Deep-learning continuous gravitational waves

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dc.identifier.uri Dreissigacker, Christoph Sharma, Rahul Messenger, Chris Zhao, Ruining Prix, Reinhard 2021-02-24T10:00:40Z 2021-02-24T10:00:40Z 2019
dc.identifier.citation Dreissigacker, C.; Sharma, R.; Messenger, C.; Zhao, R.; Prix, R.: Deep-learning continuous gravitational waves. In: Physical Review D 100 (2019), Nr. 4, 44009. DOI:
dc.description.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)10.1103/PhysRevD.97.044039; H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)10.1103/PhysRevLett.120.141103]. 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)10.1103/PhysRevD.97.123016]. 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=105 s of data, and a "harder" search using T=106 s. 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=105 s at 20 Hz) the DNN achieves pdet∼88%, 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 pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s 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. © 2019 authors. Published by the American Physical Society. eng
dc.language.iso eng
dc.publisher College Park, MD : American Physical Society
dc.relation.ispartofseries Physical Review D 100 (2019), Nr. 4
dc.rights CC BY 4.0 Unported
dc.subject.ddc 530 | Physik ger
dc.title Deep-learning continuous gravitational waves
dc.type Article
dc.type Text
dc.relation.issn 2470-0010
dc.bibliographicCitation.issue 4
dc.bibliographicCitation.volume 100
dc.bibliographicCitation.firstPage 44009
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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