AI Can Now Detect Gravitational Waves

April 13, 2018

April 13, 2018 — At the April American Physical Society Meeting, researchers from the National Center for Supercomputing Applications (NCSA) at Illinois, will continue to build on their groundbreaking research on deep learning astronomy with a new article, “Classification and Clustering of LIGO Data with Deep Transfer Learning,” which has been accepted for publication in Physics Review D this month.

Researcher, Daniel George will be featured in a live webcasting on Sunday, April 15 at 3:00pm, discussing the results of “Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Real LIGO Data.” Visit apswebcasting.com to listen to the webinar.

“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,” said 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 in Physical 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 in Physics 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.

About NCSA

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. NCSA has been advancing one third of the Fortune 50® for more than 30 years by bringing industry, researchers, and students together to solve grand challenges at rapid speed and scale.

About the Blue Waters Project

The Blue Waters petascale supercomputer is one of the most powerful supercomputers in the world, and is the fastest sustained supercomputer on a university campus. Blue Waters uses hundreds of thousands of computational cores to achieve peak performance of more than 13 quadrillion calculations per second. Blue Waters has more memory and faster data storage than any other open system in the world. Scientists and engineers across the country use the computing and data power of Blue Waters to tackle a wide range of challenges. Recent advances that were not possible without these resources include computationally designing the first set of antibody prototypes to detect the Ebola virus, simulating the HIV capsid, visualizing the formation of the first galaxies and exploding stars, and understanding how the layout of a city can impact supercell thunderstorms.


Source: NCSA

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