Deep Learning and AdS/QCD

Abstract

We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra. A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are holographically calculated. We adopt the experimentally measured spectra of $\rho$ and $a_2$ mesons as training data, and perform a supervised machine learning which determines concretely a bulk metric and a dilaton profile of an AdS/QCD model. Our deep learning (DL) architecture is based on the AdS/DL correspondence (arXiv:1802.08313) where the deep neural network is identified with the emergent bulk spacetime.

13 pages, 9 figures, v2: Figure display problem resolved, the content unchanged

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Cited by in corpus (24)

• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• Deep learning black hole metrics from shear viscosity
• Deriving dilaton potential in improved holographic QCD from meson spectrum
• Deep learning bulk spacetime from boundary optical conductivity
• Meson and glueball spectroscopy within the graviton soft wall model
• Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
• Machine Learning Lie Structures & Applications to Physics
• Learning the black hole metric from holographic conductivity
• Disentangling the gravity dual of Yang-Mills theory
• RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
• Holographic QCD and magnetic fields
• Gravitational Duals from Equations of State
• Bulk reconstruction of metrics inside black holes by complexity
• Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
• 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
• Black holes and the loss landscape in machine learning
• Generative AI for Brane Configurations and Coamoeba
• Holographic Wilsonian renormalization of a heavy quark moving through a strongly coupled plasma
• Automatic hermiticity for mixed states
• Building bulk from Wilson loops
• Learning holographic QCD with unflavored meson spectra