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Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Dittmeier, Sebastian (Heidelberg University (DE)) ; ATLAS Collaboration /ATLAS Collaboration
For the HL-LHC upgrade of the ATLAS TDAQ system, a heterogeneous computing farm deploying GPUs and/or FPGAs is under study, together with the use of modern machine learning algorithms such as Graph Neural Networks (GNNs). We present a study on the reconstruction of tracks in the ATLAS Inner Tracker using GNNs on FPGAs for the Event Filter system. [...]
ATL-DAQ-SLIDE-2024-614.- Geneva : CERN, 2024 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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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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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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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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Computational Performance of the ATLAS ITk GNN Track Reconstruction Pipeline
The ATLAS event reconstruction chain is projected to increase dramatically in computational cost with the upgrade to the HL-LHC. [...]
ATL-PHYS-PUB-2024-018. - 2024 - 9.
Original Communication (restricted to ATLAS) - Full text

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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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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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Deep learning techniques for energy clustering in the CMS electromagnetic calorimeter / Simkina, Polina (Saclay) /CMS Collaboration
The reconstruction of electrons and photons in CMS depends on the topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). The currently used algorithm cannot account for the energy deposits coming from the pileup (secondary collisions) efficiently. [...]
2023 - Published in : Nucl. Instrum. Methods Phys. Res., A 1049 (2023) 168082
In : 15th Pisa Meeting on Advanced Detectors, La Biodola - Isola D'elba, Italy, 22 - 28 May 2022, pp.168082

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Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs / Gupta, Sachin (Heidelberg University (DE)) ; ATLAS TDAQ Collaboration
The High-Luminosity LHC (HL-LHC) will provide an order of magnitude increase in integrated luminosity and the amount of data produced per event. [...]
ATL-DAQ-PROC-2023-014. - 2023.
Original Communication (restricted to ATLAS) - Full text

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10.

Machine learning for displaced vertex classification / Bekaert, Ruben Lukas
Displaced Vertices are an important signature for new physics searches at the Large Hadron Collider. [...]
CERN-STUDENTS-Note-2023-206. - 2023
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