A Detection Metric Designed for O’Connell Effect Eclipsing Binaries. (arXiv:1911.03543v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Johnston_K/0/1/0/all/0/1">Kyle B. Johnston</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Haber_R/0/1/0/all/0/1">Rana Haber</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Caballero_Nieves_S/0/1/0/all/0/1">Saida M. Caballero-Nieves</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Peter_A/0/1/0/all/0/1">Adrian M. Peter</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Petit_V/0/1/0/all/0/1">V'eronique Petit</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Knote_M/0/1/0/all/0/1">Matt Knote</a>
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.
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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