TY - JOUR
T1 - Enabling real-time multi-messenger astrophysics discoveries with deep learning
AU - Huerta, E. A.
AU - Allen, Gabrielle
AU - Andreoni, Igor
AU - Antelis, Javier M.
AU - Bachelet, Etienne
AU - Berriman, G. Bruce
AU - Bianco, Federica B.
AU - Biswas, Rahul
AU - Carrasco kind, Matias
AU - Chard, Kyle
AU - Cho, Minsik
AU - Cowperthwaite, Philip S.
AU - Etienne, Zachariah B.
AU - Fishbach, Maya
AU - Forster, Francisco
AU - George, Daniel
AU - Gibbs, Tom
AU - Graham, Matthew
AU - Gropp, William
AU - Gruendl, Robert
AU - Gupta, Anushri
AU - Haas, Roland
AU - Habib, Sarah
AU - Jennings, Elise
AU - Johnson, Margaret W. G.
AU - Katsavounidis, Erik
AU - Katz, Daniel S.
AU - Khan, Asad
AU - Kindratenko, Volodymyr
AU - Kramer, William T. C.
AU - Liu, Xin
AU - Mahabal, Ashish
AU - Marka, Zsuzsa
AU - Mchenry, Kenton
AU - Miller, J. M.
AU - Moreno, Claudia
AU - Neubauer, M. S.
AU - Oberlin, Steve
AU - Olivas, Alexander R.
AU - Petravick, Donald
AU - Rebei, Adam
AU - Rosofsky, Shawn
AU - Ruiz, Milton
AU - Saxton, Aaron
AU - Schutz, Bernard F.
AU - Schwing, Alex
AU - Seidel, Ed
AU - Shapiro, Stuart L.
AU - Shen, Hongyu
AU - Shen, Yue
AU - Singer, Leo P.
AU - Sipocz, Brigitta M.
AU - Sun, Lunan
AU - Towns, John
AU - Tsokaros, Antonios
AU - Wei, Wei
AU - Wells, Jack
AU - Williams, Timothy J.
AU - Xiong, Jinjun
AU - Zhao, Zhizhen
N1 - Funding Information:
Dl; deep learning; DOE, Department of Energy; Em, electromagnetic; GW, gravitational wave; HPC, high-performance computing; NSF, National Science Foundation.
Funding Information:
The authors gratefully acknowledge support from NVIDIA, Argonne Leadership Computing Facility, Oak Ridge Leadership Computing Facility, and the National Science Foundation through grant NSF-1848815. Artwork in this manuscript was supported in part by the National Science Foundation through grants ACI-1238993, NSF-1550514 and TG-PHY160053.
Publisher Copyright:
© 2019, Springer Nature Limited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.
AB - Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.
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U2 - 10.1038/s42254-019-0097-4
DO - 10.1038/s42254-019-0097-4
M3 - Article
SN - 2522-5820
VL - 1
SP - 600
EP - 608
JO - Nature Reviews Physics
JF - Nature Reviews Physics
IS - 10
ER -