%0 Journal Article
%T Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
%A Kyungmin Kim
%A Ian W. Harry
%A Kari A. Hodge
%A Young-Min Kim
%A Chang-Hwan Lee
%A Hyun Kyu Lee
%A John J. Oh
%A Sang Hoon Oh
%A Edwin J. Son
%J Physics
%D 2014
%I arXiv
%R 10.1088/0264-9381/32/24/245002
%X 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.
%U http://arxiv.org/abs/1410.6878v2