Speaker
Description
We open the black box of machine-learning jet taggers, asking how they compute their decisions and whether they rediscover QCD. Applying the causal mechanistic-interpretability toolkit (ablation, path patching, logit-lens, probing) to a Particle Transformer top-tagger, we isolate a sparse six-head source -> relay -> readout circuit that recovers 97.3% of full-model AUC, encodes the energy-correlator basis over N-subjettiness, and implicitly factorizes top tagging into two-prong W→qq̄ identification. A complementary physics-informed explainability study on the Lund Jet plane, three explainers across LundNet, ParticleNet, and ParT, over 1-/2-/3-prong tagging and p_T bins shows explainer importance tracking the same substructure observables (τ₂₁, τ₃₂, C₂, C₃) across architectures, confirming that taggers learn genuine energy-correlator physics.