Leveraging waveform complexity for confident detection of gravitational waves

TL;DR

This paper introduces a new method that uses waveform complexity and Bayesian evidence to improve the detection confidence of gravitational waves, effectively distinguishing true signals from noise artifacts in LIGO data.

Contribution

It proposes a hierarchical pipeline that leverages waveform complexity and Bayesian model comparison for more reliable gravitational wave detection.

Findings

High-confidence detection of various waveforms at realistic SNRs

Effective discrimination between signals and glitches

Strong performance demonstrated with a two detector network

Abstract

The recent completion of Advanced LIGO suggests that gravitational waves (GWs) may soon be directly observed. Past searches for gravitational-wave transients have been impacted by transient noise artifacts, known as glitches, introduced into LIGO data due to instrumental and environmental effects. In this work, we explore how waveform complexity, instead of signal-to-noise ratio, can be used to rank event candidates and distinguish short duration astrophysical signals from glitches. We test this framework using a new hierarchical pipeline that directly compares the Bayesian evidence of explicit signal and glitch models. The hierarchical pipeline is shown to have strong performance, and in particular, allows high-confidence detections of a range of waveforms at realistic signal-to-noise ratio with a two detector network.