Speaker
Description
Particle Transformers have achieved state-of-the-art performance in jet tagging, but the physical information underlying their decisions remains difficult to characterize. This limits our ability to assess their robustness, diagnose sensitivities to Monte Carlo mismodeling, and design effective pretraining strategies. We introduce a Jacobian-lens framework that resolves the response of a Particle Transformer into contributions associated with individual jet constituents and input features. The resulting constituent- and feature-level profiles provide a local picture of where and how discriminating information is encoded throughout the network. By comparing models trained or pretrained on different datasets, we uncover nontrivial changes in the particles, kinematic regions, and physical features emphasized by the model. The Jacobian lens therefore provides a systematic diagnostic of learned representations in jet taggers, offering a route toward more interpretable, simulation-robust, and physics-informed transformer models.