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

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

B. Beheshtipour
(
- Hannover, Max Planck Inst. Grav. and
- Hannover U.
)
,
M.A. Papa
(
)

Dec 8, 2020

10 pages

Published in:

Phys.Rev.D 103 (2021) 6, 064027

Published: Mar 16, 2021

e-Print:

2012.04381 [gr-qc]

DOI:

10.1103/PhysRevD.103.064027

View in:

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Abstract: (APS)

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 [B. Beheshtipour and M. A. Papa, Phys. Rev. D 101, 064009 (2020)PRVDAQ2470-001010.1103/PhysRevD.101.064009], 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.

General relativity, alternative theories of gravity
detector: network
gravitational radiation
programming
cluster
efficiency
numerical methods
hierarchy
numerical calculations
gravitational radiation detector

References(48)

Figures(11)

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