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A.I. for nuclear physics

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Abstract

This report is an outcome of the workshop AI for Nuclear Physics held at Thomas Jefferson National Accelerator Facility on March 4–6, 2020

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: This paper is the proceedings of a workshop. All data presented is available in the original sources.]

Notes

  1. Machine learning enables computers to learn from experience or examples.

  2. Deep learning is a class of ML algorithm that are composed of multiple hidden layers.

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Authors and Affiliations

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

    Paulo Bedaque

  2. Thomas Jefferson National Accelerator Facility, Newport News, VA, USA

    Amber Boehnlein, Markus Diefenthaler, Latifa Elouadrhiri, David Lawrence, Robert McKeown, Wally Melnitchouk, Kostas Orginos & Yves Roblin

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Mario Cromaz & Xin-Nian Wang

  4. Catholic University, Washington, D.C., USA

    Tanja Horn

  5. Davidson College, Davidson, NC, USA

    Michelle Kuchera

  6. Michigan State University, East Lansing, MI, USA

    Dean Lee, Steven Lidia & Witold Nazarewicz

  7. College of William & Mary, Williamsburg, VA, USA

    Kostas Orginos

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

    Michael Scott Smith

  9. Pacific Northwest National Laboratory, Richland, WA, USA

    Malachi Schram

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Correspondence to Amber Boehnlein.

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Communicated by Ulf Meissner

Disclaimer: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

This report is an outcome of the workshop AI for Nuclear Physics held at Thomas Jefferson National Accelerator Facility on March 4–6, 2020. The workshop brought together 184 scientists to explore opportunities for Nuclear Physics in the area of Artificial Intelligence. The workshop consisted of plenary talks, as well as six working groups.

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177. Participation of students and early career professionals was supported by NSF, Division of Physics, under the Grant ‘Artificial Intelligence (AI) Workshop in Nuclear Physics,’ Award Number 2017170. Support for the Hackathon was provided by the University of Virginia School of Data Sciences and by Amazon Web Services.

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Bedaque, P., Boehnlein, A., Cromaz, M. et al. A.I. for nuclear physics. Eur. Phys. J. A 57, 100 (2021). https://doi.org/10.1140/epja/s10050-020-00290-x

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