Bachelor Thesis Open Access
Schmitt, Frederik
{
"@context": "https://schema.org/",
"@type": "ScholarlyArticle",
"contributor": [],
"creator": [
{
"@type": "Person",
"affiliation": "KIT/ETP",
"name": "Schmitt, Frederik"
}
],
"datePublished": "2022-12-13",
"description": "<p>In the search for Axion Like Particles (ALPs) as a extension of the standard model of particle physics, one possibly arising experimental signature is <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, a training of a feed-forward neural network with a loss function inspired by the minimal detectable cross-section. This approach reproduces the signal efficiency from 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": {
"@type": "Language",
"alternateName": "eng",
"name": "English"
},
"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"
}