An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients

Understand

The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals.

• Mitigation of glitches is particularly difficult for algorithms which target generic sources of short-duration GW transients (GWT), and do not rely on GW waveform models to distinguish astrophysical signals from noise, such as Coherent WaveBurst (cWB).
• This work is part of the long-term effort to mitigate transient noises in cWB, which led to the introduction of specific estimators, and a machine-learning based signal-noise classification algorithm.
• Here, we propose an autoencoder neural network, integrated into cWB, that learns transient noises morphologies from GW time-series.