Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments

Conference · Mon Nov 16 04:00:00 EST 2020 · Transactions of the American Nuclear Society

DOI:https://doi.org/10.13182/t123-33146· OSTI ID:1814381

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

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

The new Bayesian Monte Carlo (MC) evaluation framework described in this abstract has been conceived as an attempt to improve nuclear data evaluations of differential crosssection data by removing the following two approximations conventionally employed for nuclear data evaluations: all probability density functions (PDFs) of all data and model parameters, both prior and posterior, are assumed to be normal (i.e., Gaussian) PDFs, and all uncertainties and covariances are propagated using a linear approximation. With these approximations removed, the Bayesian MC (BMC) framework could be used to account for nonlinear effects and would enable improved evaluations of differential cross sections and IBE data that are presently performed based on the assumptions itemized above. The BMC would also improve upon the uniform sampling of IBE parameters from within ranges defined by their evaluated uncertainties.

• 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: 1814381
• Conference Information: Journal Name: Transactions of the American Nuclear Society Journal Issue: 1 Journal Volume: 123
• Country of Publication: United States
• Language: English

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