In searching for continuous gravitational waves over very many ($\approx {10}^{17}$) templates, clustering is a powerful tool which increases the search sensitivity by identifying and bundling together candidates that are due to the same root cause. We implement a deep learning network that identifies clusters of signal candidates in the output of continuous gravitational wave searches and assess its performance. For loud signals, our network achieves a detection efficiency higher than 97% with a very low false alarm rate and maintains a reasonable detection efficiency for signals with lower amplitudes, i.e., at $\lesssim$ current upper limit values.

• Received 14 January 2020
• Accepted 10 February 2020

DOI:https://doi.org/10.1103/PhysRevD.101.064009