Accelerating Resonance Searches via Signature-Oriented Pre-training
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911883487870976 |
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| author | Li, Congqiao Agapitos, Antonios Drews, Jovin Duarte, Javier Fu, Dawei Gao, Leyun Kansal, Raghav Kasieczka, Gregor Moureaux, Louis Qu, Huilin Suarez, Cristina Mantilla Li, Qiang |
| author_facet | Li, Congqiao Agapitos, Antonios Drews, Jovin Duarte, Javier Fu, Dawei Gao, Leyun Kansal, Raghav Kasieczka, Gregor Moureaux, Louis Qu, Huilin Suarez, Cristina Mantilla Li, Qiang |
| contents | The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Resonance Searches via Signature-Oriented Pre-training Li, Congqiao Agapitos, Antonios Drews, Jovin Duarte, Javier Fu, Dawei Gao, Leyun Kansal, Raghav Kasieczka, Gregor Moureaux, Louis Qu, Huilin Suarez, Cristina Mantilla Li, Qiang High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense. |
| title | Accelerating Resonance Searches via Signature-Oriented Pre-training |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2405.12972 |