Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors
Abstract
Real-time 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 \textit{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 \textit{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 \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies \, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.
Keywords
Cite
@article{arxiv.2306.11366,
title = {Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors},
author = {Muhammed Saleem and Alec Gunny and Chia-Jui Chou and Li-Cheng Yang and Shu-Wei Yeh and Andy H. Y. Chen and Ryan Magee and William Benoit and Tri Nguyen and Pinchen Fan and Deep Chatterjee and Ethan Marx and Eric Moreno and Rafia Omer and Ryan Raikman and Dylan Rankin and Ritwik Sharma and Michael Coughlin and Philip Harris and Erik Katsavounidis},
journal= {arXiv preprint arXiv:2306.11366},
year = {2023}
}