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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
(Preprint), 4MB

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Citation

Beheshtipour, B., & Papa, M. A. (in preparation). Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates.


Cite as: http://hdl.handle.net/21.11116/0000-0007-AE96-C
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.