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Eclipsing binary classification with machine learning techniques

Bedri Keskin, Özgür Baştürk

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.

Eclipsing binary classification with machine learning techniques

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.

Paper Structure

This paper contains 3 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Data and Methods
  3. Results and Discussion

Figures (3)

  • Figure 1: Sample detached (top left), semidetached (top right), overcontact (bottom left), ellipsoidal (bottom right) Kepler and TESS light curves.
  • Figure 2: Sample Gaia DR3 light curves (left) and their modeled light curves (right).
  • Figure 3: Confusion matrix.