Neural Network Modeling of Heavy-Quark Potential from Holography
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915046601261056 |
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| author | Luo, Ou-Yang Chen, Xun Li, Fu-Peng Li, Xiao-Hua Zhou, Kai |
| author_facet | Luo, Ou-Yang Chen, Xun Li, Fu-Peng Li, Xiao-Hua Zhou, Kai |
| contents | Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, matching the Andreev-Zakharov model exactly and confirming the validity of MLP. Finally, we construct an analytical holographic model using KAN and study the heavy-quark potential at finite temperature and chemical potential using the KAN-based holographic model. This work demonstrates the potential of KAN to derive analytical expressions for high-energy physics applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03784 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network Modeling of Heavy-Quark Potential from Holography Luo, Ou-Yang Chen, Xun Li, Fu-Peng Li, Xiao-Hua Zhou, Kai High Energy Physics - Phenomenology Using Multi-Layer Perceptrons (MLP) and Kolmogorov-Arnold Networks (KAN), we construct a holographic model based on lattice QCD data for the heavy-quark potential in the 2+1 system. The deformation factor $w(r)$ in the metric is obtained using the two types of neural network. First, we numerically obtain $w(r)$ using MLP, accurately reproducing the QCD results of the lattice, and calculate the heavy quark potential at finite temperature and the chemical potential. Subsequently, we employ KAN within the Andreev-Zakharov model for validation purpose, which can analytically reconstruct $w(r)$, matching the Andreev-Zakharov model exactly and confirming the validity of MLP. Finally, we construct an analytical holographic model using KAN and study the heavy-quark potential at finite temperature and chemical potential using the KAN-based holographic model. This work demonstrates the potential of KAN to derive analytical expressions for high-energy physics applications. |
| title | Neural Network Modeling of Heavy-Quark Potential from Holography |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2408.03784 |