<div class="csl-bib-body">
<div class="csl-entry">Rizvanović, D. (2022). <i>Convention versus Convolution: Two methods of low-energy event classification in CRESST</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.102576</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2022.102576
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/20435
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
The introduction of new low-threshold detectors in CRESST-III has brought a new phenomenon in the low-energy region to surface. The origin of the Low-Energy Excesshas to date not been identified, with new detectors being crafted in hopes of determining the source of the excess. This work will aim to provide a raw data analysis using conventional CRESST techniques, so-called quality cuts, of event discriminationon one hand, but also build upon the foundation laid in previous works and utilise Machine Learning as a tool for event selection. By employing Convolutional Neural Networks, one of today’s most popular types of Machine Learning algorithms, this work’s analysis will not only be able to reproduce the results achieved with conventional methods, but also allow for a more granular discrimination of event types for low energies.
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
Dunkle Materie
de
dc.subject
Machine Learning
de
dc.subject
Dark Matter
en
dc.subject
machine learning
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dc.title
Convention versus Convolution: Two methods of low-energy event classification in CRESST
en
dc.title.alternative
Konvention versus Faltung: Zwei Methoden zur Klassifizierung von Niederenergieereignissen in CRESST