Researcher, Daniel George featured in a live webcasting on April 15, discussing the results of "Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Real LIGO Data".
"This article shows that we can automatically detect and group together noise anomalies in data from the LIGO detectors by using artificial intelligence algorithms based on neural networks that were already pre-trained to classify images of real-world objects", stated research scientist, Eliu Huerta.
NCSA Gravity Group researchers, Daniel George, Eliu Huerta and Hongyu Shen leveraged NCSA resources from its Innovative Systems Laboratory, Einstein Toolkit and NCSA's Blue Waters supercomputer. Also critical to this research were the GPUs - Tesla P100 and DGX-1 - provided by NVIDIA, which enabled an accelerated training of neural networks. Wolfram Research also played an important role, as the Wolfram Language was used in creating this framework for deep learning.
Daniel George and Eliu Huerta began a new chapter in gravitational wave astronomy with their groundbreaking research, "Deep Neural Networks to Enable Real-time Multimessenger Astronomy", which was also published inPhysical Review D. This was the first application of deep learning for gravitational wave astrophysics, establishing the power of deep learning to outperform other gravitational wave detection and parameter estimation algorithms when applied to simulated gravitational wave data.
These results were confirmed with outstanding accuracy when this method could detect real signals in raw LIGO data, resulting in a subsequent paper, "Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data", published inPhysics Letter B.
The April American Physical Society Meeting will be held April 14-17, 2018 in Columbus, Ohio. Ten members from the NCSA Gravity Group will present their research encompassing numerical relativity, gravitational wave astronomy and applications of HPC for large scale gravitational wave discovery.