Generation of nuclear data using Gaussian process regression

被引：15

作者：

Iwamoto, Hiroki ^{[1
]}

机构：

[1] Japan Atom Energy Agcy, Nucl Sci & Engn Ctr, Tokai, Ibaraki, Japan

来源：

JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY | 2020年 / 57卷 / 08期

关键词：

Gaussian process regression; nuclear data; nuclide production cross-section; uncertainty; CROSS-SECTIONS; NUCLIDE PRODUCTION; DATA LIBRARY; CODE; NI; SIMULATION; ELEMENTS; URANIUM; FE; MG;

D O I：

10.1080/00223131.2020.1736202

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

A new approach for generating nuclear data from experimental cross-section data is presented based on Gaussian process regression. This paper focuses on the generation of nuclear data for proton-induced nuclide production cross-sections with a nickel target. Our results provide reasonable regression curves and corresponding uncertainties and demonstrate that this approach is effective for generating nuclear data. Additionally, our results indicate that this approach can be applied in experimental design to reduce the uncertainty of generated nuclear data.

引用

页码：932 / 938

页数：7

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