Accelerating Resonance Searches via Signature-Oriented Pre-training
arxiv(2024)
Abstract
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.
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