1. Peter Capak Associate Research Scientist IPAC/Caltech

2.  What is COSMOS  What we learned from COSMOS  Photo-z (and spec-z) theory  What work needs to be done

3.  2 sq degree deep multi-wavelength survey  Designed for Photo-z  HST imaging  Ideal for lensing  Lots of spectroscopy to I~26

5. Contours : lensing DM Red : x-ray Blue : galaxy mass density COSMOS Lensing Massey et al. 2007, Rhodes et al. 2007, Leauthaud et al. 2007

6. What we learned from COSMOS Need color calibration better than 0.01 mag Need template calibration better than 1% This is the main reason photo-z are considered low-accuracy! Calibration Matters!

7. What we learned from COSMOS Shapes can be measured for much fainter galaxies than photo-z are normally used Need to carefully design space/ground depths have an extrapolation problem High-resolution and photo-z data must be of similar depth 12h in K band0.62h Hubble F814W

8. What we learned from COSMOS Lose 10-20% of area Need careful treatment of bright star artifacts Bright Stars Matter

9. What we learned from COSMOS Photo-z does not give you p(z) You get p(z|D,Model) Need prior to break degeneracy Prior should be lensing specific!(Massey et al. 2007) P(z|D,Model) ≠P(z)

10.  Spec-z measured by identifying spectral features  Accuracy is dz/(1+z)=  / =1/R  Phot-z should be accurate to ~0.2  Clearly more accurate  So where is the information?

11.  Information is in the color change as an object spectra is red- shifted Redshift

12. Photo-z Theory Photo-z error determined by: Gradient of color change with redshift photometric accuracy Redshfit

13. Photo-z Theory Generalized to any filter set: C a,b are the color Can use a range of template SEDs Provides estimate of the phot-z accuracy Applies to all methods, not just template fitting Real Galaxy Redshfit

14. Photo-z Theory Works for real galaxies Two galaxy types in COSMOS using broad band data Estimated (red line) vs actual

15. Photo-z Theory Degeneracy due to mapping from Color  Redshift Need to live with this or get more data

16.  Worse results at fainter fluxes  Need calibration accuracy of 0.01 mag or better!  More filters not necessarily better  Best accuracy at filter center  Gaps in coverage very bad  Narrow filters improve accuracy  Overlapping filters improve accuracy

17.  Better absolute calibration  Better measurement of system throughput  Better tracking of atmospheric absorption in IR  Better photometry techniques  Better tracking of errors

18.  Develop flagging and accurate error estimates  Account for template uncertainty  Develop better templates  Account for variability  Empirical codes need to work with non- representative samples  could generate templates and priors  e.g. Budivari et al. 2000, Benitez et al. 2000

19.  What priors should be used  What redshift ranges are most important  Can we live with not using some data?  Integrate complex probability distributions into lensing code

20.  Photo-z should be as robust and trustworthy as spec-z’s  Main fault is in data quality and lack of theoretical understanding  Recent improvements have come from improved data quality  Now need to focus on improved techniques  Lensing should integrate inherent uncertainties in photo-z into the quantities measured