Eur. Phys. J. C (2019) 79: 608
https://doi.org/10.1140/epjc/s10052-019-7113-9

Special Article - Tools for Experiment and Theory

Learning representations of irregular particle-detector geometry with distance-weighted graph networks

¹ CERN, Geneva, Switzerland
² National University of Sciences and Technology, Islamabad, Pakistan

^* e-mail: jan.kieseler@cern.ch

Received: 7 March 2019
Accepted: 5 July 2019
Published online: 18 July 2019

Abstract

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.