Solving Key Challenges in Collider Physics with Foundation Models

TL;DR

This paper demonstrates how a new Foundation Model for hadronic jets can address key collider physics challenges, improving computational efficiency, uncertainty quantification, and model-agnostic new physics searches, thereby advancing deep learning applications in science.

Contribution

Introduces a novel Foundation Model for hadronic jets that tackles three major collider physics challenges, enhancing practical scientific analysis capabilities.

Findings

Reduced computational costs in reconstruction algorithms

Enabled comprehensive uncertainty quantification

Facilitated model-agnostic new physics searches

Abstract

Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new Foundation Model for hadronic jets to solve three key challenges in collider physics. In particular, we 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.