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.
Highlights
The AdS/CFT correspondence [1,2,3], or the holographic principle, is a promising way to define a quantum gravity
The advantage of the deep learning method is that it can provide a systematic approach to determine the gravity dual from a given dataset of the quantum field theory (QFT) correlators, which even generalizes for prediction
According to the concept of the AdS/deep learning (DL) correspondence [11], we present a deep neural network architecture optimizing a generic soft-wall AdS/QCD model
Summary
The AdS/CFT correspondence [1,2,3], or the holographic principle, is a promising way to define a quantum gravity. The best framework to test the deep learning method is the AdS/QCD [13,14,15], a bottom-up construction of phenomenological gravity models based on symmetries and the dictionary. Model has a large arbitrariness, and in addition the dictionary is nonlocal, so solving the inverse problem is challenging This is the reason why the deep learning method can help in finding the gravity dual of QCD. The advantage of the deep learning method is that it can provide a systematic approach to determine the gravity dual from a given dataset of the QFT correlators, which even generalizes for prediction. [15], we use the experimentally measured data to train our AdS/QCD model to find an optimized, emergent background geometry and dilaton. The Appendix includes the details of our deep learning architecture
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