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Charged particle tracking via edge-classifying interaction networks
<!--HTML-->Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well-suited to address a variety of recon- struction problems in HEP. In particular, tracker events are naturally repre- sented as graphs by identifying hits as nodes and track...
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| Lenguaje: | eng |
| Publicado: |
2021
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| Acceso en línea: | http://cds.cern.ch/record/2767798 |