K-PINN: Lorentz-covariant hybrid network for Z boson decay classification

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Abstract
To address the growing demand for intelligent automation in particle physics analysis, this study proposes K-PINN, a novel hybrid architecture integrating Kolmogorov—Arnold networks (KANs) and physics-informed neural networks (PINNs) for high-precision classification of Z boson decay channels ([ ] and [ ] ee) in high-energy physics experiments. Key innovations include the synergistic combination of B-spline-based interpretable feature extraction via the KANs module with rigorous physics constraints from the PINNs module, specifically enforcing four-momentum conservation and invariant mass-shell conditions. These components are unified through a physics-guided loss function with adaptive weighting to optimize classification fidelity. Evaluated on the CERN open dataset, K-PINN achieves state-of-the-art performance with 96.3% classification accuracy and an F1-score of 0.963, demonstrating a significant improvement over the traditional threshold-based approach. The architecture’s modular design enables direct generalization to other quantum chromodynamics processes through parameter space reconfiguration, establishing an optimal balance between physical interpretability and model performance. This work provides a versatile framework for intelligent particle physics analysis systems, with immediate applications extending to Higgs boson studies and beyond, while offering critical technical infrastructure for next-generation collider experiments.
Funding Information
  • the Natural Science Foundation of Henan (232300420114)

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