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

Kim, K., Harry, I., Hodge, K. A., Kim, Y.-M., Lee, C.-H., Lee, H. K., et al. (2015). Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts. Classical and Quantum Gravity, 32(24): 245002. doi:10.1088/0264-9381/32/24/245002.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0025-B8DF-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-9976-D
Genre: Journal Article

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 Creators:
Kim, Kyungmin, Author
Harry, Ian1, Author              
Hodge, Kari A., Author
Kim, Young-Min, Author
Lee, Chang-Hwan, Author
Lee, Hyun Kyu, Author
Oh, John J., Author
Oh, Sang Hoon, Author
Son, Edwin J., Author
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1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

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Free keywords: Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,General Relativity and Quantum Cosmology, gr-qc
 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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 Dates: 2014-10-242015-03-032015
 Publication Status: Published in print
 Pages: 30 pages, 10 figures
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 Rev. Method: -
 Identifiers: arXiv: 1410.6878
URI: http://arxiv.org/abs/1410.6878
DOI: 10.1088/0264-9381/32/24/245002
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Title: Classical and Quantum Gravity
Source Genre: Journal
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Pages: - Volume / Issue: 32 (24) Sequence Number: 245002 Start / End Page: - Identifier: -