Speaker
Description
Gradient-boosted decision trees such as XGBoost remain the default for tabular high-energy-physics (HEP) classification. We investigate Kolmogorov-Arnold Networks (KAN), which replace fixed activations with learnable B-spline functions on network edges, as an alternative for Higgs boson signal/background separation. We implement KAN from scratch in PyTorch (Cox-de Boor B-splines, adaptive knot placement, coarse-to-fine grid extension, and L1 spline regularisation) and evaluate it on a stratified 2-million-event subset of the FAIR Universe HiggsML dataset (H to tau tau signal). All metrics are reported as bootstrap mean plus/minus sigma from a single deterministic, fully reproducible run.
A faithful deep KAN (AUC 0.8834, AMS 3.66) and its grid-extended variant (AUC 0.8839, AMS 3.74) both outperform a vanilla XGBoost baseline (0.8802 / 3.31) on discrimination and significance. Under momentum energy-scale systematics (gamma in [0.8, 1.2]), adversarial decorrelation makes KAN roughly 3x more robust (max AUC drop 0.026 vs 0.082). Model-agnostic permutation importance, grouped-feature importance, and a per-class-weighted calibration analysis further show KAN is competitive, well-calibrated, and interpretable for HEP.