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Unsupervised machine learning for detection of faulty beam position monitors
Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of a decision trees-based algorithm to faulty BPMs detection at the LHC. This method is f...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.18429/JACoW-IPAC2019-WEPGW081 http://cds.cern.ch/record/2693725 |