TDIS GNN Tracking

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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

Accelerated GNN Tracking

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

Retrieved from "https://hallaweb.jlab.org/wiki/index.php?title=TDIS_GNN_Tracking&oldid=44747"

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  • This page was last modified on 3 August 2023, at 16:39.
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