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

2019/01/01 by Oriel Kiss, Kiss, Oriel
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Data Analysis #FOS: Physical sciences #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1911.02501

openalex publication_date 2019/01/01 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

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.

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