27 citations · 103 across the 19 of their papers we have counts for
3 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…
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…
Improving Di-Higgs Sensitivity at Future Colliders in Hadronic Final States with Machine Learning
Artur Apresyan, Daniel Diaz, Javier Duarte +8
One of the central goals of the physics program at the future colliders is to elucidate the origin of electroweak symmetry breaking, including precision measurements of the Higgs s…