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.
@article{arxiv.2012.04381,
title = {Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates},
author = {Banafsheh Beheshtipour and Maria Alessandra Papa},
journal= {arXiv preprint arXiv:2012.04381},
year = {2021}
}