Machine-learning non-stationary noise out of gravitational wave detectors

TL;DR

This paper introduces a machine learning algorithm designed to identify and remove non-stationary noise in gravitational wave detectors, enhancing detection sensitivity without biasing source parameters.

Contribution

The work presents a novel, general algorithm for characterizing and removing non-stationary noise couplings using witness sensors, specifically applied to LIGO data.

Findings

Improved gravitational wave detection reach.

Effective removal of non-stationary noise.

No bias introduced in source parameter estimation.

Abstract

Signal extraction out of background noise is a common challenge in high precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal to noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is non-stationary, linear techniques often fail or are sub-optimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove non-stationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational wave observatory, where we could obtain an improvement of the detector gravitational wave reach without introducing any bias on the source parameter estimation.