Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts
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.
© 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).
Submitted - 1410.6878v2.pdf