Unrecognized Sources of Uncertainties (USU) in Experimental Nuclear Data

Capote, R.; Badikov, S.; Carlson, A.; Duran, I.; Gunsing, F.; Neudecker, D.; Pronyaev, V. G.; Schillebeeckx, P.; Schnabel, G.; Smith, D. L.; Wallner, A.

doi:10.48550/arxiv.1911.01825

Cited by 1 publication

(1 citation statement)

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“…Another approach (as presented in Ref. [29]), is to identify Unrecognized Sources of Uncertainties (USU) in experiments and try to include them in evaluations. These approaches were however, not utilized in this work.…”

Section: B Experimental Datamentioning

confidence: 99%

Iterative Bayesian Monte Carlo for nuclear data evaluation

Alhassan¹,

Rochman²,

Vasiliev³

et al. 2020

Preprint

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In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM) procedure for nuclear data evaluation within a Talys Evaluated Nuclear data Library (TENDL) framework. In order to identify the model and parameter combinations that reproduce selected experimental data, different physical models implemented within the TALYS code, were sampled and varied simultaneously to produce random input files with unique model combinations. All the models considered were assumed to be equal a priori. Parameters to these models were then varied simultaneously using the TALYS code system to produce a set of random ENDF files which were processed into x-y tables for comparison with selected experimental data from the EXFOR database within a Bayesian framework. To improve our fit to experimental data, we iteratively update our 'best' file -the file that maximises the likelihood function -by re-sampling model parameters around this file. The method proposed has been applied for the evaluation of p+ 111 Cd and 59 Co between 1 -100 MeV incident energy region. Finally, the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2017 and JENDL-4.0/HE nuclear data libraries.

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Section: B Experimental Datamentioning

confidence: 99%