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

TL;DR

This paper introduces a neural network framework that accurately predicts energies and radii of light nuclei from truncated no-core shell model data, enabling high-precision nuclear structure calculations beyond computational limits.

Contribution

The authors develop a universal neural network approach to extrapolate nuclear observables to infinite model space, improving predictions for energies and radii with quantified uncertainties.

Findings

Accurate predictions of ground-state energies for light nuclei.

Extension of the framework to excitation energies.

Reliable uncertainty quantification through sampling.

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.