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CERN Document Server 找到 2,031 笔记录  1 - 10下一个最後  跳到记录: 检索需时 0.42 秒. 
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Deep Learning strategies for ProtoDUNE raw data denoising / Rossi, Marco (CERN ; INFN, Milan ; Milan U.) ; Vallecorsa, Sofia (CERN)
In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. [...]
arXiv:2103.01596.- 2022-01-07 - 9 p.
- Published in : Comput. Softw. Big Sci.: 6 (2022) , no. 1, pp. 2 Fulltext: 2103.01596 - PDF; document - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.2
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Deep Learning strategies for ProtoDUNE raw data denoising / Rossi, Marco (Università degli Studi e INFN Milano (IT)) ; Vallecorsa, Sofia (CERN)
In this work we investigate different machine learning based strategies for denoising raw simulation data from ProtoDUNE experiment. [...]
CERN-IT-2021-001. - 2021. - 9 p.
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Deep Learning strategies for ProtoDUNE raw data denoising / Rossi, Marco (speaker) (CERN)
In this work we investigate different machine learning based strategies for denoising raw simulation data from ProtoDUNE experiment. ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. [...]
2021 - 1537. Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics External links: Talk details; Event details In : 25th International Conference on Computing in High Energy & Nuclear Physics
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Supervised learning-based reconstruction of magnet errors in circular accelerators / Fol, E (CERN ; Frankfurt U.) ; Tomás, R (Frankfurt U.) ; Franchetti, G (Frankfurt U. ; Darmstadt, GSI)
Magnetic field errors and misalignments cause optics perturbations, which can lead to machine safety issues and performance degradation. The correlation between magnetic errors and deviations of the measured optics functions from design can be used in order to build supervised learning models able to predict magnetic errors directly from a selection of measured optics observables. [...]
2021 - 19 p. - Published in : Eur. Phys. J. Plus 136 (2021) 365 Fulltext: PDF;
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Using deep learning techniques on hardware accelerators for particle identification studies in ProtoDUNE / Rodriguez, Manuel (speaker) (CERN)
2021 - 643. Workshops; CERN openlab Technical Workshop External links: Talk details; Event details In : CERN openlab Technical Workshop
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FPGA-Accelerated Neural Network Inference for Ultra-Low-Latency Recalibration and Classification of Physics Objects at 40 MHz within CMS / Choudhury, Diptarko (speaker) (National Institute of Science Education and Research)
In the realm of data processing and physics analysis at the Large Hadron Collider (LHC), there exists a notable advantage of deep learning-based algorithms over traditional physics-based counterparts. This study explores cutting-edge methodologies for the low latency neural network inference on Field Programmable Gate Array (FPGA) devices. [...]
2023 - 446. CERN openlab Summer Student Programme 2023; CERN openlab Summer Student Lightning Talks (2/2) External links: Talk details; Event details In : CERN openlab Summer Student Lightning Talks (2/2)
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Point Cloud Deep Learning Methods for Pion Reconstruction in the ATLAS Detector / Portillo Quintero, Dilia Maria (TRIUMF (CA)) /ATLAS Collaboration
The reconstruction and calibration of hadronic final states in the ATLAS detector present complex experimental challenges. For isolated pions, in particular, classifying 𝜋0 versus 𝜋± and calibrating pion energy deposits in the ATLAS calorimeters are key steps in the hadronic reconstruction process. [...]
ATL-PHYS-SLIDE-2022-298.- Geneva : CERN, 2022 - 1 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-PHYS-SLIDE-2022-298
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ProtoDUNE revealed
Published in: CERN Courier Volume 57, Number 2, March 2017
Geneva : CERN, 2017
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AtmoRep: Machine learning-based atmosphere dynamics / Garcia Recasens, Pol (speaker)
2022 - 296. CERN openlab Summer Student Programme 2022; CERN openlab summer student Lightning talks Session 1 External links: Talk details; Event details In : CERN openlab summer student Lightning talks Session 1
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CERN-PHOTO-201612-306
© 2016-2022 CERN 		ProtoDUNE - roof leveling
ProtoDUNE - roof leveling
02-12-2016
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