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December 4, 2020
US/Central timezone

Graph Neural Networks for Reconstruction in DUNE

Dec 4, 2020, 12:00 PM


Jeremy Hewes (University of Cincinnati)


The Deep Underground Neutrino Experiment is a next-generation long-baseline neutrino oscillation experiment, designed to measure current unknowns in neutrino oscillation phenomenology. Its high-resolution detectors benefit from ML-based reconstruction techniques, which currently provide the backbone of its projected sensitivity to primary physics goals. This talk will discuss the development of graph neural network (GNN) based low-level reconstruction techniques, using an attention message-passing network to classify the relationships between detector hits into different particle types. The current benchmark achieves 84% accuracy in graph edge classification, with more advanced techniques currently in development.

Presentation Materials