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

• DOI: 10.48550/arxiv.2102.01033
• Publication Date: 2021-01-01
• Authors: Drielsma, Francois; Terao, Kazuhiro; Dominé, Laura; Koh, Dae Heun
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; High Energy Physics - Experiment (hep-ex); 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; FOS: Physical sciences; High Energy Physics - Experiment

Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada<br/>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.<br/>