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Topology classification with deep learning to improve real-time event selection at the LHC
We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and h...
| Autores principales: | , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/s41781-019-0028-1 http://cds.cern.ch/record/2631618 |