Uncertainty quantification for optical model parameters

被引:27
|
作者
Lovell, A. E. [1 ,2 ]
Nunes, F. M. [1 ,2 ]
Sarich, J. [3 ]
Wild, S. M. [3 ]
机构
[1] Michigan State Univ, Natl Superconducting Cyclotron Lab, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA
[3] Argonne Natl Lab, Math & Comp Sci Div, Lemont, IL 60439 USA
基金
美国国家科学基金会;
关键词
NEUTRON-SCATTERING; CROSS-SECTIONS; POTENTIALS; DEUTERONS; PB-208;
D O I
10.1103/PhysRevC.95.024611
中图分类号
O57 [原子核物理学、高能物理学];
学科分类号
070202 ;
摘要
Background: Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. Purpose: The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. Method: We use statistical methods to determine a best fit and create corresponding 95% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. Results: We study a number of reactions involving neutron and deuteron projectiles with energies in the range of 5-25 MeV/u, on targets with mass A = 12-208. We investigate the correlations between the parameters in the fit. The case of deuterons on C-12 is discussed in detail: the elastic-scattering fit and the prediction of C-12(d, p) C-13 transfer angular distributions, using both uncorrelated and correlated. 2 minimization functions. The general features for all cases are compiled in a systematic manner to identify trends. Conclusions: Our work shows that, in many cases, the correlated chi(2) functions (in comparison to the uncorrelated chi(2) functions) provide a more natural parameterization of the process. These correlated functions do, however, produce broader confidence bands. Further optimization may require improvement in the models themselves and/or more information included in the fit.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] Multifidelity uncertainty quantification with models based on dissimilar parameters
    Zeng X.
    Geraci G.
    Eldred M.S.
    Jakeman J.D.
    Gorodetsky A.A.
    Ghanem R.
    Computer Methods in Applied Mechanics and Engineering, 2023, 415
  • [42] Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model
    Wu, Xu
    Mui, Travis
    Hu, Guojun
    Meidani, Hadi
    Kozlowski, Tomasz
    NUCLEAR ENGINEERING AND DESIGN, 2017, 319 : 185 - 200
  • [43] Model Verification and Validation of a Cable-Stayed Bridge: Interval-Based Uncertainty Quantification of the Model Parameters
    Zhou, Haifei
    Zong, Zhouhong
    Niu, Jie
    Liu, Lu
    Lin, Dinan
    JOURNAL OF BRIDGE ENGINEERING, 2022, 27 (08)
  • [44] Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method
    Yamamoto, Yota
    Yajima, Tomoyuki
    Kawajiri, Yoshiaki
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2021, 175 : 223 - 237
  • [45] Uncertainty Quantification of System Model Parameters with Component Level and Sub-System Level Tests
    Li, Chenzhao
    Mahadevan, Sankaran
    SAE INTERNATIONAL JOURNAL OF MATERIALS AND MANUFACTURING, 2016, 9 (02) : 345 - 354
  • [46] Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters
    Bevia, V
    Burgos, C.
    Cortes, J-C
    Navarro-Quiles, A.
    Villanueva, R-J
    CHAOS SOLITONS & FRACTALS, 2020, 138
  • [47] Conservation of forest biomass and forest-dependent wildlife population: Uncertainty quantification of the model parameters
    Fanuel, Ibrahim M.
    Mirau, Silas
    Kajunguri, Damian
    Moyo, Francis
    HELIYON, 2023, 9 (06)
  • [48] Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom
    Valleti, Mani
    Vlcek, L.
    Ziatdinov, Maxim
    Vasudevan, Rama K.
    Kalinin, Sergei, V
    JOURNAL OF APPLIED PHYSICS, 2020, 128 (21)
  • [49] Uncertainty quantification under hybrid structure of probability-fuzzy parameters in Gaussian plume model
    Rituparna Chutia
    Life Cycle Reliability and Safety Engineering, 2017, 6 (4) : 277 - 284
  • [50] Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom
    Valleti, Mani
    Vlcek, L.
    Ziatdinov, Maxim
    Vasudevan, Rama K.
    Kalinin, Sergei V.
    Journal of Applied Physics, 2020, 128 (21):