Performance of iDQ ahead of LIGO, Virgo, and KAGRA's fourth observing run

TL;DR

This paper evaluates the improved iDQ framework's ability to detect noise transients in gravitational wave detectors during the LIGO-Virgo-KAGRA fourth observing run, enhancing noise identification for better gravitational wave signal analysis.

Contribution

The paper details improvements to the iDQ pipeline and introduces a novel method for correlating glitch classifications with auxiliary channels in gravitational wave detectors.

Findings

iDQ detected 39,398 glitches out of 100,512 identified by Omicron.

Extended iDQ output to include correlations between glitch types and auxiliary channels.

Identified channels frequently recording specific glitch types like Scattered Light, Whistle, and Blip.

Abstract

The gravitational wave detectors used by the LIGO Scientific Collaboration, and the Virgo Collaboration are incredibly sensitive instruments which frequently detect non-stationary, non-Gaussian noise transients. iDQ is a statistical inference framework which leverages the use of auxiliary degrees of freedom monitored in the detectors to identify such transients. In this work, we describe the improvements to the iDQ pipeline made between the third and fourth observing run of the LIGO-Virgo-KAGRA (LVK) collaboration, and show the performance of these changes. We find that iDQ detects a total of 39,398 of the known 100,512 glitches identified by Omicron over the course of the second half of the third observing run. We construct a measure of the probability a glitch is present in the strain data of a given detector by combining information from iDQ and Omicron as well as extend the output of iDQ in a novel method which finds correlations between known glitch classifications, and auxiliary channels. We identify several channels over the course of O3b which frequently record instances of Scattered Light, Whistle, and Blip glitches and discuss use cases for this method in active observing runs.