Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
Aadet al.(ATLAS), Measurement of the Lund Jet Plane Using Charged Particles in 13 TeV Proton-Proton Collisions with the ATLAS Detector, Phys
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Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
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Neural Scaling Laws for Jet Generation
Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.