Bachelor Thesis Open Access

Improving Event Selection with Machine Learning Methods for the $B^\pm \rightarrow K^\pm a,~a\rightarrow \gamma\gamma$ Search at Belle II.

Schmitt, Frederik

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{
  "abstract": "<p>In the search for&nbsp;Axion Like Particles (ALPs) as a extension of the standard model of particle physics, one possibly arising experimental signature is&nbsp;<span class=\"math-tex\">\\(B^\\pm \\rightarrow K^\\pm a,~a\\rightarrow \\gamma\\gamma\\)</span>.<br>\nIn the context of the search for this signature at the Belle II experiment, this thesis studies an alternative signal selection algorithm on Belle II simulation samples.<br>\nHere the signal selection is based on the so-called Punzi-net, &nbsp;a training of a feed-forward neural network with a loss function inspired by the minimal detectable cross-section. This approach&nbsp;reproduces the signal efficiency&nbsp;from&nbsp;the previous cut-based selection.</p>", 
  "author": [
    {
      "family": "Schmitt, Frederik"
    }
  ], 
  "id": "22144", 
  "issued": {
    "date-parts": [
      [
        2022, 
        12, 
        13
      ]
    ]
  }, 
  "language": "eng", 
  "title": "Improving Event Selection with Machine Learning Methods for the $B^\\pm \\rightarrow K^\\pm a,~a\\rightarrow \\gamma\\gamma$ Search at Belle II.", 
  "type": "thesis"
}
Publication date:
December 13, 2022
Keyword(s):
Belle II ALPs
Awarding University:
Karlsruhe Institute of Technology (KIT)
Report Number:
ETP-KA/2022-12
Communities:

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