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 [Formula: see text] baryon resonances, we obtain strong linear correlation (R2 = 0.90) with slope [Formula: see text] GeV-2, though elevated scatter ([Formula: see text]) correlates strongly with resonance width (r = 0.88, p < 0.001). While hypergraph features did not significantly improve explanatory power beyond quality controls in this pilot dataset ([Formula: see text], p = 0.42), we introduce a hypergraph-informed confidence framework for missing resonance predictions, where structural decay coherence provides quantitative reliability metrics beyond traditional trajectory extrapolation. Together, these results demonstrate how combining trajectory analysis with network-inspired methods can improve hadron classification and provide concrete predictions for future experimental searches.
PloS one
Journal Article
English
41996658
Guideline Central and select third party use “cookies” on this website to enhance the user experience.
This technology helps us gather statistical and analytical information to optimize the relevant content for you.
The user also has the option to opt-out which may have an effect on the browsing experience.