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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Pattern recognition for particle shower reconstruction; Exploring AI-based methods for calorimetric clustering at the CMS HGCAL |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | The number of collisions in the upcoming runs of the Large Hadron Collider at CERN will increase significantly. The increasing amount of data and a higher granularity of the newly developed calorimetric detectors pose a substantial data volume and complexity challenge to the current particle shower reconstruction algorithms. This thesis aims to explore the feasibility of machine-learned models scalable to large data volumes for improving the reconstruction quality of calorimetric particle showers via calorimetric clustering. The goal of calorimetric clustering is to recognize and reconnect fragmented energetic components of particle showers described by three-dimensional spatial structures called tracksters. We show that machine-learned models are viable methods for calorimetric clustering and provide a significant reconstruction performance benefit over classical clustering approaches. Furthermore, we investigate the feasibility of node classification and link prediction problem formulations for training graph neural networks. Experimentally, we show that graph-based models provide a better reconstruction performance, more compact data representation, and better scalability on the tested datasets than feed-forward neural networks. |
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