Titolo della tesi: Studies of the Higgs boson properties with the $H \rightarrow WW$ channel and optimization of muon tracking algorithms with the ATLAS experiment
The Standard Model has been a successful framework for understanding fundamental particles and their interactions. However, it is known to be incomplete, lacking explanations for gravity, matter-antimatter asymmetry, and dark matter. Recent experimental measurements have also shown tensions with the predictions of the Standard Model, motivating the effort towards more and more precise physics measurement. This thesis focuses on the study of the Higgs boson, particularly its decay into WW pairs, using data from the ATLAS experiment at the Large Hadron Collider (LHC). The analysis explores the same flavor (SF) channel and employs a combination of cut-based selection and deep neural network discriminants to enhance sensitivity and reduce background contamination. Additionally, the thesis extends to the development of new machine learning algorithms for particle reconstruction (in particular cluster reconstruction and pattern recognition) in high occupancy environments, with a specific application to the New Small Wheels detectors in the ATLAS detector upgrade project. Preliminary results of the HWW SF channel analysis are shown, that will have to be combined with the ATLAS different flavor analysis to contribute to the total sensitivity. The performance of the machine learning algorithms for charged particles tracking show promising results, demonstrating significant improvements in the resolution of individual cluster positions and in the efficiency and background rejection of pattern recognition.