تقرير
Accelerating Resonance Searches via Signature-Oriented Pre-training
| العنوان: | Accelerating Resonance Searches via Signature-Oriented Pre-training |
|---|---|
| المؤلفون: | 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 |
| سنة النشر: | 2024 |
| المجموعة: | High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability |
| الوصف: | 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. Comment: 14 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.12972 |
| رقم الأكسشن: | edsarx.2405.12972 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |