Panoptic Segmentation for Particle Identification in LArTPC Detectors

The Deep Underground Neutrino Experiment (DUNE) will use the liquid argon time projection chamber (LArTPC) technology for its near and far detectors. LArTPC detectors can collect high-resolution data of charged particles’ trajectories. An example of this type of detector is ProtoDUNE-SP that is the prototype of the single-phase DUNE far detector using full-scale components and a charged-particle beam that allows measuring the detector’s calorimetric response to hadronic particles and electromagnetic showers. Convolutional Neural Networks have been developed and employed in the analysis of scientific data from the ProtoDUNE detector, which exploit the advantages of a liquid argon time projection chamber (LArTPC). Despite the high-resolution images and the fine details that the detector can capture, the classification of the different types of particles and interactions is still a challenge. With this motivation, we present the details of a multi-task reconstruction algorithm using a Sparse Convolutional Neural Network capable of panoptic segmentation – that is simultaneously generating a pixel-by-pixel particle ID and clustering pixels into objects.