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.
@article{arxiv.2405.12972,
title = {Accelerating Resonance Searches via Signature-Oriented Pre-training},
author = {Congqiao Li and Antonios Agapitos and Jovin Drews and Javier Duarte and Dawei Fu and Leyun Gao and Raghav Kansal and Gregor Kasieczka and Louis Moureaux and Huilin Qu and Cristina Mantilla Suarez and Qiang Li},
journal= {arXiv preprint arXiv:2405.12972},
year = {2024}
}