Cargando…
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
<!--HTML-->We explore the possibility of using graph networks to deal with irregular-geometry detectors when reconstructing particles. Thanks to their representation-learning capabilities, graph networks can exploit the detector granularity, while dealing with the event sparsity and the irreg...
| Autor principal: | |
|---|---|
| Lenguaje: | eng |
| Publicado: |
2019
|
| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2672564 |