View source for TDIS GNN Tracking

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==

[https://doi.org/10.48550/arXiv.2012.01249 Graph Neural Networks for Particle Tracking and Reconstruction]

[https://doi.org/10.48550/arXiv.2003.11603 Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors]

[https://doi.org/10.1007/s41781-021-00073-z Charged Particle Tracking via Edge‐Classifying Interaction Networks]

[https://iris-hep.org/projects/accel-gnn-tracking.html Accelerated GNN Tracking]

[https://github.com/gnn-tracking GitHub page on GNN tracking with some code]

===Belle 2 GNN Tracking===

[https://indico.jlab.org/event/459/contributions/11761/ Belle2 GNN Poster at CHEP 2023]. (See link to poster at bottom of page.)

[https://indico.belle2.org/event/7727/contributions/46515/attachments/19685/29191/22_11_30_B2Trigger_GNNTracking_lreuter.pdf GNN-based Track and Vertex Finding]

[https://publish.etp.kit.edu/record/22115 Full Event Interpretation using Graph Neural Networks - Lea Reuter Master's Thesis]

==Other Tracking Info==
[https://doi.org/10.48550/arXiv.1806.05880 Interfacing Geant4, Garfield++ and Degrad for the Simulation of Gaseous Detectors]