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arXiv · 2012.04381

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

Banafsheh Beheshtipour·Maria Alessandra Papa
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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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BibTeXRIS

Banafsheh Beheshtipour, Maria Alessandra Papa. 2020-12-08. Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates. https://doi.org/10.1103/physrevd.103.064027

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