Precise neural network predictions of energies and radii from the no-core shell model
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| Format: | Preprint |
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2023
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| _version_ | 1866916336504930304 |
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| author | Wolfgruber, Tobias Knöll, Marco Roth, Robert |
| author_facet | Wolfgruber, Tobias Knöll, Marco Roth, Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_05256 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Precise neural network predictions of energies and radii from the no-core shell model Wolfgruber, Tobias Knöll, Marco Roth, Robert Nuclear Theory 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. |
| title | Precise neural network predictions of energies and radii from the no-core shell model |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2310.05256 |