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学術論文

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

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Beheshtipour,  Banafsheh
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Papa,  Maria Alessandra
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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2012.04381.pdf
(プレプリント), 4MB

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引用

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):. doi:10.1103/PhysRevD.103.064027.


引用: https://hdl.handle.net/21.11116/0000-0007-AE96-C
要旨
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.