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Machine Learning Applied at the LHC for Beam Loss Pattern Classification

Beam losses at the LHC are constantly monitored because they can heavily impact the performance of the machine. One of the highest risks is to quench the LHC superconducting magnets in the presence of losses leading to a long machine downtime in order to recover cryogenic conditions. Smaller losses...

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Detalles Bibliográficos
Autores principales: Valentino, Gianluca, Salvachua, Belen
Lenguaje:eng
Publicado: 2018
Materias:
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2018-WEPAF078
https://dx.doi.org/10.1088/1742-6596/1067/7/072036
http://cds.cern.ch/record/2667539