Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks

Understand

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.