摘要
We propose a self-supervised learning model to denoise gravitational wave (GW) signals in the time series strain data without relying on waveform information. Denoising GW data is a crucial intermediate process for machine-learning-based data analysis techniques, as it can simplify the model for downstream tasks such as detections and parameter estimations. We use the blind-spot neural network and train it with whitened strain data with GW signals injected as both input data and target. Under the assumption of a Gaussian noise model, our model successfully denoises over 90% of GW signals from binary black hole (BBH) and neutron star-black hole mergers when the signal-to-noise ratio (SNR) is larger than 13 and 16 and denoises over 50% of binary neutron star signals when the SNR is larger than 23, with an overlap greater than 0.2, over which the denoised signal can have visually distinguishable patterns. Our model also denoises over 90% of BBH signals in H1, L1, and V1 data detected in the O1, O2, and O3 observation runs when the network SNR is larger than 11, 12, and 20, respectively. We also test the model's potential to extract glitch features, loud inspiral compact binary coalescence signals a few seconds before the merger, and unseen core-collapse supernova signals during training.