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

References in corpus (8)

• Linear Confinement and AdS/QCD
• Exploring improved holographic theories for QCD: Part II
• Machine Learning of Calabi-Yau Volumes
• Machine Learning in the String Landscape
• Evolving neural networks with genetic algorithms to study the String Landscape
• AdS/CFT as a deep Boltzmann machine
• Holography as deep learning
• Machine Learning, Deepest Learning: Statistical Data Assimilation Problems

Cited by in corpus (1)

• Building bulk from Wilson loops