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Deriving dilaton potential in improved holographic QCD from meson spectrum
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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.
Forward citations
Cited by 2 Pith papers
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Learning holographic QCD with unflavored meson spectra
Neural networks trained on rho, a1, a2 and f0 mass spectra reconstruct a holographic QCD background with scalar potential k1 X^3 + k2 X^4 (k1 ~ -8, k2 ~17) and claim a pion-mass prediction that is partly circular.
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Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model
A neural-network-parametrized dilaton field reproduces the masses and leptonic decay constants of charmonium and bottomonium with 1.26% and 3.32% RMS errors, but only because those values were used as training data.
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