Bayesian inference from photometric redshift surveys
  We show how to enhance the redshift accuracy of surveys consisting of tracers
with highly uncertain positions along the line of sight. Photometric surveys
with redshift uncertainty delta_z ~ 0.03 can yield final redshift uncertainties
of delta_z_f ~ 0.003 in high density regions. This increased redshift precision
is achieved by imposing an isotropy and 2-point correlation prior in a Bayesian
analysis and is completely independent of the process that estimates the
photometric redshift. As a byproduct, the method also infers the three
dimensional density field, essentially super-resolving high density regions in
redshift space. Our method fully takes into account the survey mask and
selection function. It uses a simplified Poissonian picture of galaxy
formation, relating preferred locations of galaxies to regions of higher
density in the matter field. The method quantifies the remaining uncertainties
in the three dimensional density field and the true radial locations of
galaxies by generating samples that are constrained by the survey data. The
exploration of this high dimensional, non-Gaussian joint posterior is made
feasible using multiple-block Metropolis-Hastings sampling. We demonstrate the
performance of our implementation on a simulation containing 2.0 x 10^7
galaxies. These results bear out the promise of Bayesian analysis for upcoming
photometric large scale structure surveys with tens of millions of galaxies.
