CLASH/HST photoz estimation: the challenges & their quality

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

Photoz for cluster galaxies

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 high-z galaxies – Arcsfor strong-lensing analysis

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

Photoz for Strong-Lensing

O. Host, D. Coe

Le Phare photoz for specz catalogue

Catastrophic redshift for high-redshift galaxies


ccWe can mistake cluster galaxies for background galaxies



Le Phare photoz for high-z galaxies

Photoz uncertainty well estimated using ACS only



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


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.

Le Phare : photoz errors validation

Specz cat

• 22 A383 • 8 MACS1149 • 17 A2261• 132 MACS1206 • 78 RXJ1347 • 14 MACS2129

Le Phare photoz errors

uncertainty

With 0.03 (ACS) 0.04 (NIR) and 0.05 (UVIS)photometric errors added in quadrature at all bands

Validation of the photoz uncertainties on the whole mag-redshift range

CLASH/HST photoz estimation: the challenges & their quality

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