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Recent inroads in Computer Vision (CV) and Machine Learning (ML) havemotivated a new approach to the analysis of particle imaging detector data.Unlike previous efforts which tackled isolated CV tasks, this paper introducesan end-to-end, ML-based data reconstruction chain for Liquid Argon TimeProjection Chambers (LArTPCs), the state-of-the-art in precision imaging at theintensity frontier of neutrino physics. The chain is a multi-task networkcascade which combines voxel-level feature extraction using SparseConvolutional Neural Networks and particle superstructure formation using GraphNeural Networks. Each algorithm incorporates physics-informed inductive biases,while their collective hierarchy is used to enforce a causal structure. Theoutput is a comprehensive description of an event that may be used forhigh-level physics inference. The chain is end-to-end optimizable, eliminatingthe need for time-intensive manual software adjustments. It is also the firstimplementation to handle the unprecedented pile-up of dozens of high energyneutrino interactions, expected in the 3D-imaging LArTPC of the DeepUnderground Neutrino Experiment. The chain is trained as a whole and itsperformance is assessed at each step using an open simulated data set.

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors