Extrapolation to infinite model space of no-core shell model calculations using machine learning

• URL: http://arxiv.org/abs/2511.05061v1
• Date: Thu, 13 Nov 2025 01:26:24 GMT
• Title: Extrapolation to infinite model space of no-core shell model calculations using machine learning
• Authors: Aleksandr Mazur, Roman Sharypov, Andrey Shirokov,
• Abstract summary: An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei.<n>We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces.
• Score: 41.99844472131922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces and with different values of the NCSM basis parameter $\hbarΩ$ for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for $^{6}$Li, $^{6}$He, and the unbound $^{6}$Be, as well as the excited $(3^{+},0)$ and $(0^{+},1)$ states of $^{6}$Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in $^{6}$Be and $^{6}$Li do not stabilize.

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