Uncertainty quantification for nuclear density functional theory and information content of new measurements
Abstract
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to presentday 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 leastsquares optimization, combined with highperformance 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, twoneutron 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. As a result, the example discussed in this study sets the stage formore »
 Authors:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Argonne National Lab. (ANL), Argonne, IL (United States)
 Michigan State Univ., East Lansing, MI (United States); Oak Ridge National Lab., Oak Ridge, TN (United States); Univ. of Warsaw, Warsaw (Poland)
 Publication Date:
 Research Org.:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1179409
 Report Number(s):
 LLNLJRNL665886
Journal ID: ISSN 00319007; PRLTAO
 DOE Contract Number:
 AC5207NA27344
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Physical Review Letters; Journal Volume: 114; Journal Issue: 12
 Country of Publication:
 United States
 Language:
 English
 Subject:
 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Citation Formats
McDonnell, J. D., Schunck, N., Higdon, D., Sarich, J., Wild, S. M., and Nazarewicz, W. Uncertainty quantification for nuclear density functional theory and information content of new measurements. United States: N. p., 2015.
Web. doi:10.1103/PhysRevLett.114.122501.
McDonnell, J. D., Schunck, N., Higdon, D., Sarich, J., Wild, S. M., & Nazarewicz, W. Uncertainty quantification for nuclear density functional theory and information content of new measurements. United States. doi:10.1103/PhysRevLett.114.122501.
McDonnell, J. D., Schunck, N., Higdon, D., Sarich, J., Wild, S. M., and Nazarewicz, W. 2015.
"Uncertainty quantification for nuclear density functional theory and information content of new measurements". United States.
doi:10.1103/PhysRevLett.114.122501. https://www.osti.gov/servlets/purl/1179409.
@article{osti_1179409,
title = {Uncertainty quantification for nuclear density functional theory and information content of new measurements},
author = {McDonnell, J. D. and Schunck, N. and Higdon, D. and Sarich, J. and Wild, S. M. and Nazarewicz, W.},
abstractNote = {Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to presentday 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 leastsquares optimization, combined with highperformance 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, twoneutron 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. As a result, 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 experimenttheory cycle in the scientific method.},
doi = {10.1103/PhysRevLett.114.122501},
journal = {Physical Review Letters},
number = 12,
volume = 114,
place = {United States},
year = 2015,
month = 3
}

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to presentday 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 leastsquaresmore »

Uncertainty quantification for nuclear density functional theory and information content of new measurements
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to presentday 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 leastsquaresmore »Cited by 24 
Uncertainty quantification for nuclear density functional theory and information content of new measurements
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to presentday 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 leastsquaresmore »Cited by 24 
Uncertainty quantification and propagation in nuclear density functional theory
Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While ongoing efforts seek to better root nuclear DFT in the theory of nuclear forces (see Duguet et al., this Topical Issue), energy functionals remain semiphenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimationmore »