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 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. Furthermore, the example discussed in this study sets the stage for quantifying andmore »
- Authors:
-
- 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:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Michigan State Univ., East Lansing, MI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1233543
- Alternate Identifier(s):
- OSTI ID: 1181478; OSTI ID: 1337830
- Grant/Contract Number:
- NA0002574; AC52-07NA27344; AC02-06CH11357; SC000851; NA0001820; AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Volume: 114; Journal Issue: 12; Journal ID: ISSN 0031-9007
- Publisher:
- American Physical Society (APS)
- 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
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. https://doi.org/10.1103/PhysRevLett.114.122501
McDonnell, J. D., Schunck, N., Higdon, D., Sarich, J., Wild, S. M., and Nazarewicz, W. Tue .
"Uncertainty quantification for nuclear density functional theory and information content of new measurements". United States. https://doi.org/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 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. Furthermore, 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.},
doi = {10.1103/PhysRevLett.114.122501},
journal = {Physical Review Letters},
number = 12,
volume = 114,
place = {United States},
year = {Tue Mar 24 00:00:00 EDT 2015},
month = {Tue Mar 24 00:00:00 EDT 2015}
}
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