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Title: Enabling real-time multi-messenger astrophysics discoveries with deep learning

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 finally, the need to build a community of experts to realize the goals of multi-messenger astrophysics.

Authors:
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+ Show Author Affiliations
  1. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  2. California Inst. of Technology, Pasadena, CA (United States)
  3. Tecnologico de Monterrey, Zapopan (Mexico). School of Engineering and Sciences,
  4. Las Cumbres Observatory, Goleta, CA (United States)
  5. Univ. of Deleware, Newark, DE (United States)
  6. Stockholm Univ., AlbaNova, Stockholm (Sweden). The Oskar Klein Centre for Cosmoparticle Physics
  7. Univ. of Chicago, Chicago, IL (United States)
  8. IBM T.J. Watson Research Center, New York, NY (United States)
  9. Observatories of the Carnegie Inst. for Science, Pasadena, CA (United States)
  10. West Virginia Univ., Morgantown, WV (United States)
  11. Center for Mathematical Modelling, Santiago (Chile)
  12. Google X, Mountain View, CA (United States)
  13. NVIDIA, Santa Clara, CA (United States)
  14. Argonne National Lab. (ANL), Argonne, IL (United States). Leadership Computing Facility
  15. Massachusetts Inst. of Technology, Cambridge, MA (United States)
  16. Columbia Univ. in the City of New York, New York, NY (United States)
  17. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  18. Univ. de Guadalajara, Guadalajara (Mexico). Centro Univers. de Ciencias Exactas e Ingenieria
  19. Univ. of Maryland, College Park, MD (United States)
  20. Cardiff Univ., Cardiff (United Kingdom)
  21. NASA Goddard Space Flight Center, Greenbelt, MD (United States)
  22. Univ. of Washington, Seattle, WA (United States)
  23. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1569373
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Nature Reviews Physics
Additional Journal Information:
Journal Volume: 1; Journal Issue: 10; Journal ID: ISSN 2522-5820
Publisher:
Springer Nature
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Huerta, E. A., Allen, Gabrielle, Andreoni, Igor, Antelis, Javier M., Bachelet, Etienne, Berriman, G. Bruce, Bianco, Federica B., Biswas, Rahul, Carrasco Kind, Matias, Chard, Kyle, Cho, Minsik, Cowperthwaite, Philip S., Etienne, Zachariah B., Fishbach, Maya, Forster, Francisco, George, Daniel, Gibbs, Tom, Graham, Matthew, Gropp, William, Gruendl, Robert, Gupta, Anushri, Haas, Roland, Habib, Sarah, Jennings, Elise, Johnson, Margaret W. G., Katsavounidis, Erik, Katz, Daniel S., Khan, Asad, Kindratenko, Volodymyr, Kramer, William T. C., Liu, Xin, Mahabal, Ashish, Marka, Zsuzsa, McHenry, Kenton, Miller, J. M., Moreno, Claudia, Neubauer, M. S., Oberlin, Steve, Olivas Jr., Alexander R., Petravick, Donald, Rebei, Adam, Rosofsky, Shawn, Ruiz, Milton, Saxton, Aaron, Schutz, Bernard F., Schwing, Alex, Seidel, Ed, Shapiro, Stuart L., Shen, Hongyu, Shen, Yue, Singer, Leo P., Sipocz, Brigitta M., Sun, Lunan, Towns, John, Tsokaros, Antonios, Wei, Wei, Wells, Jack, Williams, Timothy J., Xiong, Jinjun, and Zhao, Zhizhen. Enabling real-time multi-messenger astrophysics discoveries with deep learning. United States: N. p., 2019. Web. doi:10.1038/s42254-019-0097-4.
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Huerta, E. A., Allen, Gabrielle, Andreoni, Igor, Antelis, Javier M., Bachelet, Etienne, Berriman, G. Bruce, Bianco, Federica B., Biswas, Rahul, Carrasco Kind, Matias, Chard, Kyle, Cho, Minsik, Cowperthwaite, Philip S., Etienne, Zachariah B., Fishbach, Maya, Forster, Francisco, George, Daniel, Gibbs, Tom, Graham, Matthew, Gropp, William, Gruendl, Robert, Gupta, Anushri, Haas, Roland, Habib, Sarah, Jennings, Elise, Johnson, Margaret W. G., Katsavounidis, Erik, Katz, Daniel S., Khan, Asad, Kindratenko, Volodymyr, Kramer, William T. C., Liu, Xin, Mahabal, Ashish, Marka, Zsuzsa, McHenry, Kenton, Miller, J. M., Moreno, Claudia, Neubauer, M. S., Oberlin, Steve, Olivas Jr., Alexander R., Petravick, Donald, Rebei, Adam, Rosofsky, Shawn, Ruiz, Milton, Saxton, Aaron, Schutz, Bernard F., Schwing, Alex, Seidel, Ed, Shapiro, Stuart L., Shen, Hongyu, Shen, Yue, Singer, Leo P., Sipocz, Brigitta M., Sun, Lunan, Towns, John, Tsokaros, Antonios, Wei, Wei, Wells, Jack, Williams, Timothy J., Xiong, Jinjun, & Zhao, Zhizhen. Enabling real-time multi-messenger astrophysics discoveries with deep learning. United States. doi:10.1038/s42254-019-0097-4.
