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Automatic classification of eclipsing binaries light curves using neural networks
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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.
Forward citations
Cited by 2 Pith 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.
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Morphological classification of eclipsing binary stars using computer vision methods
Fine-tuned ResNet50 and vision transformers on polar-hexbin images classify eclipsing binaries as detached or overcontact with high accuracy on real data, but cannot reliably detect starspots.
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