Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys

TL;DR

This paper introduces a hierarchical Bayesian method to accurately infer galaxy redshift distributions and individual redshifts from noisy photometric data, improving robustness over traditional techniques and enabling better cosmological analysis.

Contribution

It presents a novel hierarchical Bayesian framework that simultaneously infers galaxy redshift distributions and individual redshifts from photometric surveys, outperforming existing methods.

Findings

Accurately recovers galaxy type and redshift distributions from simulated data.

Provides correct posterior distributions for individual galaxy redshifts.

Enables propagation of redshift uncertainties in cosmological analyses.

Abstract

Accurately characterizing the redshift distributions of galaxies is essential for analysing deep photometric surveys and testing cosmological models. We present a technique to simultaneously infer redshift distributions and individual redshifts from photometric galaxy catalogues. Our model constructs a piecewise constant representation (effectively a histogram) of the distribution of galaxy types and redshifts, the parameters of which are efficiently inferred from noisy photometric flux measurements. This approach can be seen as a generalization of template-fitting photometric redshift methods and relies on a library of spectral templates to relate the photometric fluxes of individual galaxies to their redshifts. We illustrate this technique on simulated galaxy survey data, and demonstrate that it delivers correct posterior distributions on the underlying type and redshift distributions, as well as on the individual types and redshifts of galaxies. We show that even with uninformative priors, large photometric errors and parameter degeneracies, the redshift and type distributions can be recovered robustly thanks to the hierarchical nature of the model, which is not possible with common photometric redshift estimation techniques. As a result, redshift uncertainties can be fully propagated in cosmological analyses for the first time, fulfilling an essential requirement for the current and future generations of surveys.