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CERN Document Server 2,017 record trovati  1 - 10successivofine  salta al record: La ricerca ha impiegato 0.50 secondi. 
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Performance of the ATLAS GNN4ITk Particle Track Reconstruction GPU pipeline / Poreba, Aleksandra (Heidelberg University (DE)) /ATLAS Collaboration
With the upcoming upgrade of High Luminosity LHC, the need for computation power will increase in the ATLAS trigger system by more than an order of magnitude. Therefore, new particle track reconstruction techniques are explored by the ATLAS collaboration, including the usage of Graph Neural Networks (GNN). [...]
ATL-DAQ-SLIDE-2024-642.- 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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(20)
The Trigger performance monitoring and rate predictions preparation for Run 3 at ATLAS experiment / Poreba, Aleksandra (AGH University of Science and Technology (PL))
Bespoke Cost Monitoring software collates data on the performance of all aspects of the ATLAS experiment's High Level Trigger software. [...]
ATL-DAQ-PROC-2021-010. - 2021. - 13 p.
Original Communication (restricted to ATLAS) - Full text
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(16)
Operational experience with the new ATLAS HLT framework for LHC Run 3 / Poreba, Aleksandra (Heidelberg University (DE))
Athena is the software framework used in the ATLAS experiment throughout the data processing path, from the software trigger system through offline event reconstruction to physics analysis. [...]
ATL-DAQ-PROC-2023-005. - 2024 - 6.
Original Communication (restricted to ATLAS) - Full text
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Operational experience with the new ATLAS HLT framework for LHC Run 3 / Poreba, Aleksandra (Heidelberg University (DE)) /ATLAS Collaboration
Athena is the software framework used in the ATLAS experiment throughout the data processing path, from the software trigger system through offline event reconstruction to physics analysis. For Run 3 data taking (which started in 2022) the framework has been reimplemented into a multi-threaded framework. [...]
ATL-DAQ-SLIDE-2023-538.- Geneva : CERN, 2023 - 21 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023
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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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7.
(7)
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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8.
(6)
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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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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(6)
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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