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

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

doi:10.1088/1361-6382/acb038

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arXiv · 2207.00207

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

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

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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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Jade Powell, Ling Sun, Katinka Gereb, Paul D. Lasky, Markus Dollmann. 2022-07-01. Generating transient noise artifacts in gravitational-wave detector data with generative adversarial networks. https://doi.org/10.1088/1361-6382%2Facb038

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