Eclipsing binary classification with machine learning techniques

TL;DR

This paper presents a machine learning approach for classifying eclipsing binary stars from large-scale photometric survey data, aiming to improve efficiency and accuracy in analyzing vast astronomical datasets.

Contribution

It introduces a deep learning-based method specifically designed for automated classification of eclipsing binary stars from light curves, addressing the challenge of large data volumes.

Findings

Effective classification accuracy demonstrated on survey data

Reduces manual analysis time significantly

Applicable to data from Kepler, TESS, and Gaia

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.