Deep learning for gravitational wave discovery

Mon, 29 Jan 2018, by Wayne Radinsky

GPU-accelerated deep learning for gravitational waves. "Combining deep learning algorithms, numerical relativity simulations of black hole mergers -- obtained with the Einstein Toolkit run on the Blue Waters supercomputer -- and data from the LIGO Open Science Center, NCSA Gravity Group researchers Daniel George and Eliu Huerta produced Deep Filtering, an end-to-end time-series signal processing method. Deep Filtering achieves similar sensitivities and lower errors compared to established gravitational wave detection algorithms, while being far more computationally efficient and more resilient to noise anomalies. The method allows faster than real-time processing of gravitational waves in LIGO's raw data, and also enables new physics, since it can detect new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. George and Huerta are extending this method to identify in real-time electromagnetic counterparts to gravitational wave events in future LSST data."

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