KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging

Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interaction Graph Network (KIGNet), a graph neural network that integrates kinematic variables into jet classification by constructing four graph representations per jet, each weighted by a distinct variable: angular separation (Delta), relative transverse momentum (kT), momentum fraction (z), and invariant mass squared (m2). Three of these (Delta, kT, z) are motivated by the Lund jet plane, grounded in perturbative QCD factorization; the fourth (m2) adds complementary mass-scale sensitivity for heavy-flavor identification. Using Gradient-weighted Class Activation Mapping (Grad-CAM), we determine which variables dominate classification. Angular separation and relative transverse momentum account for about 76% of the total Grad-CAM attribution (40.72% and 35.67%), with momentum fraction and invariant mass contributing the remaining 24%. This hierarchy is consistent with the soft-collinear structure of QCD radiation in the training data, showing that the network learns physically interpretable representations rather than spurious correlations. On the JetClass dataset, KIGNet achieves a macro-accuracy of 95.07%, macro-AUC of 96.61%, and macro-AUPR of 81.52%, relative improvements of 2.45%, 3.40%, and 19.11% over the state-of-the-art baseline. On the Aspen Open Jets dataset of real CMS collision data, KIGNet produces substantially more structured latent representations than the baseline, reducing the Davies-Bouldin Index by 52.15% (0.8395 rightarrow 0.4017) and increasing the Dunn Index by 42.33% (0.0189 rightarrow 0.0269), confirming that physics-informed kinematic encoding generalizes beyond idealized simulation to experimental detector conditions.