Use of neural networks in a search for single top quark production at DO

We present a search for electroweak production of single top quarks in the DO detector at the Tevatron collider. After initial selections, the signal forms less than one percent of the background, and requires a powerful analysis tool to separate it from background. For this purpose, we employ the neural network package MLPfit, and train it on Monte Cario (MC) models of two processes for signal, and on data and MC models of five processes for background. Based on an analysis of singularities in the Feynman diagrams for single-top production, we choose an optimal set of kinematic variables as inputs to the networks. We use separate networks for each signal-background pair. For the dominant backgrounds, we use sequential nets.