Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chambers

TL;DR

This paper introduces a Point Proposal Network that accurately detects and categorizes key points in 3D LArTPC images with sub-voxel precision, improving particle trajectory analysis for neutrino physics.

Contribution

The novel Point Proposal Network predicts precise 3D points and categories in LArTPC data, achieving sub-voxel accuracy and enabling effective particle trajectory clustering.

Findings

Achieved 96.8% and 97.8% detection within 3 and 10 voxels.

Median distance of 0.25 voxels for close predictions.

Clustering efficiency of 96% for particle trajectories.

Abstract

Liquid Argon Time Projection Chambers (LArTPC) 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 of 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 sub-voxel 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 3mm/voxel, our algorithm successfully predicted 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 the sub-voxel 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 50 mistakes visually scanned, 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.