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Non-linear Regge trajectories with AdS/QCD

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arxiv 2004.10286 v2 pith:RGOTMM3S submitted 2020-04-21 hep-ph hep-th

classification hep-phhep-th
keywords alphakappamesonsvaluessectorstatesexoticheavy
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

In this work, we consider a non-quadratic dilaton $\Phi(z)=(\kappa\,z)^{2-\alpha}$ in the context of the static soft wall model to describe the mass spectrum of a wide range of vector mesons from the light up to the heavy sectors. The effect of this non-quadratic approach is translated into non-linear Regge trajectories with the generic form $M^2=a\,(n+b)^\nu$. We apply this sort of fits for the isovector states of $\omega$, $\phi$, $J/\psi$ and $\Upsilon$ mesons and compare with the corresponding holographic duals. We also extend these ideas to the heavy-light sector by using the isovector set of parameters to extrapolate the proper values of $\kappa$ and $\alpha$ through the average constituent mass $\bar{m}$ for each mesonic specie considered. In the same direction, we address the description of possible non-$q\,\bar{q}$ candidates using $\bar{m}$ as a holographic threshold, associated with the structure of the exotic state, to define the values of $\kappa$ and $\alpha$. We study the $\pi_1$ mesons in the light sector, and the $Z_c$, $Y$ and $Z_b$ mesons in the heavy sector as possible exotic vector states. Finally, the RMS error for describing these twenty-seven states with fifteen parameters (four values for $\kappa$ and $\alpha$ respectively and seven values for $\bar{m}$) is $12.61\%$.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Non linear Regge trajectories of quarkonia from holography

    hep-ph 2025-12 conditional novelty 6.0 of 10

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  3. Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model

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    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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