Enabling real-time multi-messenger astrophysics discoveries with deep learning
Creators: name: Huerta, E. A.
name: Allen, Gabrielle
name: Andreoni, Igor
name: Antelis, Javier M.
name: Bachelet, Etienne
name: Berriman, Bruce
name: Bianco, Federica
name: Biswas, Rahul
name: Carrasco, Matias
name: Chard, Kyle
name: Cho, Minsik
name: Cowperthwaite, Philip S.
name: Etienne, Zachariah B.
name: Fishbach, Maya
name: Förster, Francisco
name: George, Daniel
name: Gibbs, Tom
name: Graham, Matthew
name: Gropp, William
name: Gruendl, Robert
name: Gupta, Anushri
name: Haas, Roland
name: Habib, Sarah
name: Jennings, Elise
name: Johnson, Margaret W. G.
name: Katsavounidis, Erik
name: Katz, Daniel S.
name: Khan, Asad
name: Kindratenko, Volodymyr
name: Kramer, William T. C.
name: Liu, Xin
name: Mahabal, Ashish
name: Marka, Zsuzsa
name: McHenry, Kenton
name: Miller, Jonah
name: Moreno, Claudia
name: Neubauer, Mark
name: Oberlin, Steve
name: Olivas, Alexander R.
name: Petravick, Donald
name: Rebei, Adam
name: Rosofsky, Shawn
name: Ruiz, Milton
name: Saxton, Aaron
name: Schutz, Bernard F.
name: Schwing, Alex
name: Seidel, Ed
name: Shapiro, Stuart L.
name: Shen, Hongyu
name: Shen, Yue
name: Singer, Leo
name: Sipőcz, Brigitta M.
name: Sun, Lunan
name: Towns, John
name: Tsokaros, Antonios
name: Wei, Wei
name: Wells, Jack
name: Williams, Timothy J.
name: Xiong, Jinjun
name: Zhao, Zhizhen
Publisher: name: arXiv
Year: 2019
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: High Energy Astrophysical Phenomena (astro-ph.HE)
subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: 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.
description: Invited Expert Recommendation for Nature Reviews Physics. The art work produced by E. A. Huerta and Shawn Rosofsky for this article was used by Carl Conway to design the cover of the October 2019 issue of Nature Reviews Physics