Speaker
Description
I will present recent advancements in developing inclusive, large-scale pretrained models for large-radius jets at the LHC's general-purpose experiments. The discussion will begin with the Sophon model, trained on Delphes datasets as a demonstrative benchmark, and extend to the Global Particle Transformer (GloParT) models, which have been developed and deployed within CMS over the past three years. I will highlight their impact on the LHC physics program, showcasing how they (1) enable widespread model-specific searches to reach the sensitivity frontier and (2) substantially enhance the model-agnostic approaches as well, thereby unlocking previously unreached physics potential. I will conclude with insights into the underlying deep learning methodologies and discuss directions for future advancements.
| Would you like to be considered for an oral presentation? | Yes |
|---|
