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arXiv · 2010.11829

GWSkyNet: a real-time classifier for public gravitational-wave candidates

Miriam Cabero·Ashish Mahabal·Jess McIver
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Abstract

The rapid release of accurate sky localization for gravitational-wave candidates is crucial for multi-messenger observations. During the third observing run of Advanced LIGO and Advanced Virgo, automated gravitational-wave alerts were publicly released within minutes of detection. Subsequent inspection and analysis resulted in the eventual retraction of a fraction of the candidates. Updates could be delayed by up to several days, sometimes issued during or after exhaustive multi-messenger followup campaigns. We introduce GWSkyNet, a real-time framework to distinguish between astrophysical events and instrumental artefacts using only publicly available information from the LIGO-Virgo open public alerts. This framework consists of a non-sequential convolutional neural network involving sky maps and metadata. GWSkyNet achieves a prediction accuracy of 93.5% on a testing data set.

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Miriam Cabero, Ashish Mahabal, Jess McIver. 2020-10-22. GWSkyNet: a real-time classifier for public gravitational-wave candidates. https://doi.org/10.3847/2041-8213%2Fabc5b5

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