摘要: Using Kolmogorov-Arnold Networks (KANs), we construct a holographic model informed by lattice QCD data. This neural network approach enables the derivation of an analytical solution for the deformation factor w(r) and the determination of a constant g related to the string tension. Within the KANs-based holographic framework, we further analyze heavy quark potentials under finite temperature and chemical potential conditions. Additionally, we calculate the drag force, jet quenching parameter, and diffusion coefficient of heavy quarks in this paper. Our findings demonstrate qualitative consistency with both experimental measurements and established phenomenological model.
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来自:
陈, Dr. 勋
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分类:
物理学
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核物理学
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备注:
已向《Nuclear Science and Techniques》投稿
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引用:
ChinaXiv:202505.00064
(或此版本
ChinaXiv:202505.00064V1)
DOI:10.12074/202505.00064
CSTR:32003.36.ChinaXiv.202505.00064
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科创链TXID:
eace761c-4454-4d9a-b01b-9291aafec6a2
- 推荐引用方式:
Dai, Mr. Wen-Chao,Luo, Mr. Ou-Yang,Chen, Mr. Bing,陈, Dr. 勋,Zhu, Dr. Yan Xiao,Li, Dr. Xiao-Hua.Extracting Transport Properties of Quark-Gluon Plasma from the Heavy-Quark Potential With Neural Networks in a Holographic Model.中国科学院科技论文预发布平台.[DOI:10.12074/202505.00064]
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