The Phenomenological Classification of TESS Eclipsing Binaries

TL;DR

This paper presents a machine learning approach using a neural network to classify TESS eclipsing binaries into types with over 99% accuracy, enabling rapid analysis of large datasets.

Contribution

The study introduces a high-accuracy neural network classifier and a standardized pipeline for automated classification of eclipsing binaries in large photometric surveys.

Findings

Achieved over 99% accuracy on validation and test sets.

Successfully classified over 20,000 TESS eclipsing binaries.

Provided a reliable tool for future large-scale binary star classification.

Abstract

Eclipsing binaries are crucial astrophysical laboratories for studying stellar parameters and evolutionary processes. In this study, we constructed a machine-learning-based model for systematic phenomenological classification of eclipsing binaries. We first extracted eclipsing binaries from the ASAS-SN variable star catalog and cross-matched them with TESS targets. The corresponding TESS light curves were processed through a unified pipeline, resulting in a high-quality training set of 9576 eclipsing binary light curves (2801 EA, 1930 EB, and 4845 EW systems). We designed and trained a fully connected neural network (FCNN) that achieved accuracy of 99.23% and 99.03% on the validation and test set respectively, demonstrating excellent performance. Applying the trained neural network to a total of 20196 TESS eclipsing binaries collected from multiple star catalogs and performing manual visual inspection, we finally obtained 13376 EA, 2114 EB, and 4706 EW systems. The standardized preprocessing pipeline and high-performance classifier developed in this study provide a reliable tool for the rapid automated classification of massive numbers of eclipsing binary in future photometric surveys.