Comparison of the use of binary decision trees and neural networks in top-quark detection.

Abstract

The use of neural networks for signal versus background discrimination in high-energy physics experiments 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 apply a binary decision tree to top-quark identification at the Fermilab Tevatron and find it to be comparable in performance to the neural network. Furthermore, reservations about the ``black box'' nature of neural network discriminators do not appy to binary decision trees; a binary decision tree may be reduced to a set of kinematic cuts subject to conventional error analysis.