May 16, 2023 Master Thesis Open Access

Dorwarth, Philipp Benjamin

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    <subfield code="a">Graph-Building and Input Feature Analysis for Edge Classification in the Central Drift Chamber at Belle II</subfield>
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    <subfield code="a">Belle II</subfield>
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    <subfield code="a">Graph Neural Network</subfield>
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    <subfield code="a">Deep Learning</subfield>
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    <subfield code="a">Graph-Building</subfield>
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    <subfield code="a">Pattern Recognition</subfield>
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    <subfield code="a">Central Drift Chamber</subfield>
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    <subfield code="a">Displaced Vertices</subfield>
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    <subfield code="a">&lt;p&gt;This thesis investigates components of a Graph Neural Network (GNN)-based pipeline addressing challenges of current tracking algorithms at the Belle II experiment, such as highly displaced vertices and escalating beam backgrounds. The thesis examines input features for real-time pattern recognition algorithms, systematic graph-building in the Central Drift Chamber (CDC), which serves as the primary tracking detector, and the use of the Interaction Network (IN) for edge classification and background clean-up.&lt;/p&gt;

&lt;p&gt;The study finds that Analog-to-Digital Converter (ADC) count and Time-to-Digital Converter (TDC) count, representing deposited energy and associated timing information in a CDC cell, provide orthogonal discrimination power, making them both valuable for distinguishing signal from background.&lt;/p&gt;

&lt;p&gt;Different patterns for graph-building in the CDC are analyzed to find graphs that effectively encapsulate crucial information about signal particle tracks of a simulated Inelastic Dark Matter with a Dark Higgs model. Metrics are introduced to aid in balancing between capturing essential edges connecting signal hits and excluding those associated with background.&lt;/p&gt;

&lt;p&gt;The graphs are evaluated, employing the IN as a classifier for graph edges. This process allows for the identification of signal hits and the execution of a background clean-up. The clean-up task&amp;nbsp;yields&amp;nbsp;a promising result, correctly identifying&amp;nbsp;up to (80.6 &amp;plusmn; 0.4) % of signal hits in the CDC while maintaining a purity of (67.4 &amp;plusmn; 0.4) % in the hit selection. An initial analysis towards a real-time implementation is also conducted, aligning&amp;nbsp;the input feature resolution with the anticipated resolutions at the Level 1 Trigger (L1 Trigger) stage.&lt;/p&gt;

&lt;p&gt;This thesis provides encouraging evidence that a GNN-based pipeline offers a viable solution to the challenges posed.&lt;/p&gt;</subfield>
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