Deriving dilaton potential in improved holographic QCD from meson spectrum

TL;DR

This paper derives an explicit dilaton potential in improved holographic QCD using experimental meson spectrum data and deep learning, ensuring a data-driven, consistent gravity model that exhibits quark confinement.

Contribution

The paper introduces a novel method to derive the dilaton potential in IHQCD directly from experimental data using deep learning, bridging data-driven approaches with holographic QCD models.

Findings

Derived a dilaton potential consistent with experimental meson data.

The resulting model satisfies confinement criteria.

Ensures the bulk geometry is a solution of IHQCD.

Abstract

We derive an explicit form of the dilaton potential in improved holographic QCD (IHQCD) from the experimental data of the $\rho$ meson spectrum. For this purpose we make use of the emergent bulk geometry obtained by deep learning from the hadronic data in arXiv:2005.02636. Requiring that the geometry is a solution of an IHQCD derives the corresponding dilaton potential backwards. This determines the bulk action in a data-driven way, which enables us at the same time to ensure that the deep learning proposal is a consistent gravity. Furthermore, we find that the resulting potential satisfies the requirements normally imposed in IHQCD, and that the holographic Wilson loop for the derived model exhibits quark confinement.