NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals

TL;DR

NNETFIX is a neural network-based denoising tool that improves sky localization of gravitational-wave signals affected by glitches, enhancing real-time detection accuracy for astrophysical observations.

Contribution

Introduces NNETFIX, a novel neural network algorithm that estimates and mitigates glitch effects in gravitational-wave data, improving sky localization accuracy during low-latency detections.

Findings

Denoised data yields more accurate sky localization than original or simply glitch-removed data.

High SNR signals show significant improvement in sky map overlap after denoising.

Potential for integration into future low-latency gravitational-wave search pipelines.

Abstract

Instrumental and environmental transient noise bursts in gravitational-wave detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of gravitational-wave signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning-based algorithm designed to remove glitches detected in coincidence with transient gravitational-wave signals. NNETFIX uses artificial neural networks to estimate the portion of the data lost due to the presence of the glitch, which allows the recalculation of the sky localization of the astrophysical signal. The sky localization of the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed.