Graph Neural Network Track Reconstruction for the ATLAS ITk Detector

Graph Neural Networks (GNNs) have been shown to produce high accuracy performance on a variety of HEP tasks, including track reconstruction in the TrackML challenge, and tagging in jet physics. However, GNNs are less explored in applications with noisy, heterogeneous or ambiguous data. These element...

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Detalles Bibliográficos
Autores principales: Murnane, Daniel Thomas, Vallier, Alexis, Rougier, Charline, Calafiura, Paolo, Stark, Jan, Ju, Xiangyang, Farrell, Steven Andrew, Caillou, Sylvain, Neubauer, Mark, Atkinson, Markus Julian
Lenguaje:eng
Publicado: 2022
Materias:
Particle Physics - Experiment
Acceso en línea:http://cds.cern.ch/record/2809518

Internet

http://cds.cern.ch/record/2809518

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