Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates. (arXiv:2012.04381v2 [gr-qc] UPDATED)
<a href="http://arxiv.org/find/gr-qc/1/au:+Beheshtipour_B/0/1/0/all/0/1">Banafsheh Beheshtipour</a>, <a href="http://arxiv.org/find/gr-qc/1/au:+Papa_M/0/1/0/all/0/1">Maria Alessandra Papa</a>
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.
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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