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