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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.26218 |
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| _version_ | 1866912853187887104 |
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| author | Visive, Ambre Moskvitina, Polina Nellist, Clara de Austri, Roberto Ruiz Caron, Sascha |
| author_facet | Visive, Ambre Moskvitina, Polina Nellist, Clara de Austri, Roberto Ruiz Caron, Sascha |
| contents | We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures trained to reconstruct background events via masked-token prediction, our method identifies anomalies through deviations in reconstruction performance, without prior knowledge of signal characteristics. Applied to searches for simultaneous four-top-quark production, this token-based approach shows competitive performance against established unsupervised methods and effectively captures subtle discrepancies in collider data, suggesting a promising direction for model-independent searches for new physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Event Tokenization and Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider Visive, Ambre Moskvitina, Polina Nellist, Clara de Austri, Roberto Ruiz Caron, Sascha High Energy Physics - Experiment High Energy Physics - Phenomenology We propose a novel use of Large Language Models (LLMs) as unsupervised anomaly detectors in particle physics. Using lightweight LLM-like networks with encoder-based architectures trained to reconstruct background events via masked-token prediction, our method identifies anomalies through deviations in reconstruction performance, without prior knowledge of signal characteristics. Applied to searches for simultaneous four-top-quark production, this token-based approach shows competitive performance against established unsupervised methods and effectively captures subtle discrepancies in collider data, suggesting a promising direction for model-independent searches for new physics. |
| title | Event Tokenization and Masked-Token Prediction for Anomaly Detection at the Large Hadron Collider |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.26218 |