This talk will discuss work carried out by the Exa.TrkX collaboration to explore the application of Graph Neural Network (GNN)-based techniques for reconstructing particle interactions in wire-based Liquid Argon Time Projection Chambers (LArTPCs). LArTPC detector technology is utilised by many neutrino experiments, including future flagship US neutrino experiment DUNE, and techniques for fully automated event reconstruction are still in active development. Using reference to previous applications of such GNN approaches in HEP, this talk will discuss the unique challenges posed when reconstructing in LArTPC detectors and how those challenges might be overcome. It will describe the application of different GNN-based techniques for reconstruction tasks such as formation of 3D spacepoints from 2D hits, determination of spacepoint directionality and clustering of spacepoints.
|Second most appropriate track (if necessary)||Techniques developed beyond high-energy physics|
|Consider for young scientist forum (Student or postdoc speaker)||Yes|