Foundation Models for Particle Physics

Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this presentation, I will introduce OmniLearn, a foundational model for collider physics capable of solving many tasks and adapting to multiple datasets. To showcase the benefits of OmniLearn I will show how foundational models are able to solve three key challenges in collider physics. In particular, I show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet Foundation Models beyond proof-of-principle studies and into the toolkit of practitioners.