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Deep learning for inferring cause of data anomalies
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the...
| Autores principales: | , , , , , , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/1085/4/042015 http://cds.cern.ch/record/2644811 |