No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts

TL;DR

This paper presents an extension of the Attention-boosted Waveform Reconstruction network (AWaRe) that robustly reconstructs gravitational wave signals amidst noise glitches without explicit glitch training, improving analysis reliability.

Contribution

The work introduces a glitch-agnostic waveform reconstruction method that effectively isolates signals from contaminated data, enhancing gravitational wave data analysis.

Findings

AWaRe accurately reconstructs signals in glitch-affected data.

The method performs well on real LIGO events with potential data quality issues.

Reconstructed waveforms closely match injected signals in simulated glitches.

Abstract

Gravitational wave observations by ground based detectors such as LIGO and Virgo have transformed astrophysics, enabling the study of compact binary systems and their mergers. However, transient noise artifacts, or glitches, pose a significant challenge, often obscuring or mimicking signals and complicating their analysis. In this work, we extend the Attention-boosted Waveform Reconstruction network to address glitch mitigation, demonstrating its robustness in reconstructing waveforms in the presence of real glitches from the third observing run of LIGO. Without requiring explicit training on glitches, AWaRe accurately isolates gravitational wave signals from data contaminated by glitches spanning a wide range of amplitudes and morphologies. We evaluate this capability by investigating the events GW191109 and GW200129, which exhibit strong evidence of anti-aligned spins and spin precession respectively, but may be adversely affected by data quality issues. We find that, regardless of the potential presence of glitches in the data, AWaRe reconstructs both waveforms with high accuracy. Additionally, we perform a systematic study of the performance of AWaRe on a simulated catalog of injected waveforms in real LIGO glitches and obtain reliable reconstructions of the waveforms. By subtracting the AWaRe reconstructions from the data, we show that the resulting residuals closely align with the background noise that the waveforms were injected in. The robustness of AWaRe in mitigating glitches, despite being trained exclusively on GW signals and not explicitly on glitches, highlights its potential as a powerful tool for improving the reliability of searches and characterizing noise artifacts.