Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics

TL;DR

The paper introduces the BAND framework, a Bayesian approach to unify nuclear models, data, and uncertainties, enabling advanced analysis and progress in nuclear physics research.

Contribution

It presents the BAND framework, integrating Bayesian methods into nuclear physics for model comparison, uncertainty quantification, and data analysis, with illustrative case studies.

Findings

Demonstrated the framework's application through four case studies

Showcased how Bayesian methods improve nuclear model analysis

Provided accessible tools for the nuclear physics community

Abstract

We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.