Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder

Understand

Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information.

• We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e.
• apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain data.
• The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ("glitches"), and its fidelity is tested on real gravitational-wave events from O1 and O2 LIGO-Virgo observing runs.