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arXiv 2310.04700nucl-thnucl-ex
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物理信息对基于机器学习预测重离子熔合截面的重要性

Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning

Zhilong Li, Zepeng Gao, Ling Liu, Yongjia Wang, Long Zhu, Qingfeng Li

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12 pages, 7 figures
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AI总结：

本研究采用LightGBM算法预测重离子熔合截面，验证了加入物理信息特征可显著降低预测误差，明确了物理信息在该类机器学习预测中的重要作用。

摘要：

本研究采用Light Gradient Boosting Machine（LightGBM，轻量梯度提升机）——一种现代的基于决策树的机器学习算法，研究重离子反应的熔合截面（CS）。研究将若干基础物理量（如弹核与靶核的质量数、质子数）以及由唯象公式得到的熔合截面输入LightGBM算法，以实现熔合截面的预测。研究发现，在验证集上，仅以基础物理量作为输入时，用于衡量预测熔合截面与实验熔合截面的log₁₀值绝对差平均幅度的平均绝对误差（MAE）为0.129，该数值小于经验耦合道模型得到的0.154；加入一个物理信息驱动的输入特征后，MAE可进一步降至0.08。测试集包含来自训练集未涵盖的18个反应体系的280个数据点，加入与不加入该物理信息特征时，测试集上的MAE分别约为0.19和0.53。研究还通过将预测结果与密度约束含时Hartree-Fock方法得到的⁴⁰,⁴⁸Ca+⁷⁸Ni体系熔合截面进行对比，进一步验证了LightGBM的预测效果。本研究证明了物理信息在利用机器学习预测重离子反应熔合截面中的重要性。

英文摘要：

In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree based machine-learning algorithm, is used to study the fusion cross section (CS) of heavy-ion reaction. Several basic quantities (e.g., mass number and proton number of projectile and target) and the CS obtained from phenomenological formula are fed into the LightGBM algorithm to predict the CS. It is found that, on the validation set, the mean absolute error (MAE) which measures the average magnitude of the absolute difference between $log_{10}$ of the predicted CS and experimental CS is 0.129 by only using the basic quantities as the input, this value is smaller than 0.154 obtained from the empirical coupled channel model. MAE can be further reduced to 0.08 by including an physical-informed input feature. The MAE on the test set (it consists of 280 data points from 18 reaction systems that not included in the training set) is about 0.19 and 0.53 by including and excluding the physical-informed feature, respectively. We further verify the LightGBM predictions by comparing the CS of $^{ 40,48}{\rm Ca }$+$^{78}{\rm Ni}$ obtained from the density-constrained time-dependent Hartree-Fock approach. Our study demonstrates the importance of physical information in predicting fusion cross section of heavy-ion reaction with machine learning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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