Understanding the Standard Model and Beyond Using Computational Tools

Abstract

The Standard Model is an extremely successful theory which describes the properties of fundamental particles and their interactions to highly tested precision. However there is strong evidence that it is incomplete. In this dissertation, we look at models for beyond the Standard Model physics involving the Higgs coupling to a new dark sector which produces a high multiplicity lepton final state, which could be measured at the Large Hadron Collider. We set limits on the cross section times branching fraction of these processes. We also use machine learning methods, particularly classifiers, to study how well we can distinguish jets produced from up and down quarks, as these jets are very similar kinematically. Finally, we demonstrate a method for the exact computation of Feynman integrals, given the function space in which the analytic result should lie, using numerical sampling of the data at a sufficient number of points with sufficient precision.