| dc.description.abstract | Supersymmetry is a popular extension to the Standard Model (SM) of particle physics, which predicts a supersymmetric partner to each SM particle. Among these are four charginos and four neutralinos, which are partners of the charged and neutral gauge bosons and the Higgs boson of the SM. The lightest neutralino is often considered a dark matter candidate. If supersymmetry exists in nature, we should be able to produce supersymmetric particles in high-energy particle collision experiments. In this thesis, I have analyzed measurements made by the ATLAS detector, during proton collisions provided by the Large Hadron Collider between 2015 and 2018. The goal was to search for signals that cannot be explained by the well-known SM particles. More specifically, I searched for charginos and neutralinos in collision events where two leptons (electrons or muons) have been measured by the detector. If no such signal is found, we can exclude some of the scenarios where supersymmetric particles should have shown up, had they existed. I approached the problem in two ways: first by using the conventional cut-and-count method, and finally by using machine learning to classify collision events as being either a supersymmetric signal or a SM background process. No supersymmetric signal was found, and therefore we were able to extend our exclusion limits on how light the charginos and neutralinos can be. | en_US |