Speaker
Description
One of the main goals of theoretical nuclear physics is to provide a first-principles description of the atomic nucleus, starting from interactions between nucleons (protons and neutrons). Although exciting progress has been made in recent years thanks to the development of many-body methods and nucleon-nucleon interactions derived from chiral effective field theory, performing accurate many-body calculations with quantifiable uncertainties remains a major challenge.
To address these problems, we use ab initio many-body calculations in combination with a hierarchical Bayesian neural network to develop emulators that accurately predict nuclear properties, vastly reducing computational time and enabling robust uncertainty quantification.
As a benchmark for our developments, we present results on the ground-state properties of complex nuclei, surpassing traditional surrogates where data is available and enabling predictions of nuclear properties and nuclear matter at extreme proton-to-neutron ratios, where experiments are expected in the coming years.