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

NUCLEAR SCIENCE AND TECHNIQUES 2024年第10期35卷 1-14页

作者： Huang, Jia-Li Wang, Hui Huang, Ying-Ge Xiao, Er-Xi Feng, Yu-Jie Lei, Xin Gu, Fu-Chang Zhu, Long Chen, Yong-Jing Su, Jun Sun Yat Sen Univ Sino French Inst Nucl Engn & Technol Zhuhai 519082 Peoples R China China Inst Atom Energy Key Lab Nucl Data Beijing 102413 Peoples R China China Inst Atom Energy China Nucl Data Ctr Beijing 102413 Peoples R China

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 60Co, 79Se\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{79}} {\mathrm{Se}}$$\end{document}, 93Zr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{93}}\mathrm{Zr}$$\end{document}, 107P\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{107}}\mathrm{P}$$\end{document}, 126Sn\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{126}}\mathrm{Sn}$$\end{document}, and 137Cs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepack

关键词： (n, 2n) Reaction cross-section tensor model Machine learning Collaborative filtering algorithm Selective transmutation NUCLEAR-DATA TRANSMUTATION N,2N

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Multi-dimensional EEF Criterion for Source Number Estimation

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Chinese Journal of Electronics 2014年第3期23卷 503-507页

作者： HUANG Qinghua WANG Tong School of Communication and Information Engineering Shanghai University Institute of Computer and Information Shanghai Second Polytechnic University

When the number of snapshots is smaller than the dimension of the measured data, Exponential embedded family(EEF) rule fails to choose the correct model order. It is modified in this paper to enumerate sources in three-dimensional space unlimited by the number of snapshots. Then the modified EEF criterion is extended to RD version for the multi-dimensional data model based on Higher-order singular value decomposition(HOSVD). The R-D EEF criterion exploits the multi-dimensional structure of measurements and the eigenvalues of different mode sample covariance matrices jointly. It improves accuracy and robustness compared with the modified EEF criterion. Simulation results demonstrate the performance of the proposed enumerators.

关键词： Source enumeration Exponential embedded family Multi-dimensional structure tensor model

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