Orateur
Description
Jets are ubiquitous but highly complex objects at the LHC, and learning their substructure patterns provides information about the particles that produced them and, ultimately, the underlying physics. They are therefore paramount to the LHC physics program. Foundation models have emerged as a powerful new ML paradigm for modelling jets and harnessing the information encoded in their constituents. In this talk, we will investigate how far foundation models can go toward learning and exploiting the complete information content of jets. We will approach this question from the perspective of the fundamental limits of jet tagging and generation, and their prospective scaling laws. Furthermore, we will explore the applicability of these representations to fully agnostic anomaly detection.