RODEM Jet Datasets
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917754874888192 |
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| author | Zoch, Knut Raine, John Andrew Sengupta, Debajyoti Golling, Tobias |
| author_facet | Zoch, Knut Raine, John Andrew Sengupta, Debajyoti Golling, Tobias |
| contents | We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RODEM Jet Datasets Zoch, Knut Raine, John Andrew Sengupta, Debajyoti Golling, Tobias High Energy Physics - Phenomenology We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling. |
| title | RODEM Jet Datasets |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2408.11616 |