利用机器学习确定强耦合常数的分布
高能物理 - 唯象学
2023-06-12 v3
摘要
在这项工作中，我们使用人工神经网络（ANN）方法，通过拟合现有数据来研究和预测强耦合常数的分布。我们的方法利用了 ANN 学习复杂非线性关系和优良泛化的能力，并允许对与数据相关的不确定度进行系统处理。为确保结果的可靠性，我们应用三种评价指标在训练期间评估模型的准确性。最后，我们获得了不同能标下强耦合常数的预测值，并与现有实验数据进行了比较和验证。我们的方法代表了一种有前景的途径，可改进低能下强耦合常数的确定，并可能对量子色动力学的未来实验和理论研究产生重要影响。
引用
@article{arxiv.2303.07968,
title = {Determination of the distribution of strong coupling constant with machine learning},
author = {Xiao-Yun Wang and Chen Dong and Quanjin Wang},
journal= {arXiv preprint arXiv:2303.07968},
year = {2023}
}
备注
This work is a very preliminary result of using machine learning to study strong coupling constants. It is not a very mature work, and there are still certain uncertainties in the results. In a responsible manner, we decided to withdraw this manuscript after deliberation, and readers are invited to pay attention to our follow-up work on strong coupling constants in machine learning