Bayesian Monte Carlo Evaluation Framework for Imperfect Data and Models [Abstract]

Conference · Tue Jul 26 04:00:00 EDT 2022

OSTI ID:1899767

Brown, Jesse M. ^[1]; Arbanas, Goran ^[1]; Wiarda, Dorothea ^[1]; Holcomb, Andrew M. ^[1]; Guber, Klaus H. ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Nuclear data evaluation methods conventionally make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate for minimization of a cost function (even for non-linear models); and that both the model (of, e.g., neutron cross section) and experimental data (including their covariance data) are perfect. These assumptions are inherent to the well-known generalized linear least squares (GLLS) minimization method commonly used for evaluations of resolved resonance region (RRR) neutron cross sections. However, these assumptions are often not justified due to the presence of non-normal PDFs, non-linear models (e.g. R-matrix formalism), and inherent imperfections in data and models (e.g. discrepant data sets, discrepancies between the previous evaluation and newly measured data, or imperfect covariance data). We remove the said assumptions in a mathematical framework of Bayes’ theorem, and implement it using the Metropolis-Hastings Monte Carlo method. Parameters of a new kind are introduced to parameterize inherent imperfections, e.g., any discrepancies between the theoretical model and measured data. These new parameters enable evaluators to quantify their expert judgement about any discrepancies or imperfections in a reproducible manner. We demonstrate the framework with an ongoing evaluation of ²³³U in the eV region using the ENDF-B/VIII library and transmission data measured by Guber, et al., and compare the posterior parameters to those obtained by conventional evaluation methods.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Nuclear Criticality Safety Program (NCSP)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1899767
• Country of Publication: United States
• Language: English

Related Subjects

73 NUCLEAR PHYSICS AND RADIATION PHYSICS
97 MATHEMATICS AND COMPUTING
Generalized Linear Least Squares (GLLS)
Nuclear Criticality Safety Program (NCSP)
Nuclear Data