CMS level-1 trigger muon momentum assignment with machine learning

With the advent of the High-Luminosity phase of the LHC (HL-LHC), the instantaneous luminosity of the Large Hadron Collider at CERN will increase up to 7,5 10^34 cm^-2s^-1. Therefore, new algorithmic techniques for data acquisition and processing will be necessary, in preparation for a high pile-up environment that would eventually make the current electronics and trigger devices obsolete. Nowadays, Machine Learning techniques represent a promising alternative to this problem, as they make possible the selection of multiple information - collected by the detector - and build from them different models, able to predict with a certain efficiency fundamental physical quantities, including the transverse momentum pT. The analysis presented in this Master Thesis consists in the production of such models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the Barrel region of the CMS muon chambers, and compare the results with the pT assigned by the current CMS Level 1 Barrel Muon Track Finder (BMTF) trigger system.