2015/01/15 by Jordan McDonnell, J. D. McDonnell, N. Schunck +6 · 130 citations
Engineering · Mathematics · Physics and Astronomy · #Astronomical and nuclear sciences #Computer science #Content (measure theory) #Density functional theory #Mathematics #Nuclear physics research studies #Nuclear reactor physics and engineering #Physics #Quantum mechanics #Statistical physics #nucl-th
paper · pdf · doi:10.1103/physrevlett.114.122501
published in Physical Review Letters 114(12), 122501 (American Physical Society) · 5 pages, 4 figures, 1 table; Supplemental material with 2 tables; Submitted to Physical Review Letters
arxiv created 2015/01/15 · openalex publication_date 2015/03/24 · arxiv updated 2015/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.