Bayesian Monte Carlo Evaluation of Imperfect (n, 233U) Data and Model Article Swipe
Jesse Brown
,
Goran Arbanas
,
Dorothea Wiarda
,
K. H. Guber
,
Andrew Holcomb
,
Vladimir Sobes
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202328412003
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202328412003
Conventional nuclear data evaluation methods using generalized linear least squares 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 to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g. imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233 U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/epjconf/202328412003
- https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_12003.pdf
- OA Status
- diamond
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378470744
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https://openalex.org/W4378470744Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/epjconf/202328412003Digital Object Identifier
- Title
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Bayesian Monte Carlo Evaluation of Imperfect (n, 233U) Data and ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Jesse Brown, Goran Arbanas, Dorothea Wiarda, K. H. Guber, Andrew Holcomb, Vladimir SobesList of authors in order
- Landing page
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https://doi.org/10.1051/epjconf/202328412003Publisher landing page
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https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_12003.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_12003.pdfDirect OA link when available
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Monte Carlo method, Covariance, Mathematics, Statistical physics, Applied mathematics, Gaussian, Bayesian probability, Algorithm, Physics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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