A robust machine learning algorithm to search for continuous gravitational waves

TL;DR

This paper introduces a convolutional neural network-based method to improve continuous gravitational wave searches by reducing instrumental artefacts, enhancing sensitivity, and automating the detection process across various data sets.

Contribution

The authors developed a novel CNN-based technique that classifies frequency bands to distinguish signals from instrumental lines, improving automation and sensitivity in gravitational wave searches.

Findings

Achieved 95% efficiency at a sensitivity depth of 10 Hz$^{-1/2}$

Demonstrated effectiveness across multiple data sets including LIGO data

Enhanced automation reduces need for human intervention in searches

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 penalising 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 artefacts on searches that use the SOAP algorithm. 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 data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set 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 Hz$^{-1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.