Robust machine learning algorithm to search for continuous gravitational waves
J. C. Bayley, C. Messenger, G. Woan
Abstract
Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalizing signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artifacts on searches that use the SOAP algorithm Bayley et al. [Phys. Rev. D 100, 023006 (2019)]. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different datasets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge dataset Walsh et al. [Phys. Rev. D 94, 124010 (2016)] and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge dataset and at a 1% false alarm probability we showed that at 95% efficiency a fully automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of $10\text{ }\text{ }{\mathrm{Hz}}^{\ensuremath{-}1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.