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Journal Articles

Total cross section model with uncertainty evaluated by KALMAN

Hashimoto, Shintaro; Sato, Tatsuhiko

EPJ Web of Conferences, 239, p.03015_1 - 03015_4, 2020/09

 Times Cited Count:0 Percentile:0.00(Nuclear Science & Technology)

Particle transport simulation codes based on the Monte Carlo technique have been successfully applied to shielding calculations in accelerator facilities. Estimation of not only statistical uncertainties, which depend on the number of trials, but also systemic uncertainties, which are caused by uncertainty of total cross section models, is required to confirm the reliability of the simulation results. We evaluated unclear quantities of internal parameters included in the total cross section model by the KALMAN code, which is based on the least squares technique, comparing with experimental data of the total cross section. The uncertainties in the total cross sections obtained by the new model are comparable to the experimental errors. In the present study, the systematic uncertainty included in the simulation results can be estimated by performing the transport calculations with variation of the internal parameters within their unclear quantities.

Journal Articles

Estimation method of systematic uncertainties in Monte Carlo particle transport simulation based on analysis of variance

Hashimoto, Shintaro; Sato, Tatsuhiko

Journal of Nuclear Science and Technology, 56(4), p.345 - 354, 2019/04

 Times Cited Count:7 Percentile:55.95(Nuclear Science & Technology)

Particle transport simulations based on the Monte Carlo method have been applied to shielding calculations. Estimation of not only statistical uncertainty related to the number of trials but also systematic one induced by unclear physical quantities is required to confirm the reliability of calculated results. In this study, we applied a method based on analysis of variance to shielding calculations. We proposed random- and three-condition methods. The first one determines randomly the value of the unclear quantity, while the second one uses only three values: the default value, upper and lower limits. The systematic uncertainty can be estimated adequately by the random-condition method, though it needs the large computational cost. The three-condition method can provide almost the same estimate as the random-condition method when the effect of the variation is monotonic. We found criterion to confirm convergence of the systematic uncertainty as the number of trials increases.

Oral presentation

Estimation method of systematic uncertainty associated with uncertainty of total cross section model in PHITS

Hashimoto, Shintaro; Sato, Tatsuhiko

no journal, , 

The PHITS code based on the Monte Carlo method has been successfully applied to analysis of radiation shielding in accelerator facilities. The reliability of results obtained by PHITS should be evaluated by systematic uncertainties caused by the uncertainty in total cross section model as well as statistical uncertainties associated with the number of trials. We can estimate the systematic uncertainty in the calculated results by using a statistical method ANOVA (analysis of variance). Furthermore, we developed a new total cross section model, whose internal parameters have widths to express the model uncertainty. By the KALMAN code based on the Bayes' theorem, we determined the widths comparing errors of experimental data. By performing the transport calculations with variation of the internal parameters within the widths, the systematic uncertainty associated with the model uncertainty can be estimated from the variance of calculated results.

Oral presentation

Development of estimation function of systematic uncertainty in PHITS

Hashimoto, Shintaro

no journal, , 

PHITS based on the Monte Carlo method has been used for many purposes such as radiation shielding in accelerator facilities. In order to confirm reliability of PHITS results, not only statistical errors related to the number of trials but also systematic uncertainties attributed to errors of input such as material densities are required. We implemented an estimation function of statistical errors and systematic uncertainties based on analysis of variance (ANOVA) in PHITS. In this presentation, we will introduce the estimation method using ANOVA, and explain how to use a sub-program autorun which can be used to easily obtain the error and uncertainty.

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