Classification of Eclipsing Binary Light Curves in Gaia DR3: A Machine Learning Approach

Abstract

Gaia Data Release 3 (DR3) presents a unique dataset with approximately 2.1 million eclipsing binary star candidates. The unsustainability of manually classifying such a large volume of data has necessitated the development of reliable and scalable automated techniques. In this study, a novel multimodal deep learning model has been developed for the automated classification of approximately 2 million eclipsing binary stars in the Gaia DR3 archive based on their light curve morphologies (EA, EB, EW). The developed architecture simultaneously utilizes a Convolutional Neural Network (CNN) that extracts visual features from light curve images and a Multilayer Perceptron (MLP) that processes geometric model parameters. Noise-free synthetic light curves were used during the training process to ensure the model focuses on geometric shapes. Tests showed that the model achieved an accuracy rate of over 95% for all classes, exhibiting excellent separation performance, particularly in EA-type systems. As a result of the automated classification performed with the trained model, 40% of the Gaia DR3 eclipsing binaries were classified as EA, 30% as EB, and 30% as EW. This study provides a highly accurate and transferable classification framework for future large-scale sky surveys.