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1.
Improving Computational Performance of ATLAS GNN Track Reconstruction Pipeline / ATLAS Collaboration
Track reconstruction is an essential element of modern and future collider experiments, including the ATLAS detector. The HL-LHC upgrade of the ATLAS detector brings an unprecedented tracking reconstruction challenge, both in terms of the large number of silicon hit cluster readouts and the throughput required for budget-constrained track reconstruction. [...]
ATL-SOFT-SLIDE-2024-499.- Geneva : CERN, 2024 - 18 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024
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2.
Improving Computational Performance of a GNN Track Reconstruction Pipeline for ATLAS / ATLAS Collaboration
Track reconstruction is an essential element of modern and future collider experiments, including within the ATLAS detector. The HL-LHC upgrade of the ATLAS detector brings an unprecedented tracking challenge, both in terms of number of silicon hit cluster readouts, and throughput required for both high level trigger and offline track reconstruction. [...]
ATL-SOFT-SLIDE-2024-256.- Geneva : CERN, 2024 - 24 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Stony Brook, Us, 11 - 15 Mar 2024
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3.
Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain / ATLAS Collaboration
Graph-based techniques and graph neural networks (GNNs) in particular are a promising solution for particle track reconstruction at the HL-LHC. [...]
ATL-SOFT-PROC-2023-047. - 2023.
Original Communication (restricted to ATLAS) - Full text
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4.
Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain / Caillou, Sylvain (Centre National de la Recherche Scientifique (FR)) ; Calafiura, Paolo (Lawrence Berkeley National Lab. (US)) ; Farrell, Steven Andrew ; Ju, Xiangyang (Lawrence Berkeley National Lab. (US)) ; Murnane, Daniel Thomas (Lawrence Berkeley National Lab. (US)) ; Pham, Minh Tuan (University of Wisconsin Madison (US)) ; Rougier, Charline (Centre National de la Recherche Scientifique (FR)) ; Stark, Jan (Centre National de la Recherche Scientifique (FR)) ; Vallier, Alexis (Centre National de la Recherche Scientifique (FR))
Particle tracking is vital for the ATLAS physics programs. [...]
ATL-SOFT-PROC-2023-038. - 2024 - 7.
Original Communication (restricted to ATLAS) - Full text
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5.
ATLAS ITk Track Reconstruction with a GNN-based pipeline / Caillou, Sylvain (Centre National de la Recherche Scientifique (FR)) ; Calafiura, Paolo (Lawrence Berkeley National Lab. (US)) ; Farrell, Steven Andrew ; Ju, Xiangyang (Lawrence Berkeley National Lab. (US)) ; Murnane, Daniel Thomas (Lawrence Berkeley National Lab. (US)) ; Rougier, Charline (Centre National de la Recherche Scientifique (FR)) ; Stark, Jan (Centre National de la Recherche Scientifique (FR)) ; Vallier, Alexis (Centre National de la Recherche Scientifique (FR))
In preparation for the upcoming HL-LHC era, ATLAS is pursuing several methods to reduce the resources consumption needed to reconstruct the trajectory of charged particles (tracks) in the new all-silicon Inner Tracker (ITk). [...]
ATL-ITK-PROC-2022-006. - 2022 - 11.
Original Communication (restricted to ATLAS) - Full text
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6.
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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