深度神经网络对165Ho (α,xn) 反应激发函数的预测
R Gökhan Türeci1, Ismail Hakki Sarpün2, Abdullah Aydin3
1Kırıkkale University, Kırıkkale Vocational School, Kırıkkale, Türkiye.
概括
这项研究使用人工智能增强了核反应截面预测. 深度神经网络显著提高了对传统模型的准确性,帮助核医学和天体物理学.
科学领域:
- 核物理 核物理 核物理
- 计算物理 计算物理
背景情况:
- 准确的核反应截面数据对于核医学,反应器技术和核天体物理学至关重要.
- 了解这些反应是推动这些领域发展的关键.
研究的目的:
- 为了分析特定的-α反应的激发函数.
- 将实验数据与理论预测和机器学习模型进行比较.
- 评估深度神经网络 (DNN) 在预测核截面方面的有效性.
主要方法:
- 对165Ho(α,xn) 168-165Tm反应的激发函数的分析.
- 实验数据 (EXFOR) 与TALYS代码和TENDL-2023库预测的比较.
- 使用Python和pytorch与各种激活功能 (ReLU,ELU,LeakyReLU,SiLU,Mish,PReLU) 的DNN的开发和应用.
主要成果:
- 传统模型提供了反应趋势的基本近似.
- 深度神经网络模型显示了与实验核反应数据的显著改进.
- 在DNN中不同的激活函数对预测准确性产生影响.
结论:
- 人工智能,特别是ANN,显示出作为核反应建模的补充工具的巨大潜力.
- 与传统方法相比,DNN为预测核截面提供了更准确的方法.
- 激活函数的选择对于优化核数据预测中的DNN性能至关重要.
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