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使用XGBoost和Leave-One-Out交叉验证预测 (n,3n) 核反应截面
Yiğit Ali Üncü1, Taner Danışman2, Hasan Özdoğan3
1Akdeniz University, Vocational School of Technical Sciences, Department of Biomedical Equipment Technology, 07070, Antalya, Turkey.
概括
本研究介绍了一种XGBoost机器学习模型,用于精确的核反应截面预测. 该模型准确地预测了 (n,3n) 反应截面,超过了传统方法.
科学领域:
- 核物理 核物理 核物理
- 计算科学 计算科学
- 材料科学 材料科学 材料科学
背景情况:
- 准确预测核反应截面对于核医学,能源和材料科学至关重要.
- 现有的理论和经验方法在预测这些截面方面存在局限性.
研究的目的:
- 开发一个强大的机器学习模型来预测 (n,3n) 核反应截面.
- 为了利用XGBoost算法提高预测准确度.
主要方法:
- 使用了实验横截面值的综合数据集.
- 员工休假一次性交叉验证 (LOOCV) 用于特征选择和超参数优化.
- 通过规范化值和处理缺失数据来准备数据,以便进行可靠的模型训练.
主要成果:
- XGBoost模型在预测 (n,3n) 核反应截面方面表现出高准确度.
- 评估模型性能使用平均平方误差 (MSE),平均绝对误差 (MAE) 和减少Chi-Square.
- 将XGBoost的预测与TALYS 1.95,TENDL和现象学模型进行了比较,显示了更好的准确性.
结论:
- 机器学习,特别是XGBoost,提供了一种强大的方法来克服核数据分析的局限性.
- 开发的模型提供了准确的预测,推进了核反应截面分析领域.
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