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arXiv · 1911.03543

A Detection Metric Designed for O'Connell Effect Eclipsing Binaries

Kyle B. Johnston·Rana Haber·Saida M. Caballero-Nieves·Adrian M. Peter·V'eronique Petit·Matt Knote
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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.

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Kyle B. Johnston, Rana Haber, Saida M. Caballero-Nieves, Adrian M. Peter, V'eronique Petit, Matt Knote. 2019-11-08. A Detection Metric Designed for O'Connell Effect Eclipsing Binaries. https://doi.org/10.1186/s40668-019-0031-2

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