Extracting gravitational waves whose amplitude is much smaller than the background noise and inferring accurate parameters of their sources in real-time is crucial in enabling multimessenger astrophysics. We are working on reducing the noise in the data from the LIGO detectors and extracting gravitational wave signals using denoising auto-encoders based on recurrent neural networks. This will be used as a preprocessing step to improve the accuracy of our Deep Filtering algorithm as well as that of existing detection and parameter estimation pipelines in gravitational wave detectors.
Project Members: Hongyu Shen, Daniel George, Eliu Huerta
Collaborators: Zhizhen Zhao (U. Illinois)
Funding: NCSA Faculty Fellow Program 2017-2018