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Machine learning at the energy and intensity frontiers of particle physics

Author

Listed:
  • Alexander Radovic

    (College of William and Mary)

  • Mike Williams

    (Massachusetts Institute of Technology)

  • David Rousseau

    (LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay)

  • Michael Kagan

    (SLAC National Accelerator Laboratory)

  • Daniele Bonacorsi

    (Università di Bologna
    INFN Sezione di Bologna)

  • Alexander Himmel

    (Fermi National Accelerator Laboratory)

  • Adam Aurisano

    (University of Cincinnati)

  • Kazuhiro Terao

    (SLAC National Accelerator Laboratory)

  • Taritree Wongjirad

    (Tufts University)

Abstract

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

Suggested Citation

  • Alexander Radovic & Mike Williams & David Rousseau & Michael Kagan & Daniele Bonacorsi & Alexander Himmel & Adam Aurisano & Kazuhiro Terao & Taritree Wongjirad, 2018. "Machine learning at the energy and intensity frontiers of particle physics," Nature, Nature, vol. 560(7716), pages 41-48, August.
    • Handle: RePEc:nat:nature:v:560:y:2018:i:7716:d:10.1038_s41586-018-0361-2
    DOI: 10.1038/s41586-018-0361-2
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    Cited by:

    1. Gerardo Alfonso Perez & Raquel Castillo, 2023. "Categorical Variable Mapping Considerations in Classification Problems: Protein Application," Mathematics, MDPI, vol. 11(2), pages 1-26, January.

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