Discriminating signal from background using neural networks. Application to top-quark search at the Fermilab Tevatron

The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are presented. The top--quark search is used as an example to illustrate the problems and proposed solutions.Comment: 11 pages, 3 figures, psfi

Enhancing the top signal at Tevatron using Neural Nets

We show that Neural Nets can be useful for top analysis at Tevatron. The main features of $t\bar t$ and background events on a mixed sample are projected in a single output, which controls the efficiency and purity of the $t\bar t$ signal.Comment: 11 pages, 6 figures (not included and available from the authors), Latex, UB-ECM-PF 94/1