Identifikacija miona u CMS detektoru pri proton-proton sudarima energije Vs=13 TeV metodama strojnog učenja

In this thesis, the identification of objects obtained by simulation of the Drell-Yan process is made. The reconstructed objects were divided into two classes: the signal object class and the background object class, which were then stored in a data set. Signal objects are muons most likely formed in the primary vertex, while background objects are inaccurately reconstructed as muons. Data analysis at CERN often use so-called cut-based ID methods to identify objects, therefore this paper considers the possibility of replacing this approach with a machine learning strategy. Machine learning teaches the model to distinguish objects by training on already classified data to be able to identify the signal in the experimental data collected by the operation of the CMS detector. Wanting to mimic this process, the created data set is divided into a training data set and a test data set. In achieving the goal, the main impediment were the imbalance of classes which means the number of background objects is much, approximately 14 times, less than the signal. Two approaches to the problem of data imbalance are discussed. The first approach is weight balancing which changes the weights of the error function. The second approach is to balance classes by adding or removing objects from a training data set.