Improved Photometric Redshifts with Surface Luminosity Priors

TL;DR

This paper introduces a Bayesian method using surface luminosity priors to enhance photometric redshift accuracy, significantly reducing catastrophic outliers in galaxy redshift estimates within the GOODS fields.

Contribution

The study presents a novel application of surface luminosity priors in Bayesian photometric redshift estimation, improving accuracy and reducing outliers compared to previous methods.

Findings

Reduces catastrophic outliers from 15.0% to 10.4% for z < 1.6

Maintains similar rms scatter in redshift errors

Effectively breaks degeneracies in redshift estimation

Abstract

We apply Bayesian statistics with prior probabilities of galaxy surface luminosity (SL) to improve photometric redshifts. We apply the method to a sample of 1266 galaxies with spectroscopic redshifts in the GOODS North and South fields at 0.1 < z < 2.0. We start with spectrophotometric redshifts (SPZs) based on Probing Evolution and Reionization Spectroscopically grism spectra, which cover a wavelength range of 6000-9000A, combined with (U)BViz(JHK) broadband photometry in the GOODS fields. The accuracy of SPZ redshifts is estimated to be \sigma (\Delta(z))=0.035 with an systematic offset of -0.026, where \Delta(z)=\Delta z / (1+z), for galaxies in redshift range of 0.5 < z < 1.25. The addition of the SL prior probability helps break the degeneracy of SPZ redshifts between low redshift 4000 A break galaxies and high-redshift Lyman break galaxies which are mostly catastrophic outliers. For the 1138 galaxies at z < 1.6, the fraction of galaxies with redshift deviation \Delta (z) > 0.2 is reduced from 15.0% to 10.4%, while the rms scatter of the fractional redshift error does not change much.