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De Saint Jean C, Tamagno P, Archier P, Noguere G. CONRAD – a code for nuclear data modeling and evaluation. EPJ NUCLEAR SCIENCES & TECHNOLOGIES 2021. [DOI: 10.1051/epjn/2021011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The CONRAD code is an object-oriented software tool developed at CEA since 2005. It aims at providing nuclear reaction model calculations, data assimilation procedures based on Bayesian inference and a proper framework to treat all uncertainties involved in the nuclear data evaluation process: experimental uncertainties (statistical and systematic) as well as model parameter uncertainties. This paper will present the status of CONRAD-V1 developments concerning the theoretical and evaluation aspects. Each development is illustrated with examples and calculations were validated by comparison with existing codes (SAMMY, REFIT, ECIS, TALYS) or by comparison with experiment. At the end of this paper, a general perspective for CONRAD (concerning the evaluation and theoretical modules) and actual developments will be presented.
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Kumar D, Alam SB, Sjöstrand H, Palau J, De Saint Jean C. Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters. EPJ WEB OF CONFERENCES 2020. [DOI: 10.1051/epjconf/202023913003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The mathematical models used for nuclear data evaluations contain a large number of theoretical parameters that are usually uncertain. These parameters can be calibrated (or improved) by the information collected from integral/differential experiments. The Bayesian inference technique is used to utilize measurements for data assimilation. The Bayesian approximation is based on the least-square or Monte-Carlo approaches. In this process, the model parameters are optimized. In the adjustment process, it is essential to include the analysis related to the influence of model parameters on the adjusted data. In this work, some statistical indicators such as the concept of Cook’s distance; Akaike, Bayesian and deviance information criteria; effective degrees of freedom are developed within the CONRAD platform. Further, these indicators are applied to a test case of 155Gd to evaluate and compare the influence of resonance parameters.
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Leal-Cidoncha E, Noguere G, Bouland O, Serot O. Covariance generation for the prompt neutron multiplicity of 239Pu including the (n, γf) process. EPJ WEB OF CONFERENCES 2020. [DOI: 10.1051/epjconf/202023912002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Fission cross section of 239Pu can be seen as a sum of the “immediate" fission and “two-step" (n,γf) reactions. In the Resolved Resonance Range of the reaction cross sections, the contribution of the (n,γf) process has an impact on the determination of the partial widths magnitude involved in the Reich-Moore approximation of the R-matrix theory. The present work aims to investigate this impact by using the CONRAD code and the partial width Γγf for the (n,γf) reaction calculated by Lynn et al. [1]. A special attention will be paid to the covariance matrix obtained on νp.
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