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CERN Document Server 60 のレコードが見つかりました。  1 - 10次最後  レコードへジャンプ： 検索にかかった時間： 0.84 秒 
1.
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 Cost Optimization and Sustainability for Public Cloud Provider / Rocha, Ricardo (speaker) (CERN)
2025 - 843. Workshops and Training; 2025 CERN openlab Technical Workshop External links: Talk details; Event details In : 2025 CERN openlab Technical Workshop
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2.
Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning / DUNE Collaboration
The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. [...]
arXiv:2502.06637; FERMILAB-PUB-25-0037-LBNF.- 2025-06-25 - 24 p. - Published in : 10.1140/epjc/s10052-025-14313-8 Fulltext: 14efb83d5a7785c0edf297cd09123c2c - PDF; 2502.06637 - PDF; d3edfddb498a61df9dd782411ca4ae78 - PDF; document - PDF; Fulltext from Publisher: PDF; External link: Fermilab Accepted Manuscript
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3.
Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure / Cruz-Martinez, Juan (CERN) ; Jansen, Aaron (Netherlands eScience Center) ; van Oord, Gijs (Netherlands eScience Center) ; Rabemananjara, Tanjona R. (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Rocha, Carlos M.R. (Netherlands eScience Center) ; Rojo, Juan (CERN ; Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. These hyperparameters must be determined separately from the model parameters such as network weights, and are often fixed by ad-hoc methods or by manual inspection of the results. [...]
arXiv:2410.16248; CERN-TH-2024-168.- 2025-04-28 - 27 p. - Published in : Mach. Learn. Sci. Tech. 6 (2025) 025027 Fulltext: 2410.16248 - PDF; document - PDF;
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4.
An Update on the CERN Journey from Bare Metal to Orchestrated Containerization for Controls / Oulevey, Thomas (CERN) ; Copy, Brice (CERN) ; Locci, Frank (CERN) ; Page, Stephen (CERN) ; Rocha, R (CERN) ; Roderick, Chris (CERN) ; Vanden Eynden, Marc (CERN) ; de Martel, Jean-Baptiste (CERN)
At CERN, work has been undertaken since 2019 to transition from running Accelerator controls software on bare metal to running in an orchestrated, containerized environment. This will allow engineers to optimise infrastructure cost, to improve disaster recovery and business continuity, and to streamline DevOps practices along with better security. [...]
2023 - 7 p. - Published in : JACoW ICALEPCS 2023 (2023) TH2AO03 Fulltext: PDF;
In : 19th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2023), Cape Town, South Africa, 7 - 13 Oct 2023, pp.TH2AO03
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AI and Kubernetes @ CERN - Challenges / Rocha, Ricardo (speaker) (CERN) ; Nappi, Antonio (speaker) (CERN)
2024 - 1042. Workshops and Training; 2024 CERN openlab Technical Workshop External links: Talk details; Event details In : 2024 CERN openlab Technical Workshop
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6.
Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment / DUNE Collaboration
A primary goal of the upcoming Deep Underground Neutrino Experiment (DUNE) is to measure the $\mathcal{O}(10)$ MeV neutrinos produced by a Galactic core-collapse supernova if one should occur during the lifetime of the experiment. The liquid-argon-based detectors planned for DUNE are expected to be uniquely sensitive to the $\nu_e$ component of the supernova flux, enabling a wide variety of physics and astrophysics measurements. [...]
arXiv:2303.17007; FERMILAB-PUB-23-132-CSAID-LBNF-ND-T.- 2023-06-01 - 25 p. - Published in : Phys. Rev. D 107 (2023) 112012 Fulltext: 2303.17007 - PDF; FERMILAB-PUB-23-132-CSAID-LBNF-ND-T - PDF; Publication - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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7.
Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector / DUNE Collaboration
Measurements of electrons from $\nu_e$ interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. [...]
arXiv:2211.01166; FERMILAB-PUB-22-784; CERN-EP-DRAFT-MISC-2022-008.- 2023-05-01 - 19 p. - Published in : Phys. Rev. D 107 (2023) 092012 Fulltext: 2211.01166 - PDF; FERMILAB-PUB-22-784 - PDF; FERMILAB-PUB-22-784 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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8.
A Gaseous Argon-Based Near Detector to Enhance the Physics Capabilities of DUNE / DUNE Collaboration
This document presents the concept and physics case for a magnetized gaseous argon-based detector system (ND-GAr) for the Deep Underground Neutrino Experiment (DUNE) Near Detector. [...]
arXiv:2203.06281 ; FERMILAB-FN-1169-ND. - 31.
Fermilab Library Server - eConf - Fulltext - Fulltext
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9.
Snowmass Neutrino Frontier: DUNE Physics Summary / DUNE Collaboration
The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment with a primary physics goal of observing neutrino and antineutrino oscillation patterns to precisely measure the parameters governing long-baseline neutrino oscillation in a single experiment, and to test the three-flavor paradigm. [...]
arXiv:2203.06100 ; FERMILAB-FN-1168-LBNF. - 44.
Fermilab Library Server - eConf - Fulltext - Fulltext
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10.
Training and Serving ML workloads with Kubeflow at CERN / Golubovic, Dejan (CERN) ; Rocha, Ricardo (CERN)
Machine Learning (ML) has been growing in popularity in multiple areas and groups at CERN, covering fast simulation, tracking, anomaly detection, among many others. We describe a new service available at CERN, based on Kubeflow and managing the full ML lifecycle: data preparation and interactive analysis, large scale distributed model training and model serving. [...]
2021 - 10 p. - Published in : EPJ Web Conf. 251 (2021) 02067 Fulltext: PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.02067
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CERN Document Server : 60 のレコードが見つかりました。   1 - 10次最後  レコードへジャンプ：
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1 Rocha, R B D
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