Total-variation methods for gravitational-wave denoising: performance tests on Advanced LIGO data

TL;DR

This paper evaluates total-variation denoising methods on real Advanced LIGO data containing gravitational-wave signals, exploring parameter selection strategies and introducing a machine-learning approach for automatic regularization tuning.

Contribution

It extends previous Gaussian noise studies to real noise conditions and proposes an automatic, machine-learning-informed method for selecting the regularization parameter.

Findings

Total-variation methods effectively denoise real gravitational-wave data.

Parameter selection significantly impacts denoising quality.

Machine learning can automate optimal regularization parameter choice.

Abstract

We assess total-variation methods to denoise gravitational-wave signals in real noise conditions, by injecting numerical-relativity waveforms from core-collapse supernovae and binary black hole mergers in data from the first observing run of Advanced LIGO. This work is an extension of our previous investigation where only Gaussian noise was used. Since the quality of the results depends on the regularization parameter of the model, we perform an heuristic search for the value that produces the best results. We discuss various approaches for the selection of this parameter, either based on the optimal, mean, or multiple values, and compare the results of the denoising upon these choices. Moreover, we also present a machine-learning-informed approach to obtain the Lagrange multiplier of the method through an automatic search. Our results provide further evidence that total-variation methods can be useful in the field of Gravitational-Wave Astronomy as a tool to remove noise.