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arXiv · 1902.00522

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

Gabrielle Allen·Igor Andreoni·Etienne Bachelet·G. Bruce Berriman·Federica B. Bianco·Rahul Biswas·Matias Carrasco Kind·Kyle Chard·Minsik Cho·Philip S. Cowperthwaite·Zachariah B. Etienne·Daniel George·Tom Gibbs·Matthew Graham·William Gropp·Anushri Gupta·Roland Haas·E. A. Huerta·Elise Jennings·Daniel S. Katz·Asad Khan·Volodymyr Kindratenko·William T. C. Kramer·Xin Liu·Ashish Mahabal·Kenton McHenry·J. M. Miller·M. S. Neubauer·Steve Oberlin·Alexander R. Olivas Jr·Shawn Rosofsky·Milton Ruiz·Aaron Saxton·Bernard Schutz·Alex Schwing·Ed Seidel·Stuart L. Shapiro·Hongyu Shen·Yue Shen·Brigitta M. Sipőcz·Lunan Sun·John Towns·Antonios Tsokaros·Wei Wei·Jack Wells·Timothy J. Williams·Jinjun Xiong·Zhizhen Zhao
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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.

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Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao. 2019-02-01. Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. https://arxiv.org/abs/1902.00522

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