peter capak associate research scientist ipac/caltech

Peter CapakAssociate Research Scientist

IPAC/Caltech

What is COSMOSWhat we learned from COSMOSPhoto-z (and spec-z) theoryWhat work needs to be done

2 sq degree deep multi-wavelength survey

Designed for Photo-z

HST imaging Ideal for lensing

Lots of spectroscopy to I~26

Contours : lensing DM

Red : x-ray

Blue : galaxy mass density

COSMOS LensingMassey et al. 2007, Rhodes et al. 2007, Leauthaud et al. 2007

•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!

•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

12h in K band

0.62h Hubble F814W

•Lose 10-20% of area

•Need careful treatment of bright star artifacts

•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)

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?

Information is in the color change as an object spectra is red-shifted

Redshift

•Photo-z error determined by:

•Gradient of color change with redshift•photometric accuracy

Redshfit

•Generalized to any filter set:

• Ca,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

•Works for real galaxies

•Two galaxy types in COSMOS using broad band data

•Estimated (red line) vs actual

•Degeneracy due to mapping from ColorRedshift

•Need to live with this or get more data

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

Better absolute calibrationBetter measurement of system

throughputBetter tracking of atmospheric

absorption in IRBetter photometry techniquesBetter tracking of errors

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

What priors should be usedWhat redshift ranges are most

important Can we live with not using some data?

Integrate complex probability distributions into lensing code

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