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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  "creator": [
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      "@type": "Person", 
      "affiliation": "KIT/ETP", 
      "name": "Schmitt, Frederik"
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  "datePublished": "2022-12-13", 
  "description": "<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>", 
  "headline": "Improving Event Selection with Machine Learning Methods for the $B^\\pm \\rightarrow K^\\pm a,~a\\rightarrow \\gamma\\gamma$ Search at Belle II.", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
  "inLanguage": {
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    "alternateName": "eng", 
    "name": "English"
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  "keywords": [
    "Belle II", 
    "ALPs"
  ], 
  "name": "Improving Event Selection with Machine Learning Methods for the $B^\\pm \\rightarrow K^\\pm a,~a\\rightarrow \\gamma\\gamma$ Search at Belle II.", 
  "url": "https://publish.etp.kit.edu/record/22144"
}

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