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  Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates

Beheshtipour, B., & Papa, M. A. (2021). Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates. Physical Review D, 103(6): 064027. doi:10.1103/PhysRevD.103.064027.

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 Creators:
Beheshtipour, Banafsheh1, Author              
Papa, Maria Alessandra1, Author              
Affiliations:
1Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_2630691              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,Computer Science, Learning, cs.LG
 Abstract: Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [1], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to much fainter signals. These two networks are complementary and we show that a cascade of the two networks achieves an excellent detection efficiency across a wide range of signal strengths, with a false alarm rate comparable/lower than that of methods currently in use.

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 Dates: 2020-12-082021
 Publication Status: Published in print
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 Identifiers: arXiv: 2012.04381
DOI: 10.1103/PhysRevD.103.064027
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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: 103 (6) Sequence Number: 064027 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: https://pure.mpg.de/cone/journals/resource/111088197762258