EBOP MAVEN: A machine learning model to estimate the input parameters for analytic fitting of detached eclipsing binary light curves

Abstract

Detached eclipsing binary stars (dEBs) are a key source of data on fundamental stellar parameters. Within the light curve databases of survey missions such as Kepler and TESS are a wealth of new systems awaiting characterisation. We aim to improve the scalability of efforts to process these data by developing a Convolutional Neural Network (CNN) machine learning model to assist in the automation of their analysis. From a phase-folded and binned dEB light curve the model predicts system parameters relating to stellar fractional radii, orbital inclination and eccentricity, and the stellar brightness ratio, for use as input values in subsequent formal analysis with the established JKTEBOP analytic code. We find the model able to predict these parameters for a previously unseen test dataset of 20000 synthetic dEB systems with a mean error of 14.1% when compared with the label values, improving to 8.6% against a subset representative of real systems. When tested with the TESS light curves of a set of real well-characterised systems, the model's predictions yield a mean error of $8.7\pm0.7\%$ when compared with label values derived from existing published analyses. Subsequent fitting of the TESS light curves with the JKTEBOP analytic code while using the model predictions as input values finds 27 of the 28 systems achieving a good fit. On the strength of these results, we plan to build a new characterisation pipeline based on the machine learning model and JKTEBOP code with the intention of producing a target catalogue of dEB systems for potential observation with the forthcoming PLATO mission.