Learning holographic QCD with unflavored meson spectra

Mathew Thomas Arun; Ritik Pal

doi:10.1007/JHEP06(2026)226

paper

Learning holographic QCD with unflavored meson spectra

arXiv:2512.16450 · doi:10.1007/JHEP06(2026)226

Abstract

We develop a data-driven neural network framework to reconstruct the five-dimensional background geometry, the dilaton potential, and the chiral-symmetry-breaking scalar potential of holographic QCD from hadron mass spectra. Framed as an inverse problem, the model is trained using a discretized form of the Schrödinger-like equation, which resembles a linear moose in ``deconstructed" 5 dimensions with Dirichlet boundary conditions, in contrast to the AdS/DL with ``emergent" space-time. Using the masses of the unflavored mesons $ρ$ , $a_{1}$ , $a_{2}$ , and $f_{0}$ and their excitations as training data, the model learns confining effective potentials and computes a dilaton profile that satisfies the null energy condition. The network predicts that the dilaton's IR behavior will be much steeper than its quadratic form. Moreover, the symmetry-breaking bulk potential of the scalar field, $V (X) \sim k_{1} X^{3} + k_{2} X^{4}$ , was computed, and the parameters $k_{1}$ and $k_{2}$ predicted to be $\sim - 4$ and $\sim 9$ respectively. The deep-learned parameters, metric, and the dilaton profile were then used to predict the pion mass and its spectrum with good accuracy. A Python code, along with the trained models, is provided to facilitate further studies\footnote{Available at Github, https://github.com/rp-winter/NN-AdS-QCD

23 pages, 7 Figures, The Python code is available at Github, https://github.com/rp-winter/NN-AdS-QCD

Learning holographic QCD with unflavored meson spectra

Abstract

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