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arXiv · 1410.6878

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

Kyungmin Kim·Ian W. Harry·Kari A. Hodge·Young-Min Kim·Chang-Hwan Lee·Hyun Kyu Lee·John J. Oh·Sang Hoon Oh·Edwin J. Son
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Abstract

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

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Kyungmin Kim, Ian W. Harry, Kari A. Hodge, Young-Min Kim, Chang-Hwan Lee, Hyun Kyu Lee, John J. Oh, Sang Hoon Oh, Edwin J. Son. 2014-10-25. Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts. https://doi.org/10.1088/0264-9381%2F32%2F24%2F245002

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