The Bulletin of The Korean Astronomical Society (천문학회보)
- Volume 39 Issue 2
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- Pages.107.1-107.1
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- 2014
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- 1226-2692(pISSN)
Application of Artificial Neural Networks to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
- Oh, Sang Hoon (National Institute of Mathematical Science) ;
- Kim, Kyungmin (Hanyang University) ;
- Harry, Ian W. (Syracusl University) ;
- Hodge, Kari A. (California Institute of Technology) ;
- Kim, Young-Min (Pusan National University) ;
- Lee, Chang-Hwan (Pusan National University) ;
- Lee, Hyun Kyu (Hanyang University) ;
- Oh, John J. (National Institute of Mathematical Science) ;
- Son, Edwin J. (National Institute of Mathematical Science)
- Published : 2014.10.13
Abstract
We apply a machine learning algorithm, 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. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that 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.
Keywords