Results 91 to 100 of about 1,288,945 (304)

Operational data for fault prognosis in particle accelerators with machine learning. [PDF]

Data Brief, 2023
Radaideh MI, Pappas C, Wezensky M, Cousineau S.   +3 more
europepmc   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Review of particle physics

, 2004
This biennial Review summarizes much of Particle Physics. Using data from previous editions, plus 1726 new measurements from 512 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons.
Particle Data Group, Particle Data Grp
core   +1 more source

Multilayer thermal insulation for superconducting particle accelerators

, 2022
Superconducting particle accelerators are an indispensable tool for high energy physics and basic sciences research. These accelerators use superconducting magnet and superconducting cavities for particle beam manipulation, needing cryogenic temperatures
D, Ram
core   +1 more source

Cherenkov Radiation in Optical Fibres as a Versatile Machine Protection System in Particle Accelerators. [PDF]

Sensors (Basel), 2023
Wolfenden J, Alexandrova AS, Jackson F, Mathisen S, Morris G, Pacey TH, Kumar N, Yadav M, Jones A, Welsch CP.   +9 more
europepmc   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Particle acceleration by wave scattering off dielectric spheres at whispering-gallery-mode resonance

Physical Review Special Topics. Accelerators and Beams, 2007
The large electromagnetic fields, created in wave scattering near a perfect dielectric sphere at the condition of whispering-gallery-mode resonances, are investigated as driving units for high energy charged particle accelerators.
Władysław Żakowicz
doaj   +1 more source

A novel experimental approach to characterize neutron fields at high- and low-energy particle accelerators. [PDF]

Sci Rep, 2022
Braccini S, Casolaro P, Dellepiane G, Mateu I, Mercolli L, Pola A, Rastelli D, Scampoli P.   +7 more
europepmc   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

Explainable physics-based constraints on reinforcement learning for accelerator optimization

Machine Learning: Science and Technology
We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior.
Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian   +3 more
doaj   +1 more source