Graph Neural Network for Neutrino Physics Event Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915359710248960 |
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| author | Hewes, V Aurisano, Adam Cerati, Giuseppe Kowalkowski, Jim Lee, Claire Liao, Wei-keng Grzenda, Daniel Gumpula, Kaushal Zhang, Xiaohe |
| author_facet | Hewes, V Aurisano, Adam Cerati, Giuseppe Kowalkowski, Jim Lee, Claire Liao, Wei-keng Grzenda, Daniel Gumpula, Kaushal Zhang, Xiaohe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network for Neutrino Physics Event Reconstruction Hewes, V Aurisano, Adam Cerati, Giuseppe Kowalkowski, Jim Lee, Claire Liao, Wei-keng Grzenda, Daniel Gumpula, Kaushal Zhang, Xiaohe Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment 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. |
| title | Graph Neural Network for Neutrino Physics Event Reconstruction |
| topic | Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2403.11872 |