{"ID":2844842,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05061","arxiv_id":"2511.05061","title":"Extrapolation to infinite model space of no-core shell model calculations using machine learning","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.","short_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 b...","url_abs":"https://arxiv.org/abs/2511.05061","url_pdf":"https://arxiv.org/pdf/2511.05061v2","authors":"[\"Aleksandr Mazur\",\"Roman Sharypov\",\"Andrey Shirokov\"]","published":"2025-11-07T08:16:12Z","proceeding":"nucl-th","tasks":"[\"nucl-th\",\"cs.LG\",\"physics.comp-ph\"]","methods":"[]","has_code":false}