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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Abstract
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 |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| 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: | 0031-9007 |
| 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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