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Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Journal Article · · EPJ Web of Conferences (Online)
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  1. Univ. of Cincinnati, OH (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  3. Northwestern Univ., Evanston, IL (United States)
  4. California Institute of Technology (CalTech), Pasadena, CA (United States)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
+ Show Author Affiliations
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model’s strengths and weaknesses are discussed, and plans for developing this technique further are summarised.
Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-05CH11231; AC02-07CH11359
OSTI ID:
1826698
Alternate ID(s):
OSTI ID: 1831483
OSTI ID: 23172512
Journal Information:
EPJ Web of Conferences (Online), Journal Name: EPJ Web of Conferences (Online) Vol. 251; ISSN 2100-014X
Publisher:
EDP SciencesCopyright Statement
Country of Publication:
United States
Language:
English

References (5)

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journal July 2003
The GENIE neutrino Monte Carlo generator
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journal February 2010
Liquid argon TPC signal formation, signal processing and reconstruction techniques journal July 2017
Operation of the ATLAS trigger system in Run 2 journal October 2020
Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data journal July 2020

Cited By (1)

Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking text January 2021

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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS