Master Thesis Open Access

Search for Dark Matter with Graph Neural Networks at CMS

von den Driesch, Jost

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  <dc:creator>von den Driesch, Jost</dc:creator>
  <dc:date>2022-05-13</dc:date>
  <dc:description>The Standard Model of particle physics is very precise yet it fails to describe a growing set of observations, such as the rotation curves of galaxies. Introducing Dark Matter is a convenient way to deal with many cosmic problems the Standard Model falls short to describe. However, Dark Matter (DM) has not been observed to date and is therefore heavily searched, for example at the CMS experiment at CERN. Here, DM might be created following the collision of two high-energy protons. As DM usually leaves the detector without having interacted, missing transverse momentum (MET) is an important quantity that might hint at DM production.

This thesis deals with a new method to calculate MET which is based on Graph Neural Networks and therefore called GraphMET. Compared to previous MET estimation methods, it has a better resolution but not a better response. In order to investigate the effect on an analysis, the GraphMET is compared to the former MET estimation methods in a sensitivity analysis of a Search for DM.
This sensitivity analysis yields smaller expected asymptotic upper limits on the DM production cross section, which is a promising result and paves the way for further investigations and possible application in future analyses.</dc:description>
  <dc:identifier>https://publish.etp.kit.edu/record/22146</dc:identifier>
  <dc:identifier>oai:publish.etp.kit.edu:22146</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://publish.etp.kit.edu/communities/cms</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>GNN</dc:subject>
  <dc:subject>MET</dc:subject>
  <dc:subject>GraphMET</dc:subject>
  <dc:subject>Dark Matter</dc:subject>
  <dc:title>Search for Dark Matter with Graph Neural Networks at CMS</dc:title>
  <dc:type>info:eu-repo/semantics/masterThesis</dc:type>
  <dc:type>thesis-master-thesis</dc:type>
</oai_dc:dc>
Publication date:
May 13, 2022
Keyword(s):
GNN MET GraphMET Dark Matter
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2022-05
Communities:

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