An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients

TL;DR

This paper introduces an autoencoder neural network integrated into the cWB gravitational-wave search algorithm to better distinguish noise glitches from true signals, significantly improving detection sensitivity especially for blip glitches.

Contribution

The novel integration of an autoencoder neural network into cWB enhances noise transient rejection, improving gravitational-wave detection sensitivity without relying on specific waveform models.

Findings

Sensitivity volume increases by up to 30% for blip-like glitches.

Significant improvement in detection sensitivity for binary black hole mergers.

Effective noise classification adaptable to future observing runs.

Abstract

The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification algorithm. Here, we propose an autoencoder neural network, integrated into cWB, that learns transient noises morphologies from GW time-series. We test its performance on the glitch family known as blip. The resulting sensitivity to generic GWT and binary black hole mergers significantly improves when tested on LIGO detectors data from the last observation period (O3b). At false alarm rate of one event per 50 years the sensitivity volume increases up to 30% for signal morphologies similar to blip glitches. In perspective, this tool can adapt to classify different transient noise classes that may affect future observing runs, enhancing GWT searches.