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O'Connell Effect Detector using Push-Pull Learning
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With the advent of digital astronomy, new benefits and new challenges have been presented to the modern day astronomer. Here we focus on the construction and application of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm for the targeted identification of eclipsing binaries which demonstrate a feature known as the O’Connell Effect. Our proposed methodology maps stellar variable observations (time-domain data) to a new representation known as Distribution Fields (DF), whose properties enable us to efficiently handle issues such as irregular sampling and multiple values per time instance. Given this novel representation, we develop a metric learning technique directly on the DF space 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. Our framework demonstrates favorable performance on Kepler EB data, taking a crucial step to prepare the way for large-scale data volumes from next generation telescopes such as LSST

The data are not included as part of this repository (as they are too big),

For LINEAR Data see

For Kepler Data see

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