Advancements in Glitch Subtraction Systems for Enhancing Gravitational Wave Data Analysis: A Brief Review

TL;DR

This review summarizes various glitch subtraction techniques in gravitational wave data analysis, highlighting their effectiveness, challenges, and future potential to improve detection accuracy in observatories like LIGO and Virgo.

Contribution

It provides a comprehensive comparison of classic, frequency-domain, and machine learning methods for glitch subtraction in gravitational wave data.

Findings

Machine learning shows promise for automatic glitch identification.

Frequency-domain methods effectively detect non-stationary glitches.

Classic time-domain techniques are useful for real-time applications.

Abstract

Glitches are transitory noise artifacts that degrade the detection sensitivity and accuracy of interferometric observatories such as LIGO and Virgo in gravitational wave astronomy. Reliable glitch subtraction techniques are essential for separating genuine gravitational wave signals from background noise and improving the accuracy of astrophysical investigations. This review study summarizes the main glitch subtraction methods used in the industry. We talk about the efficacy of classic time-domain techniques in real-time applications, like matched filtering and regression methods. The robustness of frequency-domain approaches, such as wavelet transformations and spectral analysis, in detecting and mitigating non-stationary glitches is assessed. We also investigate sophisticated machine learning methods, demonstrating great potential in automatically identifying and eliminating intricate glitch patterns. We hope to provide a thorough understanding of these approaches' uses, difficulties, and potential for future development in gravitational wave data analysis by contrasting their advantages and disadvantages. Researchers looking to enhance glitch subtraction procedures and raise the accuracy of gravitational wave detections will find great value in this paper.