Precise neural network predictions of energies and radii from the no-core shell model
| Autor: | Wolfgruber, Tobias, Knöll, Marco, Roth, Robert |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Phys. Rev. C 110, 014327 (2024) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1103/PhysRevC.110.014327 |
| Popis: | For light nuclei, ab initio many-body methods such as the no-core shell model are the tools of choice for predictive, high-precision nuclear structure calculations. The applicability and the level of precision of these methods, however, is limited by the model-space truncation that has to be employed to make such computations feasible. We present a universal framework based on artificial neural networks to predict the value of observables for an infinite model-space size based on finite-size no-core shell model data. Expanding upon our previous ansatz of training the neural networks to recognize the observable-specific convergence pattern with data from few-body nuclei, we improve the results obtained for ground-state energies and show a way to handle excitation energies within this framework. Furthermore, we extend the framework to the prediction of converged root-mean-square radii, which are more difficult due to the much less constrained convergence behavior. For all observables robust and statistically significant uncertainties are extracted via the sampling over a large number of network realizations and evaluation data samples. Comment: 12 pages, 11 figures, 1 table, LaTeX; corrected typos, increased font size in some figures, added references |
| Databáze: | arXiv |
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