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Hypergraph-based decay analysis for predicting and classifying states beyond regge trajectories

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  • Julia Gao

Abstract

Regge trajectories provide a simple geometric picture of hadron spectra, but particles at higher-spin and missing states often scatter from linear fits, raising questions about both experimental completeness and theoretical limits of the model. Here, we develop a unified data framework that integrates standard particle listings with hypergraph-based decay features, enabling systematic comparison across baryons and mesons. We employed orthogonal distance regression with bootstrap resampling to quantify uncertainties in slope and intercept estimates, while hypergraph-derived structural invariants (community purity, motif z-scores, and product entropy) serve as quantitative predictors of spectroscopic regularity, establishing decay topology as a microscopic determinant of macroscopic Regge behavior. Applying this hybrid approach to 20 Δ baryon resonances, we obtain strong linear correlation (R2 = 0.90) with slope α′=1.53±0.03 GeV−2, though elevated scatter (χ2/dof=18.5) correlates strongly with resonance width (r = 0.88, p

Suggested Citation

  • Julia Gao, 2026. "Hypergraph-based decay analysis for predicting and classifying states beyond regge trajectories," PLOS ONE, Public Library of Science, vol. 21(4), pages 1-13, April.
    • Handle: RePEc:plo:pone00:0346334
    DOI: 10.1371/journal.pone.0346334
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