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Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

Hunter Gabbard, Michael Williams, Fergus Hayes, and Chris Messenger
Phys. Rev. Lett. 120, 141103 – Published 6 April 2018

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.

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  • Received 16 December 2017
  • Revised 12 February 2018

DOI:https://doi.org/10.1103/PhysRevLett.120.141103

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Hunter Gabbard*, Michael Williams, Fergus Hayes, and Chris Messenger

  • SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom

  • *Corresponding author. h.gabbard.1@research.gla.ac.uk

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Issue

Vol. 120, Iss. 14 — 6 April 2018

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