Accelerating Resonance Searches via Signature-Oriented Pre-training
- Peking U., SKLNPT
- Hamburg U.
- UC, San Diego
- CERN
- Fermilab
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.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 2467539
- Report Number(s):
- FERMILAB-PUB-24-0699-V; arXiv:2405.12972; oai:inspirehep.net:2788738
- Journal Information:
- TBD, Journal Name: TBD
- Country of Publication:
- United States
- Language:
- English
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