1 citations · 1 across the 3 of their papers we have counts for
3 papers
hep-ex2025
Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
Lukas Péron, Paolo Calafiura, Xiangyang Ju +1
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the Track…
physics.data-an2025
Physics and Computing Performance of the EggNet Tracking Pipeline
Jay Chan, Brandon Wang, Paolo Calafiura
Particle track reconstruction is traditionally computationally challenging due to the combinatorial nature of the tracking algorithms employed. Recent developments have focused on…
physics.data-an2024★ 1 cited
EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
Paolo Calafiura, Jay Chan, Loic Delabrouille +1
Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks…