Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained using self-supervision with masking due to the lack of labels. In many scientific domains, accurate simulations are plentiful and facilitate large, labeled datasets. This opens up new possibilities for pre-training. We present a systematic comparison of pre-training methods using the OmniLearned High Energy Physics FM framework. We test supervised classification, flow-matching generation, and self-supervised masked particle modeling. All models are pre-trained on the JetClass dataset and fine-tuned on two represent...
| Authors | Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni et al. |
|---|---|
| Primary category | hep-ph |
| Published | 12 June 2026 |
| Last updated at source | 19 August 2026 |
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