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CERN Document Server 2,019 записей найдено  1 - 10следующийконец  перейти к записи: Поиск длился 0.37 секунд. 
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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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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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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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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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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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High Performance Graph Segmentation for ATLAS GNN Track Reconstruction / Murnane, Daniel Thomas (University of Copenhagen (DK)) ; Liu, Ryan (Lawrence Berkeley National Lab. (US)) ; Condren, Levi Harris Jaxon (University of California Irvine (US)) ; Vallier, Alexis (Centre National de la Recherche Scientifique (FR)) ; Whiteson, Daniel (University of California Irvine (US)) ; Lazar, Alina (Youngstown State University (US)) ; Ju, Xiangyang (Lawrence Berkeley National Lab. (US)) /ATLAS Collaboration
Graph neural networks and deep geometric learning have been successfully proven in the task of track reconstruction in recent years. The GNN4ITk project employs these techniques in the context of the ATLAS upgrade ITk detector to produce similar physics performance as traditional techniques, while scaling sub-quadratically. [...]
ATL-SOFT-SLIDE-2024-503.- Geneva : CERN, 2024 - 39 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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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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Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain / Torres, Heberth (Centre National de la Recherche Scientifique (FR)) /ATLAS Collaboration
Applying graph-based techniques, and graph neural networks (GNNs) in particular, has been shown to be a promising solution to the high-occupancy track reconstruction problems posed by the upcoming HL- LHC era. Simulations of this environment present noisy, heterogeneous and ambiguous data, which previous GNN-based algorithms for ATLAS ITk track reconstruction could not handle natively. [...]
ATL-SOFT-SLIDE-2023-591.- Geneva : CERN, 2023 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Connecting The Dots (CTD 2023), Toulouse, Fr, 10 - 13 Oct 2023
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New approaches for fast and efficient graph construction on CPU, GPU and heterogeneous architectures for the ATLAS event reconstruction / Collard, Christophe (Centre National de la Recherche Scientifique (FR)) /ATLAS Collaboration
Graph neural networks (GNN) have emerged as a cornerstone of ML-based reconstruction and analysis algorithms in particle physics. Many of the proposed algorithms are intended to be deployed close to the beginning of the data processing chain, e.g. [...]
ATL-SOFT-SLIDE-2024-549.- Geneva : CERN, 2024 - 1 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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The ATLAS ITk detector for the HL-LHC / ATLAS Collaboration ; Calderini, Giovanni (Centre National de la Recherche Scientifique (FR)) /ATLAS Collaboration
To be filled
ATL-ITK-SLIDE-2021-711.- Geneva : CERN, 2021 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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