Mass Predictions: Baryon Resonances with Hypergraph Decay Features

20 baryon resonances analyzed using orthogonal distance regression and hypergraph-derived structural invariants. The dataset, created by Julia Gao and updated in April 2026, demonstrates a strong linear correlation (R²=0.90) for Regge trajectories. It introduces a hypergraph-informed confidence framework for predicting missing resonances, linking decay topology to spectroscopic regularity.

Use Cases

• Predicting missing baryon resonances based on hypergraph-informed confidence metrics described in the study.
• Analyzing the correlation between resonance width and scatter in Regge trajectory fits, as reported with r=0.88.
• Comparing spectroscopic regularity across particles using predictors like community purity and motif z-scores.
• Testing the explanatory power of hypergraph features for hadron spectra beyond traditional trajectory extrapolation.

Strengths

• Dataset size is 5.5 KB, indicating a compact and focused collection.
• Analysis includes 20 baryon resonances with quantified uncertainties from bootstrap resampling.
• Results show a strong linear correlation (R²=0.90) for Regge trajectories.
• Licensed under CC-BY-4.0, permitting open sharing and adaptation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The pilot dataset found hypergraph features did not significantly improve explanatory power (p=0.42).

Provenance

Source

figshare

Collection Method

Integrated standard particle listings with hypergraph-based decay features for systematic comparison.

Time Range

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Freshness

Last updated 2026-04-17 20:02:12; freshness should be verified.

Geography

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