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