Master Thesis Open Access

Anomaly Detection in Searches for Inelastic Dark Matter with a Dark Higgs

Eppelt, Jonas

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Eppelt, Jonas</dc:creator>
  <dc:date>2022-11-14</dc:date>
  <dc:description>Anomaly Detection presents a complementary ansatz to traditional searches for Dark Matter. Instead of searching for specific Dark Matter models, anomalous data is identified and compared to different models. This thesis explores different autoencoder architectures as tools for Anomaly Detection at the Belle II experiments and presents the Inelastic Dark Matter model with a Dark Higgs as a potential use case.</dc:description>
  <dc:identifier>https://publish.etp.kit.edu/record/22152</dc:identifier>
  <dc:identifier>oai:publish.etp.kit.edu:22152</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://publish.etp.kit.edu/communities/belle2</dc:relation>
  <dc:relation>url:https://publish.etp.kit.edu/communities/etp</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>Belle II, Machine Learning, Autoencoder, Dark Matter</dc:subject>
  <dc:title>Anomaly Detection in Searches for Inelastic Dark Matter with a Dark Higgs</dc:title>
  <dc:type>info:eu-repo/semantics/masterThesis</dc:type>
  <dc:type>thesis-master-thesis</dc:type>
</oai_dc:dc>
Publication date:
November 14, 2022
Keyword(s):
Belle II, Machine Learning, Autoencoder, Dark Matter
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2022-17
Communities:

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