Speaker
Description
Jet tagging is essential for identifying hadronic decays of boosted particles at collider experiments. Transformer-based deep learning models have become state-of-the-art due to their ability to capture complex particle interactions. This talk reviews the development of Transformer architectures in jet tagging, from Particle Transformer to our More Interaction Particle Transformer (MIParT), which improves interaction modeling with reduced complexity. We highlight recent advances such as Lorentz-equivariant models (L-GATr), Interaction-Aware architectures (IAFormer), and new directions including foundation models (HEP-JEPA), auxiliary-task-enhanced transformers (GN2). We conclude with a discussion on the fundamental limits of jet tagging and future prospects for AI in collider physics.
