Title: Convention versus Convolution: Two Methods of Low-Energy Event Classification in CRESST
Other Titles: Konvention versus Faltung: Zwei Methoden zur Klassifizierung von Niederenergieereignissen in CRESST
Language: input.forms.value-pairs.iso-languages.en
Authors: Rizvanović, Damir ItemCrisRefDisplayStrategy.rp.student.icon
Qualification level: Diploma
Advisor: Schieck, Jochen 
Assisting Advisor: Reindl, Florian 
Issue Date: 2022
Rizvanović, D. (2022). Convention versus Convolution: Two Methods of Low-Energy Event Classification in CRESST [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.102576
Number of Pages: 84
Qualification level: Diploma
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.
Keywords: Dunkle Materie; Machine Learning
Dark Matter; machine learning
URI: https://doi.org/10.34726/hss.2022.102576
DOI: 10.34726/hss.2022.102576
Library ID: AC16557451
Organisation: E141 - Atominstitut 
Publication Type: Thesis
Appears in Collections:Thesis

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