  Deep convolutional neural networks (CNNs) show strong promise for analyzing
scientific data in many domains including particle imaging detectors such as a
liquid argon time projection chamber (LArTPC). Yet the high sparsity of LArTPC
data challenges traditional CNNs which were designed for dense data such as
photographs. A naive application of CNNs on LArTPC data results in inefficient
computations and a poor scalability to large LArTPC detectors such as the Short
Baseline Neutrino Program and Deep Underground Neutrino Experiment. Recently
Submanifold Sparse Convolutional Networks (SSCNs) have been proposed to address
this challenge. We report their performance on a 3D semantic segmentation task
on simulated LArTPC samples. In comparison with standard CNNs, we observe that
the computation memory and wall-time cost for inference are reduced by factor
of 364 and 33 respectively without loss of accuracy. The same factors for 2D
samples are found to be 93 and 3.1 respectively. Using SSCN, we present the
first machine learning-based approach to the reconstruction of Michel electrons
using public 3D LArTPC samples. We find a Michel electron identification
efficiency of 93.9% with 96.7% of true positive rate. Reconstructed Michel
electron clusters yield 95.4% in average pixel clustering efficiency and 95.5%
in purity. The results are compelling to show strong promise of scalable data
reconstruction technique using deep neural networks for large scale LArTPC
detectors.
