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.

利用液体氩时间投影室（LArTPC）探测器技术进行粒子重建是本文描述的主要技术，使用了基于图神经网络（Graph Neural Network，GNN）的 NuGraph2 算法对模拟中微子相互作用进行低级重建，同时提供了高效的背景过滤和语义标记方法，旨在用于中微子物理学中的粒子重建。

NuGraph2：中微子物理事件重建的图神经网络

NuGraph2: A Graph Neural Network for Neutrino Physics Event  Reconstruction

We explore the use of graph networks to deal with irregular-geometry
detectors in the context of particle reconstruction. Thanks to their
representation-learning capabilities, graph networks can exploit the full
detector granularity, while natively managing the event sparsity and
arbitrarily complex detector geometries. We introduce two distance-weighted
graph network architectures, dubbed GarNet and GravNet layers, and apply them
to a typical particle reconstruction task. The performance of the new
architectures is evaluated on a data set of simulated particle interactions on
a toy model of a highly granular calorimeter, loosely inspired by the endcap
calorimeter to be installed in the CMS detector for the High-Luminosity LHC
phase. We study the clustering of energy depositions, which is the basis for
calorimetric particle reconstruction, and provide a quantitative comparison to
alternative approaches. The proposed algorithms provide an interesting
alternative to existing methods, offering equally performing or less
resource-demanding solutions with less underlying assumptions on the detector
geometry and, consequently, the possibility to generalize to other detectors.

本论文探索了使用图网络处理粒子重建中不规则几何探测器，并提出了两个基于距离加权的图网络架构， GarNet 和 GravNet，性能通过与替代方法的定量比较来评价，得出了这些算法提供了一种有趣的替代方法，既能够提供同等性能，也能够用更少的资源消耗和更少的基于探测器几何结构的假设而进行推广到其他探测器