EPJ Web Conf.
Volume 251, 202125th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|Number of page(s)||7|
|Published online||23 August 2021|
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
1 University of Cincinnati, Cincinnati, OH, USA
2 Fermilab, Batavia, IL, USA
3 Northwestern University, Evanston, IL, USA
4 California Institute of Technology, Pasadena, CA, USA
5 Lawrence Berkeley National Laboratory, Berkeley, CA, USA
* e-mail: firstname.lastname@example.org
Published online: 23 August 2021
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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