Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
Abstract
This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Cite
@article{arxiv.1902.00522,
title = {Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era},
author = {Gabrielle Allen and Igor Andreoni and Etienne Bachelet and G. Bruce Berriman and Federica B. Bianco and Rahul Biswas and Matias Carrasco Kind and Kyle Chard and Minsik Cho and Philip S. Cowperthwaite and Zachariah B. Etienne and Daniel George and Tom Gibbs and Matthew Graham and William Gropp and Anushri Gupta and Roland Haas and E. A. Huerta and Elise Jennings and Daniel S. Katz and Asad Khan and Volodymyr Kindratenko and William T. C. Kramer and Xin Liu and Ashish Mahabal and Kenton McHenry and J. M. Miller and M. S. Neubauer and Steve Oberlin and Alexander R. Olivas and Shawn Rosofsky and Milton Ruiz and Aaron Saxton and Bernard Schutz and Alex Schwing and Ed Seidel and Stuart L. Shapiro and Hongyu Shen and Yue Shen and Brigitta M. Sipőcz and Lunan Sun and John Towns and Antonios Tsokaros and Wei Wei and Jack Wells and Timothy J. Williams and Jinjun Xiong and Zhizhen Zhao},
journal= {arXiv preprint arXiv:1902.00522},
year = {2019}
}
Comments
15 pages, no figures. White paper based on the "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at NCSA, October 17-19, 2018 http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/