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Deep Learning Pioneered For Real-Time Gravitational Wave Discovery

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Blue Waters numerical relativity simulation of two colliding black holes.

Scientists at the National Center for Supercomputing Applications have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves.

Credit: R. Haas

Researchers at the University of Illinois at Urbana-Champaign's National Center for Supercomputing Applications (NCSA) have developed a method for using graphics processing unit-accelerated deep learning for detecting and characterizing gravitational waves.

The researchers used deep learning algorithms, numerical relativity simulations of black hole mergers, and data from the LIGO Open Science Center to produce Deep Filtering, an end-to-end time-series signal processing method.

Deep Filtering achieves similar sensitivities and lower error rates when compared to conventional gravitational wave detection algorithms, and is more computationally efficient and resilient to noise anomalies. The method enables faster-than-real-time processing of gravitational waves in LIGO's raw data, thanks to NCSA's Blue Waters supercomputer.

In addition, the researchers created a demonstration to visualize the architecture of Deep Filtering, and gained insights into its neuronal activity during the detection and characterization of real gravitational wave events.

Their research won first place in the ACM Student Research Competition at the SuperComputing 2017 (SC17) conference in Denver, CO last November.

From Space Daily
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