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.
Original languageEnglish
Article number245002
Number of pages29
JournalClassical and Quantum Gravity
Volume32
Issue number24
DOIs
Publication statusPublished - 24 Nov 2015

Keywords

  • astro-ph.IM
  • astro-ph.HE
  • gr-qc

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