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  Detection of gravitational-wave signals from binary neutron star mergers using machine learning

Schäfer, M., Ohme, F., & Nitz, A. H. (2020). Detection of gravitational-wave signals from binary neutron star mergers using machine learning. Physical Review D, 102: 063015. doi:10.1103/PhysRevD.102.063015.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-1A9B-E Version Permalink: http://hdl.handle.net/21.11116/0000-0007-1A9F-A
Genre: Journal Article

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 Creators:
Schäfer, Marlin1, Author              
Ohme, Frank1, Author              
Nitz, Alexander H.2, Author              
Affiliations:
1Binary Merger Observations and Numerical Relativity, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_2461691              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

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Free keywords: Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,Computer Science, Learning, cs.LG,General Relativity and Quantum Cosmology, gr-qc
 Abstract: As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches.

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 Dates: 2020-06-022020-09-282020
 Publication Status: Published in print
 Pages: 14 pages, 6 figures, 1 table, supplemental materials at https://github.com/gwastro/bns-machine-learning-search
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 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2006.01509
DOI: 10.1103/PhysRevD.102.063015
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Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
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Publ. Info: Lancaster, Pa. : American Physical Society
Pages: - Volume / Issue: 102 Sequence Number: 063015 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: https://pure.mpg.de/cone/journals/resource/111088197762258