Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
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.
Cite
@article{arxiv.2012.07704,
title = {Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics},
author = {D. R. Phillips and R. J. Furnstahl and U. Heinz and T. Maiti and W. Nazarewicz and F. M. Nunes and M. Plumlee and M. T. Pratola and S. Pratt and F. G. Viens and S. M. Wild},
journal= {arXiv preprint arXiv:2012.07704},
year = {2021}
}
Comments
47 pages, 10 figures. Revised version includes minor corrections and changes in presentation. Matches journal version