  Recent inroads in Computer Vision (CV) and Machine Learning (ML) have
motivated a new approach to the analysis of particle imaging detector data.
Unlike previous efforts which tackled isolated CV tasks, this paper introduces
an end-to-end, ML-based data reconstruction chain for Liquid Argon Time
Projection Chambers (LArTPCs), the state-of-the-art in precision imaging at the
intensity frontier of neutrino physics. The chain is a multi-task network
cascade which combines voxel-level feature extraction using Sparse
Convolutional Neural Networks and particle superstructure formation using Graph
Neural Networks. Each algorithm incorporates physics-informed inductive biases,
while their collective hierarchy is used to enforce a causal structure. The
output is a comprehensive description of an event that may be used for
high-level physics inference. The chain is end-to-end optimizable, eliminating
the need for time-intensive manual software adjustments. It is also the first
implementation to handle the unprecedented pile-up of dozens of high energy
neutrino interactions, expected in the 3D-imaging LArTPC of the Deep
Underground Neutrino Experiment. The chain is trained as a whole and its
performance is assessed at each step using an open simulated data set.
