Tracking with Graph Neural Networks

Recent work has demonstrated that graph neural networks (GNNs) trained for charged particle tracking can match the performance of traditional algorithms while improving scalability. This project uses a learned clustering strategy: GNNs are trained to embed the hits of the same particle close to each other in a latent space, such that they can easily be collected by a clustering algorithm. The project is fully open source and available at https://github.com/gnn-tracking/gnn_tracking/. In this talk, we will present the basic ideas while demonstrating the execution of our pipeline with a Jupyter notebook. We will also show how participants can plug in their own model