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

## TL;DR

This paper introduces novel distance-weighted graph network architectures, GarNet and GravNet, for particle reconstruction in irregular detector geometries, demonstrating their effectiveness and efficiency on simulated calorimeter data.

## Contribution

The paper presents two new graph network architectures tailored for irregular detector geometries, enabling improved particle reconstruction with less resource demand and greater generalization.

## Key findings

- Comparable or better performance than existing methods
- Reduced computational resource requirements
- Enhanced generalization to different detector geometries

## 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.