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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14330 |
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| _version_ | 1866916444861628416 |
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| author | Malara, Andrea |
| author_facet | Malara, Andrea |
| contents | The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration, employing innovative approaches including advanced neural network architectures, attention-based mechanisms, and adversarial training. These proceedings provide a comprehensive review of the state-of-the-art methods employed by both collaborations, highlighting their similarities, unique strengths, and limitations through a comparative analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring jets: substructure and flavour tagging in CMS and ATLAS Malara, Andrea High Energy Physics - Experiment The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration, employing innovative approaches including advanced neural network architectures, attention-based mechanisms, and adversarial training. These proceedings provide a comprehensive review of the state-of-the-art methods employed by both collaborations, highlighting their similarities, unique strengths, and limitations through a comparative analysis. |
| title | Exploring jets: substructure and flavour tagging in CMS and ATLAS |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.14330 |