CaltechAUTHORS
  A Caltech Library Service

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

Kim, Kyungmin and Harry, Ian W. and Hodge, Kari A. and Kim, Young-Min and Lee, Chang-Hwan and Lee, Hyun Kyu and Oh, John J. and Oh, Sang Hoon and Son, Edwin J. (2015) Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts. Classical and Quantum Gravity, 32 (24). Art. No. 245002. ISSN 0264-9381. https://resolver.caltech.edu/CaltechAUTHORS:20160107-105509801

[img] PDF - Submitted Version
See Usage Policy.

2170Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20160107-105509801

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1088/0264-9381/32/24/245002DOIArticle
http://iopscience.iop.org/article/10.1088/0264-9381/32/24/245002/metaPublisherArticle
http://arxiv.org/abs/1410.6878arXivDiscussion Paper
Additional Information:© 2015 IOP Publishing Ltd. Received 3 March 2015, revised 24 August 2015. Accepted for publication 2 October 2015. Published 24 November 2015. We thank the LIGO Scientific Collaboration and the Virgo Collaboration for the use of the data. We are also grateful for computational resources provided by the Leonard E Parker Center for Gravitation, Cosmology and Astrophysics at University of Wisconsin-Milwaukee (NSF-0923409). The authors would like to thank S Bose, K Cannon, T Dent, C Hanna, H M Lee, C Kim, and R Vaulin for helpful comments and useful discussions. KK would like to specially thank J Burguet-Castell, A Dietz, and N Fotopoulos for suggesting the initial motivation of this work. KK, YMK, CHL, HKL, JJO, SHO, and EJS were supported in part by the Global Research Network program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT, and Future Planning of Korea (MSIP) (NRF-2011-220-C00029). KK, YMK, CHL, HKL, JJO, SHO, and EJS were also supported in part by the Global Science experimental Data hub Center (GSDC) at KISTI. The work of YMK and CHL was also supported by the NRF funded by the MSIP (NRF-2015R1A2A2A01004238).
Funders:
Funding AgencyGrant Number
NSF0923409
ICT & Future Planning of KoreaUNSPECIFIED
National Research Foundation of Korea (NRF)NRF-2011-220- C00029
KISTI Global Science Experimental Data Hub Center (GSDC)UNSPECIFIED
National Research Foundation of Korea (NRF)NRF-2015R1A2A2A01004238
Subject Keywords:gravitational-waves, short gamma-ray bursts, artificial neural networks
Issue or Number:24
Record Number:CaltechAUTHORS:20160107-105509801
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160107-105509801
Official Citation:Kyungmin Kim et al 2015 Class. Quantum Grav. 32 245002
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63446
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:08 Jan 2016 20:30
Last Modified:03 Oct 2019 09:28

Repository Staff Only: item control page