<div class="csl-bib-body">
<div class="csl-entry">Fischl, L. (2022). <i>Data analysis of the XBox-2 radiofrequency cavity at CERN using machine learning techniques</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.102625</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2022.102625
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/20402
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
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.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
maschinelles lernen
de
dc.subject
CERN
de
dc.subject
Datenanalyse
de
dc.subject
machine learning
en
dc.subject
CERN
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dc.subject
data analysis
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dc.title
Data analysis of the XBox-2 radiofrequency cavity at CERN using machine learning techniques
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dc.title.alternative
Daten Analyse der XBox-2 Radiofrequency Cavity am CERN mittels Machine Learning
de
dc.type
Thesis
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dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2022.102625
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Lorenz Fischl
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E101 - Institut für Analysis und Scientific Computing
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC16553367
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dc.description.numberOfPages
93
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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item.languageiso639-1
en
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.fulltext
with Fulltext
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item.openairetype
master thesis
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item.grantfulltext
open
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item.openaccessfulltext
Open Access
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item.cerifentitytype
Publications
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crisitem.author.dept
E104-05 - Forschungsbereich Kombinatorik und Algorithmen
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crisitem.author.parentorg
E104 - Institut für Diskrete Mathematik und Geometrie