The Bayesian Analysis of Nuclear Dynamics Framework

The atomic nucleus is a complex system and simulation models provide essential insights into many nuclear physics (NP) phenomena. However, simulation models are imperfect representations of reality and they almost always include uncertainties associated with model assumptions, inputs, and experiments. Uncertainty quantification aims to systematically account for relevant uncertainties for more accurate and precise predictions. Quantifying the uncertainty in a model is crucial to establishing trust in its predictions, and it requires cutting edge research in machine and statistical learning that can handle the cost of running high-fidelity NP models. The goal of the Bayesian Analysis of Nuclear Dynamics (BAND) Framework is to facilitate the uncertainty quantification in NP models. The framework integrates four Bayesian statistical tools: 1) model emulation, 2) model calibration, 3) model mixing, and 4) experimental design. The project aims to transform computational and theoretical research in those four areas into tools and techniques for making reliable predictions of complex NP systems with well-quantified uncertainties. These techniques are delivered as publicly-available open-source software tools along with guidelines for users. The goal is for these software tools to play a transformative role in facilitating and producing a full assessment of the uncertainty in NP predictions. In this talk, we introduce the BAND framework and overview the four main statistical tools. Finally, we illustrate some of BAND's uncertainty quantification tools and techniques with real NP examples.