Title: Data Analysis of the XBox-2 Radiofrequency Cavity at CERN using Machine Learning Techniques
Other Titles: Daten Analyse der XBox-2 Radiofrequency Cavity am CERN mittels Machine Learning
Language: input.forms.value-pairs.iso-languages.en
Authors: Fischl, Lorenz 
Qualification level: Diploma
Advisor: Feischl, Michael 
Issue Date: 2022
Fischl, L. (2022). Data Analysis of the XBox-2  Radiofrequency Cavity at CERN using Machine Learning Techniques [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.102625
Number of Pages: 100
Qualification level: Diploma
This master’s thesis starts with an introduction to particle physics. Thereby,the basic operating principles of high-gradient linear accelerators are explained. One of the main limitations in these devices is the occurrence of breakdowns,which is investigated in the experimental accelerating structure XBox-2 locatedat CERN. An adaptable framework for data analysis using machine learning iscreated with the goal of deriving analysis results from raw experimental data. Astrong focus lies on its optimized implementation, which is described in detail. The framework is applied to the data of the XBox-2 accelerator with unsupervised andsupervised machine learning techniques. A hypothesis for breakdown indicators isderived from the trained models and tested in the lab. However, further testingon accelerating structures is required before the results of the analysis can bevalidated.
Keywords: maschinelles lernen; CERN; Datenanalyse
machine learning; CERN; data analysis
URI: https://doi.org/10.34726/hss.2022.102625
DOI: 10.34726/hss.2022.102625
Library ID: AC16553367
Organisation: E101 - Institut für Analysis und Scientific Computing 
Publication Type: Thesis
Appears in Collections:Thesis

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