Accelerating Resonance Searches via Signature-Oriented Pre-training

TL;DR

This paper introduces Sophon, a deep learning pre-training method that enhances the sensitivity and broadens the scope of heavy resonance searches at the LHC by learning complex jet signatures from extensive datasets.

Contribution

The paper presents a novel Signature-Oriented Pre-training approach that improves both model-specific and model-agnostic resonance search capabilities at the LHC.

Findings

Enhanced sensitivity in resonance searches

Broad coverage of boosted final states

Improved transfer learning performance

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.