Precise neural network predictions of energies and radii from the no-core shell model

Abstract

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.

12 pages, 11 figures, 1 table, LaTeX; corrected typos, increased font size in some figures, added references

References in corpus (10)

• Recent developments in no-core shell-model calculations
• Machine Learning in Nuclear Physics
• Ab initio no-core full configuration calculations of light nuclei
• Importance Truncation for Large-Scale Configuration Interaction Approaches
• Convergence in the no-core shell model with low-momentum two-nucleon interactions
• An artificial neural network application on nuclear charge radii
• Infrared length scale and extrapolations for the no-core shell model
• Nuclei with up to $\mathbf{A}=6$ nucleons with artificial neural network wave functions
• Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations
• Uncertainties in ab initio nuclear structure calculations with chiral interactions

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