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Found 5 papers, 1 papers with code
Combined track finding with GNN & CKF
The application of Graph Neural Networks (GNN) in track reconstruction is a promising approach to cope with the challenges arising at the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC).
Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays
The layout of multi-dimensional data can have a significant impact on the efficacy of hardware caches and, by extension, the performance of applications.
Machine learning for surface prediction in ACTS
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction.
The Tracking Machine Learning challenge : Throughput phase
Both were measured on the Codalab platform where the participants had to upload their software.
Hashing and metric learning for charged particle tracking
We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search.
