ETD

• CERN
• CMS
• convolutional neural networks
• Deep learning
• high-energy phisics
• machine learning
• neural networks
• track reconstruction

Track reconstruction is one of the most expensive tasks performed during the analysis due to its combinatorial nature and the high number of fake tracks generated. In this thesis we develop a deep learning model capable of recognizing fake tracks using only two hit shapes of a track seed.