Difference between revisions of "TDIS GNN Tracking"
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[https://iris-hep.org/projects/accel-gnn-tracking.html Accelerated GNN Tracking] | [https://iris-hep.org/projects/accel-gnn-tracking.html Accelerated GNN Tracking] | ||
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===Belle 2 GNN Tracking=== | ===Belle 2 GNN Tracking=== | ||
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[https://publish.etp.kit.edu/record/22115 Full Event Interpretation using Graph Neural Networks - Lea Reuter Master's Thesis] | [https://publish.etp.kit.edu/record/22115 Full Event Interpretation using Graph Neural Networks - Lea Reuter Master's Thesis] | ||
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==Other Tracking Info== | ==Other Tracking Info== | ||
[https://doi.org/10.48550/arXiv.1806.05880 Interfacing Geant4, Garfield++ and Degrad for the Simulation of Gaseous Detectors] | [https://doi.org/10.48550/arXiv.1806.05880 Interfacing Geant4, Garfield++ and Degrad for the Simulation of Gaseous Detectors] | ||
Revision as of 15:35, 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
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
