HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917914848788480 |
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| author | Bardhan, Jai Agrawal, Radhikesh Tilak, Abhiram Neeraj, Cyrin Mitra, Subhadip |
| author_facet | Bardhan, Jai Agrawal, Radhikesh Tilak, Abhiram Neeraj, Cyrin Mitra, Subhadip |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture Bardhan, Jai Agrawal, Radhikesh Tilak, Abhiram Neeraj, Cyrin Mitra, Subhadip Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology 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/ |
| title | HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture |
| topic | Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2502.03933 |