Cargando…
Detection of faulty beam position monitors using unsupervised learning
Optics measurements at the LHC are mainly based on turn-by-turn signal from hundreds of beam position monitors (BPMs). Faulty BPMs produce erroneous signal causing unreliable computation of optics functions. Therefore, detection of faulty BPMs prior to optics computation is crucial for adequate opti...
Autores principales: | Fol, E, Tomás, R, Coello de Portugal, J, Franchetti, G |
---|---|
Lenguaje: | eng |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1103/PhysRevAccelBeams.23.102805 http://cds.cern.ch/record/2744101 |
Ejemplares similares
-
Unsupervised machine learning for detection of faulty beam position monitors
por: Fol, Elena, et al.
Publicado: (2019) -
Application of Machine Learning to Beam Diagnostics
por: Fol, Elena, et al.
Publicado: (2019) -
Optics corrections using machine learning in the LHC
por: Fol, Elena, et al.
Publicado: (2019) -
Faulty Connections in the Beam Position Monitors and their effect on the Beam
por: Ellonen, Otto Henrik
Publicado: (2019) -
Application of Machine Learning to Beam Diagnostics
por: Fol, Elena, et al.
Publicado: (2019)