Deep learning for clustering of continuous gravitational wave candidates

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.