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Direkt detektion av mörk materia
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Direct detection of dark matter (English)
Abstract [sv]

Trots att nästan 85 % av all massa i universum består av mörk materia är dess egenskaper okända. Medan vissa delar av den okända materian kan beskrivas med hjälp av standardmodellen, är egenskaperna av den övriga oidentifierad. Den vanligaste förklaringen är en okänd partikel som kan interagera med baryonisk materia. Om partikeln finns kommer den att kunna bli detekterad genom svag växelverkan med andra partiklar. I denna rapport presenteras bevis för existensen av mörk materia. Dessutom kommer olika metoder att detektera den okända materian diskuteras, där största fokuset ligger på direkt detektion. I rapporten kommer det teoretiska antalet detektionsutfall och dess beroenden att undersökas. Slutligen kommer det förväntade totala antalet beräknade detektionsutfall att studeras. Dessa resultat kommer att jämföras med totala antalet utfall hos existerande experiment.

Abstract [en]

Despite accounting for almost 85 % of all mass in the Universe, the properties of dark matter are mostly unknown. While some of this unknown matter can be explained by the standard model, the properties of the rest are unspecified. The most common explanation is an unknown particle that can interact with baryonic matter. If that is true, dark matter could be detected through weak interactions with other particles. In this report we will explain some evidence for dark matter and discuss different avenues of detecting dark matter while focusing on direct detection. We will examine theoretical detection rates and their dependencies. Finally we will examine numerically calculated expected total number of detector events, compare them to experimental total number of detector events and discuss current efforts in direct detection of dark matter.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:210
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255702OAI: oai:DiVA.org:kth-255702DiVA, id: diva2:1341339
Supervisors
Examiners
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved

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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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