Accelerating Resonance Searches via Signature-Oriented Pre-training
Reviewed by Pithpith:MRBP4ZL7open to challenge →
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
"Hadron-in-fat-jet'' AI Tagging to Detect Rare Decays Such as $W^{\pm}\to\pi^{\pm}\gamma$
Proof-of-principle for hadron-in-fat-jet AI tagging that yields an expected 95% CL limit of B(W±→π±γ) < 2.78×10^{-5} at 450 fb^{-1}.
-
Towards Engineering Scaling Laws with Pretraining Data Composition
Pretraining data composition can be used to engineer neural scaling laws in hadronic jet classification toward data-heavy rather than model-size-heavy regimes.
-
Towards Engineering Scaling Laws with Pretraining Data Composition
Pretraining data composition can engineer scaling laws for jet classification to favor data scaling over model scaling.
-
Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.