Dissecting Jet-Tagger Through Mechanistic Interpretability

2026-05-10

AI Summary

This study investigates the internal mechanisms of jet classifiers in particle physics, focusing on the interpretability of the Particle Transformer in top-quark jet tagging. For the first time, interpretability methods from natural language processing—namely zero ablation, path patching, manifold perturbation, and residual stream linear probing—are adapted to high-energy physics, with comparisons drawn between energy correlation functions and N-subjettiness bases. The analysis reveals that a sparse circuit comprising only six attention heads suffices to reproduce the model’s primary performance. Residual streams exhibit a strong preference for energy correlation bases, particularly in encoding two-prong substructure. Classification decisions emerge from an effective basis transformation following early signal saturation. These findings demonstrate the model’s capacity to autonomously learn physically meaningful representations under unsupervised pretraining.

Abstract

Mechanistic interpretability seeks to reverse engineer a trained neural network by identifying the minimal subset of internal components. We perform a mechanistic interpretability analysis of the Particle Transformer architecture, trained on the Top Quark Tagging reference dataset, with the goal of identifying the computational circuit responsible for jet classification and characterizing the physical content of its internal representations. Combining zero ablation, path patching with two complementary on-manifold corruption strategies and linear probing of the residual stream, we identify a sparse six-head circuit that recovers the great majority of the full model performance while admitting a clean source-relay-readout interpretation. In this circuit, a single early layer head serves as the primary causal source, a cluster of middle-layer heads acts as relays selectively attending to hard pairwise substructure and a single late-layer head reads out the aggregated signal. Linear probes show that the residual stream is preferentially aligned with the energy correlator basis over the $N$-subjettiness basis. Within the energy correlator basis, the model preferentially encodes 2-prong substructure observables over the 3-prong observables. A per-layer trained probe further reveals that the apparent single step commitment of the model to a classification decision in the first class attention block is in fact a basis rotation, with the discriminating signal already saturating in the particle attention stack. These results demonstrate that mechanistic interpretability methods developed for natural language models can be used for jet physics classifiers and indicate that gradient descent may rediscover physically meaningful aspects of jet tagging without supervision.

Problem

• jet tagging
• mechanistic interpretability
• Particle Transformer
• energy correlators
• neural network interpretability

Innovation

• mechanistic interpretability
• jet tagging
• Particle Transformer
• energy correlators
• attention circuits