CLASH/HST photoz estimation: the challenges & their quality

1. CLASH/HST photoz estimation:the challenges & their quality Stephanie Jouvel, Ofer Lahav, Ole Host

2. 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

3. 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

4. How do we assess the photoz quality Robust estimator of the photoz scatter : NMAD Normalised Median Absolute Deviation => 1.48*median[|zp-zs|/1+zs] Fraction of catastrophic : h => zp-zs>0.1 Median (zp-zs)/(1+zs)

5. 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

6. 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

7. The CLASH/HST specz data oct 2011 Specz sample of 271 galaxies covering the first 6 CLASH clusters completed Specz 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

8. Photoz for cluster galaxies

9. 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

10. Le Phare photozuncertainty Photoz errors underestimated => We then modify the photometric errors bands to achieve this.

11. 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

12. BPZ and Le Phare 0pts #FILTERS  zp_offset err_zp F225W     1.888 0.446 F275W     0.109 0.472 F336W     0.012 0.233 F390W     -0.074 0.258 F435W     0.000 0.030 F475W     0.000 0.030 F606W     0.000 0.030 F625W     0.000 0.030 F775W     0.000 0.030 F814W     0.000 0.030 F850LP  0.000   0.030 F105W    -0.052 0.040 F110W    -0.057 0.060 F125W    -0.078 0.074 F140W    -0.119 0.138 F160W    -0.095 0.108 1 0.39684 2 0.41308 3 0.23674 4 0.18033 5 0.06161 6 0.05412 7 -0.03166 8 -0.06276 9 -0.10776 10 -0.07174 11 -0.08302 12 0.00659 13 0.00655 14 0.02828 15 0.02167 16 0.02489 Problems with UVIS ? Or with templates photoz codes are using ?

13. Dan’s photoz results Steph’s photoz results 1sz<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% Txitxo’s results 0.15<z<0.65 & odds>0.9 #                         median            NMAD         n_obj   ACS                          0.0018          0.0285           144   ACS+IR                   -0.0075          0.0282           144   ACS+UVIS               -0.0077          0.027           133 ACS+UVIS+IR         -0.0095          0.0276          136   UVIS_ACS very close to UVIS_ACS_NIR "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%

14. 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

15. Photoz for high-z galaxies – Arcsfor strong-lensing analysis

16. Photoz for arcs : Strong-Lensing Photoz for arcs => High-z galaxies 1<z<6 • Balmer break at l>8000 AA • Lyman break at l>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 ?

17. Redshift and Cosmology Lens Efficiency: For a fixed lens redshift, the efficiency increase with source redshift Weak cosmology dependence Bartelmann & Schneider

18. Photoz for Strong-Lensing O. Host, D. Coe

19. Le Phare photoz for specz catalogue Catastrophic redshift for high-redshift galaxies

20. Le Phare photoz for specz catalogue Catastrophic redshift for high-redshift galaxies

21. Le Phare photoz for specz catalogue Catastrophic redshift for high-redshift galaxies

22. Le Phare photoz for specz catalogue c We can mistake cluster galaxies for background galaxies Catastrophic redshift for high-redshift galaxies

23. Le Phare photoz for high-z galaxies Photoz uncertainty well estimated using ACS only Catastrophic redshift for high-redshift galaxies

24. Le Phare photoz for high-z galaxies Catastrophic redshift for high-redshift galaxies

25. 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

26. 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 Catastrophic redshift for high-redshift galaxies => can’t tell from photoz uncertainty Having all 16 filters improves the statistics and %outliers 41 galaxies at z>0.65

27. 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.

28. Le Phare : photoz errors validation

29. Le Phare : photoz errors validation

30. Le Phare : photoz errors validation

31. Le Phare : photoz errors validation

32. Specz cat • 22 A383   • 8 MACS1149  • 17 A2261 • 132 MACS1206  • 78 RXJ1347  • 14 MACS2129

33. 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