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 analysis, from variable selection to systematic errors, are presented. The top-quark search is used as an example to illustrate the problems and proposed solutions. @S0556-2821~96!06013-4# PACS number~s!: 14.65.Ha, 02.50.Sk, 13.85.Qk It is well known that neural networks ~NN’s ! are useful tools for pattern recognition. In high energy physics, they have been used or proposed as good candidates for tasks of signal versus background classification. However, most of the existing studies are somewhat academic, in the sense that they essentially compare the NN performances with other classical techniques of classification using Monte Carlo ~MC! events for that purpose. In realistic applications, real events should be analyzed and compared with simulated events, introducing systematic effects which have to be taken into account and could significantly modify the efficiency of the analysis. We try to give some insight in this direction using the top quark search at the Fermilab Tevatron as illustration. The top quark has been observed by the Collider Detector at Fermilab ~CDF !@ 1 #and D0 @2# collaborations. Recently, NN’s have been applied to experimental top-quark searches by the D0 Collaboration @3#, for a fixed top-quark mass, concluding that NN’s are more efficient than traditional methods, in agreement with previous parton level studies @4#. In this paper we continue and complete the analysis of Ref. @4# for the top-quark search at the Tevatron. A more realistic study is performed by including parton hadronization and detector simulation with jet reconstruction. In addition, contrary to Ref. @4# where the top mass was fixed, the present study is valid for a large range of top mass values. Moreover, the number of kinematical variables considered is enlarged and different ways of selecting subsets of the most relevant ones to the process under consideration are discussed. Finally, the influence of systematic errors on the NN results is studied. The analysis is focused on the top-quark search at the ,