The Exa.TrkX team has developed a Graph Neural Network (GNN) for reconstruction of liquid argon time projection chamber (LArTPC) data. We discuss the network architecture, a multi-head attention message passing network that classifies detector hits according to the particle type that produced them. By utilizing a heterogeneous graph structure with independent subgraphs for each 2D plane’s hits and for 3D space points, the model achieves a consistent description of the neutrino interaction across all planes.
Performance results will be presented based on publicly available samples from MicroBooNE. These will include both physics performance metrics, achieving ~95% accuracy when integrated over all particle classes, and computational metrics for training on single or distributed GPU systems and for inference on CPU or GPU.
We will discuss applications of the network for additional LArTPC reconstruction tasks, such as event classification, cosmic rejection and particle instance segmentation. Prospects for integration in the data processing chains of experiments will also be presented.
|Consider for long presentation||Yes|