Noise reduction in gravitational-wave data via deep learning

Abstract

With the advent of gravitational-wave astronomy, techniques to extend the reach of gravitational-wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine-learning algorithms to gravitational-wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, nonlinear, and nonstationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise-reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal signal-to-noise ratio (SNR) of the injected signal is enhanced by ≈21.6% and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and nonlinear noise sources and discuss its impact on astrophysical searches by gravitational-wave detectors.