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CERN Document Server 518,609 záznamov nájdených  1 - 10ďalšíkoniec  skoč na záznam: Hľadanie trvalo 0.85 sekúnd. 
1.
Common ATLAS, CMS and LHCb summary plots for Heavy Neutral Leptons (Fall 2025)
Summary plots for Heavy Neutral Leptons. [...]
ATL-PHYS-PUB-2025-048. - 2025 - 5.
Original Communication (restricted to ATLAS) - Full text
Úplný záznam
2.
Edge SpAIce: Deep Learning Deployment Pipeline for Onboard Data Reduction on Satellite FPGAs / D’Abbondanza, Noemi (U. Camerino (main) ; Italian Inst. Tech., Genoa) ; Tzelepis, Stylianos (CERN ; Natl. Tech. U., Athens) ; Ghielmetti, Nicolò (CERN) ; Kakogeorgiou, Ioannis (Natl. Tech. U., Athens) ; Buchova, Vanya (Unlisted, BG) ; Karantzalos, Konstantinos (Natl. Tech. U., Athens) ; Kikaki, Katerina (Natl. Tech. U., Athens ; Unlisted, GR) ; Lemoine, Nicolas-Marcel (Unlisted, FR) ; Pierini, Maurizio (CERN) ; Summers, Sioni (CERN) et al.
Earth Observation satellites are essential for monitoring global phenomena. However, their potential is often constrained by limited downlink bandwidth and ground-based processing capabilities. [...]
2025 - 7 p. - Published in : IEEE IPDPSW 25 (2025) 1243-1249
Úplný záznam
3.
Learning to Predict Network Paths: A Transformer Model with Confidence-Based Imputation / Vasileva, Petya (U. Michigan, Ann Arbor ; Plovdiv U. (main)) ; Babik, Marian (CERN) ; McKee, Shawn (U. Michigan, Ann Arbor) ; Vukotic, Ilija (U. Chicago (main))
The research and education community relies on a robust network to access the vast amounts of data generated by scientific experiments. The underlying infrastructure connects hundreds of sites around the world, requiring reliable and efficient transfers of increasingly large datasets. [...]
2025 - 8 p. - Published in : EPJ Web Conf. 337 (2025) 01115 Fulltext: PDF;
In : 27th International Conference on Computing in High Energy & Nuclear Physics (CHEP2024), Kraków, Poland, 19 - 25 Oct 2024, pp.01115
Úplný záznam
4.
Enhanced sensitivity for electron affinity measurements of rare elements / Maier, F M (Greifswald U. ; CERN ; TRIUMF ; Michigan State U.) ; Leistenschneider, E (CERN ; LBNL, NSD) ; Au, M (CERN) ; Bērziņš, U (Latvia U.) ; Gracia, Y N Vila (CERN) ; Hanstorp, D (U. Gothenburg (main)) ; Kanitz, C (CERN) ; Lagaki, V (Greifswald U. ; CERN) ; Lechner, S (CERN) ; Leimbach, D (CERN ; U. Gothenburg (main)) et al.
Abstract The electron affinity (EA), the energy released when a neutral atom binds an additional electron, is a fundamental property of atoms that is governed by electron-electron correlations and is strongly related to an element’s chemical reactivity. However, conventional techniques for EA determination lack the experimental sensitivity to probe very scarce samples [...]
2025 - 12 p. - Published in : Nature Commun. 16 (2025) 9576 Fulltext: PDF; External link: Interactions.org article
Úplný záznam
5.
RF design and optimization of the high-energy linac for the FCC-ee injector complex / Kurtulus, A (CERN ; ETH, Zurich (main)) ; Grudiev, A (CERN) ; Latina, A (CERN) ; Bettoni, S (PSI, Villigen) ; Craievich, P (PSI, Villigen) ; Raguin, J -Y (PSI, Villigen)
The high-energy linac of the Future Circular Collider electron-positron (FCC-ee) injector complex requires high-performance rf accelerating structures to efficiently accelerate beams up to 20 GeV while ensuring operational stability. This study presents an analytical approach to the rf design of traveling-wave structures, incorporating a pulse compression system to enhance power efficiency and meet the demanding FCC-ee specifications. [...]
2025 - 11 p. - Published in : Phys. Rev. Accel. Beams 28 (2025) 101601
Úplný záznam
6.
FPGA Implementation of a CNN-Based Topological Trigger for HL-LHC / Brooke, J (Bristol U.) ; Clement, E (Bristol U.) ; Glowacki, M (CERN) ; Paramesvaran, S (Bristol U.) ; Segal, J (Bristol U.)
The implementation of convolutional neural networks in programmable logic, for applications in fast online event selection at hadron colliders, is studied. In particular, an approach based on full event images for classification is studied, including hardware-aware optimisation of the network architecture, and evaluation of physics performance using simulated data. [...]
2025 - 12 p. - Published in : Comput. Softw. Big Sci. 9 (2025) 18 Fulltext: PDF;
Úplný záznam
7.
Brain MRI Screening Tool with Federated Learning / Stoklasa, Roman (CERN ; Masaryk U., Brno (main)) ; Stathopoulos, Ioannis (CERN ; Athens Natl. Capodistrian U.) ; Karavasilis, Efstratios (Democritus U., Thrace) ; Efstathopoulos, Efstathios (Athens Natl. Capodistrian U.) ; Dostál, Marek (Masaryk U., Brno (main)) ; Keřkovský, Miloš (Masaryk U., Brno (main)) ; Kozubek, Michal (Masaryk U., Brno (main)) ; Serio, Luigi (CERN)
In clinical practice, we often see significant delays between MRI scans and the diagnosis made by radiologists, even for severe cases. [...]
2024 - 5.
Úplný záznam
8.
Physical Properties of Materials and Testing Methods / Arauzo, Ana (U. Zaragoza (main))
The use of materials in particle accelerator applications implies the consideration of physical properties in different environments than usual industrial applications. [...]
arXiv:2509.19872. - 2025 - 15.
Fulltext
Úplný záznam
9.
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers / Santos, Diogo Reis (CERN) ; Aillet, Albert Sund (CERN) ; Boiano, Antonio (Unlisted, IT) ; Milasheuski, Usevalad (Unlisted, IT ; CNR, Italy) ; Giusti, Lorenzo (CERN) ; Gennaro, Marco Di (Unlisted, IT) ; Kianoush, Sanaz (CNR, Italy) ; Barbieri, Luca (Unlisted, IT) ; Nicoli, Monica (CERN) ; Carminati, Michele (Unlisted, IT) et al.
The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. [...]
2024 - 7.
Úplný záznam
10.
LEMURS dataset: Large-scale multi-detector ElectroMagnetic Universal Representation of Showers / McKeown, Peter (CERN) ; Raikwar, Piyush (CERN) ; Zaborowska, Anna (CERN)
We present LEMURS: an extensive dataset of simulated calorimeter showers designed to support the development and benchmarking of fast simulation methods in high-energy physics, most notably providing a step towards the development of foundation models. [...]
arXiv:2509.05108. - 2025 - 17.
Fulltext
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