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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...
| Autores principales: | , |
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| Lenguaje: | eng |
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2018
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| 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 |