Title: Pulse-shape dicrimination with deep learning in CRESST
Other Titles: Pulse-Shape Diskriminierung mit Deep Learning beim CRESST-Experiment
Language: English
Authors: Mühlmann, Christoph  
Qualification level: Diploma
Advisor: Schieck, Jochen 
Assisting Advisor: Reindl, Florian 
Issue Date: 2019
Number of Pages: 98
Qualification level: Diploma
Abstract: 
Many unsolved mysteries in our universe such as galaxy rotation curves, mass distribution of clusters etc. can be reasonably explained by the concept of dark matter. WIMPs (Weakly Interacting Massive Particles) are the most favored dark matter candidate. In the quest to experimentally observe WIMP dark matter, CRESST is an outstanding experiment setting the best exclusion limits for low-mass WIMPs ever since. The analysis of the raw data observed by a particular cryogenic detector TUM40 used by CRESST is very challenging as two distinct pulse shapes are observed leading to a two-class classification problem. Neural networks and deep learning evolved to high potential tools in the field of data science. This work uses state-of-the-art deep learning techniques to investigate the two-class classification problem observed in TUM40 data.
Keywords: Machine Learning Dark Matter
Machine Learning Dark Matter
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-122102
http://hdl.handle.net/20.500.12708/14865
Library ID: AC15325061
Organisation: E141 - Atominstitut 
Publication Type: Thesis
Hochschulschrift
Appears in Collections:Thesis

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