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CERN Document Server 14 ჩანაწერია ნაპოვნი  1 - 10შემდეგი  ჩანაწერთან გადასვლა: ძიებას დასჭირდა 1.69 წამი. 
1.
Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Burleson, Jared Dynes (Univ. Illinois at Urbana Champaign (US)) ; ATLAS Collaboration /ATLAS Collaboration
The next phase of high energy particle physics research at CERN will involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo upgrades to the online software tracking capabilities. [...]
ATL-DAQ-SLIDE-2024-675.- Geneva : CERN, 2025 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2024, West Lafayette, Us, 15 - 18 Oct 2024
დეტალური ჩანაწერი
2.
Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Burleson, Jared Dynes (Univ. Illinois at Urbana Champaign (US)) ; ATLAS Collaboration /ATLAS Collaboration
The next phase of high energy particle physics research at CERN will involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo upgrades to the online software tracking capabilities. [...]
ATL-DAQ-SLIDE-2024-674.- Geneva : CERN, 2025 - 16 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2024, West Lafayette, Us, 15 - 18 Oct 2024
დეტალური ჩანაწერი
3.
Unsupervised Machine Learning for Anomaly Detection in LHC Collider Searches / D'Avanzo, Antonio (University Federico II and INFN, Naples (IT)) /ATLAS Collaboration
Searches for new physics at the LHC traditionally use advanced simulations to model Standard Model (SM) processes in high-energy collisions. These are then compared with predictions from new-physics theories like dark matter and supersymmetry [...]
ATL-PHYS-SLIDE-2025-281.- Geneva : CERN, 2025 - 24 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
დეტალური ჩანაწერი
4.
Flavour Tagging with Graph Neural Networks with the ATLAS Detector / Santos, Helena (Laboratory of Instrumentation and Experimental Particle Physics (PT)) /ATLAS Collaboration
The identification of jets containing b-hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate b-jets from jets stemming from lighter quarks will be presented. [...]
ATL-PHYS>-SLIDE-2025-061.- Geneva : CERN, 2025 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : XXXII International Workshop on Deep Inelastic Scattering and Related Subjects, Cape Town, Za, 24 - 28 Mar 2025
დეტალური ჩანაწერი
5.
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
დეტალური ჩანაწერი
6.
Track Reconstruction for the ATLAS Detector’s Phase II Trigger and Data Acquisition System using Graph Neural Networks / Ahuja, Sudha (University of London (GB)) /ATLAS Collaboration
Track Reconstruction for the ATLAS Detector’s Phase II Trigger and Data Acquisition System using Graph Neural Networks
ATL-DAQ-SLIDE-2024-184.- Geneva : CERN, 2024 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
დეტალური ჩანაწერი
7.
Track reconstruction for the ATLAS Phase-II Event Filter using Graph Neural Networks on FPGAs / Dittmeier, Sebastian (Heidelberg University (DE)) /ATLAS Collaboration
The High-Luminosity LHC poses new challenges for the trigger and data acquisition system of the ATLAS experiment. The reconstruction of charged particle tracks is already now the computationally most intensive task of the trigger. [...]
ATL-DAQ-SLIDE-2024-027.- Geneva : CERN, 2024 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 2001 Spring Meeting Of Fachverbaende Gravitation Und Relativitaetstheorie (GR), Teilchenphysik (T), And Theoretische Und Mathematische Grundlagen Der Physik (MP) Of Deutsche Physikalische Gesellschaft, Bonn, Germany, 26 - 30 Mar 2001
დეტალური ჩანაწერი
8.
Track reconstruction for the ATLAS Phase-II Event Filter using GNNs on FPGAs / Dittmeier, Sebastian (Heidelberg University (DE)) ; ATLAS TDAQ Collaboration /ATLAS Collaboration
The High-Luminosity LHC (HL-LHC) will provide an order of magnitude increase in integrated luminosity and enhance the discovery reach for new phenomena. The increased pile-up foreseen during the HL-LHC necessitates major upgrades to the ATLAS detector and trigger. [...]
ATL-DAQ-SLIDE-2023-531.- Geneva : CERN, 2023 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
დეტალური ჩანაწერი
9.
Flavour Tagging with Graph Neural Networks with the ATLAS Detector / Froch, Alexander (Albert Ludwigs Universitaet Freiburg (DE)) /ATLAS Collaboration
The identification of jets containing $b$-hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate $b$-jets from jets stemming from lighter quarks will be presented. [...]
ATL-PHYS-SLIDE-2023-370.- Geneva : CERN, 2023 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 2023 European Physical Society Conference on High Energy Physics (EPS-HEP2023), Hamburg, Germany, 20 - 25 Aug 2023
დეტალური ჩანაწერი
10.
A ROOT feature for parsing PyTorch Geometric graph neural networks into C++ code for fast inference / Van Berkum, Stefan
Graph neural networks have proven effective in many different fields — including particle physics — and are typically trained using Python tools such as PyTorch Geometric. [...]
CERN-STUDENTS-Note-2023-076. - 2023
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