Search for top squark Production at the LHC at $\sqrt{\text{s}}=13$ TeV with the ATLAS Detector Using Multivariate Analysis Techniques

Supersymmetry is a very promising extension of the Standard Model. It predicts new heavy particles, which are currently searched for in the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy of 13 TeV. So far, all searches for supersymmetric particles use a cut-based signal selection. In this thesis, the use of multivariate selection techniques, Boosted Decision Trees and Artificial Neural Networks, is explored for the search for top squarks, the supersymmetric partner of the top quark. The multivariate methods increase the expected lower limit in the mass of top squarks by approximately 90 GeV from currently 990 GeV for small neutralino masses.