Bayesian Optimization for machine learning algorithms in the context of Higgs searches at the CMS experiment

Abstract

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.