DSpace-CRIS at TU Wienhttps://repositum.tuwien.atThe reposiTUm digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 25 Sep 2021 20:56:20 GMT2021-09-25T20:56:20Z5011Calculation of dark-matter exclusions-limits using a maximum Likelihood approachhttp://hdl.handle.net/20.500.12708/5351Title: Calculation of dark-matter exclusions-limits using a maximum Likelihood approach
Authors: Schmiedmayer, Daniel
Abstract: The search for dark matter (DM) is one of the most fundamental questions in modern physics. Evidence for matter beyond the Standard Model (SM) of particle physics is found in various astrophysical observations. These observations also provide the basis for theories about the type and properties of potential DM particles. One of the most promising explanations involves weakly interacting massive particles (WIMPs) and similar WIMP-like particles, which are also referred to as WIMPs in the course of this work. Numerous experiments attempt to find proof of their existence and measure characteristic properties. So far no conclusive proof has been found, it was however possible to put limits on the expected cross section for the interaction of potential particles with SM particles. One of these experiments is the Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) which is the currently leading experiment for establishing cross section exclusion limits for WIMPs in the low-mass region. In this work an extended maximum likelihood method capable of calculating exclusion limits from the data measured with CRESST detectors is presented and tested. At the beginning an overview of the evidence for dark matter, its possible particle models and detection methods with focus on WIMPs and direct detection is given. Then, a short introduction into the CRESST experiment, its setup and capabilities is presented. The performance of the extended maximum likelihood method is compared against another method for limit calculation, namely Yellins optimal interval method. Therefore, Yellins method is introduced alongside the maximum likelihood formalism. To evaluate the measured data in the maximum likelihood framework a model for the properties of the detector as well as the known backgrounds is established. Finally, the maximum likelihood method is used to calculate WIMP exclusion limits using data from two CRESST experimental data taking campaigns with the purpose of achieving stronger limits.
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Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/20.500.12708/53512019-01-01T00:00:00Z