On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors

TL;DR

This paper presents methods for real-time identification and whitening of noise power spectral density in interferometric gravitational wave detectors, demonstrating effective modeling of VIRGO's spectrum using parametric and adaptive techniques.

Contribution

It introduces adaptive parametric techniques for online noise spectral density estimation and whitening in gravitational wave detectors, tailored to VIRGO's specific spectrum.

Findings

Successfully modeled VIRGO's noise spectrum with finite parameters

Demonstrated effectiveness of adaptive techniques for online data whitening

Analyzed performance of stochastic gradient and Least Squares methods

Abstract

In this paper we address both to the problem of identifying the noise Power Spectral Density of interferometric detectors by parametric techniques and to the problem of the whitening procedure of the sequence of data. We will concentrate the study on a Power Spectral Density like the one of the Italian-French detector VIRGO and we show that with a reasonable finite number of parameters we succeed in modeling a spectrum like the theoretical one of VIRGO, reproducing all its features. We propose also the use of adaptive techniques to identify and to whiten on line the data of interferometric detectors. We analyze the behavior of the adaptive techniques in the field of stochastic gradient and in the Least Squares ones.