Point proposal network for reconstructing 3D particle endpoints with subpixel precision in liquid argon time projection chambers

Liquid argon time projection chambers (LArTPCs) are particle imaging detectors recording 2D or 3D images of trajectories of charged particles. Identifying points of interest in these images, namely, the initial and terminal points of track-like particle trajectories such as muons and protons, and the initial points of electromagnetic shower-like particle trajectories such as electrons and gamma rays, is a crucial step in identifying and analyzing these particles and impacts the inference of physics signals such as neutrino interaction. The Point Proposal Network is designed to discover these specific points of interest. The algorithm predicts with a subvoxel precision their spatial location, and also determines the category of the identified points of interest. Using as a benchmark the PILArNet public LArTPC data sample in which the voxel resolution is 3 mm / voxel , our algorithm successfully predicts 96.8% and 97.8% of 3D points within a distance of 3 and 10 voxels from the provided true point locations, respectively. For the predicted 3D points within 3 voxels of the closest true point locations, the median distance is found to be 0.25 voxels, achieving subvoxel-level precision. In addition, we report our analysis of the mistakes where our algorithm prediction differs from the provided true point positions by more than 10 voxels. Among the mistakes we visually scanned 50 events: 25 were due to the definition of true position location, 15 were legitimate mistakes where a physicist cannot visually disagree with the algorithm's prediction, and 10 were genuine mistakes that we wish to improve in the future. Further, using these predicted points, we demonstrate a simple algorithm to cluster 3D voxels into individual track-like particle trajectories with a clustering efficiency, purity, and adjusted Rand index of 96%, 93%, and 91%, respectively.