Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics
Daniel R. Phillips, R. J. Furnstahl, Ulrich Heinz, Tapabrata Maiti, W. Nazarewicz, F. M. Nunes, Matthew Plumlee, Matthew T. Pratola, Scott Pratt, Frédéri Viens, Stefan M. Wild
Abstract
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 insights 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 in complex, far-ranging problems in nuclear physics (NP). 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 NP and statistics communities can contribute to and build upon the BAND framework.