Generalised gravitational burst generation with generative adversarial networks

McGinn, J., Messenger, C. , Williams, M.J. and Heng, I.S. (2021) Generalised gravitational burst generation with generative adversarial networks. Classical and Quantum Gravity, 38(15), 155005. (doi: 10.1088/1361-6382/ac09cc)

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We introduce the use of conditional generative adversarial networks (CGANs) for generalised gravitational wave (GW) burst generation in the time domain. Generative adversarial networks are generative machine learning models that produce new data based on the features of the training data set. We condition the network on five classes of time-series signals that are often used to characterise GW burst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binary black hole merger. We show that the model can replicate the features of these standard signal classes and, in addition, produce generalised burst signals through interpolation and class mixing. We also present an example application where a convolutional neural network (CNN) classifier is trained on burst signals generated by our CGAN. We show that a CNN classifier trained only on the standard five signal classes has a poorer detection efficiency than a CNN classifier trained on a population of generalised burst signals drawn from the combined signal class space.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Heng, Professor Ik Siong and McGinn, Mr Jordan and Williams, Michael and Messenger, Dr Christopher
Authors: McGinn, J., Messenger, C., Williams, M.J., and Heng, I.S.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Classical and Quantum Gravity
Publisher:IOP Publishing
ISSN (Online):1361-6382
Published Online:30 June 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Classical and Quantum Gravity 38(15): 155005
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300569Strengthening capacity in big data and engineering through LIGO-IndiaGiles HammondScience and Technology Facilities Council (STFC)ST/R001928/1P&S - Physics & Astronomy
301541Gravitational-wave Excellence through Alliance Training (GrEAT) Network with ChinaIk Siong HengScience and Technology Facilities Council (STFC)ST/R002770/1P&S - Physics & Astronomy
169451Investigations in Gravitational RadiationSheila RowanScience and Technology Facilities Council (STFC)ST/L000946/1P&S - Physics & Astronomy