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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. Furthermore, the example discussed in this study sets the stage for quantifying andmore » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [4];  [5]
  1. Francis Marion Univ., Florence, SC (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
  5. 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 Lab. (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. doi: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. 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 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 = {2015},
month = {3}
}

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Works referenced in this record:

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


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


Needs and Issues of Covariance Data Application
journal, December 2008


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


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

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


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


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 landscape in covariant density functional theory
journal, November 2013


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


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

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

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


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


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


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


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


Bayesian inference in physics: case studies
journal, August 2003


Bayesian inference in physics
journal, September 2011


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

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


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

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


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


Proton radius from Bayesian inference
journal, November 2014


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

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


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


Nuclear energy density optimization: Large deformations
journal, February 2012


Nuclear energy density optimization
journal, August 2010


Nuclear energy density optimization: Shell structure
journal, May 2014


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

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

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

Nuclear structure and astrophysics
journal, August 2007


Fission properties for r -process nuclei
journal, February 2012


    Works referencing / citing this record:

    Bayesian calibration of strength parameters using hydrocode simulations of symmetric impact shock experiments of Al-5083
    journal, November 2018

    • Walters, David J.; Biswas, Ayan; Lawrence, Earl C.
    • Journal of Applied Physics, Vol. 124, Issue 20
    • DOI: 10.1063/1.5051442

    Information processing in the “not-in-my-backyard” strategy: An empirical study of anti-nuclear behavioral responses
    journal, October 2019

    • Hu, Xiaoli; Xie, Yundong; Zhang, Shaofeng
    • Human and Ecological Risk Assessment: An International Journal, Vol. 26, Issue 8
    • DOI: 10.1080/10807039.2019.1672138

    Microscopic theory of nuclear fission: a review
    journal, October 2016


    Challenges in nuclear structure theory
    journal, March 2016


    An introduction to bootstrap for nuclear physics
    journal, April 2019


    r -process nucleosynthesis: connecting rare-isotope beam facilities with the cosmos
    journal, July 2019

    • Horowitz, C. J.; Arcones, A.; Côté, B.
    • Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 8
    • DOI: 10.1088/1361-6471/ab0849

    From the microscopic to the macroscopic world: from nucleons to neutron stars
    journal, August 2019

    • Gandolfi, S.; Lippuner, J.; Steiner, A. W.
    • Journal of Physics G: Nuclear and Particle Physics, Vol. 46, Issue 10
    • DOI: 10.1088/1361-6471/ab29b3

    In-medium similarity renormalization group for closed and open-shell nuclei
    journal, December 2016


    Simultaneous fitting of neutron star structure and cooling data
    journal, November 2019


    Exploring experimental conditions to reduce uncertainties in the optical potential
    journal, December 2019


    Impact of statistical uncertainties on the composition of the outer crust of a neutron star
    journal, March 2020


    Statistical error propagation in ab initio no-core full configuration calculations of light nuclei
    journal, December 2015


    Nuclear charge and neutron radii and nuclear matter: Trend analysis in Skyrme density-functional-theory approach
    journal, May 2016


    Covariant energy density functionals: Nuclear matter constraints and global ground state properties
    journal, May 2016


    Applying the density matrix expansion with coordinate-space chiral interactions
    journal, May 2017


    Assessing theoretical uncertainties in fission barriers of superheavy nuclei
    journal, May 2017


    Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach
    journal, December 2017


    Equation of state for dense nucleonic matter from metamodeling. I. Foundational aspects
    journal, February 2018

    • Margueron, Jérôme; Hoffmann Casali, Rudiney; Gulminelli, Francesca
    • Physical Review C, Vol. 97, Issue 2
    • DOI: 10.1103/physrevc.97.025805

    Equation of state for dense nucleonic matter from metamodeling. II. Predictions for neutron star properties
    journal, February 2018

    • Margueron, Jérôme; Hoffmann Casali, Rudiney; Gulminelli, Francesca
    • Physical Review C, Vol. 97, Issue 2
    • DOI: 10.1103/physrevc.97.025806

    Bayesian approach to model-based extrapolation of nuclear observables
    journal, September 2018


    Incorporating Brueckner-Hartree-Fock correlations in energy density functionals
    journal, December 2018


    Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data
    journal, February 2019

    • Alvarez-Ruso, Luis; Graczyk, Krzysztof M.; Saul-Sala, Eduardo
    • Physical Review C, Vol. 99, Issue 2
    • DOI: 10.1103/physrevc.99.025204

    Effect of high-order empirical parameters on the nuclear equation of state
    journal, February 2019


    Precision Mass Measurements of Cd 129 131 and Their Impact on Stellar Nucleosynthesis via the Rapid Neutron Capture Process
    journal, December 2015


    Impact of Nuclear Mass Uncertainties on the r Process
    journal, March 2016


    Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap
    journal, December 2017


    Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus
    journal, December 2019


    Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System
    text, January 2020