March 2, 2024 Master Thesis Open Access

Schmidt, Kylian

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    <subfield code="a">Graph Neural Networks, Axion-Like Particles</subfield>
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    <subfield code="a">Photon Reconstruction of Axion-Like Particles with Graph Neural Networks at Beamdump Experiments</subfield>
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    <subfield code="a">&lt;p&gt;Axion-Like Particles (ALPs) are hypothetical weakly interacting light particles predicted&lt;br&gt;
by theories Beyond the Standard Model which could be mediators between a dark sector&lt;br&gt;
and the Standard Model. Some of these theories predict light ALPs which decay into two&lt;br&gt;
photons and could be detected at future beamdump experiments such as LUXE - New&lt;br&gt;
Physics search at Optical Dump.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
To investigate the properties of such ALPs, an accurate reconstruction of the common&lt;br&gt;
photon vertex from the hits measured in the detector can aid the search significantly. For&lt;br&gt;
this purpose, the photon shower direction needs to be reconstructed precisely, combining&lt;br&gt;
techniques from cluster and track reconstruction. This task is a prime candidate for modern&lt;br&gt;
methods of reconstruction based on Machine Learning such as Graph Neural Networks.&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
This thesis presents a Graph Neural Network composed of GravNet and GarNet, which&lt;br&gt;
is able to reconstruct the decay vertex of ALPs from the sparse detector hits of the two&lt;br&gt;
photon showers. The performance of the network is assessed on a data-set simulated with a&lt;br&gt;
high granularity calorimeter.&lt;/p&gt;</subfield>
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