Total-variation-based methods for gravitational wave denoising

TL;DR

This paper introduces Total Variation denoising algorithms, adapted from image processing, for effectively removing noise from gravitational wave signals without prior signal knowledge, enhancing detection capabilities.

Contribution

The paper presents novel application of Total Variation denoising to gravitational wave signals, demonstrating its effectiveness across different waveform types and noise conditions.

Findings

Successful noise removal across various signal morphologies

Enhanced detection when combined with spectrogram analysis

Applicable to simulated core collapse and black hole merger signals

Abstract

We describe new methods for denoising and detection of gravitational waves embedded in additive Gaussian noise. The methods are based on Total Variation denoising algorithms. These algorithms, which do not need any a priori information about the signals, have been originally developed and fully tested in the context of image processing. To illustrate the capabilities of our methods we apply them to two different types of numerically-simulated gravitational wave signals, namely bursts produced from the core collapse of rotating stars and waveforms from binary black hole mergers. We explore the parameter space of the methods to find the set of values best suited for denoising gravitational wave signals under different conditions such as waveform type and signal-to-noise ratio. Our results show that noise from gravitational wave signals can be successfully removed with our techniques, irrespective of the signal morphology or astrophysical origin. We also combine our methods with spectrograms and show how those can be used simultaneously with other common techniques in gravitational wave data analysis to improve the chances of detection.