HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture

• URL: http://arxiv.org/abs/2502.03933v1
• Date: Thu, 06 Feb 2025 10:16:27 GMT
• Title: HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture
• Authors: Jai Bardhan, Radhikesh Agrawal, Abhiram Tilak, Cyrin Neeraj, Subhadip Mitra,
• Abstract summary: We present a transformer architecture-based foundation model for tasks at high-energy particle colliders. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. Our model fares well with other datasets for standard classification benchmark tasks.
• Score: 0.0
• License:
• Abstract: 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/

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