Transformer networks for Heavy flavor jet tagging
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913580037701632 |
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| author | Hammad, A. Nojiri, Mihoko M |
| author_facet | Hammad, A. Nojiri, Mihoko M |
| contents | In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the performance of state-of-the-art deep learning networks and further improvement coming from the modification of networks based on physics insights. Additionally, we discuss interpretable methods to understand network decision-making, which are crucial when employing highly complex and deep networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11519 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer networks for Heavy flavor jet tagging Hammad, A. Nojiri, Mihoko M High Energy Physics - Phenomenology In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on attention-based Transformer networks. We report the performance of state-of-the-art deep learning networks and further improvement coming from the modification of networks based on physics insights. Additionally, we discuss interpretable methods to understand network decision-making, which are crucial when employing highly complex and deep networks. |
| title | Transformer networks for Heavy flavor jet tagging |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2411.11519 |