TENDL-based evaluation and adjustment of p+111Cd between 1 and 100 MeV

• Published in: Applied radiation and isotopes Vol. 198; p. 110832
• Main Authors: Alhassan, E., Rochman, D., Vasiliev, A., Koning, A.J., Ferroukhi, H.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-08-2023
• Subjects: File weights; Iterative Bayesian Monte Carlo (iBMC); Model parameters; Nuclear data; Nuclear reaction models; TALYS code system; Nuclear reaction models; TALYS code system; Iterative Bayesian Monte Carlo (iBMC); Nuclear data; Model parameters; File weights
• ISSN: 0969-8043; 1872-9800; 1872-9800
• DOI: 10.1016/j.apradiso.2023.110832

Proton induced reaction data are needed in the optimization of various radioisotope production routes, among others. In this work, the evaluation of proton-induced reactions on 111Cd between 1 and 100 MeV using the TALYS code system within an iterative Bayesian Monte Carlo (iBMC) framework, is presented. The method involves the simultaneous variation of a large number of nuclear reaction models included in the TALYS code system as well as their parameters. Each random TALYS calculation yields a vector of calculated values of cross section observables as well as the angular distributions, among others, which were compared with corresponding vectors of carefully selected differential experimental data for reaction channels where data were available. The random nuclear data file with the maximum likelihood function value obtained from combining the individual χ2s computed for the considered reaction channels was chosen as the parent vector and the starting point for the generation of a further set of random TALYS calculations. This was repeated multiple times until a targeted convergence of 5% was reached. The final evaluated file was compared with available experimental data from the EXFOR database as well as with the evaluations from the TENDL-2021 and JENDL5.0 libraries, and found to compare favorably.
•TENDL-based evaluation of p+111Cd in the fast energy region below 100 MeV was carried out.•Model calculations were optimized to experimental data within an iterative Bayesian Monte Carlo (iBMC) framework.•A large non-informative prior was used by searching both the model and parameter spaces.•Results from this work compared favorably with experimental data and the TENDL-2021 and JENDL5.0 libraries.