Optimizing Vetoes for Gravitational-wave Transient Searches

TL;DR

This paper introduces an algorithm to optimize the use of auxiliary channels in gravitational-wave detectors for vetoing noise events, improving detection confidence and identifying noise sources.

Contribution

The paper presents a novel algorithm for ordering auxiliary channels to optimize veto conditions in gravitational-wave transient searches, demonstrated on LIGO and Virgo data.

Findings

The method robustly identifies important auxiliary channels.

It significantly reduces noise background in gravitational-wave data.

The approach helps characterize detector noise sources.

Abstract

Interferometric gravitational-wave detectors like LIGO, GEO600 and Virgo record a surplus of information above and beyond possible gravitational-wave events. These auxiliary channels capture information about the state of the detector and its surroundings which can be used to infer potential terrestrial noise sources of some gravitational-wave-like events. We present an algorithm addressing the ordering (or equivalently optimizing) of such information from auxiliary systems in gravitational-wave detectors to establish veto conditions in searches for gravitational-wave transients. The procedure was used to identify vetoes for searches for unmodelled transients by the LIGO and Virgo collaborations during their science runs from 2005 through 2007. In this work we present the details of the algorithm; we also use a limited amount of data from LIGO's past runs in order to examine the method, compare it with other methods, and identify its potential to characterize the instruments themselves. We examine the dependence of Receiver Operating Characteristic curves on the various parameters of the veto method and the implementation on real data. We find that the method robustly determines important auxiliary channels, ordering them by the apparent strength of their correlations to the gravitational-wave channel. This list can substantially reduce the background of noise events in the gravitational-wave data. In this way it can identify the source of glitches in the detector as well as assist in establishing confidence in the detection of gravitaional-wave transients.