Photometric Redshift with Bayesian Priors on Physical Properties of
  Galaxies
  We present a proof-of-concept analysis of photometric redshifts with Bayesian
priors on physical properties of galaxies. This concept is particularly suited
for upcoming/on-going large imaging surveys, in which only several broad-band
filters are available and it is hard to break some of the degeneracies in the
multi-color space. We construct model templates of galaxies using a stellar
population synthesis code and apply Bayesian priors on physical properties such
as stellar mass and star formation rate. These priors are a function of
redshift and they effectively evolve the templates with time in an
observationally motivated way. We demonstrate that the priors help reduce the
degeneracy and deliver significantly improved photometric redshifts.
Furthermore, we show that a template error function, which corrects for
systematic flux errors in the model templates as a function of rest-frame
wavelength, delivers further improvements. One great advantage of our technique
is that we simultaneously measure redshifts and physical properties of galaxies
in a fully self-consistent manner, unlike the two-step measurements with
different templates often performed in the literature. One may rightly worry
that the physical priors bias the inferred galaxy properties, but we show that
the bias is smaller than systematic uncertainties inherent in physical
properties inferred from the SED fitting and hence is not a major issue. We
will extensively test and tune the priors in the on-going Hyper Suprime-Cam
survey and will make the code publicly available in the future.
