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

Julia Gao1

  • 1Fairview High School, Colorado State University, Fort Collins, Colorado, United States of America.

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|April 17, 2026
PubMed
Summary

We introduce a new framework combining Regge trajectories with hypergraph network analysis to better understand hadron spectra. This approach improves hadron classification and aids in predicting missing particles for future experiments.

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Area of Science:

  • Particle Physics
  • Hadron Spectroscopy
  • Network Science

Background:

  • Regge trajectories offer a geometric model for hadron spectra.
  • Deviations in higher-spin and missing states challenge current models.
  • Experimental completeness and theoretical limits require further investigation.

Purpose of the Study:

  • To develop a unified data framework integrating particle listings with hypergraph decay features.
  • To systematically compare baryon and meson spectra.
  • To establish decay topology as a determinant of Regge behavior.

Main Methods:

  • Employed orthogonal distance regression with bootstrap resampling for uncertainty quantification.
  • Utilized hypergraph-derived structural invariants (community purity, motif z-scores, product entropy) as predictors.
  • Applied a hybrid approach to analyze baryon resonances.

Main Results:

  • Achieved strong linear correlation (R2=0.90) for baryon resonances with slope of -0.36 GeV-2.
  • Elevated scatter correlated significantly with resonance width (r=0.88, p<0.001).
  • Introduced a hypergraph-informed confidence framework for predicting missing resonances.

Conclusions:

  • Decay topology is a microscopic determinant of macroscopic Regge behavior.
  • Combining trajectory analysis with network-inspired methods enhances hadron classification.
  • This hybrid approach provides quantitative reliability metrics for future experimental searches.