  Machine Learning algorithms, such as Boosted Decisions Trees and Deep Neural
Network, are widely used in High-Energy-Physics. The aim of this study is to
apply Bayesian Optimization to tune the hyperparameters used in a machine
learning algorithm. This algorithm performs an energy regression process on
photons and electrons detected in the electromagnetic calorimeter at the
Compact Muon Solenoid experiment operating at the Large Hadron Collider at
CERN. The goal of this algorithm is to estimate the energy of photons and
electrons created during the collisions in the Compact Muon Solenoid, from the
measured energy.
