Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

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:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [3];  [4]; ORCiD logo [2];  [5]; ORCiD logo [6];  [1];  [7];  [8];  [9];  [10];  [7];  [11];  [12];  [13];  [2]; ORCiD logo [1];  [1] more »; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [14]; ORCiD logo [1];  [15]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [2];  [16];  [1];  [17]; ORCiD logo [18];  [1];  [13];  [19];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [20];  [1];  [1];  [1]; ORCiD logo [1];  [1];  [21]; ORCiD logo [22];  [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [23]; ORCiD logo [14];  [8];  [1] « less
+ 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.
Copy to clipboard
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. https://doi.org/10.1038/s42254-019-0097-4
Copy to clipboard
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. https://doi.org/10.1038/s42254-019-0097-4. https://www.osti.gov/servlets/purl/1569373.
Copy to clipboard
@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}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s42254-019-0097-4
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Lasair: The Transient Alert Broker for LSST:UK
journal, January 2019

Matched filtering of gravitational waves from inspiraling compact binaries: Computational cost and template placement
journal, June 1999

Deep Fluids: A Generative Network for Parameterized Fluid Simulations
journal, May 2019

  • Kim, Byungsoo; Azevedo, Vinicius C.; Thuerey, Nils
  • Computer Graphics Forum, Vol. 38, Issue 2
  • DOI: 10.1111/cgf.13619

Community Organizations: Changing the Culture in Which Research Software Is Developed and Sustained
journal, March 2019

  • Katz, Daniel S.; McInnes, Lois Curfman; Bernholdt, David E.
  • Computing in Science & Engineering, Vol. 21, Issue 2
  • DOI: 10.1109/MCSE.2018.2883051

Deep learning
journal, May 2015

  • LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
  • Nature, Vol. 521, Issue 7553
  • DOI: 10.1038/nature14539

The Einstein Toolkit: a community computational infrastructure for relativistic astrophysics
journal, May 2012

Deep-learning continuous gravitational waves
journal, August 2019

OPACITIES AND SPECTRA OF THE r -PROCESS EJECTA FROM NEUTRON STAR MERGERS
journal, August 2013

Supernova 1987A
journal, September 1989

Neutron star mergers as sites of r-process nucleosynthesis and short gamma-ray bursts
journal, October 2018

  • Hotokezaka, Kenta; Beniamini, Paz; Piran, Tsvi
  • International Journal of Modern Physics D, Vol. 27, Issue 13
  • DOI: 10.1142/S0218271818420051

Graph Neural Networks for IceCube Signal Classification
conference, December 2018

  • Choma, Nicholas; Bruna, Joan; Monti, Federico
  • 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
  • DOI: 10.1109/ICMLA.2018.00064

What is the most Promising Electromagnetic Counterpart of a Neutron star Binary Merger?
journal, January 2012

Light curves of the neutron star merger GW170817/SSS17a: Implications for r-process nucleosynthesis
journal, October 2017

Design and evaluation of a parallel k-nearest neighbor algorithm on CUDA-enabled GPU
conference, August 2010

Results from the Supernova Photometric Classification Challenge
journal, December 2010

  • Kessler, Richard; Bassett, Bruce; Belov, Pavel
  • Publications of the Astronomical Society of the Pacific, Vol. 122, Issue 898
  • DOI: 10.1086/657607

Binary neutron star mergers: a review of Einstein’s richest laboratory
journal, July 2017

Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
journal, November 2017

  • E., Weinan; Han, Jiequn; Jentzen, Arnulf
  • Communications in Mathematics and Statistics, Vol. 5, Issue 4
  • DOI: 10.1007/s40304-017-0117-6

Supporting High-Performance and High-Throughput Computing for Experimental Science
journal, February 2019

  • Huerta, E. A.; Haas, Roland; Jha, Shantenu
  • Computing and Software for Big Science, Vol. 3, Issue 1
  • DOI: 10.1007/s41781-019-0022-7

Effective image differencing with convolutional neural networks for real-time transient hunting
journal, April 2018

