A Detection Metric Designed for O'Connell Effect Eclipsing Binaries

TL;DR

This paper introduces a new time-domain signature extraction method and a metric learning algorithm to identify eclipsing binaries with the O'Connell effect, aiding large-scale astronomical surveys.

Contribution

It proposes a novel representation called distribution fields and a specialized metric learning technique for targeted binary star detection.

Findings

Successfully identified 124 potential O'Connell effect eclipsing binaries

Demonstrated effective performance on Kepler data

Prepares for large-scale surveys like LSST and SKA

Abstract

We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification of eclipsing binaries that demonstrate a feature known as the O'Connell effect. Our proposed methodology maps stellar variable observations to a new representation known as distribution fields (DFs). Given this novel representation, we develop a metric learning technique directly on the DF space that is capable of specifically identifying our stars of interest. The metric is tuned on a set of labeled eclipsing binary data from the Kepler survey, targeting particular systems exhibiting the O'Connell effect. The result is a conservative selection of 124 potential targets of interest out of the Villanova Eclipsing Binary Catalog. Our framework demonstrates favorable performance on Kepler eclipsing binary data, taking a crucial step in preparing the way for large-scale data volumes from next-generation telescopes such as LSST and SKA.