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 - • Improve coupling and cross-talk between numerical relativity and modelling tools and build a pipeline for predicting observables • Accelerate community efforts to develop and release open-source versions of modelling codes, especially microphysics and transport packages • Explore publicly funded opportunities to develop numerical relativity software critical for mmA interpretations such as through the National Science Foundation (NSF) or the Department of Energy (DOE) office of science
Dl; deep learning; DOE, Department of Energy; Em, electromagnetic; GW, gravitational wave; HPC, high-performance computing; NSF, National Science Foundation.
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.
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.
UR - https://www.scopus.com/pages/publications/85077465113
UR - https://www.scopus.com/pages/publications/85077465113#tab=citedBy
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 -