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arXiv 2411.14902hep-ph
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夸克-反夸克相互作用的机器学习洞察:探究QCD中的场分布与弦张力

Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD

Wei Kou, Xurong Chen

AI总结:

本研究利用 MLP 和 KAN 等机器学习技术分析格点 QCD 模拟数据,探究夸克-反夸克间的场分布,计算色通量管的弦张力与宽度,并提出场分布的解析表达式,展示了机器学习在 QCD 研究中的潜力。

摘要:

理解夸克-反夸克对之间的相互作用对于阐明量子色动力学(QCD)框架内的夸克禁闭至关重要。本研究采用先进的机器学习技术,即多层感知机(MLP)和 Kolmogorov-Arnold 网络(KAN),分析来自格点 QCD 模拟的数据,以研究这些夸克对之间产生的场分布模式。通过训练开发的模型随后被用于计算与色通量管相关的弦张力和宽度,并将这些结果与格点 QCD 推导的结果进行了严格比较。此外,我们利用 KAN 方法引入了一个初步的解析表达式,将场分布表征为夸克间距的函数。我们全面的定量分析强调了将机器学习方法整合到传统 QCD 研究中的潜力。

英文摘要:

Understanding the interactions between quark-antiquark pairs is essential for elucidating quark confinement within the framework of quantum chromodynamics (QCD). This study investigates the field distribution patterns that arise between these pairs by employing advanced machine learning techniques, namely multilayer perceptrons (MLP) and Kolmogorov-Arnold networks (KAN), to analyze data obtained from lattice QCD simulations. The models developed through this training are then applied to calculate the string tension and width associated with chromo flux tubes, and these results are rigorously compared to those derived from lattice QCD. Moreover, we introduce a preliminary analytical expression that characterizes the field distribution as a function of quark separation, utilizing the KAN methodology. Our comprehensive quantitative analysis underscores the potential of integrating machine learning approaches into conventional QCD research.

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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