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Title: 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. The example discussed in this study sets the stage for quantifying and maximizingmore » 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.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1392587
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
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

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. Sun . "Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements". United States. doi:10.1103/PhysRevLett.114.122501.
@article{osti_1392587,
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. 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},
issn = {0031-9007},
number = 12,
volume = 114,
place = {United States},
year = {2015},
month = {3}
}

Works referenced in this record:

Propagation of uncertainties in the Skyrme energy-density-functional model
journal, March 2013


Nuclear data sensitivity, uncertainty and target accuracy assessment for future nuclear systems
journal, May 2006


Uncertainties of mass extrapolations in Hartree-Fock-Bogoliubov mass models
journal, May 2014


Error analysis in nuclear density functional theory
journal, February 2015

  • Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3
  • DOI: 10.1088/0954-3899/42/3/034024

Studies of neutron-rich isotopes with the CPT mass spectrometer and the CARIBU project
journal, April 2006

  • Savard, G.; Wang, J. C.; Sharma, K. S.
  • International Journal of Mass Spectrometry, Vol. 251, Issue 2-3
  • DOI: 10.1016/j.ijms.2006.01.047

Computer Model Calibration Using High-Dimensional Output
journal, June 2008

  • Higdon, Dave; Gattiker, James; Williams, Brian
  • Journal of the American Statistical Association, Vol. 103, Issue 482
  • DOI: 10.1198/016214507000000888

The limits of the nuclear landscape
journal, June 2012

  • Erler, Jochen; Birge, Noah; Kortelainen, Markus
  • Nature, Vol. 486, Issue 7404
  • DOI: 10.1038/nature11188

Nuclear energy density optimization: Shell structure
journal, May 2014


Nuclear structure and astrophysics
journal, August 2007


First Gogny-Hartree-Fock-Bogoliubov Nuclear Mass Model
journal, June 2009


Computational nuclear quantum many-body problem: The UNEDF project
journal, October 2013


The role of experiments and of sensitivity analysis in simulation validation strategies with emphasis on reactor physics
journal, February 2013


Fission properties for r -process nuclei
journal, February 2012


Statistical significance of theoretical predictions: A new dimension in nuclear structure theories (II)
journal, January 2011


Nuclear energy density optimization
journal, August 2010


Bayesian inference in physics
journal, September 2011


The Equation of State from Observed Masses and Radii of Neutron Stars
journal, September 2010

  • Steiner, Andrew W.; Lattimer, James M.; Brown, Edward F.
  • The Astrophysical Journal, Vol. 722, Issue 1
  • DOI: 10.1088/0004-637X/722/1/33

Needs and Issues of Covariance Data Application
journal, December 2008


Self-consistent mean-field models for nuclear structure
journal, January 2003

  • Bender, Michael; Heenen, Paul-Henri; Reinhard, Paul-Gerhard
  • Reviews of Modern Physics, Vol. 75, Issue 1
  • DOI: 10.1103/RevModPhys.75.121

Nuclear landscape in covariant density functional theory
journal, November 2013


Mass measurements near the r -process path using the Canadian Penning Trap mass spectrometer
journal, April 2012


Variations on a theme by Skyrme: A systematic study of adjustments of model parameters
journal, March 2009


Derivative-free optimization for parameter estimation in computational nuclear physics
journal, February 2015

  • Wild, Stefan M.; Sarich, Jason; Schunck, Nicolas
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3
  • DOI: 10.1088/0954-3899/42/3/034031

Determining fundamental properties of matter created in ultrarelativistic heavy-ion collisions
journal, March 2014


Neutron-skin uncertainties of Skyrme energy density functionals
journal, September 2013


Bayesian Analysis of Pentaquark Signals from CLAS Data
journal, February 2008


Information content of a new observable: The case of the nuclear neutron skin
journal, May 2010


Proton radius from Bayesian inference
journal, November 2014


Algorithmic construction of optimal symmetric Latin hypercube designs
journal, September 2000


A Bayesian approach for parameter estimation and prediction using a computationally intensive model
journal, February 2015

  • Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3
  • DOI: 10.1088/0954-3899/42/3/034009

Enhancing the interaction between nuclear experiment and theory through information and statistics
journal, February 2015


Bayesian inference in physics: case studies
journal, August 2003


First Results from the CARIBU Facility: Mass Measurements on the r -Process Path
journal, August 2013


Uncertainty Quantification, Sensitivity Analysis, and Data Assimilation for Nuclear Systems Simulation
journal, December 2008


Error estimates of theoretical models: a guide
journal, May 2014

  • Dobaczewski, J.; Nazarewicz, W.; Reinhard, P-G
  • Journal of Physics G: Nuclear and Particle Physics, Vol. 41, Issue 7
  • DOI: 10.1088/0954-3899/41/7/074001

Nuclear energy density optimization: Large deformations
journal, February 2012


Evaluation and Propagation of the 239 Pu Fission Cross-Section Uncertainties Using a Monte Carlo Technique
journal, May 2006

  • Kawano, T.; Hanson, K. M.; Frankle, S.
  • Nuclear Science and Engineering, Vol. 153, Issue 1
  • DOI: 10.13182/NSE06-A2589