Physics – High Energy Physics – High Energy Physics - Phenomenology
Scientific paper
1992-09-04
Phys.Rev. D47 (1993) 1900-1905
Physics
High Energy Physics
High Energy Physics - Phenomenology
14pp. Plain TeX + mtexsis.tex (latter available through 'get mtexsis.tex'.) Two postscript files avail. by email
Scientific paper
10.1103/PhysRevD.47.1900
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.
Bowser-Chao David
Dzialo Debra L.
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