Machine learning for ttH mechanism Higgs boson detection from CERN ATLAS data

One aspect of studying subatomic particles by observing proton-proton collision is being able to identify those collisions where the particles of interest occur, since thousands of collisions are happening in an accelerator such as the Large Hadron Collider (LHC) at any given time. Machine learning methods have shown the potential to improve the performance of the detection while using either hand-engineered features or low-level measurements from the detector as input features. One such particle, which has been studied by multiple research groups, is the Higgs boson. The aim of this thesis is to test and compare several machine learning algorithms and compare the usage of hand-engineered features with the usage of direct measurements of the detector on the task of detecting Higgs boson events, namely the $t