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.

arXiv comment7pages, 5figures. v2: footnote 6 added

References in corpus (30)

• Gauge Theory Correlators from Non-Critical String Theory
• The Large N Limit of Superconformal Field Theories and Supergravity
• Wilson loops in large N field theories
• Macroscopic strings as heavy quarks: Large-N gauge theory and anti-de Sitter supergravity
• Low energy hadron physics in holographic QCD
• QCD and a Holographic Model of Hadrons
• Linear Confinement and AdS/QCD
• Towards a holographic dual of large-N_c QCD
• Exploring improved holographic theories for QCD: Part II
• Exploring improved holographic theories for QCD: Part I
• Heavy-Quark Potentials and AdS/QCD
• Mimicking the QCD equation of state with a dual black hole
• Holography and Thermodynamics of 5D Dilaton-gravity
• Deconfinement and Gluon Plasma Dynamics in Improved Holographic QCD
• Running Coupling and the Lambda-Parameter from SU(3) Lattice Simulations
• Deep Learning and AdS/CFT
• Tensors Mesons in AdS/QCD
• Machine Learning Spatial Geometry from Entanglement Features
• The Cornell Potential from General Geometries in AdS / QCD
• Deep Learning and Holographic QCD
• AdS/CFT as a deep Boltzmann machine
• Deep Learning and AdS/QCD
• Machine Learning Holographic Mapping by Neural Network Renormalization Group
• Effective holographic models for QCD: glueball spectrum and trace anomaly
• A gauge/gravity duality model for gauge theory thermodynamics
• Quantum fluctuations of Wilson loops from string models
• Deep learning black hole metrics from shear viscosity
• Neural ODE and Holographic QCD
• AdS/Deep-Learning made easy: simple examples
• Deep learning the holographic black hole with charge

Cited by in corpus (12)

• Exploring QCD matter in extreme conditions with Machine Learning
• Deep learning bulk spacetime from boundary optical conductivity
• Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
• Learning the black hole metric from holographic conductivity
• Gravitational Duals from Equations of State
• Reconstructing black hole exteriors and interiors using entanglement and complexity
• Deep learning-based holography for T-linear resistivity
• The Algebraic Structure Underlying Pole-Skipping Points
• Confinement Potential in a Soft-Wall Holographic Model with a Hydrogen-like Spectrum
• Oscillating nuclear charge radii as sensors for ultralight dark matter
• Automatic hermiticity for mixed states
• Learning holographic QCD with unflavored meson spectra