Frameworks: Bayesian Analysis for Nuclei in Diverse Theories

The United States’ investment in nuclear physics has yielded tremendous gains in our understanding of matter as well as many applications that benefit society. Progress in the theory of nuclei and nuclear matter has produced a multitude of models that describe extant data well. But a paradigm shift in the analysis of nuclear-physics data is now necessary: predictions and quantified uncertainties must use the collective wisdom of the best models, constrained by data, and include a unified treatment of all uncertainties. This will enable reliable predictions for experimentally inaccessible environments, e.g. the properties and dynamics of matter at the core of neutron stars or in the first microsecond after the Big Bang. And it will make possible quantitative evaluation of the impact of new experiments, so facilitating optimal use of taxpayers’ investment in this science. Bayesian Analysis for Nuclei in Diverse Theories (BANDIT) will address these uncertainty quantification needs by creating and supporting an extendible software base that is augmented by human advice. Our Cyberinfrastructure (CI) Framework is Bayesian, and so leverages deep scientific prior knowledge with experimental data for powerful statistical inference. BANDIT will provide extrapolations and uncertainty quantification that exploit multiple theoretical models. It will be built through intensive collaboration between statisticians, computer scientists and experts in nuclear theory. The suite of codes will be useful for a broad community of nuclear physicists, and coupled with support that helps users reliably incorporate their expert knowledge to use the framework intelligently and effectively. Both codes and support will be sustainable.