We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of various known particles to pre-train the model with a data-centric approach -- the model uses a fraction of the jet constituents as the context to predict the embeddings of the unseen target constituents. Our pre-trained model fares well with other datasets for standard classification benchmark tasks. We test our model on two additional downstream tasks: top tagging and differentiating light-quark jets from gluon jets. We also evaluate our model with task-specific metrics and baselines and compare it with state-of-the-art models in high-energy physics. Project site: https://hep-jepa.github.io/
Cite
@article{arxiv.2502.03933,
title = {HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture},
author = {Jai Bardhan and Radhikesh Agrawal and Abhiram Tilak and Cyrin Neeraj and Subhadip Mitra},
journal= {arXiv preprint arXiv:2502.03933},
year = {2025}
}