Interpretable deep learning models for the inference and classification of LHC data
Abstract The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background proce...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
SpringerOpen
2024-05-01
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| Colecção: | Journal of High Energy Physics |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/JHEP05(2024)004 |
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