ABSTRACT:
In this talk i will present a technique for improving Higgs analyses involving WBF at the LHC.
It is common nowadays to use multivariate techniques to construct classification rules with
which to separate signal from background events. This is a vital aspect of many LHC analyses.
The choice of variables used to train the classification rule has so far been rather straightforward,
i.e. distributions in the rapidity, transverse momentum, and invariant mass of the leading jets.
In this talk i will show that by incorporating Fox Wolfram moments - variables that describe
different types of correlations between jets - into the multivariate analyses there is a significant
improvement in the ability to discern a Higgs boson signal produced via WBF from background processes.
As examples I will consider the Higgs diphoton and Higgs tau tau channels.