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
Machine learning enables computers to learn from experience or examples.
Deep learning is a class of ML algorithm that are composed of multiple hidden layers.
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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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DOI: https://doi.org/10.1140/epja/s10050-020-00290-x