Regression of Environmental Noise in LIGO Data

TL;DR

This paper extends Wiener-Kolmogorov filtering techniques to improve environmental noise regression in LIGO gravitational-wave data, incorporating multi-channel and time-frequency analysis, and presents initial results on bi-coherent noise regression.

Contribution

The paper introduces an advanced Wiener-Kolmogorov filter framework with multi-channel and time-frequency analysis for better noise regression in LIGO data.

Findings

Enhanced noise regression performance demonstrated.

First results on bi-coherent noise regression in LIGO data.

Versatile approach applicable to various environmental noise sources.

Abstract

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.