What question did this study set out to answer?

The aim is to derive transport properties from a holographic model using neural networks.

May 8, 2026Open Access

Extracting Transport Properties from the Heavy-Quark Potential with Neural Networks in a Holographic Model

Key Points

The aim is to derive transport properties from a holographic model using neural networks.
Constructed a holographic model informed by lattice QCD data using Kolmogorov-Arnold Networks.
Analyzed heavy quark potentials under finite temperature and chemical potential.
Calculated drag force, jet quenching parameter, and diffusion coefficient for heavy quarks.
The derived deformation factor w(r) and constant g show consistency with existing models.
Drag force and jet quenching parameter calculations align qualitatively with experimental data.
Indicated effective diffusion coefficient values support theoretical predictions.

Abstract

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.

Extracting Transport Properties from the Heavy-Quark Potential with Neural Networks in a Holographic Model

Key Points

Abstract

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