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

Jade Powell, Ling Sun, Katinka Gereb, Paul D. Lasky, Markus Dollmann

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

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 (GANs). We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. We show how our GAN-generated images can easily be converted to time series, which would allow us to use GAN-generated glitches in simulations and mock data challenges to improve the robustness of gravitational-wave 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%.

Original languageEnglish
Article number035006
Number of pages14
JournalClassical and Quantum Gravity
Volume40
Issue number3
DOIs
Publication statusPublished - 2 Feb 2023

Keywords

  • detector characterisation
  • gravitational waves
  • machine learning

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