# Charged particle tracking via edge-classifying interaction networks

- Institution: CERN
- Department: Conferences
- Speaker: Gage DeZoort
- Year: 2021
- Language: English
- Subjects: Physics, Computer science, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3008502)
- 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)

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well-suited to address a variety of recon- struction problems in HEP. In particular, tracker events are naturally repre- sented as graphs by identifying hits as nodes and track segments as edges; given a set of hypothesized edges, edge-classifying GNNs predict which rep- resent real track segments. In this work, we adapt the physics-motivated Inter- action Network (IN) GNN to the problem of charged-particle tracking in the high-pileup conditions expected at the HL-LHC. We demonstrate the IN's ex- cellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge- classification, and track building. Notably, the proposed IN architecture is sub- stantially smaller than previously studied GNN tracking architectures; this type of reduction in size critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN is easily expressed as a set of matrix operations, making it a promising candidate for acceleration via hetero- geneous computing resources..

0:00:00 Slide 1

0:03:11 Slide 2

0:06:21 Slide 3

0:09:31 Slide 4

0:12:41 Slide 5

0:15:51 Slide 6

0:19:01 Slide 7

0:22:11 Slide 8

0:25:21 Slide 9

0:28:31 Slide 10

## Lectures

1. [Charged particle tracking via edge-classifying interaction networks](https://invisiblenetwork.ai/learn/cern/3008502/3e7421c87dc5), 32 min (CERN's player)