NuGraph2: A Graph Neural Network for Neutrino Physics Event
  Reconstruction
V Hewes Adam Aurisano Giuseppe Cerati Jim Kowalkowski Claire Lee Wei-keng Liao Daniel Grzenda Kaushal Gumpula Xiaohe Zhang
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.