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Anomaly Detection on the High Throughput Network of the ATLAS TDAQ System
As the volume of data recorded from systems increases, there is a need to effectively analyse this data to gain insights about the system. One such analysis requirement is anomaly detection. Data-driven approaches such as machine learning, are by construction, able to \emph{learn} (to some degree) t...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2747194 |