Study of (n,2n) reaction cross section of fission product based on neural network and decision tree models

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 a...

Full description

Bibliographic Details
Published in:EPJ Web of Conferences
Main Authors: 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
Format: Article
Language:English
Published: EDP Sciences 2024-01-01
Online Access:https://www.epj-conferences.org/articles/epjconf/pdf/2024/04/epjconf_wonder2024_04008.pdf
Description
Summary: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.
ISSN:2100-014X