Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Infrared Safety of a Neural-Net Top Tagging Algorithm   Neural network-based algorithms provide a promising approach to jet
classification problems, such as boosted top jet tagging. To date, NN-based top
taggers demonstrated excellent performance in Monte Carlo studies. In this
paper, we construct a top-jet tagger based on a Convolutional Neural Network
(CNN), and apply it to parton-level boosted top samples, with and without an
additional gluon in the final state. We show that the jet observable defined by
the CNN obeys the canonical definition of infrared safety: it is unaffected by
the presence of the extra gluon, as long as it is soft or collinear with one of
the quarks. Our results indicate that the CNN tagger is robust with respect to
possible mis-modeling of soft and collinear final-state radiation by Monte
Carlo generators.

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The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: stat
11
The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org