– Galaxies: supernovae
• Photometric Blends– Transient blends by moving
objects
– Close neighbours
– Line-of-sight projections, strong lenses
– Binaries, WD+M etc.
• SED composition– AGN component
– Composite stellar populations
PSF-Matched Photometry
• Basic method:– Assume Gaussian PSF– Convolve to worst PSF– Photometry in aperture A
• Problems:– Local PSF variations– Non-Gaussian PSF
(Capak et al. 2007)
• Special case: Gaussian aperture & PSF
– Stronger weight to brighter object centre
– Aeff = PSF A(“space-based aperture”)
– If A and PSF Gaussian, then Aeff Gaussian as well
– Minimize computations:Fix Aeff & adjust A to PSF
2A = 2
eff - 2PSF
(Röser & Meisenheimer 1991)
• Non-gaussian aperture & PSF– Shapelet-based method
(Kuijken 2008)
What We Understand by Now
• Origin of local z bias– Observed-frame + rest-frame calibration– Non-flat priors: p(zph|zsp) vs. p(zsp|zph), z>0
• Origin of catastrophic outliers– Unrecognised z ambiguity in colour space– Wrong data / errors: blends, instrumental issues
• Minimum z variance levels– Intrinsic SED variations– Spectral resolution
Local Z Bias: Calibration
• N = number of filters, i.e. independent data points
• Calibration offsets in N=3 D– 1-D normalisation– 1-D z-bias– 1-D restframe SED bias– 1 out of N offset dimensions
causes a photo-z bias Δz
• More filters smaller Δz (proj. component ~1/√N)
• Narrow filters small Δz (larger col/z on feature)
• Spectroscopy with N~102..3: z without flux calibration
SbSb
EE
z=0.52
z=0.56
colourco
lour
Few-filter photo-z’s limited by calibration accuracy
Many-filter photo-z’s limited by number and resolution of filters
Catastrophic OutliersResult from undetected ambiguities: Also: wrong data/errors- Example: see shrinking training sample- 20% sample in 1:20 ambiguities causes overall 1% unflagged outliers
Intrinsic Variety: Z Error Support
• Example: – QSO near g-r~1 or z~3.7– Main signal: Ly forest in g,
but SEDs >0-D family
• Training sample in box– Redshift distribution:
mean 3.66, rms 0.115– RMS/(1+z) = 0.024
• Testing sample in box– RMS/(1+z) error 0.023
What To Work On: Data
• Define most effective & efficient data sets:
– From simulations (…which don’t rule out outliers)
• Describe data correctly:
– Consistent apertures across bands
– True photometric scatter by object
• Minimise unrecognised error sources in data:
– Error floor from photometric blends & transients
What To Work On: Models
• Templates etc.:– Best templates, rare objects with “different SEDs”
– Best priors, best extrapolation in (mag, z)
• Training samples:– Discretization effects, confidence limits on random-ness
– Propagation into n(z) errors and outlier risks
– Size matters: “What do I need?”
• Combine all approaches? – Empirical + extrapolated template model for all kinds of use