<div class="csl-bib-body">
<div class="csl-entry">Loesener, M. E. M. (2019). <i>Application of deep learning to the reconstruction of electron tracks in the CMS experiment</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.62403</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.62403
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/8533
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
This work proposes an implementation of a Deep Regression model for the purpose of predicting electron track parameters from collision events at the CMS Experiment at CERN. It is entirely written in Python, one of the most popular programming languages in the field of machine learning, and makes use of PyTorch, a cutting-edge Deep Learning framework, widely known for its dynamic graph structure. Several architectures were used, including the base architecture proposed by Bernkopf (Masters Thesis in preparation) and convolutional neural networks. They were trained and tested using a variety of algorithms and hyper-parameters to assess their performance. A training error reduction of a factor 2 to 3.5 was achieved with respect to the baseline, depending on the parameter. With under 5k network parameters the model offers a light-weight and precise tool to predict electron tracks. With automated predictions of streaming particle track data in mind, a possible bottleneck regarding data imports using different data formats was tested.
en
dc.description.abstract
null
de
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
Electron reconstruction
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dc.subject
deep learning
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dc.title
Application of deep learning to the reconstruction of electron tracks in the CMS experiment
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dc.title.alternative
Anwendung von Deep Learning auf die Rekonstruktion von Elektron-Spuren im CMS-Experiment
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.62403
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Martin Eduard Michael Loesener
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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
E105 - Institut für Stochastik und Wirtschaftsmathematik