Application of a new transient-noise analysis tool for an unmodeled gravitational-wave search pipeline

TL;DR

This paper introduces PyChChoo, a new software tool that analyzes control system and environmental data to identify the origins of transient noise glitches in gravitational-wave detectors, improving data quality for astrophysical signal detection.

Contribution

The paper presents PyChChoo, a novel software that uses time series data to trace the causes of glitches, enhancing the understanding of noise sources in GW detectors.

Findings

PyChChoo identified causes for 80% of glitches analyzed.

The tool successfully correlated glitches with existing vetoes.

Improves understanding of noise coupling in GW detectors.

Abstract

Excess transient noise events, or glitches, impact the data quality of ground-based ravitational-wave (GW) detectors and impair the detection of signals produced by astrophysical sources. Identification of the causes of these glitches is a crucial starting point for the improvement of GW signal detectability. However, glitches are the product of linear and non-linear couplings among the interrelated detector-control systems that include mitigation of ground motion and regulation of optic motion, which generally makes it difficult to find their origin. We present a new software called PyChChoo which uses time series recorded in the instrumental control systems and environmental sensors around times when glitches are present in the detector's output to reveal essential clues about their origin. Applying PyChChoo on the most adversely affecting glitches on background triggers generated by one of unmodeled GW detection pipelines called coherent WaveBurst (cWB) operated in the data from the LIGO detectors between January 1st, 2020 and February 3rd, 2020, we find that 80% of triggers are marked as either being vetoed or unvetoed in common between our analysis and the current LIGO infrastructure.