Multivariate Discrimination Methods for Top Quark Analysis
Lasse Holmström, Stephan R. Sain, Lasse Holmström · Technometrics · 1997
Multivariate discrimination techniques are being considered as alternatives to many data-analysis methods conventionally used in experimental high-energy physics. In this article, the applications of four different methods—a quadratic classifier, kernel discriminant analysis, a feedforward neural network, and multivariate adaptive regression splines—are compared for the detection of the top quark. Simulated particle-collision data that correspond to two classes, a rare signal and a dominant background, are used.