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Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

Title
Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts
Author
이현규
Keywords
gravitational-waves; short gamma-ray bursts; artificial neural networks
Issue Date
2015-11
Publisher
IOP PUBLISHING LTD
Citation
CLASSICAL AND QUANTUM GRAVITY, v. 32, NO 24, Page. 1-2
Abstract
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). 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 (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. 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 GRBs.
URI
http://iopscience.iop.org/article/10.1088/0264-9381/32/24/245002/meta;jsessionid=B0F61BCC4FA4D446C91B7E002F73A999.c3.iopscience.cld.iop.orghttp://hdl.handle.net/20.500.11754/28701
ISSN
0264-9381; 1361-6382
DOI
10.1088/0264-9381/32/24/245002
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > PHYSICS(물리학과) > Articles
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