1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Scaling Graph Neural Networks for Particle Track Reconstruction
Alok Tripathy, Alina Lazar, Xiangyang Ju +3
Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithm…
physics.comp-ph2024★ 1 cited
Graph Neural Network-based Tracking as a Service
Haoran Zhao, Andrew Naylor, Shih-Chieh Hsu +8
Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computati…
hep-ph2024★ 1 cited
A Language Model for Particle Tracking
Andris Huang, Yash Melkani, Paolo Calafiura +4
Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. Ho…