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Graph neural network for neutrino physics event reconstruction

Journal Article · · Physical Review. D.
 [1];  [1];  [2];  [2];  [3];  [3];  [4];  [4];  [5]
  1. Univ. of Cincinnati, OH (United States)
  2. Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
  3. Northwestern Univ., Evanston, IL (United States)
  4. Univ. of Chicago, IL (United States)
  5. Univ. of Chicago, IL (United States); Univ. of California, Los Angeles, CA (United States)
+ Show Author Affiliations
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. Here, this article describes NUGRAPH2, a graph neural network 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 multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-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 paper.
Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
89243024CSC000002; AC02-07CH11359; SC0021399; SC0024698
OSTI ID:
2333005
Alternate ID(s):
OSTI ID: 2474054
Report Number(s):
FERMILAB-PUB--24-0118-CSAID-ETD; oai:inspirehep.net:2769932; arXiv:2403.11872
Journal Information:
Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 3 Vol. 110; ISSN 2470-0029; ISSN 2470-0010
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

References (18)

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers journal January 2021
MicroBooNE Public Data Sets: A Collaborative Tool for LArTPC Software Development journal January 2024
An Object Condensation Pipeline for Charged Particle Tracking at the High Luminosity LHC journal January 2024
Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events journal February 2023
LArSoft: toolkit for simulation, reconstruction and analysis of liquid argon TPC neutrino detectors journal October 2017
A convolutional neural network neutrino event classifier journal September 2016
Design and construction of the MicroBooNE detector journal February 2017
Context-enriched identification of particles with a convolutional network for neutrino events journal October 2019
Neutrino interaction classification with a convolutional neural network in the DUNE far detector journal November 2020
Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE journal March 2021
Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber journal May 2021
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber journal May 2019
The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector journal January 2018
Performance of a geometric deep learning pipeline for HL-LHC particle tracking journal October 2021
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network journal October 2022
ImageNet classification with deep convolutional neural networks journal May 2017
MicroBooNE BNB Electron Neutrino Overlay Sample (No Wire Info) dataset January 2022
MicroBooNE BNB Inclusive Overlay Sample (No Wire Info) dataset January 2023

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