A new chi-square morphology method plus CNN classifies Kepler eclipsing binaries at 90% accuracy and flags four new temporally varying systems linked to magnetic activity.
Automatic classification of eclipsing binaries light curves using neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
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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.
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astro-ph.SR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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A New Methodology for Classifying Eclipsing Binaries with Kepler Data and Deep Learning
A new chi-square morphology method plus CNN classifies Kepler eclipsing binaries at 90% accuracy and flags four new temporally varying systems linked to magnetic activity.