Deriving dilaton potential in improved holographic QCD from meson spectrum

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.

7pages, 5figures. v2: footnote 6 added

References in corpus (7)

• Linear Confinement and AdS/QCD
• Exploring improved holographic theories for QCD: Part II
• Mimicking the QCD equation of state with a dual black hole
• Deconfinement and Gluon Plasma Dynamics in Improved Holographic QCD
• The Cornell Potential from General Geometries in AdS / QCD
• AdS/CFT as a deep Boltzmann machine
• Deep Learning and AdS/QCD

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