The fundamental limit of jet tagging: Beyond top jets
Jet tagging, i.e. determining the origin of high-energy hadronic jets, is a key challenge in particle physics. Machine-learning-based taggers have achieved remarkable progress, raising the question of how close current methods are to the theoretical limit of performance. Previous work addressed this question for boosted top-quark jets using transformer-based generative models that provide realistic synthetic jet data with known probability density functions. This enables a direct comparison between modern taggers and the optimal likelihood-ratio classifier. In this note, we summarize the approach and extend the study to boosted W, Z, and H$\rightarrow gg$ jets. We find that the gap to the estimated optimal limit is strongly jet dependent and is substantially reduced for these seemingly more challenging tagging tasks. Ongoing work aimed at understanding the interpretation, robustness, and scaling of these limits is also briefly discussed.