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  • Expert Recommendation
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Enabling real-time multi-messenger astrophysics discoveries with deep learning

Nature Reviews Physics volume 1, pages 600–608 (2019)Cite this article

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Abstract

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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Fig. 1: Visualization of the results of a numerical relativity simulation of two neutron stars before and after a merger.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    E. A. Huerta, Gabrielle Allen, Matias Carrasco Kind, William Gropp, Robert Gruendl, Anushri Gupta, Roland Haas, Sarah Habib, Margaret W. G. Johnson, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Kenton McHenry, M. S. Neubauer, Donald Petravick, Adam Rebei, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei & Zhizhen Zhao

  2. California Institute of Technology, Pasadena, CA, USA

    Igor Andreoni, G. Bruce Berriman, Matthew Graham & Ashish Mahabal

  3. Tecnologico de Monterrey, School of Engineering and Sciences, Zapopan, Mexico

    Javier M. Antelis

  4. Las Cumbres Observatory, Goleta, CA, USA

    Etienne Bachelet

  5. University of Delaware, Newark, DE, USA

    Federica B. Bianco

  6. The Oskar Klein Centre for Cosmoparticle Physics, Stockholm University, AlbaNova, Stockholm, Sweden

    Rahul Biswas

  7. The University of Chicago, Chicago, IL, USA

    Kyle Chard & Maya Fishbach

  8. IBM T.J. Watson Research Center, Yorktown Heights, New York, NY, USA

    Minsik Cho & Jinjun Xiong

  9. Observatories of the Carnegie Institute for Science, Pasadena, CA, USA

    Philip S. Cowperthwaite

  10. West Virginia University, Morgantown, WV, USA

    Zachariah B. Etienne

  11. Center for Mathematical Modelling, Santiago, Chile

    Francisco Forster

  12. Google X, Mountain View, CA, USA

    Daniel George

  13. NVIDIA, Santa Clara, CA, USA

    Tom Gibbs & Steve Oberlin

  14. Argonne National Laboratory, Leadership Computing Facility, Lemont, IL, USA

    Elise Jennings & Timothy J. Williams

  15. Massachusetts Institute of Technology, Cambridge, MA, USA

    Erik Katsavounidis

  16. Columbia University in the City of New York, New York, NY, USA

    Zsuzsa Marka

  17. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA

    J. M. Miller

  18. Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenieria, Guadalajara, Mexico

    Claudia Moreno

  19. University of Maryland, College Park, MD, USA

    Alexander R. Olivas Jr

  20. Cardiff University, Cardiff, UK

    Bernard F. Schutz

  21. NASA Goddard Space Flight Center, Code 661, Greenbelt, MD, USA

    Leo P. Singer

  22. University of Washington, Seattle, WA, USA

    Brigitta M. Sipocz

  23. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Jack Wells

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  1. E. A. Huerta
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Contributions

E.A.H. led and coordinated the writing of this Expert Recommendation. All authors contributed to developing the ideas, and writing and reviewing this manuscript. S.R. produced the artwork in figure 1.

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Correspondence to E. A. Huerta.

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The authors declare no competing interests.

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Nature Reviews Physics thanks Brant Robertson, Viviana Acquaviva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Huerta, E.A., Allen, G., Andreoni, I. et al. Enabling real-time multi-messenger astrophysics discoveries with deep learning. Nat Rev Phys 1, 600–608 (2019). https://doi.org/10.1038/s42254-019-0097-4

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  • DOI: https://doi.org/10.1038/s42254-019-0097-4

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