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1.
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
2.
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
3.
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
4.
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

See also: similar author names
15 Collard, C
81 Collard, C.
1455 Collard, Caroline
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