Issue |
EPJ Web Conf.
Volume 281, 2023
5th International Workshop on Nuclear Data Covariances (CW2022)
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Article Number | 00023 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/epjconf/202328100023 | |
Published online | 29 March 2023 |
https://doi.org/10.1051/epjconf/202328100023
Test of models for 235U(nth,f) charge distributions and their impacts on the covariance analysis
CEA, DES, IRESNE, DER, SPRC, LEPh, Cadarache center, F-13108 Saint Paul lez Durance, France
Published online: 29 March 2023
Fission yields are of major interest for the nuclear industry and the study of the fission process. Fission yields are determined by evaluation process based on the analysis of experimental datasets and completed by phenomenological models. The aim is to provide the best estimation of independent yields of 235U(nth,f) reaction. In this paper, we will focus on the methodology of assessment of the nuclear charge distribution per mass. In the JEFF-3.1.1, the Wahl’s parametrization of the Zp-model is used to complete the experimental knowledge. Nevertheless, the lack of this parametrization consists in the absence of uncertainty information. Consequently, it is impossible to generate the components of independent yield correlation matrix from the Zp-model. In this context, we propose to use the Zp model with a new parametrization to assess the charge distributions. Two different calculations have been developed: the first one is based on a Wahl-like linear approximation of the parameters as a function of mass. The second approach consists to apply a “direct-Zp model” without linear assumptions. This analysis is achieved using the Markov-Chain Monte-Carlo (MCMC) method to adjust the model parameters and then to deduce the covariance of charge distributions. Moreover, with our development, we have directly and consistently described the pre-neutron and post-neutron charge distributions.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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