9 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Subash Katel, Haoyang Li, Zihan Zhao +3
In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a vari…
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Haoyang Li, Marko Stamenkovic, Alexander Shmakov +12
The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to b…
Novel machine learning applications at the LHC
Javier M. Duarte
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle phy…
Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics
Zihan Zhao, Farouk Mokhtar, Raghav Kansal +2
This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the incr…
Accelerating Resonance Searches via Signature-Oriented Pre-training
Congqiao Li, Antonios Agapitos, Jovin Drews +9
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 signifi…