OmniJet-$α$: The first cross-task foundation model for particle physics
This work addresses the need for general-purpose models in particle physics to improve performance and reduce training time and data requirements, representing a major step but still incremental progress.
The authors tackled the challenge of developing a cross-task foundation model for particle physics by introducing a higher-fidelity tokenization and demonstrating transfer learning between unsupervised jet generation and supervised jet tagging with their OmniJet-α model, achieving the first successful transfer between these task classes.
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of training time and data. We report significant progress on this challenge on several fronts. First, a comprehensive set of evaluation methods is introduced to judge the quality of an encoding from physics data into a representation suitable for the autoregressive generation of particle jets with transformer architectures (the common backbone of foundation models). These measures motivate the choice of a higher-fidelity tokenization compared to previous works. Finally, we demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-$α$ model. This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.