Extracting Transport Properties of Quark-Gluon Plasma from the Heavy-Quark Potential With Neural Networks in a Holographic Model
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914526666948608 |
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| author | Dai, Wen-Chao Luo, Ou-Yang Chen, Bing Chen, Xun Zhu, Xiao-Yan Li, Xiao-Hua |
| author_facet | Dai, Wen-Chao Luo, Ou-Yang Chen, Bing Chen, Xun Zhu, Xiao-Yan Li, Xiao-Hua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extracting Transport Properties of Quark-Gluon Plasma from the Heavy-Quark Potential With Neural Networks in a Holographic Model Dai, Wen-Chao Luo, Ou-Yang Chen, Bing Chen, Xun Zhu, Xiao-Yan Li, Xiao-Hua High Energy Physics - Phenomenology 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. |
| title | Extracting Transport Properties of Quark-Gluon Plasma from the Heavy-Quark Potential With Neural Networks in a Holographic Model |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2503.10213 |