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Huerta, E. A., Allen, Gabrielle, Andreoni, Igor, Antelis, Javier M., Bachelet, Etienne, Berriman, G. Bruce, Bianco, Federica B., Biswas, Rahul, Carrasco Kind, Matias, Chard, Kyle, Cho, Minsik, Cowperthwaite, Philip S., Etienne, Zachariah B., Fishbach, Maya, Forster, Francisco, George, Daniel, Gibbs, Tom, Graham, Matthew, Gropp, William, Gruendl, Robert, Gupta, Anushri, Haas, Roland, Habib, Sarah, Jennings, Elise, Johnson, Margaret W. G., Katsavounidis, Erik, Katz, Daniel S., Khan, Asad, Kindratenko, Volodymyr, Kramer, William T. C., Liu, Xin, Mahabal, Ashish, Marka, Zsuzsa, McHenry, Kenton, Miller, J. M., Moreno, Claudia, Neubauer, M. S., Oberlin, Steve, Olivas Jr., Alexander R., Petravick, Donald, Rebei, Adam, Rosofsky, Shawn, Ruiz, Milton, Saxton, Aaron, Schutz, Bernard F., Schwing, Alex, Seidel, Ed, Shapiro, Stuart L., Shen, Hongyu, Shen, Yue, Singer, Leo P., Sipocz, Brigitta M., Sun, Lunan, Towns, John, Tsokaros, Antonios, Wei, Wei, Wells, Jack, Williams, Timothy J., Xiong, Jinjun, and Zhao, Zhizhen. Thu . "Enabling real-time multi-messenger astrophysics discoveries with deep learning". United States. doi:10.1038/s42254-019-0097-4. https://www.osti.gov/servlets/purl/1569373.
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@article{osti_1569373,
title = {Enabling real-time multi-messenger astrophysics discoveries with deep learning},
author = {Huerta, E. A. and Allen, Gabrielle and Andreoni, Igor and Antelis, Javier M. and Bachelet, Etienne and Berriman, G. Bruce and Bianco, Federica B. and Biswas, Rahul and Carrasco Kind, Matias and Chard, Kyle and Cho, Minsik and Cowperthwaite, Philip S. and Etienne, Zachariah B. and Fishbach, Maya and Forster, Francisco and George, Daniel and Gibbs, Tom and Graham, Matthew and Gropp, William and Gruendl, Robert and Gupta, Anushri and Haas, Roland and Habib, Sarah and Jennings, Elise and Johnson, Margaret W. G. and Katsavounidis, Erik and Katz, Daniel S. and Khan, Asad and Kindratenko, Volodymyr and Kramer, William T. C. and Liu, Xin and Mahabal, Ashish and Marka, Zsuzsa and McHenry, Kenton and Miller, J. M. and Moreno, Claudia and Neubauer, M. S. and Oberlin, Steve and Olivas Jr., Alexander R. and Petravick, Donald and Rebei, Adam and Rosofsky, Shawn and Ruiz, Milton and Saxton, Aaron and Schutz, Bernard F. and Schwing, Alex and Seidel, Ed and Shapiro, Stuart L. and Shen, Hongyu and Shen, Yue and Singer, Leo P. and Sipocz, Brigitta M. and Sun, Lunan and Towns, John and Tsokaros, Antonios and Wei, Wei and Wells, Jack and Williams, Timothy J. and Xiong, Jinjun and Zhao, Zhizhen},
abstractNote = {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 finally, the need to build a community of experts to realize the goals of multi-messenger astrophysics.},
doi = {10.1038/s42254-019-0097-4},
journal = {Nature Reviews Physics},
number = 10,
volume = 1,
place = {United States},
year = {2019},
month = {10}
}
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