A Method to Simultaneously Facilitate All Jet Physics Tasks

Abstract

Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system (pp versus ep), for generative models, for likelihood ratio estimation, and for anomaly detection. We consider, our OmniLearn approach thus as a jet-physics foundation model. It is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure.

12 pages, 7 figures

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• Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
• BitHEP -- The Limits of Low-Precision ML in HEP
• Jet Reconstruction with Mamba Networks in Collider Events
• OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
• CaloHadronic: a diffusion model for the generation of hadronic showers
• jBOT: Semantic Jet Representation Clustering Emerges from Self-Distillation
• Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction