Potential sources of uncertainties in nuclear reaction modeling - Archive ouverte HAL Access content directly
Journal Articles EPJ N - Nuclear Sciences & Technologies Year : 2018

Potential sources of uncertainties in nuclear reaction modeling

(1) , (1) , (1) , (1) , (1) , (1) , (1) , (2)


Nowadays, reliance on nuclear models to interpolate or extrapolate between experimental data points is very common, for nuclear data evaluation. It is also well known that the knowledge of nuclear reaction mechanisms is at best approximate, and that their modeling relies on many parameters which do not have a precise physical meaning outside of their specific implementations in nuclear model codes: they carry both specific physical information, and effective information that is related to the deficiencies of the model itself. Therefore, to improve the uncertainties associated with evaluated nuclear data, the models themselves must be refined so that their parameters can be rigorously derived from theory. Examples of such a process will be given for a wide sample of models like: detailed theory of compound nucleus decay through multiple nucleon or gamma emission, or refinements to the width fluctuation factor of the Hauser-Feshbach model. All these examples will illustrate the reduction in the effective components of nuclear model parameters, through the reduced dynamics of parameter adjustment needed to account for experimental data. The significant progress, recently achieved for the non-fission channels, also highlights the difficult path ahead to improve our quantitative understanding of fission in a similar way: by relying on microscopic theory.
Fichier principal
Vignette du fichier
epjn170060.pdf (1.47 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

cea-01893731 , version 1 (11-10-2018)



Stéphane Hilaire, Eric Baugé, Pierre Chau Huu-Tai, Marc Dupuis, Sophie Péru, et al.. Potential sources of uncertainties in nuclear reaction modeling. EPJ N - Nuclear Sciences & Technologies, 2018, 4 (16), ⟨10.1051/epjn/2018014⟩. ⟨cea-01893731⟩


57 View
182 Download



Gmail Facebook Twitter LinkedIn More