muon track
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2021 ◽  
Vol 16 (08) ◽  
pp. P08034
Author(s):  
R. Abbasi ◽  
M. Ackermann ◽  
J. Adams ◽  
J.A. Aguilar ◽  
M. Ahlers ◽  
...  

2021 ◽  
Author(s):  
José Roberto Angeles Camacho ◽  
Anushka Udara Abeysekara ◽  
Andrea Albert ◽  
Ruben Alfaro ◽  
César Alvarez ◽  
...  
Keyword(s):  

2021 ◽  
Vol 251 ◽  
pp. 03054 ◽  
Author(s):  
Jeremy Hewes ◽  
Adam Aurisano ◽  
Giuseppe Cerati ◽  
Jim Kowalkowski ◽  
Claire Lee ◽  
...  

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.


Author(s):  
S. Abovyan ◽  
D. Cieri ◽  
V. Danielyan ◽  
M. Fras ◽  
Ph. Gadow ◽  
...  
Keyword(s):  

2019 ◽  
Author(s):  
Federica Bradascio ◽  
Thorsten Gluesenkamp ◽  

2019 ◽  
Author(s):  
Georgios Karathanasis ◽  
Costas Foudas ◽  
Panagiotis Katsoulis ◽  
T. Lama ◽  
S. Mallios ◽  
...  

2019 ◽  
Author(s):  
Davide Cieri ◽  
S. Abovyan ◽  
V. Danielyan ◽  
M. Fras ◽  
P. Gadow ◽  
...  
Keyword(s):  

2019 ◽  
Vol 14 (02) ◽  
pp. P02027-P02027
Author(s):  
D. Cieri ◽  
S. Abovyan ◽  
V. Danielyan ◽  
M. Fras ◽  
P. Gadow ◽  
...  
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