Uncertainty quantification for nuclear density functional theory and information content of new measurements

McDonnell, J. D.; Schunck, N.; Higdon, D.; Sarich, J.; Wild, S. M.; Nazarewicz, W.

doi:10.1103/PhysRevLett.114.122501

Title: Uncertainty quantification for nuclear density functional theory and information content of new measurements

Journal Article · Tue Mar 24 00:00:00 EDT 2015 · Physical Review Letters

DOI:https://doi.org/10.1103/PhysRevLett.114.122501· OSTI ID:1179409

McDonnell, J. D. ^[1]; Schunck, N. ^[1]; Higdon, D. ^[2]; Sarich, J. ^[3]; Wild, S. M. ^[3]; Nazarewicz, W. ^[4]

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)

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. 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 experiment-theory cycle in the scientific method.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: AC52-07NA27344

OSTI ID:: 1179409

Report Number(s):: LLNL-JRNL-665886; PRLTAO

Journal Information:: Physical Review Letters, Vol. 114, Issue 12; ISSN 0031-9007

Publisher:: American Physical Society (APS)

Country of Publication:: United States

Language:: English

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