Speaker
Jiri Franc
(Czech Technical University in Prague)
Description
The application of multivariate analysis techniques in experimental high energy
physics have been accepted as one of the fundamental tools in the discrimination
phase, when signal is rare and background dominates. The purpose of this study is
to present new approaches to the variable selection based on phi-divergences,
together with various statistical tests, and the combination of new applied MVA
methods together with familiar ROOT TMVA methods in the real data analysis.
The results and quality of separation of the Generalized Linear Models (GLM),
Gaussian Mixture Models (GMM), Neural Networks with Switching Units (NNSU), TMVA
Boosted Decision Trees, and Multi-layer Perceptron (MLP) in the measurement of the
inclusive top pair production cross section employing $D0$ Tevatron full RunII data
($9.7 fb^{-1}$) will be presented. Possibilities of improvement in discrimination
will be discussed.
Primary author
Jiri Franc
(Czech Technical University in Prague)
