Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Neural Network for Object Reconstruction in Liquid Argon Time
  Projection Chambers   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.

36
The categories of this paper are: cmp-lg
1
The categories of this paper are: cs
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas