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  • Letter
  • Open Access

Solving key challenges in collider physics with foundation models

Vinicius Mikuni1,* and Benjamin Nachman2,3,†

  • *Contact author: vmikuni@lbl.gov
  • †Contact author: bpnachman@lbl.gov
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Phys. Rev. D 111, L051504 – Published 17 March, 2025

DOI: https://doi.org/10.1103/PhysRevD.111.L051504

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.

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Method to simultaneously facilitate all jet physics tasks

Vinicius Mikuni and Benjamin Nachman
Phys. Rev. D 111, 054015 (2025)

Article Text

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Phys. Rev. D 111, L051504– Published 17 March, 2025
  • Received 21 May 2024
  • Accepted 25 February 2025
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