Neural Network Modeling of Heavy-Quark Potential from Holography
arxiv(2024)
Abstract
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.
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