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Graph Neural Network Track Reconstruction for the ATLAS ITk Detector
/ Murnane, Daniel Thomas (Lawrence Berkeley National Lab. (US)) ; Vallier, Alexis (Centre National de la Recherche Scientifique (FR)) ; Rougier, Charline (Centre National de la Recherche Scientifique (FR)) ; Calafiura, Paolo (Lawrence Berkeley National Lab. (US)) ; Stark, Jan (Centre National de la Recherche Scientifique (FR)) ; Ju, Xiangyang (Lawrence Berkeley National Lab. (US)) ; Farrell, Steven Andrew ; Caillou, Sylvain (Centre National de la Recherche Scientifique (FR)) ; Neubauer, Mark (Univ. Illinois at Urbana Champaign (US)) ; Atkinson, Markus Julian (Univ. Illinois at Urbana Champaign (US))
/ATLAS Collaboration
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. [...]
ATL-ITK-SLIDE-2022-119.-
Geneva : CERN, 2022 - 31 p.
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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