E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features

Abstract

The identification and classification of collimated particle sprays, or jets, are essential for interpreting data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Explainable Particle Chebyshev Network (E-PCN), a graph neural network extending the Particle Chebyshev Network (PCN). E-PCN integrates kinematic variables into jet classification by constructing four graph representations per jet, each weighted by a distinct variable: angular separation (), transverse momentum (), momentum fraction (), and invariant mass squared (). We use the concept of Gradient-weighted Class Activation Mapping (Grad-CAM) to determine which kinematic variables dominate classification outcomes. Analysis reveals that angular separation and transverse momentum collectively account for approximately 76% of classification decisions (40 …

Cite

@article{isC4tDSrTZIC,
  title     = {E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features},
  author    = {Md Raqibul Islam and Adrita Khan and Mir Sazzat Hossain and Choudhury Ben Yamin Siddiqui and Md Zakir Hossan and Tanjib Khan and M Arshad Momen and Amin Ahsan Ali and AKM Mahbubur Rahman},
  journal = {arXiv e-prints},
  year      = {2025}
}