# Jet Tagging

Jet taggers in high-energy physics are judged mainly by accuracy, yet it has been hard to say whether their decisions track the radiation structure inside a jet. Researchers fit a Kolmogorov-Arnold Network (KAN) locally to a trained model's perturbation response, then read two-body and three-body interaction scores from the surrogate's mixed partial derivatives and rank-correlate them with the dominant contributions to two-point and three-point energy correlation functions. On JetClass, the Particle Transformer shows clear positive two-body alignment while a standard multi-head attention baseline does not; three-body alignment is weaker for both, with ParT still stronger and its error bars excluding null correlation.

Jet taggers in high-energy physics are judged mainly by accuracy, yet it has been hard to say whether their decisions track the radiation structure inside a jet. Researchers fit a Kolmogorov-Arnold Network (KAN) locally to a trained model's perturbation response, then read two-body and three-body interaction scores from the surrogate's mixed partial derivatives and rank-correlate them with the dominant contributions to two-point and three-point energy correlation functions. On JetClass, the Particle Transformer shows clear positive two-body alignment while a standard multi-head attention baseline does not; three-body alignment is weaker for both, with ParT still stronger and its error bars excluding null correlation.

Track reconstruction at the High-Luminosity LHC usually keeps only charged particles above a transverse-momentum threshold, and that cut discards more than ninety percent of the particles. AlphaHits-TrackFormer instead processes all pixel and strip hits in complete TrackML events without a transverse-momentum selection on target tracks, reaching a TrackML score of 0.959 on 820 test events. The same framework, pretrained on tracking and then jointly fine-tuned, uses a shared hit-level representation to output tracks, jets, jet flavour and event-level observables together, with a Higgs classification AUC of 0.944.

Track reconstruction at the High-Luminosity LHC usually keeps only charged particles above a transverse-momentum threshold, and that cut discards more than ninety percent of the particles. AlphaHits-TrackFormer instead processes all pixel and strip hits in complete TrackML events without a transverse-momentum selection on target tracks, reaching a TrackML score of 0.959 on 820 test events. The same framework, pretrained on tracking and then jointly fine-tuned, uses a shared hit-level representation to output tracks, jets, jet flavour and event-level observables together, with a Higgs classification AUC of 0.944.

A trained physics surrogate can be made to respect a known symmetry at test time by transforming the input over the symmetry group, transforming the prediction back, and averaging, without changing the architecture or retraining. For compact groups this averaging carries a known non-increasing-risk guarantee and lowered errors on several The Well surrogates; for non-compact groups such as unit rescalings and the Lorentz group, normalized averaging no longer exists, and the Weyl trick and frame averaging gave clear gains mainly out of distribution or in low-data regimes, while models trained directly on invariant features matched or beat them.

A trained physics surrogate can be made to respect a known symmetry at test time by transforming the input over the symmetry group, transforming the prediction back, and averaging, without changing the architecture or retraining. For compact groups this averaging carries a known non-increasing-risk guarantee and lowered errors on several The Well surrogates; for non-compact groups such as unit rescalings and the Lorentz group, normalized averaging no longer exists, and the Weyl trick and frame averaging gave clear gains mainly out of distribution or in low-data regimes, while models trained directly on invariant features matched or beat them.

High-energy-physics detector data can be compressed into discrete tokens, with track and topocluster tokenizers holding jet resolution below 1% at roughly 4–11x compression, while calorimeter cells stay slightly above that target.

High-energy-physics detector data can be compressed into discrete tokens, with track and topocluster tokenizers holding jet resolution below 1% at roughly 4–11x compression, while calorimeter cells stay slightly above that target.