  Liquid Argon Time Projection Chambers (LArTPCs) are high resolution particle
imaging detectors, employed by accelerator-based neutrino oscillation
experiments for high precision physics measurements. While images of particle
trajectories are intuitive to analyze for physicists, the development of a high
quality, automated data reconstruction chain remains challenging. One of the
most critical reconstruction steps is particle clustering: the task of grouping
3D image pixels into different particle instances that share the same particle
type. In this paper, we propose the first scalable deep learning algorithm for
particle clustering in LArTPC data using sparse convolutional neural networks
(SCNN). Building on previous works on SCNNs and proposal free instance
segmentation, we build an end-to-end trainable instance segmentation network
that learns an embedding of the image pixels to perform point cloud clustering
in a transformed space. We benchmark the performance of our algorithm on
PILArNet, a public 3D particle imaging dataset, with respect to common
clustering evaluation metrics. 3D pixels were successfully clustered into
individual particle trajectories with 90% of them having an adjusted Rand index
score greater than 92% with a mean pixel clustering efficiency and purity above
96%. This work contributes to the development of an end-to-end optimizable full
data reconstruction chain for LArTPCs, in particular pixel-based 3D imaging
detectors including the near detector of the Deep Underground Neutrino
Experiment. Our algorithm is made available in the open access repository, and
we share our Singularity software container, which can be used to reproduce our
work on the dataset.
