EBOP MAVEN: A machine learning model for predicting eclipsing binary light curve fitting parameters

TL;DR

This paper introduces EBOP MAVEN, a machine learning model designed to efficiently predict parameters of eclipsing binary star light curves, facilitating faster analysis of large survey datasets like Kepler and TESS.

Contribution

The paper presents a novel machine learning approach to estimate eclipsing binary parameters, improving the speed and scalability of analyzing vast survey data.

Findings

Model accurately predicts light curve parameters

Reduces time needed for detailed binary star analysis

Enhances processing of large astronomical datasets

Abstract

Detached eclipsing binary stars (dEBs) are a key source of data on fundamental stellar parameters. While there is a vast source of candidate systems in the light curve databases of survey missions such as Kepler and TESS, published catalogues of well-characterised systems fall short of reflecting this abundance. We seek to improve the efficiency of efforts to process these data with the development of a machine learning model to inspect dEB light curves and predict the input parameters for subsequent formal analysis by the jktebop code.