New Statistical Techniques in the Measurement of the inclusive Top Pair   Production Cross Section
  We present several different types of multivariate statistical techniques used in the measurement of the inclusive top pair production cross section in $p \bar{p}$-collisions at $\sqrt{s} = 1.96 \text{TeV}$ employing the full RunII data ($9.7\textrm{ fb}^{-1}$) collected with the D0 detector at the Fermilab Tevatron Collider. We consider the final state of the top quark pair decays containing one electron or muon and at least two jets. We proceed various statistical homogeneity tests such as Anderson - Darling, Kolmogorov - Smirnov, and $\varphi$-divergences tests to determine, which variables have good data-MC agreement, as well as a good separation power. We adjusted all tests for using weighted empirical distribution functions. Further we separate $t\bar{t}$ signal from the background by the application of Generalized Linear Models, Gaussian Mixture Models, Neural Networks with Switching Units and confront them with familiar methods from ROOT TMVA package such as Boosted Decision Trees, and Multi-layer Perceptron. We compare results by area under receiver operating characteristic curve and verify the quality of the discrimination from all methods. 
Ji\v{r}\'i Franc, Petr Bou\v{r}, Michal \v{S}t\v{e}p\'anek, and   V\'aclav K\r{u}s
hep-ex physics.data-an