Classification of eclipsing binary light curves in Gaia DR3: A machine learning approach
New Astronomy, cilt.127, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 127
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.newast.2026.102585
- Dergi Adı: New Astronomy
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC
- Anahtar Kelimeler: Binary stars, Eclipsing binaries, Light curve classification, Machine learning, Multimodal
- Ankara Üniversitesi Adresli: Evet
Özet
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.