1. Deep learning for clustering of continuous gravitational wave candidates. II. Identification of low-SNR candidates.
- Author
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Beheshtipour, B. and Papa, M. A.
- Subjects
- *
GRAVITATIONAL waves , *CASCADE connections , *DEEP learning , *FALSE alarms - 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 [B. Beheshtipour and M. A. Papa, Phys. Rev. D 101, 064009 (2020)], 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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