2025-03-05 14:59 |
詳細記錄 - 相似記錄
|
2025-03-05 14:57 |
詳細記錄 - 相似記錄
|
2025-03-04 15:06 |
詳細記錄 - 相似記錄
|
2025-03-04 09:44 |
詳細記錄 - 相似記錄
|
2025-03-04 08:53 |
詳細記錄 - 相似記錄
|
2025-03-04 08:52 |
詳細記錄 - 相似記錄
|
2025-03-03 16:17 |
A New Heart for ATLAS
/ Vormwald, Benedikt (CERN)
/ATLAS Collaboration
The upgrade of the LHC to the High Luminosity LHC (HL-LHC) by the end of this decade will impose significant challenges on the detectors of the LHC experiments. Increased luminosity of up to with up to 200 simultaneous p-p interactions per bunch crossing and foreseen run-times equivalent to up to make it necessary to develop new detectors that can cope with the corresponding radiation damage, occupancy, and bandwidth needs. [...]
ATL-ITK-SLIDE-2025-020.-
Geneva : CERN, 2025 - 26 p.
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Vienna Conference on Instrumentation, Vienna, Austria, At, 17 - 21 Feb 2025
|
詳細記錄 - 相似記錄
|
2025-02-26 13:13 |
詳細記錄 - 相似記錄
|
2025-02-25 14:59 |
The ATLAS RPC Phase II upgrade for High Luminosity LHC era
/ Falsetti, Gregorio (Universita della Calabria e INFN (IT))
/ATLAS Collaboration
Resistive Plate Chamber detectors play a crucial role in triggering events with muons in the ATLAS central region. In view of the High Luminosity LHC program, this system is facing a significant upgrade In the next few years, 306 triplets of new generation RPCs will be installed in the innermost region of the ATLAS Muon Barrel Spectrometer, increasing the number of tracking layers from 6 to 9, doubling the trigger lever arm and increasing the coverage. [...]
ATL-MUON-SLIDE-2025-018.-
Geneva : CERN, 2025 - 1 p.
Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
|
詳細記錄 - 相似記錄
|
2025-02-25 10:54 |
AthenaTriton: A Tool for running Machine Learning Inference as a Service in Athena
/ Chou, Yuan-Tang (University of Washington (US)) ; Stanislaus, Beojan (Lawrence Berkeley National Lab. (US)) ; Leggett, Charles (Lawrence Berkeley National Lab. (US)) ; Zhao, Haoran (University of Washington (US)) ; Esseiva, Julien (Lawrence Berkeley National Lab. (US)) ; Calafiura, Paolo (Lawrence Berkeley National Lab. (US)) ; Hsu, Shih-Chieh (University of Washington (US)) ; Tsulaia, Vakhtang (Lawrence Berkeley National Lab. (US)) ; Ju, Xiangyang (Lawrence Berkeley National Lab. (US))
/ATLAS Collaboration
Machine Learning (ML)-based algorithms play increasingly important roles in almost all aspects of the data analyses in ATLAS. Diverse ML models are used in detector simulations, event reconstructions, and data analyses. [...]
ATL-SOFT-SLIDE-2024-663.-
Geneva : CERN, 2025 - 14 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
|
詳細記錄 - 相似記錄
|
|
|