NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction
<|reference_start|>NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction: Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. This article describes NuGraph2, a Graph Neural Network (GNN) for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multi-head attention message-passing mechanism to perform background filtering and semantic labelling on these graph nodes, identifying those associated with the primary physics interaction with 98.0\% efficiency and labelling them according to particle type with 94.9\% efficiency. The network operates directly on detector observables across multiple 2D representations, but utilizes a 3D-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s/event on a CPU, and 0.005 s/event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this article.<|reference_end|>
@article{hewes2024nugraph2:,
    title={NuGraph2: A Graph Neural Network for Neutrino Physics Event
  Reconstruction},
    author={V Hewes and Adam Aurisano and Giuseppe Cerati and Jim Kowalkowski and
  Claire Lee and Wei-keng Liao and Daniel Grzenda and Kaushal Gumpula and
  Xiaohe Zhang},
    journal={arXiv preprint arXiv:2403.11872},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11872},
    primaryClass={physics.data-an cs.LG hep-ex}
}