CAOSP abstracts, Volume: 55, No.: 3, year: 2025

Author(s): Keskin, B.; Baştürk, Ö.
Journal: Contributions of the Astronomical Observatory Skalnaté Pleso, vol. 55, no. 3, p. 357-361.
Date: 04/2025
Title: Eclipsing binary classification with machine learning techniques
Keyword(s): stars:binary stars, stars:eclipsing binaries, techniques:light curve classification, techniques:machine learning
Pages: 357 -- 361
DOI: https://doi.org/10.31577/caosp.2025.55.3.357
NASA ADS Abstract Service: 2025CoSka..55c.357K
Citation:
License: This work is licensed under a Creative Commons Attribution 4.0 International License

Abstract: We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for manual analysis. By using machine learning to classify eclipsing binary stars based on light curve morphology, this study aims to contribute to the efforts to efficiently process and accurately interpret massive data from projects Kepler, TESS and Gaia missions.