Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks

TL;DR

This paper demonstrates how generative adversarial networks can create realistic synthetic transient noise glitches for gravitational-wave detectors, aiding in improving detection algorithms.

Contribution

It introduces a method to generate high-fidelity synthetic glitches for 22 common types, enhancing data analysis and detector sensitivity.

Findings

Synthetic glitches match real glitches with 99% classification accuracy.

Generated data can improve search and parameter estimation algorithms.

The approach covers the most common glitch types in multiple detectors.

Abstract

Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this Paper, we show how glitches can be simulated using generative adversarial networks. We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. The artificial glitches can be used to improve the performance of searches and parameter-estimation algorithms. We perform a neural network classification to show that our artificial glitches are an excellent match for real glitches, with an average classification accuracy across all 22 glitch types of 99.0%.