Matching matched filtering with deep networks for gravitational-wave astronomy

Gabbard, H., Williams, M. , Hayes, F. and Messenger, C. (2018) Matching matched filtering with deep networks for gravitational-wave astronomy. Physical Review Letters, 120(14), 141103. (doi: 10.1103/PhysRevLett.120.141103) (PMID:29694122)

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We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Messenger, Dr Christopher and Hayes, Dr Fergus and Gabbard, Hunter Arthur and Williams, Michael
Authors: Gabbard, H., Williams, M., Hayes, F., and Messenger, C.
College/School:College of Science and Engineering > School of Physics and Astronomy
Research Centre:College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research
Journal Name:Physical Review Letters
Publisher:American Physical Society
ISSN (Online):1079-7114
Published Online:06 April 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Physica Review Letters 120(14):141103
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
624341Investigations in Gravitational Radiation / Particle Astrophysics Capital equipmentSheila RowanScience & Technology Facilities Council (STFC)ST/L000946/1S&E P&A - PHYSICS & ASTRONOMY