Identification of long-duration noise transients in LIGO and Virgo

TL;DR

This paper introduces an algorithm that detects and identifies long-duration environmental noise transients in LIGO and Virgo data, improving the ability to distinguish genuine gravitational waves from noise artifacts.

Contribution

The paper presents a novel pattern recognition algorithm to identify and classify long-duration noise sources coupling into gravitational-wave detectors.

Findings

Successfully identified noise from airplanes, helicopters, and thunderstorms.

Analyzed data from LIGO S6 and Virgo VSR3 runs to demonstrate effectiveness.

Enhanced noise characterization improves gravitational-wave data quality.

Abstract

The LIGO and Virgo detectors are sensitive to a variety of noise sources, such as instrumental artifacts and environmental disturbances. The Stochastic Transient Analysis Multi-detector Pipeline (STAMP) has been developed to search for long-duration (t$\gtrsim$1s) gravitational-wave (GW) signals. This pipeline can also be used to identify environmental noise transients. Here we present an algorithm to determine when long-duration noise sources couple into the interferometers, as well as identify what these noise sources are. We analyze the cross-power between a GW strain channel and an environmental sensor, using pattern recognition tools to identify statistically significant structure in cross-power time-frequency maps. We identify interferometer noise from airplanes, helicopters, thunderstorms and other sources. Examples from LIGO's sixth science run, S6, and Virgo's third scientific run, VSR3, are presented.