Robust machine learning algorithm to search for continuous gravitational waves

Bayley, J. , Messenger, C. and Woan, G. (2020) Robust machine learning algorithm to search for continuous gravitational waves. Physical Review D, 102(8), 083024. (doi: 10.1103/PhysRevD.102.083024)

Abstract

Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalizing signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artifacts on searches that use the SOAP algorithm Bayley et al. [Phys. Rev. D 100, 023006 (2019)]. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different datasets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge dataset Walsh et al. [Phys. Rev. D 94, 124010 (2016)] and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge dataset and at a 1% false alarm probability we showed that at 95% efficiency a fully automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10  Hz−1/2, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.

• Item Type: Articles
• Additional Information: This research is supported by the Science and Technology Facilities Council., J. B. G.W. and C. M. are supported by the Science and Technology Research Council (Grant No. ST/L000946/1). C. M. is also supported by the European Cooperation in Science and Technology (COST) action CA17137. The authors are grateful for computational resources provided by the LIGO Laboratory supported by National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Messenger, Dr Christopher and Woan, Professor Graham and Bayley, Mr Joseph
• Authors: Bayley, J., Messenger, C., and Woan, G.
• 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 D
• Publisher: American Physical Society
• ISSN: 2470-0010
• ISSN (Online): 2470-0029
• Published Online: 21 October 2020
• Copyright Holders: Copyright © 2020 American Physical Society
• First Published: First published in Physical Review D 102(8): 083024
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Related URLs

• arXiv

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
169451		Investigations in Gravitational Radiation	Sheila Rowan	Science and Technology Facilities Council (STFC)	ST/L000946/1	P&S - Physics & Astronomy