Deep Learning Methods for Particle Reconstruction in the HGCal

The High Granularity end-cap Calorimeter is part of the phase-2 CMS upgrade (see Figure 1)[1]. It’s goal it to provide measurements of high resolution in time, space and energy. Given such measurements, the purpose of this work is to discuss the use of Deep Neural Networks for the task of particle and trajectory reconstruction, identification and energy estimation, during my participation in the CERN Summer Students Program.