Adaptive filtering techniques for gravitational wave interferometric data: Removing long-term sinusoidal disturbances and oscillatory transients

TL;DR

This paper introduces an adaptive filtering method using LMS techniques to effectively remove long-term sinusoidal disturbances and transients from gravitational wave interferometric data, improving signal detection.

Contribution

It presents a robust, model-free adaptive filtering algorithm tailored for gravitational wave data to suppress non-Gaussian noise components.

Findings

Effective removal of sinusoidal noise in simulated data

Successful application to real LIGO prototype data

Enhanced clarity of gravitational wave signals

Abstract

It is known by the experience gained from the gravitational wave detector proto-types that the interferometric output signal will be corrupted by a significant amount of non-Gaussian noise, large part of it being essentially composed of long-term sinusoids with slowly varying envelope (such as violin resonances in the suspensions, or main power harmonics) and short-term ringdown noise (which may emanate from servo control systems, electronics in a non-linear state, etc.). Since non-Gaussian noise components make the detection and estimation of the gravitational wave signature more difficult, a denoising algorithm based on adaptive filtering techniques (LMS methods) is proposed to separate and extract them from the stationary and Gaussian background noise. The strength of the method is that it does not require any precise model on the observed data: the signals are distinguished on the basis of their autocorrelation time. We believe that the robustness and simplicity of this method make it useful for data preparation and for the understanding of the first interferometric data. We present the detailed structure of the algorithm and its application to both simulated data and real data from the LIGO 40meter proto-type.