Nuclear Theory IV

Presentations

• Constraining optical potentials with charge exchange to isobaric analogue states

  ORAL

  Oct. 9, 2024, 2:00 PM – Oct. 9, 2024, 2:12 PM

  Presenters

  • Kyle Beyer

    • Michigan State University

  Authors

  • Kyle Beyer

    • Michigan State University

  • Filomena Nunes

    • Michigan State University

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• Impact of Collective Correlations on Fission Fragment Properties

  ORAL

  Oct. 9, 2024, 2:12 PM – Oct. 9, 2024, 2:24 PM

  Presenters

  • Nicolas Schunck

    • Lawrence Livermore National Laboratory

  Authors

  • Nicolas Schunck

    • Lawrence Livermore National Laboratory

  • Pooja Siwach

    • Lawrence Livermore National Laboratory

  • Marc Verriere

    • Lawrence Livermore National Laboratory

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• Uncertainty quantification in (p,n) reactions

  ORAL

  Oct. 9, 2024, 2:24 PM – Oct. 9, 2024, 2:36 PM

  Publication: A. J. Smith, C. Hebborn, F. M. Nunes, and R. G. T. Zegers, Uncertainty quantification in (p, n) reactions (2024), arXiv:2403.18629.

  Presenters

  • Andrew J Smith

    • Michigan State University

  Authors

  • Andrew J Smith

    • Michigan State University

  • Chloë Hebborn

    • Michigan State University and Facility for Rare Isotope Beams

  • Filomena Nunes

    • Michigan State University

  • Remco G.T. Zegers

    • Michigan State University

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• Semi-classical treatment of photon cascades in nuclei

  ORAL

  Oct. 9, 2024, 2:36 PM – Oct. 9, 2024, 2:48 PM

  Publication: J. Randrup and T. Døssing, Phys. Rev. C 109, 054613 (2024)

  Presenters

  • Jorgen Randrup

    • Lawrence Berkeley National Laboratory

  Authors

  • Jorgen Randrup

    • Lawrence Berkeley National Laboratory

  • Thomas Døssing

    • Niels Bohr Institute

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• Fission modes are not always Gaussian

  ORAL

  Oct. 9, 2024, 2:48 PM – Oct. 9, 2024, 3:00 PM

  Presenters

  • Patrick McGlynn

    • Facility for Rare Isotope Beams

  Authors

  • Patrick McGlynn

    • Facility for Rare Isotope Beams

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• A New Formula for Phase Shift Calculations Using Square-Integrable Bases

  ORAL

  Oct. 9, 2024, 3:00 PM – Oct. 9, 2024, 3:12 PM

  Presenters

  • Minh-Loc Bui

    • San Diego State University

  Authors

  • Calvin W Johnson

    • San Diego State University

  • Austin Keller

    • San Diego State University

  • Minh-Loc Bui

    • San Diego State University

  • Kenneth M Nollett

    • San Diego State University

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• Unmasking the Underlying Correlations in Nuclear Reaction Cross Sections

  ORAL

  Oct. 9, 2024, 3:12 PM – Oct. 9, 2024, 3:24 PM

  Publication: Unmasking Correlations in Nuclear Cross Sections with Graph Neural Networks: arXiv:2404.02332
  Illuminating Systematic Trends in Nuclear Data with Generative Machine Learning Models: arXiv:2403.16389

  Presenters

  • Kyle A Wendt

    • Lawrence Livermore National Laboratory

  Authors

  • Kyle A Wendt

    • Lawrence Livermore National Laboratory

  • Sinjini Mitra

    • Arizona State University

  • Hongjun Choi

    • Lawrence Livermore National Laboratory

  • Shusen Liu

    • Lawrence Livermore National Laboratory

  • Ruben Glatt

    • Lawrence Livermore National Laboratory

  • Nicolas Schunck

    • Lawrence Livermore National Laboratory

  • Xiao Chen

    • Lawrence Livermore Natl Lab

  • Jordan Fox

    • Argonne National Laboratory

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• Coupled Channels Reduced Order Scattering Emulators

  ORAL

  Oct. 9, 2024, 3:24 PM – Oct. 9, 2024, 3:36 PM

  Presenters

  • Manuel Catacora-Rios

    • MIchigan State University

  Authors

  • Manuel Catacora-Rios

    • MIchigan State University

  • Filomena Nunes

    • Michigan State University

  • Pablo G Giuliani

    • Facility for Rare Isotopes Beams
    • Facility for Rare Isotope Beams

  • Richard J Furnstahl

    • Ohio State University

  • Kyle S Godbey

    • Michigan State University
    • FRIB, Michigan State University
    • Facility for Rare Isotope Beams

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• Description of nuclear properties using Symbolic Machine Learning

  ORAL

  Oct. 9, 2024, 3:36 PM – Oct. 9, 2024, 3:48 PM

  Publication: Discovering Nuclear Models from Symbolic Machine Learning (https://arxiv.org/pdf/2404.11477)

  Presenters

  • Jose M Munoz

    • MIT

  Authors

  • Jose M Munoz

    • MIT

  • Ronald Fernando F Garcia Ruiz

    • MIT Laboratory for Nuclear Science
    • Massachusetts Institute of Technology

  • Silviu-Marian M Udrescu

    • Massachusetts Institute of Technology

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