Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in
  High Energy Physics   Rapid advancement of machine learning solutions has often coincided with the
production of a test public data set. Such datasets reduce the largest barrier
to entry for tackling a problem -- procuring data -- while also providing a
benchmark to compare different solutions. Furthermore, large datasets have been
used to train high-performing feature finders which are then used in new
approaches to problems beyond that initially defined. In order to encourage the
rapid development in the analysis of data collected using liquid argon time
projection chambers, a class of particle detectors used in high energy physics
experiments, we have produced the PILArNet, first 2D and 3D open dataset to be
used for a couple of key analysis tasks. The initial dataset presented in this
paper contains 300,000 samples simulated and recorded in three different volume
sizes. The dataset is stored efficiently in sparse 2D and 3D matrix format with
auxiliary information about simulated particles in the volume, and is made
available for public research use. In this paper we describe the dataset,
tasks, and the method used to procure the sample.

26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: patt-sol