Speaker
Description
Event reconstruction in liquid argon time projection chambers (LArTPCs) requires separating detector activity from overlapping beam particles and cosmic rays into physically meaningful particle hierarchies. In the Pandora event reconstruction chain, event slicing groups particle-flow particles (PFPs) into candidate hierarchies using topological association criteria. We investigate whether this task can instead be formulated as a pairwise classification problem using simulated ProtoDUNE-SP data. Each event is represented as a graph in which reconstructed PFPs are nodes and edges encode the probability that two PFPs originate from the same true hierarchy. A graph neural network (GNN) is trained using reconstructed pair-level features and then evaluated based on Monte Carlo (MC) ground truth information. The GNN performance is compared with Pandora and machine learning (ML) baseline models. These findings motivate further investigation of deep learning (DL) adoption on event slicing in LArTPC data.