Abstract

The neutron induced nuclear reaction cross sections of fission products are related with the neutron fiux and the reactor burnup, which are important for the accurate of nuclear engineering design. To predict the (n,2n) reaction cross section, especially those lack of experimental measurements, we analyzed the relevant features and establish the experimental data set on the basis of sorting out the experimental data recorded in EXFOR library. The back propagation artificial neural network (ANN) and decision tree (DT) models are built to learn the experimental data set, respectively, adopting PyTorch and XGBOOST toolboxes. we report that machine learning models are applied to analysis and predicate (n,2n) reaction cross section.

Details

Title
Study of (n,2n) reaction cross section of fission product based on neural network and decision tree models
Author
Sun, Xiaodong; Wei, Zihao; Wang, Duan; Xu, Ruirui; Tian, Yuan; Tao, Xi; Zhang, Yingxun; Zhang, Yue; Zhang, Zhi; Ge, Zhigang; Wang, Jimin; Xia, Houqiong; Shu, Nengchuan
Section
Evaluation of Nuclear Data
Publication year
2024
Publication date
2024
Publisher
EDP Sciences
ISSN
21016275
e-ISSN
2100014X
Source type
Conference Paper
Language of publication
English
ProQuest document ID
3041484981
Copyright
© 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.