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

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%.

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Cited by in corpus (10)

• The Science of the Einstein Telescope
• Gravity Spy: Lessons Learned and a Path Forward
• Applications of machine learning in gravitational wave research with current interferometric detectors
• A novel stacked hybrid autoencoder for imputing LISA data gaps
• Reconstruction of binary black hole harmonics in LIGO using deep learning
• cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
• Generative adversarial network for stellar core-collapse gravitational waves
• State Space Modelling for detecting and characterising Gravitational Waves afterglows
• Searching for topological dark matter in LIGO data
• Unveiling gravitational waves from core-collapse supernovae with MUSE