Prediction of(n,2n)reaction cross-sections of long-lived fission products based on tensor model  

作　　者：Jia-Li Huang Hui Wang Ying-Ge Huang Er-Xi Xiao Yu-Jie Feng Xin Lei Fu-Chang Gu Long Zhu Yong-Jing Chen Jun Su 

机构地区：[1]Sino-French Institute of Nuclear Engineering and Technology,Sun Yat-sen University,Zhuhai 519082,China [2]China Nuclear Data Center,China Institute of Atomic Energy,Beijing 102413,China [3]Key Laboratory of Nuclear Data,China Institute of Atomic Energy,Beijing 102413,China

出　　处：《Nuclear Science and Techniques》2024年第10期208-221,共14页核技术（英文）

基　　金：supported by the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C157)。

摘　　要：Interest has recently emerged in potential applications of(n,2n)reactions of unstable nuclei.Challenges have arisen because of the scarcity of experimental cross-sectional data.This study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor model.This tensor model is an extension of the collaborative filtering algorithm used for nuclear data.It is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training data.The reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different databases.Predictions were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive isotopes.This method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.

关 键 词：(n 2n)Reaction cross-section Tensor model Machine learning Collaborative filtering algorithm Selective transmutation 

分 类 号：O571[理学—粒子物理与原子核物理]