OmegaNeuron: Applying GravitySpy Similarity Methods to the Search for LIGO Glitch Witnesses

TL;DR

OmegaNeuron is a machine-learning tool that automates the identification of glitch witness channels in LIGO data, improving analysis speed and accuracy by integrating similarity methods with transient analysis.

Contribution

It introduces OmegaNeuron, a novel approach combining GravitySpy's similarity techniques with Omega Scan to automate glitch witness identification in gravitational-wave data.

Findings

OmegaNeuron effectively highlights plausible witness channels.

It shows strong agreement with existing correlation tools.

Provides clearer ranking through a quantitative similarity metric.

Abstract

Gravitational-wave (GW) astronomy has advanced our understanding of compact mergers through instruments like the Laser Interferometer Gravitational-Wave Observatory (LIGO). However, the extreme sensitivity required for these detections makes the instruments susceptible to short-duration transient noise, or glitches, which obscure GW data. Current tools such as Omega Scan and GravitySpy assist in identifying and classifying such noise, but are limited by manual inspection or dependence on large training sets. To address these challenges, we present \textit{OmegaNeuron}, a machine-learning tool that integrates GravitySpy's image similarity methods with Omega Scan's transient analysis to automate the identification of auxiliary channels that witness glitches. Applied to multiple glitch examples, OmegaNeuron consistently highlighted plausible witness channels and showed strong agreement with existing correlation tools, while providing clearer ranking through a quantitative similarity metric. Integrated into the \texttt{gwdetchar} package, OmegaNeuron enables faster analysis that improves glitch witness identification, enhancing both detector sensitivity and the reliability of gravitational-wave observations.