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71.
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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
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72.
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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
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73.
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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
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74.
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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;
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75.
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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.
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76.
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77.
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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.
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78.
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79.
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UA9 apparatus for testing beam merging
/ Scandale, W (Imperial Coll., London) ; Hall, G (Imperial Coll., London) ; Pesaresi, M (Imperial Coll., London) ; Rossi, R (Imperial Coll., London) ; Uchida, K (Imperial Coll., London) ; Cerutti, F (CERN) ; Esposito, L S (CERN) ; Gilardoni, S (CERN) ; Losito, R (CERN) ; Galluccio, F (INFN, Naples) et al.
The UA9 Collaboration has used a high-resolutionsilicon-strip telescope and a high-precision goniometer toinvestigate coherent and incoherent crystal-particle interactions inthe North Experimental Area of the CERN SPS. Over the last fifteenyears, a multitude of observations have provided a solidunderstanding of the revealed process, fostering the development offundamentally novel and useful applications for particleaccelerators, such as crystal-assisted collimation in the LHCcollider and crystal-aided extraction in the SPS synchrotron.The UA9 setup has recently been modified to test a two-beam mergingprocess assisted by bent crystals, a complex beam manipulationproposed to merge the bunch intensity of two converging beams. [...]
2025 - 15 p.
- Published in : JINST 20 (2025) P10049
Fulltext: PDF;
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80.
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