  • Sedaghat, Nima; Mahabal, Ashish
  • Monthly Notices of the Royal Astronomical Society, Vol. 476, Issue 4
  • DOI: 10.1093/mnras/sty613

Improving galaxy morphologies for SDSS with Deep Learning
journal, February 2018

  • Domínguez Sánchez, H.; Huertas-Company, M.; Bernardi, M.
  • Monthly Notices of the Royal Astronomical Society, Vol. 476, Issue 3
  • DOI: 10.1093/mnras/sty338

Three-dimensional supernova explosion simulations of 9-, 10-, 11-, 12-, and 13-M⊙ stars
journal, February 2019

  • Burrows, Adam; Radice, David; Vartanyan, David
  • Monthly Notices of the Royal Astronomical Society, Vol. 485, Issue 3
  • DOI: 10.1093/mnras/stz543

Relativistic Simulations of Black Hole–Neutron star Coalescence: the jet Emerges
journal, June 2015

  • Paschalidis, Vasileios; Ruiz, Milton; Shapiro, Stuart L.
  • The Astrophysical Journal, Vol. 806, Issue 1
  • DOI: 10.1088/2041-8205/806/1/L14

Numerical relativity of compact binaries in the 21st century
journal, November 2018

Serendipitous discoveries of kilonovae in the LSST main survey: maximizing detections of sub-threshold gravitational wave events
journal, February 2019

  • Setzer, Christian N.; Biswas, Rahul; Peiris, Hiranya V.
  • Monthly Notices of the Royal Astronomical Society, Vol. 485, Issue 3
  • DOI: 10.1093/mnras/stz506

Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey
journal, August 2019

Classification and unsupervised clustering of LIGO data with Deep Transfer Learning
journal, May 2018

Early Dark Energy can Resolve the Hubble Tension
journal, June 2019

Complete waveform model for compact binaries on eccentric orbits
journal, January 2017

Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence
journal, May 2019

Searching for gravitational waves from compact binaries with precessing spins
journal, July 2016

Observing gravitational waves from core-collapse supernovae in the advanced detector era
journal, February 2016

Randomized approximate nearest neighbors algorithm
journal, September 2011

  • Jones, P. W.; Osipov, A.; Rokhlin, V.
  • Proceedings of the National Academy of Sciences, Vol. 108, Issue 38
  • DOI: 10.1073/pnas.1107769108

Characterizing the Gravitational Wave Signal from Core-collapse Supernovae
journal, April 2019

  • Radice, David; Morozova, Viktoriya; Burrows, Adam
  • The Astrophysical Journal, Vol. 876, Issue 1
  • DOI: 10.3847/2041-8213/ab191a

How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?
journal, December 2017

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
journal, October 2017

A mildly relativistic wide-angle outflow in the neutron-star merger event GW170817
journal, December 2017

  • Mooley, K. P.; Nakar, E.; Hotokezaka, K.
  • Nature, Vol. 554, Issue 7691
  • DOI: 10.1038/nature25452

On the Nature of Core-Collapse Supernova Explosions
journal, September 1995

  • Burrows, Adam; Hayes, John; Fryxell, Bruce A.
  • The Astrophysical Journal, Vol. 450
  • DOI: 10.1086/176188

Local adaptive mesh refinement for shock hydrodynamics
journal, May 1989

Mary , a Pipeline to Aid Discovery of Optical Transients
journal, January 2017

  • Andreoni, I.; Jacobs, C.; Hegarty, S.
  • Publications of the Astronomical Society of Australia, Vol. 34
  • DOI: 10.1017/pasa.2017.33

Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
journal, August 2019

Subgrid modelling for two-dimensional turbulence using neural networks
journal, November 2018

A Standard Siren Measurement of the Hubble Constant from GW170817 without the Electromagnetic Counterpart
journal, January 2019

  • Fishbach, M.; Gray, R.; Hernandez, I. Magaña
  • The Astrophysical Journal, Vol. 871, Issue 1
  • DOI: 10.3847/2041-8213/aaf96e

