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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.07867 |
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| _version_ | 1866915058208997376 |
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| author | Wildridge, Andrew J. Rodgers, Jack P. Colbert, Ethan M. yao, Yao Jung, Andreas W. Liu, Miaoyuan |
| author_facet | Wildridge, Andrew J. Rodgers, Jack P. Colbert, Ethan M. yao, Yao Jung, Andreas W. Liu, Miaoyuan |
| contents | Bumblebee is a foundation model for particle physics discovery, inspired by BERT. By removing positional encodings and embedding particle 4-vectors, Bumblebee captures both generator- and reconstruction-level information while ensuring sequence-order invariance. Pre-trained on a masked task, it improves dileptonic top quark reconstruction resolution by 10-20% and excels in downstream tasks, including toponium discrimination (AUROC 0.877) and initial state classification (AUROC 0.625). The flexibility of Bumblebee makes it suitable for a wide range of particle physics applications, especially the discovery of new particles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07867 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bumblebee: Foundation Model for Particle Physics Discovery Wildridge, Andrew J. Rodgers, Jack P. Colbert, Ethan M. yao, Yao Jung, Andreas W. Liu, Miaoyuan High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology Bumblebee is a foundation model for particle physics discovery, inspired by BERT. By removing positional encodings and embedding particle 4-vectors, Bumblebee captures both generator- and reconstruction-level information while ensuring sequence-order invariance. Pre-trained on a masked task, it improves dileptonic top quark reconstruction resolution by 10-20% and excels in downstream tasks, including toponium discrimination (AUROC 0.877) and initial state classification (AUROC 0.625). The flexibility of Bumblebee makes it suitable for a wide range of particle physics applications, especially the discovery of new particles. |
| title | Bumblebee: Foundation Model for Particle Physics Discovery |
| topic | High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2412.07867 |