GSpyNetTreeS: a machine learning solution for glitch localization in time and frequency

TL;DR

GSpyNetTreeS is an automated machine learning tool that detects, classifies, and localizes noise artifacts in gravitational wave detector data, aiming to improve the consistency and efficiency of data quality assessment.

Contribution

It extends GSpyNetTree with YOLO-based detection for automatic glitch localization, enhancing gravitational wave data analysis by reducing human bias.

Findings

Accurately identifies common detector glitches

Captures precise time-frequency localization of noise transients

Demonstrates potential as an automatic validation tool

Abstract

Data from ground-based gravitational wave detectors are often contaminated by non-Gaussian instrumental artifacts or detector noise transients. Unbiased source property estimation relies on the ability to correctly identify and characterize these artifacts and remove them if necessary. To this end, the LIGO-Virgo-KAGRA Collaboration has implemented candidate vetting for all significant candidates to identify the presence of artifacts and assess the need for mitigation. The current candidate vetting process requires human experts to identify the frequency ranges and the time windows associated with any data quality issues present. Differences in judgment between human experts may cause inconsistency, making results difficult to reproduce across gravitational wave events. We present GSpyNetTreeS, an extension to GSpyNetTree based on the You Only Look Once algorithm, for the automatic detection, classification, and time-frequency localization of detector noise transients. As a proof of concept, we tested GSpyNetTreeS's performance on the data collected by the LIGO detectors during the third observing run for gravitational waves as well as common detector glitch classes included in GSpyNetTree: Blip, Low frequency blip, Low frequency line and Scratchy. We also demonstrated that GSpyNetTreeS is capable of accurately identifying common glitch classes and capturing the frequency and time information associated with detected detector noise transients, establishing its potential as an automatic event validation tool for LIGO-Virgo-KAGRA's observing runs.