Star/galaxy separation at faint magnitudes: Application to a simulated Dark Energy Survey

TL;DR

This paper develops a new machine learning-based method for star/galaxy separation in the Dark Energy Survey, improving accuracy at faint magnitudes to meet cosmological measurement requirements.

Contribution

It introduces a PCA-ANN approach that enhances star/galaxy classification accuracy at faint magnitudes compared to traditional morphometric methods.

Findings

Increased purity by up to 20% for stars at faint magnitudes.

Achieved better separation performance than existing classifiers.

Met the science requirements for cosmological parameter estimation.

Abstract

We address the problem of separating stars from galaxies in future large photometric surveys. We focus our analysis on simulations of the Dark Energy Survey (DES). In the first part of the paper, we derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the Gravitational Weak Lensing and Large Scale Structure probes. These requirements are dictated by the need to control both the statistical and systematic errors on the cosmological parameters, and by Point Spread Function calibration. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements at faint magnitudes, we propose a new method for star/galaxy separation in the second part of the paper. We first use Principal Component Analysis to outline the correlations between the objects parameters and extract from it the most relevant information. We then use the reduced set of parameters as input to an Artificial Neural Network. This multi-parameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes: it increases the purity by up to 20% for stars and by up to 12% for galaxies, at i-magnitude fainter than 23.