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  Deep learning for clustering of continuous gravitational wave candidates

Beheshtipour, B., & Papa, M. A. (2020). Deep learning for clustering of continuous gravitational wave candidates. Physical Review D, 101: 064009. doi:10.1103/PhysRevD.101.064009.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-78E7-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-01BA-7
Genre: Journal Article

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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, Instrumentation and Methods for Astrophysics, astro-ph.IM, Physics, Data Analysis, Statistics and Probability, physics.data-an
 Abstract: In searching for continuous gravitational waves over very many ($\approx 10^{17}$) templates , clustering is a powerful tool which increases the search sensitivity by identifying and bundling together candidates that are due to the same root cause. We implement a deep learning network that identifies clusters of signal candidates in the output of continuous gravitational wave searches and assess its performance.

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 Dates: 2020-01-092020
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: arXiv: 2001.03116
URI: http://arxiv.org/abs/2001.03116
DOI: 10.1103/PhysRevD.101.064009
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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: 101 Sequence Number: 064009 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: https://pure.mpg.de/cone/journals/resource/111088197762258