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Title Learning representations of irregular particle-detector geometry with distance-weighted graph networks
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Author(s) Kieseler, Jan (speaker) (CERN)
Corporate author(s) CERN. Geneva
Imprint 2019-04-17. - 0:20:53.
Series (LPCC Workshops)
(3rd IML Machine Learning Workshop)
Lecture note on 2019-04-17T15:30:00
Subject category LPCC Workshops
Abstract 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 irregular detector geometry. In this context, we introduce two distance-weighted graph network architectures, the GarNet and the GravNet layers and we apply them to a typical particle reconstruction task. As an example, we consider a high granularity calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We focus the study on the basis for calorimeter reconstruction, clustering, and provide a quantitative comparison to alternative approaches. The proposed methods outperform previous methods or reach competitive performance while keeping favourable computing-resource consumption. Being geometry agnostic, they can be easily generalized to other use cases and to other detectors, e.g., tracking in silicon detectors.
Copyright/License © 2019-2024 CERN
Submitted by paul.seyfert@cern.ch

 


 Record created 2019-04-25, last modified 2022-11-02


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