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

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

Pattern Recognition and Reconstruction

The topic of the chapter is track and vertex reconstruction in particle detectors. The reconstruction of (charged) tracks and the subsequent reconstruction of interaction vertices are important steps in the data analysis chain of an experiment in high-energy physics. Track reconstruction typically proceeds in three steps: Track Finding; Track Fitting; and Testing the track hypothesis. The most important pattern recognition algorithms and statistical estimation methods applied in this context are presented in the first section of the chapter, along with material on track-based alignment and formulas for the quick assessment of the momentum resolution of a tracking system. The following section deals with the reconstruction of interaction vertices, including both the primary interaction vertex in the collision zone of an experiment and secondary decay vertices of unstable particles. The pattern recognition methods and statical estimators involved are in many respects similar to the ones deployed in track reconstruction. The final section presents a brief overview of track and vertex reconstruction in the four LHC experiments. The methods applied by the experiments are described and their performance is illustrated by results from a few selected physics channels