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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    <subfield code="a">&lt;p&gt;In the search for&amp;nbsp;Axion Like Particles (ALPs) as a extension of the standard model of particle physics, one possibly arising experimental signature is&amp;nbsp;&lt;span class="math-tex"&gt;\(B^\pm \rightarrow K^\pm a,~a\rightarrow \gamma\gamma\)&lt;/span&gt;.&lt;br&gt;
In 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.&lt;br&gt;
Here the signal selection is based on the so-called Punzi-net, &amp;nbsp;a training of a feed-forward neural network with a loss function inspired by the minimal detectable cross-section. This approach&amp;nbsp;reproduces the signal efficiency&amp;nbsp;from&amp;nbsp;the previous cut-based selection.&lt;/p&gt;</subfield>
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    <subfield code="a">Improving Event Selection with Machine Learning Methods for the $B^\pm \rightarrow K^\pm a,~a\rightarrow \gamma\gamma$ Search at Belle II.</subfield>
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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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