Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ABCNet: An attention-based method for particle tagging   In high energy physics, graph-based implementations have the advantage of
treating the input data sets in a similar way as they are collected by collider
experiments. To expand on this concept, we propose a graph neural network
enhanced by attention mechanisms called ABCNet. To exemplify the advantages and
flexibility of treating collider data as a point cloud, two physically
motivated problems are investigated: quark-gluon discrimination and pileup
reduction. The former is an event-by-event classification while the latter
requires each reconstructed particle to receive a classification score. For
both tasks ABCNet shows an improved performance compared to other algorithms
available.

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The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin