Foundation models for HEP

• Anna Maria Cecilia Hallin (Universität Hamburg)

Foundation models are machine learning models designed to handle a wide range of datasets and tasks. After being pre-trained on a specific task on a specific dataset, these models can be fine-tuned for various downstream applications, including different tasks and datasets. Developing such models for physics data could significantly enhance performance in the field and substantially cut down the necessary training time and data requirements. In this talk, I will give an introduction to foundation models and provide an overview of the foundation models that exist for particle physics today. I will also present our foundation model OmniJet-ɑ, and discuss some challenges and outlooks for the future.