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 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. In addition, the example discussed in this study sets the stage for quantifyingmore »
 Authors:

^{[1]};
^{[2]};
^{[3]};
^{[4]};
^{[4]};
^{[5]}
 Francis Marion Univ., Florence, SC (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 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. (ORNL), Oak Ridge, TN (United States); Univ. of Warsaw, Warsaw (Poland)
 Publication Date:
 Grant/Contract Number:
 NA0002574; AC5207NA27344; AC0206CH11357; SC000851; NA0001820; AC0500OR22725
 Type:
 Accepted Manuscript
 Journal Name:
 Physical Review Letters
 Additional Journal Information:
 Journal Volume: 114; Journal Issue: 12; Journal ID: ISSN 00319007
 Publisher:
 American Physical Society (APS)
 Research Org:
 Michigan State Univ., East Lansing, MI (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org:
 USDOE National Nuclear Security Administration (NNSA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
 OSTI Identifier:
 1233543
 Alternate Identifier(s):
 OSTI ID: 1181478; OSTI ID: 1337830
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.,
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/1233543.
@article{osti_1233543,
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. In addition, 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}
}