In this work,we explore the use of an iterative Bayesian Monte Carlo(iBMC)method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library(TENDL)framework.The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data.The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code.The'best'model set with its parameter set was obtained by comparing model calculations with selected experimental data.Three experimental data types were used:(1)reaction cross sections,(2)residual production cross sections,and(3)the elastic angular distributions.To improve our fit to experimental data,we update our'best'parameter set—the file that maximizes the likelihood function—in an iterative fashion.Convergence was determined by monitoring the evolution of the maximum likelihood estimate(MLE)values and was considered reached when the relative change in the MLE for the last two iterations was within 5％.Once the final'best'file is identified,we infer parameter uncertainties and covariance information to this file by varying model parameters around this file.In this way,we ensured that the parameter distributions are cen-tered on our evaluation.The proposed method was applied to the evaluation of p+59Co between 1 and 100 MeV.Finally,the adjusted files were compared with experi-mental data from the EXFOR database as well as with evaluations from the TENDL-2019,JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.

Erwin Alhassan;Dimitri Rochman;Alexander Vasiliev;Mathieu Hursin;Arjan J.Koning;Hakim Ferroukhi

Laboratory for Reactor Physics and Thermal-Hydraulics,Paul Scherrer Institute,5232 Villigen,Switzerland;Division Large Research Facilities(GFA),Paul Scherrer Institute,Villigen,Switzerland;école Polytechnique Fédérale de Lausanne,Lausanne,Switzerland;Nuclear Data Section,International Atomic Energy Commission(IAEA),Vienna,Austria;Division of Applied Nuclear Physics,Department of Physics and Astronomy,Uppsala University,Uppsala,Sweden

核技术（英文版）

[1]Erwin Alhassan;Dimitri Rochman;Alexander Vasiliev;Mathieu Hursin;Arjan J.Koning;Hakim Ferroukhi-.Iterative Bayesian Monte Carlo for nuclear data evaluation)[J].核技术（英文版）,2022(04):105-135

iBMC,TENDL,p+59Co

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