Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison of the Use of Binary Decision Trees and Neural Networks in
  Top Quark Detection   The use of neural networks for signal vs.~background discrimination in
high-energy physics experiment has been investigated and has compared favorably
with the efficiency of traditional kinematic cuts. Recent work in top quark
identification produced a neural network that, for a given top quark mass,
yielded a higher signal to background ratio in Monte Carlo simulation than a
corresponding set of conventional cuts. In this article we discuss another
pattern-recognition algorithm, the binary decision tree. We have applied a
binary decision tree to top quark identification at the Tevatron and found it
to be comparable in performance to the neural network. Furthermore,
reservations about the "black box" nature of neural network discriminators do
not apply to binary decision trees; a binary decision tree may be reduced to a
set of kinematic cuts subject to conventional error analysis.

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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cs