Demonstration of machine learning-assisted low-latency noise regression in gravitational wave detectors

Low-latency noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of DeepClean, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use DeepClean to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that DeepClean does not do any harm to the underlying astrophysical signals in the data while it can enhance the signal-to-noise ratio of potential signals. We show that DeepClean can be used for low-latency noise regression to produce cleaned output data at latencies ~1–2 s. We also discuss various considerations that may be made while training DeepClean for low latency applications.