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

Paper Info

Title
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 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.

Year	Venue	DocType
2019	arXiv: Instrumentation and Methods for Astrophysics	Journal
Volume	Citations	PageRank
abs/1902.00522	0	0.34
References	Authors
0	48

Authors (48 rows)

Cited by (0 rows)

References (0 rows)

Name	Order	Citations	PageRank
Gabrielle Allen	1	839	116.72
Igor Andreoni	2	0	0.34
Etienne Bachelet	3	0	0.34
G. Bruce Berriman	4	994	69.33
Federica B. Bianco	5	1	1.40
R. Biswas	6	1	1.16
M. Carrasco Kind	7	5	1.53
Kyle Chard	8	515	56.70
Minsik Cho	9	3	3.77
Philip S. Cowperthwaite	10	0	0.34
Zachariah B. Etienne	11	0	0.68
Daniel George	12	20	2.83
Tom Gibbs	13	1	0.70
Matthew J. Graham	14	43	7.87
William D. Gropp	15	5547	548.31
Anushri Gupta	16	0	0.34
Roland Haas	17	5	2.89
E. A. Huerta	18	25	5.72
Elise Jennings	19	0	0.34
Daniel S. Katz	20	1496	121.04
Asad Khan	21	0	0.34
Volodymyr V. Kindratenko	22	141	20.56
William T. C. Kramer	23	0	0.68
Xin Liu	24	23	18.64
Ashish Mahabal	25	77	10.22
Kenton McHenry	26	54	11.15
J. M. Miller	27	0	0.34
Mark Neubauer	28	0	0.34
Steve Oberlin	29	0	0.34
Alexander Reinefeld	30	889	126.08
Shawn Rosofsky	31	0	0.34
Milton Ruiz	32	0	0.68
Aaron Saxton	33	0	0.34
Bernard Schutz	34	0	0.34
Alexander G. Schwing	35	696	51.78
Ed Seidel	36	0	0.34
Stuart L. Shapiro	37	0	0.34
Hongyu Shen	38	3	2.42
Yue Shen	39	19	6.48
Brigitta Miksztai-Réthey	40	0	0.34
Lunan Sun	41	0	0.34
John Towns	42	6	1.41
Antonios Tsokaros	43	0	0.68
Wei Wei	44	87	18.59
Jack Wells	45	0	0.34
Timothy J. Williams	46	0	0.34
Xiong Jinjun	47	801	86.79
Zhizhen Zhao	48	0	1.01