Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields

TL;DR

This paper develops a highly accurate method for estimating galaxy redshifts by combining template fitting and machine learning techniques using a hierarchical Bayesian model, applied to large multi-wavelength survey data.

Contribution

It introduces a novel hierarchical Bayesian approach to combine different photometric redshift estimation methods, improving accuracy for large galaxy catalogs.

Findings

Achieved root mean square error of ~0.08-0.09 in redshift estimates.

Reduced outlier fraction to ~3-4% compared to spectroscopic redshifts.

Produced the most accurate large-scale photometric redshift catalog for COSMOS and XMM-LSS fields.

Abstract

In this paper we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and Active Galactic Nuclei templates within LePhare) and machine learning (using GPz) methods are run on the aperture photometry of sources selected in the Ks-band. The resulting predictions are then combined using a Hierarchical Bayesian model, to produce consensus photometric redshift point estimates and probability distribution functions that outperform each method individually. Our point estimates have a root mean square error of ~0.08-0.09, and an outlier fraction of ~3-4 percent when compared to spectroscopic redshifts. We also compare our results to the COSMOS2020 photometric redshifts, which contains fewer sources, but had access to a larger number of bands and greater wavelength coverage, finding that comparable photo-z quality can be achieved (for bright and intermediate luminosity sources where a direct comparison can be made). Our resulting redshifts represent the most accurate set of photometric redshifts (for a catalogue this large) for these deep multi-square degree multi-wavelength fields to date.