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

Raman and X-ray investigations of LiFeSi2O6 pyroxene under pressure

In situ Raman spectroscopy at high pressure was utilized to follow the phase transition of a synthetic sample of Li-aegerine pyroxene (LiFeSi2O6) from its low-pressure (C2/c) phase to its high-pressure (P21/c) phase. The phase change occurred between 0.7 and 1 GPa andwas accompanied by a change in coordination of the Li atom from 4 to 5, which was confirmed by single-crystal X-ray diffraction. This is the first report of the Raman spectrum of Li-aegerine in the P21/c phase. As was previously observed with other pyroxenes, additional changes in the Raman spectra were observed at pressures higher than the phase transition, including the splitting of the peak near 700 cm−1, which has traditionally been utilized to indicate the phase transition. Comparisons with the Raman spectra of spodumene in both symmetries are utilized for a discussion of modes. Copyright 2005 John Wiley & Sons, Ltd