Photometric redshifts for the next generation of deep radio continuum surveys - I: Template fitting

TL;DR

This study evaluates photometric redshift accuracy for radio-detected galaxies using multi-wavelength data, showing that combining multiple template sets improves redshift estimates over individual templates.

Contribution

It introduces a hierarchical Bayesian method to combine different template-based photo-z estimates, enhancing accuracy for diverse radio galaxy populations.

Findings

No single template set performs best across all subsets.

Hierarchical Bayesian combination improves photo-z accuracy.

Performance varies with redshift, luminosity, and properties.

Abstract

We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Bo\"otes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of $\sim 4,500$ radio continuum sources with spectroscopic redshifts relative to those of $\sim 63,000$ non radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio detected population, with variation in the optimum template set both between subsets and between fields. Through a hierarchical Bayesian combination of the photo-z estimates from all three template sets, we are able to produce a consensus photo-z estimate which equals or improves upon the performance of any individual template set.