Machine Learning in the Problem of No-Core Shell Model Result Extrapolations

A method for extrapolating the results of variational calculations to the case of the infinite basis using an ensemble of artificial neural networks is proposed. Extrapolations of the no-core shell model results obtained with the nucleon–nucleon interaction Daejeon16 for the ground state energies, as well as for the root-mean-square (rms) point-proton, point-neutron, and point-nucleon (matter) radii of the $${}^{6}$$ Li and $${}^{6}$$ He nuclei, are performed.

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Publication Details

Journal
Physics of Atomic Nuclei
Published
2024-12-01
DOI
https://doi.org/10.1134/s1063778824700868
Citations
2
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.66
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article

Machine Learning in the Problem of No-Core Shell Model Result Extrapolations

A. I. Mazur, A. M. Shirokov, R. E. Sharypov
2 citations
Physics of Atomic Nuclei
Fault Detection and Control Systems
0.66
article

Machine Learning in the Problem of No-Core Shell Model Result Extrapolations

A. I. Mazur, A. M. Shirokov, R. E. Sharypov
article en
2 citations

Abstract

A method for extrapolating the results of variational calculations to the case of the infinite basis using an ensemble of artificial neural networks is proposed. Extrapolations of the no-core shell model results obtained with the nucleon–nucleon interaction Daejeon16 for the ground state energies, as well as for the root-mean-square (rms) point-proton, point-neutron, and point-nucleon (matter) radii of the $${}^{6}$$ Li and $${}^{6}$$ He nuclei, are performed.

Physics of Atomic NucleiVol. 87(S2)
Lomonosov Moscow State University (RU), Pacific National University (RU)
Openalex Percentile: Top 27%
Fault Detection and Control Systems
0.66
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