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arXiv · 2005.02636

Deep Learning and AdS/QCD

Tetsuya Akutagawa·Koji Hashimoto·Takayuki Sumimoto
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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.

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Tetsuya Akutagawa, Koji Hashimoto, Takayuki Sumimoto. 2020-05-06. Deep Learning and AdS/QCD. https://doi.org/10.1103/physrevd.102.026020

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