Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. KatzAsad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, Jinjun Xiong, Zhizhen Zhao

Research output: Book/ReportCommissioned report

Abstract

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Original languageEnglish (US)
Number of pages15
StatePublished - Feb 1 2019

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Astrophysics
Artificial intelligence
Gravity waves
Astronomy
Big data
Deep learning
Computer science
Learning systems
Signal processing
Physics

Keywords

  • astro-ph.IM
  • astro-ph.HE
  • cs.LG
  • gr-qc

Cite this

Allen, G., Andreoni, I., Bachelet, E., Berriman, G. B., Bianco, F. B., Biswas, R., ... Zhao, Z. (2019). Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era.

Deep Learning for Multi-Messenger Astrophysics : A Gateway for Discovery in the Big Data Era. / Allen, Gabrielle; Andreoni, Igor; Bachelet, Etienne; Berriman, G. Bruce; Bianco, Federica B.; Biswas, Rahul; Kind, Matias Carrasco; Chard, Kyle; Cho, Minsik; Cowperthwaite, Philip S.; Etienne, Zachariah B.; George, Daniel; Gibbs, Tom; Graham, Matthew; Gropp, William; Gupta, Anushri; Haas, Roland; Huerta, E. A.; Jennings, Elise; Katz, Daniel S.; Khan, Asad; Kindratenko, Volodymyr; Kramer, William T. C.; Liu, Xin; Mahabal, Ashish; McHenry, Kenton; Miller, J. M.; Neubauer, M. S.; Oberlin, Steve; Jr, Alexander R. Olivas; Rosofsky, Shawn; Ruiz, Milton; Saxton, Aaron; Schutz, Bernard; Schwing, Alex; Seidel, Ed; Shapiro, Stuart L.; Shen, Hongyu; Shen, Yue; Sipőcz, Brigitta M.; Sun, Lunan; Towns, John; Tsokaros, Antonios; Wei, Wei; Wells, Jack; Williams, Timothy J.; Xiong, Jinjun; Zhao, Zhizhen.

2019. 15 p.

