Automatic classification of eclipsing binaries light curves using neural networks

Abstract

In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary sistems according to their geometrical configuration in a modified version of the traditional classification scheme. The classification is performed by a Bayesian ensemble of neural networks trained with {\em Hipparcos} data of seven different categories including eccentric binary systems and two types of pulsating light curve morphologies.

arXiv comment11 pages (+ 5 pages online material) accepted for publication by A&A

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• Automated supervised classification of variable stars I. Methodology
• Kepler Eclipsing Binary Stars. III. Classification of Kepler Eclipsing Binary Light Curves with Locally Linear Embedding
• Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
• Variable stars across the observational HR diagram
• Automated supervised classification of variable stars II. Application to the OGLE database
• Unsupervised classification of eclipsing binary light curves through k-medoids clustering
• Automatic classification of eclipsing binary stars using deep learning methods