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

Author(s): Overall, S.; Southworth, J.
Journal: Contributions of the Astronomical Observatory Skalnaté Pleso, vol. 55, no. 3, p. 366-369.
Date: 04/2025
Title: EBOP MAVEN. A machine learning model for predicting eclipsing binary light curve fitting parameters
Keyword(s): binaries: eclipsing, methods: data analysis, methods: machine-learning
Pages: 366 -- 369
DOI: https://doi.org/10.31577/caosp.2025.55.3.366
NASA ADS Abstract Service: 2025CoSka..55c.366O
Citation:
License: This work is licensed under a Creative Commons Attribution 4.0 International License

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.