May 16, 2023 Master Thesis Open Access

Dorwarth, Philipp Benjamin

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      "affiliation": "KIT/ETP", 
      "name": "Dorwarth, Philipp Benjamin"
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  ], 
  "datePublished": "2023-05-16", 
  "description": "<p>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.</p>\n\n<p>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.</p>\n\n<p>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.</p>\n\n<p>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&nbsp;yields&nbsp;a promising result, correctly identifying&nbsp;up to (80.6 &plusmn; 0.4) % of signal hits in the CDC while maintaining a purity of (67.4 &plusmn; 0.4) % in the hit selection. An initial analysis towards a real-time implementation is also conducted, aligning&nbsp;the input feature resolution with the anticipated resolutions at the Level 1 Trigger (L1 Trigger) stage.</p>\n\n<p>This thesis provides encouraging evidence that a GNN-based pipeline offers a viable solution to the challenges posed.</p>", 
  "headline": "Graph-Building and Input Feature Analysis for Edge Classification in the Central Drift Chamber at Belle II", 
  "image": "https://publish.etp.kit.edu/static/img/logos/zenodo-gradient-round.svg", 
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    "alternateName": "eng", 
    "name": "English"
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  "keywords": [
    "Belle II", 
    "Graph Neural Network", 
    "Deep Learning", 
    "Graph-Building", 
    "Pattern Recognition", 
    "Central Drift Chamber", 
    "Displaced Vertices"
  ], 
  "name": "Graph-Building and Input Feature Analysis for Edge Classification in the Central Drift Chamber at Belle II", 
  "url": "https://publish.etp.kit.edu/record/22197"
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