Title: Bayesian Optimization for machine learning algorithms in the context of Higgs searches at the CMS experiment Show Chinese title

Title: 基于贝叶斯优化的机器学习算法在CMS实验中希格斯粒子搜索中的应用

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. Show Chinese abstract

Abstract: 机器学习算法，如提升决策树和深度神经网络，在高能物理中被广泛使用。 本研究的目的是将贝叶斯优化应用于机器学习算法中使用的超参数调优。 该算法对在大型强子对撞机位于欧洲核子研究中心的紧凑缪子线圈实验中检测到的光子和电子的电磁量能器进行能量回归处理。 该算法的目标是从测量的能量估计在紧凑缪子线圈中碰撞产生的光子和电子的能量。