Deep Learning at Scale for Multimessenger Astrophysics Through the NCSA-Argonne Collaboration

PI Eliu Huerta, University of Illinois at Urbana-Champaign
Huerta ADSP2020

This image is a snapshot from a visualization that shows the output of the penultimate layer of a deep neural network during training as it learns to classify spiral and elliptical galaxies. Credit: Janet Knowles, Joseph A. Insley, and Silvio Rizzi, Argonne National Laboratory

Multimessenger astrophysics refers to the contemporaneous observation of astrophysical phenomena using gravitational waves, electromagnetic radiation, neutrinos, and cosmic rays. Full realization of the goals of multimessenger astrophysics requires the resolution of outstanding computational challenges, which this project seeks to address through the development of algorithms that significantly increase the depth and speed of gravitational wave searches and that process terabyte-size datasets of telescope images in real-time. A crucial impact will be the real-time, at-scale discovery of multimessenger sources, providing a more complete picture of some of the universe’s most mysterious and powerful events.