Wavelet Scattering Transform for Gravitational Waves Analysis. An Application to Glitch Characterization

TL;DR

This paper explores the use of Wavelet Scattering Transform (WST) for analyzing gravitational wave data, demonstrating its advantages in simplifying classification and enhancing detection when combined with existing methods.

Contribution

It introduces WST as a novel, stable, and equivariant signal analysis technique for gravitational wave data, improving classification and detection performance.

Findings

WST simplifies classification tasks in gravitational wave data.

Ensemble methods combining WST and Q-transform improve performance.

WST enables more efficient architectures for signal analysis.

Abstract

Gravitational waves, first predicted by Albert Einstein within the framework of general relativity, were confirmed in 2015 by the LIGO/Virgo collaboration, marking a pivotal breakthrough in astrophysics. Despite this achievement, a key challenge remains in distinguishing true gravitational wave signals from noise artifacts, or "glitches," which can distort data and affect the quality of observations. Current state-of-the-art methods, such as the Q-transform, are widely used for signal processing, but face limitations when addressing certain types of signals. In this study, we investigate the Wavelet Scattering Transform (WST), a recent signal analysis method, as a complementary approach. Theoretical motivation for WST arises from its stability under signal deformations and its equivariance properties, which make it particularly suited for the complex nature of gravitational wave data. Our experiments on the LIGO O1a dataset show that WST simplifies classification tasks and enables the use of more efficient architectures compared to traditional methods. Furthermore, we explore the potential benefits of integrating WST with the Q-transform, demonstrating that ensemble methods exploiting both techniques can capture complementary features of the signal and improve overall performance. This work contributes to advancing machine learning applications in gravitational wave analysis, introducing refined preprocessing techniques that improve signal detection and classification.