Redshift Inference from the Combination of Galaxy Colors and Clustering in a Hierarchical Bayesian Model

Photometric galaxy surveys constitute a powerful cosmological probe but rely on the accurate

characterization of their redshift distributions using only broadband imaging, and can be very sensitive

to incomplete or biased priors used for redshift calibration. Several techniques for estimating the redshift

distributions of imaging surveys have been developed in the last decades, which can be broadly

separated in three categories: 1) direct calibration through spectroscopic redshifts, 2) mapping the

relation between photometry and redshift with a mix of theoretical models of galaxy spectra and

empirical knowledge from direct spectroscopy, and 3) comparing the sky positions of galaxies to the

positions of a tracer population with secure redshifts. In this work we extend a hierarchical Bayesian

model which combines these three main sources of information so it can be applied to real data. We test

the method in N-body simulations and find the incorporation of clustering information on top of

photometry to tighten the redshift posteriors and overcome biases in the prior that mimic those

happening in spectroscopic samples. This robustness to flaws in the redshift prior or training samples

would constitute a milestone for the control of redshift systematic uncertainties in future weak lensing

analyses.