# Graph Neural Network for Neutrino Physics Event Reconstruction

arXiv: https://arxiv.org/abs/2403.11872 · HTML: https://mlsanity.com/paper/2403.11872/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking](https://mlsanity.com/paper/2103.06995/) (2021)
- [A Convolutional Neural Network Neutrino Event Classifier](https://mlsanity.com/paper/1604.01444/) (2016)
- [Graph Neural Networks for IceCube Signal Classification](https://mlsanity.com/paper/1809.06166/) (2018)
- [Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors](https://mlsanity.com/paper/2102.01033/) (2021)
- [Hierarchical Graph Neural Networks for Particle Track Reconstruction](https://mlsanity.com/paper/2303.01640/) (2023)
- [Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers](https://mlsanity.com/paper/2007.03083/) (2020)
- [Equivariant Graph Neural Networks for Charged Particle Tracking](https://mlsanity.com/paper/2304.05293/) (2023)
- [Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing](https://mlsanity.com/paper/2301.04633/) (2023)

## Built on by (most cited first, 2 of 2)

- [Particle Trajectory Representation Learning with Masked Point Modeling](https://mlsanity.com/paper/2502.02558/) (2025)
- [Overlap-aware segmentation for topological reconstruction of obscured objects](https://mlsanity.com/paper/2510.06194/) (2025)

## Abstract (arXiv)

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 0.005s/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.