Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches

IF 1.9 4区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY Journal of Fusion Energy Pub Date : 2024-10-09 DOI:10.1007/s10894-024-00461-4
Naima Amrani, Cafer Mert Yeşilkanat, Serkan Akkoyun
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Abstract

This study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14–15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, α) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)²/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.

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通过机器学习方法确定中子-阿尔法反应截面
这项研究的重点是利用强大的机器学习方法来确定 14-15 MeV 能量范围内的中子-α 反应截面。研究利用了一个实验数据集,其中包括对 133 个原子核的(n,α)反应截面的测量结果。这些数据按照既定规程分为训练子集和验证子集,其中 80% 用于模型训练,20% 用于测试。关键核特征,包括中子数(N)、质量数(A)和对称性表示[(N-Z)²/A],被用作机器学习模型的输入变量。在本研究使用的其他机器学习方法中,SVR 和 XGBoost 方法表现出更优越的性能。此外,还开发了一种基于机器学习的在线计算工具来估算反应截面。
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来源期刊
Journal of Fusion Energy
Journal of Fusion Energy 工程技术-核科学技术
CiteScore
2.20
自引率
0.00%
发文量
24
审稿时长
2.3 months
期刊介绍: The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews. This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.
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