Global Framework for Emulation of Nuclear Calculations
This work addresses the challenge of global emulation in nuclear physics, enabling sensitivity analysis for nuclear binding energies and charge radii, but it appears incremental as it builds on existing methods like Bayesian neural networks and ab initio calculations.
The authors tackled the problem of predicting nuclear properties across isotopic chains by introducing a hierarchical framework combining ab initio many-body calculations with a Bayesian neural network, achieving accurate results for ground-state energies and nuclear charge radii in the oxygen isotopic chain with robust uncertainty quantification.
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.