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" }