Details

Autor(en) / Beteiligte

• Kim, Kyungmin
• Harry, Ian W
• Hodge, Kari A
• Kim, Young-Min
• Lee, Chang-Hwan
• Lee, Hyun Kyu
• Oh, John J
• Oh, Sang Hoon
• Son, Edwin J

Titel

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

Ist Teil von

• Classical and quantum gravity, 2015-12, Vol.32 (24), p.245002-245030

Ort / Verlag

IOP Publishing

Erscheinungsjahr

2015

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.