The SHiP experiment is a proposed experiment aiming to search for new super-weakly interacting particles. The concept is based on using a very intense and high energy proton beam at the CERN Super Proton Synchrotron (SPS) which is delivered to the new Beam Dump Facility (BDF), where the experiment will search for New Physics (NP) in a zero background environment.
This thesis describes several studies for the optimisation of this concept, in order to maximise its physics potential. These include studies of a benchmark signal model to understand acceptance effects, studies of the muon induced background using both simulation and a dedicated experiment at the SPS, and the optimisation of the muon shield —a crucial component of SHiP— using machine learning techniques.