Large-Scale Data Analysis for Higgs Boson Mass Reconstruction in ttH Production

This thesis deals with the problem of reconstruction of the invariant mass of the Higgs boson using machine learning techniques – neural networks. It focuses on the 2lSS + 1τhad decay channel. In the first part, the problem of mass reconstruction is explained and some used approaches to the problem are described. The next part describes the phase of reconstructing the invariant mass of all the particles using exact formulas and algorithms. Then, the data obtained during reconstruction of the other particles of the ttH system are used. Several neural networks are trained and tested on different datasets to predict/estimate the invariant mass of the Higgs boson on truth level. Finally, neural networks for estimating the invariant mass of the Higgs boson on detector level and distinguishing signal events from background are prepared.