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End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks
We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with objec...
| Autores principales: | , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1140/epjc/s10052-022-10665-7 http://cds.cern.ch/record/2807254 |