Star-galaxy classification in the Dark Energy Survey Y1 dataset

TL;DR

This paper compares various star-galaxy classification methods using Dark Energy Survey Year 1 data, evaluating their performance in cosmology and Milky Way studies, and highlights improvements with external data integration.

Contribution

It provides a comprehensive evaluation of star-galaxy classifiers on DES Y1 data and demonstrates how external datasets can improve classification accuracy.

Findings

Default classifiers maintain low contamination for cosmology.

External data reduces stellar misclassification to ~1%.

Stellar sample can be increased by ~20% for Milky Way studies.

Abstract

We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performing a wide range of tests with and without external `truth' information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O(1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ~20% for a given flux limit. Reference catalogs used in this work will be made available upon publication.