Global Framework for Emulation of Nuclear Calculations

Abstract

We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.

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Cited by in corpus (3)

• Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation
• Ab initio short-range nuclear matrix elements for neutrinoless double-beta decay
• Hartree-Fock emulators for nuclei: Application to charge radii of ${}^{48,52}$Ca