Solving Key Challenges in Collider Physics with Foundation Models

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.

5 pages, 2 figures

References in corpus (5)

• Herwig++ Physics and Manual
• Classification without labels: Learning from mixed samples in high energy physics
• Extending the Bump Hunt with Machine Learning
• Particle-level pileup subtraction for jets and jet shapes
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Cited by in corpus (9)

• Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
• Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
• BitHEP -- The Limits of Low-Precision ML in HEP
• Reconstructing hadronically decaying tau leptons with a jet foundation model
• Anomaly preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC
• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
• Tools for Unbinned Unfolding
• Large Language Models -- the Future of Fundamental Physics?
• jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation