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Learning representations of irregular particle-detector geometry with distance-weighted graph networks
We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1140/epjc/s10052-019-7113-9 http://cds.cern.ch/record/2666193 |
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