Snagging the top quark with a neural net

The search for the top quark at $p\overline{p}$ colliders in the one-lepton-plus-jets channel is plagued by an irremovable background from $W$-boson-plus-multijet production. In this paper, we show how the top-quark signal can be distinguished from background in the distribution of neural network output. By making a cut on the network output, we maximize the ratio of signal to background in a final event sample, and compare our results with those obtained by making kinematical cuts on the data sample. We also demonstrate the robustness of the neural network method by training the neural network on signal events of one top mass and testing upon another.