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CERN Accelerating science

CERN Document Server 2,018 εγγραφές βρέθηκαν  1 - 10επόμενοτέλος  μετάβαση στην εγγραφή: Η έρευνα πήρε 0.66 δευτερόλεπτα. 
1.
ATLAS Data Analysis using a Parallel Workflow on Distributed Cloud-based Services with GPUs / Sandesara, Jay Ajitbhai (Amherst College (US)) ; Coelho Lopes De Sa, Rafael (University of Massachusetts (US)) ; Martinez Outschoorn, Verena Ingrid (University of Massachusetts (US)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) ; Elmsheuser, Johannes (Brookhaven National Laboratory (US)) ; Klimentov, Alexei (Brookhaven National Laboratory (US))
A new type of parallel workflow is developed for the ATLAS experiment at the Large Hadron Collider, that makes use of distributed computing combined with a cloud-based infrastructure. [...]
ATL-SOFT-PROC-2023-023.
- 2024 - 8.
Original Communication (restricted to ATLAS) - Full text
2.
ATLAS data analysis using a parallelized workflow on distributed cloud-based services with GPUs / Sandesara, Jay Ajitbhai (Amherst College (US)) ; Coelho Lopes De Sa, Rafael (University of Massachusetts (US)) ; Martinez Outschoorn, Verena Ingrid (University of Massachusetts (US)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) ; Elmsheuser, Johannes (Brookhaven National Laboratory (US)) ; Klimentov, Alexei (Brookhaven National Laboratory (US)) /ATLAS Collaboration
We present a new implementation of simulation-based inference using data collected by the ATLAS experiment at the LHC. The method relies on large ensembles of deep neural networks to approximate the exact likelihood. [...]
ATL-SOFT-SLIDE-2023-169.- 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
3.
Accelerating science: the usage of commercial clouds in ATLAS Distributed Computing / ATLAS Collaboration
The ATLAS experiment at CERN is one of the largest scientific ma- chines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. [...]
ATL-SOFT-PROC-2023-020.
- 2024 - 11.
Original Communication (restricted to ATLAS) - Full text
4.
Accelerating science: the usage of commercial clouds in ATLAS distributed computing / ATLAS Collaboration
The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D; projects on Amazon and Google clouds as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. [...]
ATL-SOFT-SLIDE-2023-151.- Geneva : CERN, 2023 - 13 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
5.
Parameter Estimation with Neural Simulation-Based Inference in ATLAS / Coelho Lopes De Sa, Rafael (University of Massachusetts (US)) ; Ghosh, Aishik (University of California Irvine (US)) ; Louppe, Gilles Claude ; Martinez Outschoorn, Verena Ingrid (University of Massachusetts (US)) ; Maury, Arnaud Jean (Université Paris-Saclay (FR)) ; Rousseau, David (Université Paris-Saclay (FR)) ; Sandesara, Jay Ajitbhai (University of Massachusetts (US)) ; Schaffer, R D (Université Paris-Saclay (FR)) ; Whiteson, Daniel (University of California Irvine (US)) /ATLAS Collaboration
Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. [...]
ATL-PHYS-SLIDE-2024-566.- Geneva : CERN, 2024 - 41 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : ML4Jets2024, Paris, Fr, 4 - 8 Nov 2024
6.
Measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel / Sandesara, Jay Ajitbhai (University of Massachusetts (US)) /ATLAS Collaboration
A measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel is presented. The measurement uses the 140~fb$^{-1}$ of integrated luminosity collected by the ATLAS detector during the Run 2 proton-proton collisions of the Large Hadron Collider at $\sqrt{s}=13$~TeV and supersedes our previous result in this decay channel using the same data set. [...]
ATL-PHYS-SLIDE-2024-575.- Geneva : CERN, 2024 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Higgs 2024, Uppsala, Se, 4 - 8 Nov 2024
7.
intelligent Data Delivery Service (iDDS) / Bockelman, Brian Paul (University of Wisconsin Madison (US)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) ; Guan, Wen (University of Wisconsin Madison (US)) ; Lin, Fa-Hui (University of Texas at Arlington (US)) ; Maeno, Tadashi (Brookhaven National Laboratory (US)) ; Weber, Christian (Brookhaven National Laboratory (US)) ; Wenaus, Torre (Brookhaven National Laboratory (US)) ; Zhang, Rui (University of Wisconsin Madison (US)) /ATLAS Collaboration
The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. It has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and primary processing in large scale workflows. [...]
ATL-SOFT-SLIDE-2022-249.- Geneva : CERN, 2022 - 15 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 41st International Conference on High Energy Physics (ICHEP 2022), Bologna, Italy, 6 - 13 Jul 2022, pp.ATL-SOFT-SLIDE-2022-249
8.
A Function-as-a-Task Workflow Management Approach with PanDA and iDDS / ATLAS Collaboration
The growing complexity of high energy physics analysis often involves running various distinct applications. [...]
ATL-SOFT-PROC-2024-001.
- 2024. - 7 p.
Original Communication (restricted to ATLAS) - Full text
9.
A Function-as-a-Task Workflow Management Approach with PanDA and iDDS / ATLAS Collaboration
The growing complexity of high energy physics analysis often involves running a large number of different tools. This demands a multi-step data processing approach, with each step requiring different resources and carrying dependencies on preceding steps. [...]
ATL-SOFT-SLIDE-2024-037.- Geneva : CERN, 2024 - 21 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
10.
Seamless integration of commercial Clouds with ATLAS Distributed Computing / Elmsheuser, Johannes (Brookhaven National Laboratory (US)) ; Barreiro Megino, Fernando Harald (University of Texas at Arlington (US)) ; Bawa, Harinder Singh (California State University (US)) ; De, Kaushik (University of Texas at Arlington (US)) ; Klimentov, Alexei (Brookhaven National Laboratory (US)) ; Lassnig, Mario (CERN) ; Serfon, Cedric (Brookhaven National Laboratory (US)) ; Wegner, Tobias (Bergische Universitaet Wuppertal (DE)) /ATLAS Collaboration
The CERN ATLAS Experiment successfully uses a worldwide distributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700'000 concurrently running production and analysis jobs to process simulation and detector data. [...]
ATL-SOFT-SLIDE-2021-130.- Geneva : CERN, 2021 - 11 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021

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