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Identifying Merged Tracks in Dense Environments with Machine Learning
Tracking in high density environments plays an important role in many physics analyses at the LHC. In such environments, it is possible that two nearly collinear particles contribute to the same hits as they travel through the ATLAS pixel detector and semiconductor tracker. If the two particles are...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2676509 |