• Open Access

Deep-learning continuous gravitational waves

Christoph Dreissigacker, Rahul Sharma, Chris Messenger, Ruining Zhao, and Reinhard Prix
Phys. Rev. D 100, 044009 – Published 7 August 2019

Abstract

We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an “easy” search using T=105s of data, and a “harder” search using T=106s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105s at 20 Hz) the DNN achieves pdet88%, corresponding to a loss in sensitivity depth of 5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet13% and a loss of 66% in sensitivity depth in the hardest case (T=106s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

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  • Received 6 May 2019

DOI:https://doi.org/10.1103/PhysRevD.100.044009

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

Christoph Dreissigacker1,2,*, Rahul Sharma3,1,2, Chris Messenger4, Ruining Zhao5,6,7, and Reinhard Prix1,2

  • 1Max Planck Institute for Gravitational Physics (Albert-Einstein-Institute), D-30167 Hannover, Germany
  • 2Leibniz Universität Hannover, D-30167 Hannover, Germany
  • 3Birla Institute of Technology and Science, Pilani, Rajasthan 333031, India
  • 4SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom
  • 5Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
  • 6University of Chinese Academy of Sciences, Beijing 100049, China
  • 7Department of Astronomy, Beijing Normal University, Beijing 100875, China

  • *christoph.dreissigacker@aei.mpg.de

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Vol. 100, Iss. 4 — 15 August 2019

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