Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks

TL;DR

This paper demonstrates the use of Generative Adversarial Networks to efficiently generate realistic transient noise glitches in gravitational-wave detector data, aiding in improved modeling and testing of data analysis pipelines.

Contribution

It introduces a novel GAN-based method for fast, realistic simulation of GW detector glitches, enhancing data augmentation and pipeline stress testing.

Findings

GAN can generate ~1000 glitches in less than a second

The generated glitches closely match real detector data based on multiple metrics

Method can be extended to various glitch types for comprehensive modeling

Abstract

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $ 1 \text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning algorithm inspired by Game Theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time-domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford and Livingston detectors in less than one second. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.