Research output: Book/ReportCommissioned report

Allen, G, Andreoni, I, Bachelet, E, Berriman, GB, Bianco, FB, Biswas, R, Kind, MC, Chard, K, Cho, M, Cowperthwaite, PS, Etienne, ZB, George, D, Gibbs, T, Graham, M, Gropp, W, Gupta, A, Haas, R, Huerta, EA, Jennings, E, Katz, DS, Khan, A, Kindratenko, V, Kramer, WTC, Liu, X, Mahabal, A, McHenry, K, Miller, JM, Neubauer, MS, Oberlin, S, Jr, ARO, Rosofsky, S, Ruiz, M, Saxton, A, Schutz, B, Schwing, A, Seidel, E, Shapiro, SL, Shen, H, Shen, Y, Sipőcz, BM, Sun, L, Towns, J, Tsokaros, A, Wei, W, Wells, J, Williams, TJ, Xiong, J & Zhao, Z 2019, Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era.
Allen G, Andreoni I, Bachelet E, Berriman GB, Bianco FB, Biswas R et al. Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. 2019. 15 p.
Allen, Gabrielle ; Andreoni, Igor ; Bachelet, Etienne ; Berriman, G. Bruce ; Bianco, Federica B. ; Biswas, Rahul ; Kind, Matias Carrasco ; Chard, Kyle ; Cho, Minsik ; Cowperthwaite, Philip S. ; Etienne, Zachariah B. ; George, Daniel ; Gibbs, Tom ; Graham, Matthew ; Gropp, William ; Gupta, Anushri ; Haas, Roland ; Huerta, E. A. ; Jennings, Elise ; Katz, Daniel S. ; Khan, Asad ; Kindratenko, Volodymyr ; Kramer, William T. C. ; Liu, Xin ; Mahabal, Ashish ; McHenry, Kenton ; Miller, J. M. ; Neubauer, M. S. ; Oberlin, Steve ; Jr, Alexander R. Olivas ; Rosofsky, Shawn ; Ruiz, Milton ; Saxton, Aaron ; Schutz, Bernard ; Schwing, Alex ; Seidel, Ed ; Shapiro, Stuart L. ; Shen, Hongyu ; Shen, Yue ; Sipőcz, Brigitta M. ; Sun, Lunan ; Towns, John ; Tsokaros, Antonios ; Wei, Wei ; Wells, Jack ; Williams, Timothy J. ; Xiong, Jinjun ; Zhao, Zhizhen. / Deep Learning for Multi-Messenger Astrophysics : A Gateway for Discovery in the Big Data Era. 2019. 15 p.
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title = "Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era",
abstract = "This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded {"}Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale{"} workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.",
keywords = "astro-ph.IM, astro-ph.HE, cs.LG, gr-qc",
author = "Gabrielle Allen and Igor Andreoni and Etienne Bachelet and Berriman, {G. Bruce} and Bianco, {Federica B.} and Rahul Biswas and Kind, {Matias Carrasco} and Kyle Chard and Minsik Cho and Cowperthwaite, {Philip S.} and Etienne, {Zachariah B.} and Daniel George and Tom Gibbs and Matthew Graham and William Gropp and Anushri Gupta and Roland Haas and Huerta, {E. A.} and Elise Jennings and Katz, {Daniel S.} and Asad Khan and Volodymyr Kindratenko and Kramer, {William T. C.} and Xin Liu and Ashish Mahabal and Kenton McHenry and Miller, {J. M.} and Neubauer, {M. S.} and Steve Oberlin and Jr, {Alexander R. Olivas} and Shawn Rosofsky and Milton Ruiz and Aaron Saxton and Bernard Schutz and Alex Schwing and Ed Seidel and Shapiro, {Stuart L.} and Hongyu Shen and Yue Shen and Sipőcz, {Brigitta M.} and Lunan Sun and John Towns and Antonios Tsokaros and Wei Wei and Jack Wells and Williams, {Timothy J.} and Jinjun Xiong and Zhizhen Zhao",
note = "15 pages, no figures. White paper based on the {"}Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale{"} workshop, hosted at NCSA, October 17-19, 2018 http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/",
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T2 - A Gateway for Discovery in the Big Data Era

AU - Allen, Gabrielle

AU - Andreoni, Igor

AU - Bachelet, Etienne

AU - Berriman, G. Bruce

AU - Bianco, Federica B.

AU - Biswas, Rahul

AU - Kind, Matias Carrasco

AU - Chard, Kyle

AU - Cho, Minsik

AU - Cowperthwaite, Philip S.

AU - Etienne, Zachariah B.

AU - George, Daniel

AU - Gibbs, Tom

AU - Graham, Matthew

AU - Gropp, William

AU - Gupta, Anushri

AU - Haas, Roland

AU - Huerta, E. A.

AU - Jennings, Elise

AU - Katz, Daniel S.

AU - Khan, Asad

AU - Kindratenko, Volodymyr

AU - Kramer, William T. C.

AU - Liu, Xin

AU - Mahabal, Ashish

AU - McHenry, Kenton

AU - Miller, J. M.

AU - Neubauer, M. S.

AU - Oberlin, Steve

AU - Jr, Alexander R. Olivas

AU - Rosofsky, Shawn

AU - Ruiz, Milton

AU - Saxton, Aaron

AU - Schutz, Bernard

AU - Schwing, Alex

AU - Seidel, Ed

AU - Shapiro, Stuart L.

AU - Shen, Hongyu

AU - Shen, Yue

AU - Sipőcz, 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 - 15 pages, no figures. White paper based on the "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at NCSA, October 17-19, 2018 http://www.ncsa.illinois.edu/Conferences/DeepLearningLSST/

PY - 2019/2/1

Y1 - 2019/2/1

N2 - This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

AB - This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

KW - astro-ph.IM

KW - astro-ph.HE

KW - cs.LG

KW - gr-qc

UR - https://arxiv.org/abs/1902.00522

M3 - Commissioned report

BT - Deep Learning for Multi-Messenger Astrophysics

ER -