Optimizing searches for electromagnetic counterparts of gravitational wave triggers
journal, April 2018

  • Coughlin, Michael W.; Tao, Duo; Chan, Man Leong
  • Monthly Notices of the Royal Astronomical Society, Vol. 478, Issue 1
  • DOI: 10.1093/mnras/sty1066

Data Access for LIGO on the OSG
conference, January 2017

  • Weitzel, Derek; Bockelman, Brian; Brown, Duncan A.
  • Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17
  • DOI: 10.1145/3093338.3093363

Deep neural networks to enable real-time multimessenger astrophysics
journal, February 2018

The physics of core-collapse supernovae
journal, December 2005

  • Woosley, Stan; Janka, Thomas
  • Nature Physics, Vol. 1, Issue 3
  • DOI: 10.1038/nphys172

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
journal, October 2016

  • Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy
  • Journal of Fluid Mechanics, Vol. 807
  • DOI: 10.1017/jfm.2016.615

Neutrino-Driven Convection in Core-Collapse Supernovae: High-Resolution Simulations
journal, March 2016

  • Radice, David; Ott, Christian D.; Abdikamalov, Ernazar
  • The Astrophysical Journal, Vol. 820, Issue 1
  • DOI: 10.3847/0004-637X/820/1/76

r -process Nucleosynthesis from Three-dimensional Magnetorotational Core-collapse Supernovae
journal, September 2018

  • Mösta, Philipp; Roberts, Luke F.; Halevi, Goni
  • The Astrophysical Journal, Vol. 864, Issue 2
  • DOI: 10.3847/1538-4357/aad6ec

Producing Magnetar Magnetic Fields in the Merger of Binary Neutron Stars
journal, August 2015

  • Giacomazzo, Bruno; Zrake, Jonathan; Duffell, Paul C.
  • The Astrophysical Journal, Vol. 809, Issue 1
  • DOI: 10.1088/0004-637X/809/1/39

A convolutional neural network neutrino event classifier
journal, September 2016

SkyNet: A Modular Nuclear Reaction Network Library
journal, December 2017

  • Lippuner, Jonas; Roberts, Luke F.
  • The Astrophysical Journal Supplement Series, Vol. 233, Issue 2
  • DOI: 10.3847/1538-4365/aa94cb

Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts, and Nucleosynthesis
journal, December 2018

  • Radice, David; Perego, Albino; Hotokezaka, Kenta
  • The Astrophysical Journal, Vol. 869, Issue 2
  • DOI: 10.3847/1538-4357/aaf054

Binary Neutron star Mergers: a jet Engine for Short Gamma-Ray Bursts
journal, June 2016

  • Ruiz, Milton; Lang, Ryan N.; Paschalidis, Vasileios
  • The Astrophysical Journal, Vol. 824, Issue 1
  • DOI: 10.3847/2041-8205/824/1/L6

Observing gravitational-wave transient GW150914 with minimal assumptions
journal, June 2016

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
journal, April 2018

Hungary rewards highly cited scientists with bonus grants
journal, November 2017

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817
journal, December 2017

Determining the Hubble constant from gravitational wave observations
journal, September 1986

Comparison of various methods to extract ringdown frequency from gravitational wave data
journal, June 2019

Galaxy formation and evolution science in the era of the Large Synoptic Survey Telescope
journal, June 2019

Large Magellanic Cloud Cepheid Standards Provide a 1% Foundation for the Determination of the Hubble Constant and Stronger Evidence for Physics beyond ΛCDM
journal, May 2019

  • Riess, Adam G.; Casertano, Stefano; Yuan, Wenlong
  • The Astrophysical Journal, Vol. 876, Issue 1
  • DOI: 10.3847/1538-4357/ab1422

Three-dimensional GRMHD Simulations of Neutrino-cooled Accretion Disks from Neutron Star Mergers
journal, May 2018

Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-encoders
conference, May 2019

  • Shen, Hongyu; George, Daniel; Huerta, Eliu. A.
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • DOI: 10.1109/ICASSP.2019.8683061

