The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample to r<19.5

TL;DR

This paper introduces a new training set for galaxy photometric redshift estimation in the 2dFLenS project, demonstrating high accuracy and low bias using kernel-density estimation with ugriz and W1/W2 photometry.

Contribution

The study provides a large, nearly complete training sample for empirical photometric redshift estimation and evaluates the performance of various methods, highlighting the effectiveness of KDE.

Findings

Kernel-density estimation yields the lowest bias and errors.

Redshift scatter at r<19.5 is approximately 0.028.

Redshift estimates are unbiased and accurate, with small outlier rates.

Abstract

We present a new training set for estimating empirical photometric redshifts of galaxies, which was created as part of the 2dFLenS project. This training set is located in a 700 sq deg area of the KiDS South field and is randomly selected and nearly complete at r<19.5. We investigate the photometric redshift performance obtained with ugriz photometry from VST-ATLAS and W1/W2 from WISE, based on several empirical and template methods. The best redshift errors are obtained with kernel-density estimation, as are the lowest biases, which are consistent with zero within statistical noise. The 68th percentiles of the redshift scatter for magnitude-limited samples at r<(15.5, 17.5, 19.5) are (0.014, 0.017, 0.028). In this magnitude range, there are no known ambiguities in the colour-redshift map, consistent with a small rate of redshift outliers. In the fainter regime, the KDE method produces p(z) estimates per galaxy that represent unbiased and accurate redshift frequency expectations. The p(z) sum over any subsample is consistent with the true redshift frequency plus Poisson noise. Further improvements in redshift precision at r<20 would mostly be expected from filter sets with narrower passbands to increase the sensitivity of colours to small changes in redshift.