A differentiable end-to-end model combining graph attention networks with clustering and fitting improves muon track reconstruction and pT estimation at the LHC compared to factorized approaches.
Zhao,et al., Track reconstruction as a service for collider physics, JINST20 (2025) no.06, P06002
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
hep-ex 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC
A differentiable end-to-end model combining graph attention networks with clustering and fitting improves muon track reconstruction and pT estimation at the LHC compared to factorized approaches.