Cosmology at a crossroads
journal, May 2017

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
journal, April 2018

Physics of Core-Collapse Supernovae in Three Dimensions: A Sneak Preview
journal, October 2016

Optical Follow-up of Gravitational-wave Events with Las Cumbres Observatory
journal, October 2017

  • Arcavi, Iair; McCully, Curtis; Hosseinzadeh, Griffin
  • The Astrophysical Journal, Vol. 848, Issue 2
  • DOI: 10.3847/2041-8213/aa910f

Gravitational wave denoising of binary black hole mergers with deep learning
journal, January 2020

Evolution of the magnetized, neutrino-cooled accretion disk in the aftermath of a black hole-neutron star binary merger
journal, April 2018

Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
journal, May 2018

  • Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.
  • The Astrophysical Journal Supplement Series, Vol. 236, Issue 1
  • DOI: 10.3847/1538-4365/aab781

BOSS-LDG: A Novel Computational Framework That Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery
conference, October 2017

  • Huerta, E. A.; Haas, Roland; Fajardo, Edgar
  • 2017 IEEE 13th International Conference on e-Science (e-Science)
  • DOI: 10.1109/eScience.2017.47

Cosmology at a Crossroads
journal, September 2009

GW170817: Observation of gravitational waves from a binary neutron star inspiral
text, January 2017

  • Abbott, B. P.; Abbott, R.; Abbott, T. D.
  • American Physical Society
  • DOI: 10.5167/uzh-147231

Deep-learning continuous gravitational waves
other, January 2019

  • Dreissigacker, Christoph; Sharma, Rahul; Messenger, Chris
  • College Park, MD : American Physical Society
  • DOI: 10.15488/10439

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
other, January 2017

  • Abbott, B. P.; Abbott, R.; Abbott, T. D.
  • College Park : American Physical Society
  • DOI: 10.15488/4798

Deep Learning
text, January 2018

LSST Science Book, Version 2.0
preprint, January 2009

Results from the Supernova Photometric Classification Challenge
text, January 2010

The Einstein Toolkit: A Community Computational Infrastructure for Relativistic Astrophysics
text, January 2011

The Dark Energy Survey: more than dark energy - an overview
text, January 2016

Searching for Gravitational Waves from Compact Binaries with Precessing Spins
text, January 2016

Binary neutron-star mergers: a review of Einstein's richest laboratory
text, January 2016

Mary, a pipeline to aid discovery of optical transients
text, January 2017

GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
text, January 2017

Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817
text, January 2017

Optical Follow-up of Gravitational-wave Events with Las Cumbres Observatory
text, January 2017

A mildly relativistic wide-angle outflow in the neutron star merger GW170817
text, January 2017

Neutron Star Mergers as sites of r-process Nucleosynthesis and Short Gamma-Ray Bursts
text, January 2018

Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
text, January 2018

Optimizing searches for electromagnetic counterparts of gravitational wave triggers
text, January 2018

Deep Fluids: A Generative Network for Parameterized Fluid Simulations
text, January 2018

Numerical Relativity of Compact Binaries in the 21st Century
text, January 2018

Binary Neutron Star Mergers: Mass Ejection, Electromagnetic Counterparts and Nucleosynthesis
text, January 2018

Characterizing the Gravitational Wave Signal from Core-Collapse Supernovae
text, January 2018

Three-Dimensional Supernova Explosion Simulations of 9-, 10-, 11-, 12-, and 13-M$_{\odot}$ Stars
text, January 2019

The Physics of Core-Collapse Supernovae
text, January 2006

    Sort by:
    [ × clear filter / sort ]

    Works referencing / citing this record:

    Luminal propagation of gravitational waves in scalar-tensor theories: The case for torsion
    journal, December 2019

    • Barrientos, José; Cordonier-Tello, Fabrizio; Corral, Cristóbal
    • Physical Review D, Vol. 100, Issue 12
    • DOI: 10.1103/physrevd.100.124039
      Sort by:
      [ × clear filter / sort ]

      Similar Records in DOE PAGES and OSTI.GOV collections: