Application of artificial neural network to search for gravitationalwave signals associated with short gammaray bursts
Abstract
We apply a machine learning algorithm, the artificial neural network, to the search for gravitationalwave signals associated with short gammaray bursts (GRBs). The multidimensional 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 gravitationalwave 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 gravitationalwave 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 gravitationalwave signals related to the short GRBs.
 Publication:

Classical and Quantum Gravity
 Pub Date:
 December 2015
 DOI:
 10.1088/02649381/32/24/245002
 arXiv:
 arXiv:1410.6878
 Bibcode:
 2015CQGra..32x5002K
 Keywords:

 gravitationalwaves;
 short gammaray bursts;
 artificial neural networks;
 02.50.Sk;
 95.85.Sz;
 98.70.Rz;
 Astrophysics  Instrumentation and Methods for Astrophysics;
 Astrophysics  High Energy Astrophysical Phenomena;
 General Relativity and Quantum Cosmology
 EPrint:
 30 pages, 10 figures