Bachelorarbeiten zu Software
| Thema: | Graph Neural Network track reconstruction for particles decaying in flight |
| Zusammenfassung: | Graph Neural Networks (GNNs) have demonstrated excellent performance in track reconstruction at the Belle II experiment. However, reconstructing tracks of particles that decay in flight (e.g. a muon decaying into an electron and a neutrino) presents a unique challenge, as the resulting trajectories exhibit kinks where the decay occurs. These kinks can lead to inefficiencies in standard GNN-based tracking algorithms, causing incomplete or misidentified track segments. You will develop reconstruction techniques to refine the GNN-performances of our CATFinder for such tracks. |
| Sie lernen kennen: | advanced track reconstruction techniques in particle physics, machine learning |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Lea Reuter |
| Letzte Änderung: | 14.02.2025 |
| Thema: | Automatisches Monitoring der Datenqualität von Photonen in Belle II |
| Zusammenfassung: | Belle II ist ein Teilchenphysikexperiment das unter anderem darauf optimiert ist, neutrale Teilchen wie Photonen zu messen. Um präzise Messungen und Suchen nach neuer Physik zu ermöglichen, muss der Detektor hierfür genau kalibriert werden. Wechselnde Beschleuniger- und Detektorbedingungen erfordern ein zuverlässiges und automatisiertes Monitoring der Energie- und Richtungsauflösung von Photonen. Im Rahmen der Bachelorarbeit soll Richtungsauflösung von Photonen in echten Daten bestimmt und automatisiert werden. |
| Sie lernen kennen: | detector calibration, workflow management |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Alexander Heidelbach |
| Letzte Änderung: | 02.02.2025 |
| Thema: | Automated process monitoring with workflow management software towards sustainable computing |
| Zusammenfassung: | In High Energy Physics many calculations are done on complex data. Since every calculation increases energy consumption, it is important to do these as sensible and efficient as possible. While some computing sites already provide data on the power usage of these calculations, it is still difficult to monitor the energy consumption of whole analysis workflows. In this project, an existing workflow management based on the software luigi will be extended to automatically collect data on the energy usage of its tasks. |
| Sie lernen kennen: | workflow management software (luigi), monitoring and accounting of computing resources, databases |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Jonas Eppelt |
| Letzte Änderung: | 02.02.2025 |
| Thema: | Performance optimization for Machine Learning reconstruction algorithms |
| