Photometric redshift analysis in the Dark Energy Survey Science Verification data

TL;DR

This study evaluates the photometric redshift accuracy of the Dark Energy Survey using early data, demonstrating that most photo-z methods meet the survey's performance requirements with core resolution around 0.08.

Contribution

It provides a comprehensive assessment of various photo-z algorithms on DES SV data, highlighting the effectiveness of empirical methods like neural networks and random forests.

Findings

Empirical photo-z methods achieve core resolution of ~0.08.

Most photo-z codes meet DES performance requirements.

The weighting method effectively mimics full survey conditions.

Abstract

We present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification (SV) period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq.~deg.~at the nominal depth of the survey. We assess the photometric redshift performance using about 15000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-$z$'s are obtained and studied using most of the existing photo-$z$ codes. A weighting method in a multi-dimensional color-magnitude space is applied to the spectroscopic sample in order to evaluate the photo-$z$ performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. Empirical photo-$z$ methods using, for instance, Artificial Neural Networks or Random Forests, yield the best performance in the tests, achieving core photo-$z$ resolutions $\sigma_{68} \sim 0.08$. Moreover, the results from most of the codes, including template fitting methods, comfortably meet the DES requirements on photo-$z$ performance, therefore, providing an excellent precedent for future DES data sets.