Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

O. M. Molchanov, K. D. Launey, A. Mercenne, G. H. Sargsyan, T. Dytrych, and J. P. Draayer
Phys. Rev. C 105, 034306 – Published 3 March 2022

Abstract

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the Ne20 ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the Mg2042 isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of Si24 and Mg40 of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as Er166,168 and U236, that build on first-principles considerations.

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  • Received 8 July 2021
  • Accepted 14 January 2022

DOI:https://doi.org/10.1103/PhysRevC.105.034306

©2022 American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

O. M. Molchanov1, K. D. Launey1, A. Mercenne1,2, G. H. Sargsyan1, T. Dytrych1,3, and J. P. Draayer1

  • 1Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana 70803, USA
  • 2Center for Theoretical Physics, Sloane Physics Laboratory, Yale University, New Haven, Connecticut 06520, USA
  • 3Nuclear Physics Institute, Academy of Sciences of the Czech Republic, 25068 Rez, Czech Republic

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Vol. 105, Iss. 3 — March 2022

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