Difference between revisions of "TDIS GNN Tracking"
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[https://github.com/gnn-tracking GitHub page on GNN tracking with some code] | [https://github.com/gnn-tracking GitHub page on GNN tracking with some code] | ||
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| + | [https://doi.org/10.48550/arXiv.2303.01640 Hierarchical Graph Neural Networks for Particle Track Reconstruction] | ||
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| + | [https://indico.cern.ch/event/955026/contributions/4012924/attachments/2127589/3582355/IRIS-Topical_GNN_Tracking.pdf GNN Tracking] (CERN Powerpoint) | ||
===Belle 2 GNN Tracking=== | ===Belle 2 GNN Tracking=== | ||
Latest revision as of 15:39, 3 August 2023
Graph Neural Networks (GNN) are a type of machine learning. GNN algorithms are being increasingly used for track finding and could be the best method of tracking for the mTPC.
Articles and resources
Graph Neural Networks for Particle Tracking and Reconstruction
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Charged Particle Tracking via Edge‐Classifying Interaction Networks
GitHub page on GNN tracking with some code
Hierarchical Graph Neural Networks for Particle Track Reconstruction
GNN Tracking (CERN Powerpoint)
Belle 2 GNN Tracking
Belle2 GNN Poster at CHEP 2023. (See link to poster at bottom of page.)
GNN-based Track and Vertex Finding
Full Event Interpretation using Graph Neural Networks - Lea Reuter Master's Thesis
Other Tracking Info
Interfacing Geant4, Garfield++ and Degrad for the Simulation of Gaseous Detectors
