Learning representations of irregular particle-detector geometry with distance-weighted graph networks
| Autor: | Qasim, Shah Rukh, Kieseler, Jan, Iiyama, Yutaro, Pierini, Maurizio |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Eur. Phys. J. C, 79 7 (2019) 608 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1140/epjc/s10052-019-7113-9 |
| Popis: | 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 detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors. Comment: 10p, v2: published version |
| Databáze: | arXiv |
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