Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations

Abstract

The prediction of nuclear observables beyond the finite model spaces that are accessible through modern ab initio methods, such as the no-core shell model, pose a challenging task in nuclear structure theory. It requires reliable tools for the extrapolation of observables to infinite many-body Hilbert spaces along with reliable uncertainty estimates. In this work we present a universal machine learning tool capable of capturing observable-specific convergence patterns independent of nucleus and interaction. We show that, once trained on few-body systems, artificial neural networks can produce accurate predictions for a broad range of light nuclei. In particular, we discuss neural-network predictions of ground-state energies from no-core shell model calculations for 6Li, 12C and 16O based on training data for 2H, 3H and 4He and compare them to classical extrapolations.

7 pages, 5 figures, 1 table

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• Uncertainties in ab initio nuclear structure calculations with chiral interactions
• Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies
• Spectroscopic factor calculations in the \textit{ab initio} no-core shell model
• High-Precision Ab Initio Radius Calculations of Boron Isotopes