Background discrimination techniques using Artificial Neural Networks for the GERDA experiment

The aim of this thesis is to perform an analysis of Signal recognition and Background rejection to outline a framework for data analysis based on Artificial Neural Network (ANN) suitable to the discrimination of samples from the GERDA experiment. More precisely, this work will focus on setting up an ANN that, through Pulse Shape Analysis techniques, can recognize a possible 0vbb decay from background events.