CLASH/HST photoz estimation: the challenges & their quality Stephanie Jouvel, Ofer Lahav, Ole Host.
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Transcript of CLASH/HST photoz estimation: the challenges & their quality Stephanie Jouvel, Ofer Lahav, Ole Host.
Presentation overview
- CLASH observations and photoz codes and photoz quality definitions
- Photoz for cluster galaxies- Photoz for high-z galaxies : Arcs for the strong
lensing analysis- Conclusion
Data and photoz quality• CLASH/HST observations = > 16 filters covering
2000 to 17000 AA => detect up to z~4 gal (Balmer)• We currently have 3 template fitting codes :
- BPZ- Le Phare- Munich’s code (Seitz et al.)
• We use these codes to estimate the photoz quality in terms of :- Photoz accuracy : NMAD- Number of catastrophic redshift- Median of the zphot-zspec (zp-zs) distribution
How do we assess the photoz quality
Robust estimator of the photoz scatter : NMADNormalised Median Absolute Deviation => 1.48*median[|zp-zs|/1+zs]
Fraction of catastrophiczp-zs>0.1
Median (zp-zs)/(1+zs)
Identify the photoz problems• The data :
– Photometry• Calibration which will result in big 0pts corrections=> Produce biases in photoz results• Photometric errors=> Impact the photoz scatter and PDZ
– Number of filters => Impact photoz accuracy
• Photoz codes :– Template representativity and diversity– Priors in redshift/template=> More likely to produce catastrophic redshifts and bias
Template representativity
We use Le Phare with the template optimised for the COSMOS survey
The COSMOS template fill the color-color space defined by the CLASH observation which is a first validation of the template representativity
The CLASH/HST specz data oct 2011
Specz sample of 271 galaxies covering the first 6 CLASH clusters completedSpecz catalogue mainly composed by cluster members 0.1<z<0.65.
Need to separate the specz catalogue in 2 samples :• foreground structure and cluster members
(z<0.65)• Arcs z>0.65
Le Phare’s photoz results for specz cat
ACS only (7 filters)
UVIS+ACS (11 filters)
ACS+NIR (12 filters)
UVIS+ACS+NIR (16 filters)
NMAD 4.3 to 5.4%(1+z)Median -0.007 to -0.026
Le Phare photoz
uncertainty
Photoz errors underestimated => We then modify the photometric errors bands to achieve this.
Le Phare photoz
uncertainty
With 0.03 photometric errors added in quadrature at all bands
Validation of the photoz uncertainties on the whole mag-redshift range
BPZ and Le Phare 0pts 1 0.396842 0.413083 0.236744 0.180335 0.061616 0.054127 -0.031668 -0.062769 -0.1077610 -0.0717411 -0.0830212 0.0065913 0.0065514 0.0282815 0.0216716 0.02489
#FILTERS zp_offset err_zpF225W 1.888 0.446F275W 0.109 0.472F336W 0.012 0.233F390W -0.074 0.258F435W 0.000 0.030F475W 0.000 0.030F606W 0.000 0.030F625W 0.000 0.030F775W 0.000 0.030F814W 0.000 0.030F850LP 0.000 0.030F105W -0.052 0.040F110W -0.057 0.060F125W -0.078 0.074F140W -0.119 0.138F160W -0.095 0.108
Problems with UVIS ? Or with templates photoz codes are using ?
Steph’s photoz results z<0.1
filters (chisq2<1)&(odds>0.9) NMAD abs(zp-zs)<0.1 RMS "outliers” > 0.1 ACS 74 objects 3.9%(1+z) 64 objects 3.0%(1+z) 23%ACS+IR 105 objects 4.4%(1+z) 78 objects 2.8%(1+z) 25%ACS+UVIS 97 objects 4.0%(1+z) 81 objects 3.1%(1+z) 16%ACS+UVIS+IR 109 objects 3.9%(1+z) 83 objects 2.9%(1+z) 23%
# median NMAD n_obj ACS 0.0018 0.0285 144 ACS+IR -0.0075 0.0282 144 ACS+UVIS -0.0077 0.027 133ACS+UVIS+IR -0.0095 0.0276 136
Txitxo’s results 0.15<z<0.65 & odds>0.9
Dan’s photoz results
"ACS" -0.01806 0.0403 152 1.6% "ACS_NIR" -0.01239 0.0423 175 5.0% "UVIS_ACS" -0.009524 0.0387 170 3.9% "UVIS_ACS_NIR” -0.01268 0.0379 187 4.2%
summary• We do not need both UVIS and NIR data to find
good photoz for galaxies z<0.65 which is expected since the color gradient produced by the Balmer break is in the optical for these redshift range
• The UVIS data seem to have a calibration problem since both BPZ and Le Phare find big 0pt in this wavelength range
• Le Phare derives good uncertainty after an addition factor of 0.03 in the photometric errors
• Both BPZ and Le Phare have consistent results. We reach an NMAD 2.8 to 4%(1+z), median -0.01 and low catastrophic redshift rate for confident redshift
Photoz for arcs : Strong-Lensing
Photoz for arcs => High-z galaxies 1<z<6Balmer break at 8000 AA Lyman break at 2400 AA Need of UVIS or NIR data to detect a color
gradient which will help the photoz estimation What photoz quality is necessary for the strong-
lensing analysis ?
Redshift and Cosmology
Lens Efficiency:
For a fixed lens redshift, the efficiency increase with source redshift
Weak cosmology dependence
Bartelmann & Schneider
Catastrophic redshift for high-redshift galaxies
ccWe can mistake cluster galaxies for background galaxies
Le Phare photoz for specz catalogue
Catastrophic redshift for high-redshift galaxies
Le Phare photoz for high-z galaxies
Photoz uncertainty well estimated using ACS only
Catastrophic redshift for high-redshift galaxies
Le Phare photoz for high-z galaxies
Photoz uncertainty not as well estimated than using ACS only=> Add information that do not help at the color gradient
Catastrophic redshift for high-redshift galaxies => can’t tell from photoz uncertainty
Le Phare photoz for high-z galaxies
Filt NMAD median %outlier”A" 0.3663 -0.129 73.17 ”UA" 0.1014 -0.038 65.85 ”AN" 0.0945 -0.038 70.73 ”All” 0.0544 -0.023 48.78
Having all 16 filters improves the statistics and %outliers 41 galaxies at z>0.65
Summary• For the cluster galaxies, you only need optical
data to derive unbiased redshifts. Since most of our specz catalogue are composed by cluster galaxies UVIS/NIR do not make a big difference.
• For high-z galaxies z>0.65, the full HST filters does make a difference. It allows to reduce the number of catastrophic redshifts, the scatter, and gives less skewed distribution.
• Need to understand the number of catastrophic redshift for high-z galaxies.