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

- Institution: CERN
- Department: LPCC Workshops
- Speaker: Jan Kieseler
- Year: 2019
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3006297)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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..

0:00:00 Slide 1

0:00:27 Slide 2

0:01:36 Slide 3

0:02:36 Slide 4

0:04:05 Slide 5

0:05:11 Slide 6

0:07:24 Slide 7

0:09:36 Slide 8

0:11:18 Slide 9

0:12:22 Slide 10

0:14:03 Slide 11

0:15:13 Slide 12

0:15:59 Slide 13

0:17:26 Slide 14

## Lectures

1. [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://invisiblenetwork.ai/learn/cern/3006297/f47ac86a2892), 21 min (CERN's player)