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 s...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 centered on our evaluation.The proposed method was applied to the evaluation of p+^(59)Co between 1 and 100 MeV.Finally,the adjusted files were compared with experimental 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.展开更多
To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen s...To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.展开更多
A cross section evaluation of neutron induced reactions on^(48)Ti is undertaken using the Unified Monte Carlo-B(UMC-B)approach.The evaluation concentrates on estimating the covariance and the use of the UMC-B allows a...A cross section evaluation of neutron induced reactions on^(48)Ti is undertaken using the Unified Monte Carlo-B(UMC-B)approach.The evaluation concentrates on estimating the covariance and the use of the UMC-B allows avoiding the deficiencies of linear regression brought by the traditional least squares method.Eight main neutron and charged particle emission reactions from n+^(48)Ti in the fast neutron energy region below 20 MeV are studied in this work.The posterior probability density function(PDF)of each neutron cross section is obtained in a UMC-B Bayesian approach by convoluting the model PDFs sampled based on model parameters and the likelihood functions for the experimental data.Nineteen model parameters including level density,pair corrections,optical model and Kalbach matrix element parameter are stochastically sampled with the assumption of normal distributions to estimate the model uncertainty.The Cholesky factorization approach is applied to consider potential parameter correlations.Finally,the posterior covariance matrices are generated using the UMC-B generated weights.The new evaluated results are compared with the CENDL-3.2,ENDF/B-VIII.0,JEFF-3.3,TENDL-2021 and JENDL-5 evaluations and differences are discussed.展开更多
基金Funding Open Access funding provided by Lib4RI–Library for the Research Institutes within the ETH Domain:Eawag,Empa,PSI&WSLthe Paul Scherrer Institute through the NES/GFA-ABE Cross Project.
文摘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 centered on our evaluation.The proposed method was applied to the evaluation of p+^(59)Co between 1 and 100 MeV.Finally,the adjusted files were compared with experimental 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.
基金funding from the Paul ScherrerInstitute,Switzerland through the NES/GFA-ABE Cross Project。
文摘To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.
基金Supported by the National Key Research and Development(R&D)Program(2022YFA1602403)Continuous Support Basic Scientific Research Project BJ010261223282。
文摘A cross section evaluation of neutron induced reactions on^(48)Ti is undertaken using the Unified Monte Carlo-B(UMC-B)approach.The evaluation concentrates on estimating the covariance and the use of the UMC-B allows avoiding the deficiencies of linear regression brought by the traditional least squares method.Eight main neutron and charged particle emission reactions from n+^(48)Ti in the fast neutron energy region below 20 MeV are studied in this work.The posterior probability density function(PDF)of each neutron cross section is obtained in a UMC-B Bayesian approach by convoluting the model PDFs sampled based on model parameters and the likelihood functions for the experimental data.Nineteen model parameters including level density,pair corrections,optical model and Kalbach matrix element parameter are stochastically sampled with the assumption of normal distributions to estimate the model uncertainty.The Cholesky factorization approach is applied to consider potential parameter correlations.Finally,the posterior covariance matrices are generated using the UMC-B generated weights.The new evaluated results are compared with the CENDL-3.2,ENDF/B-VIII.0,JEFF-3.3,TENDL-2021 and JENDL-5 evaluations and differences are discussed.
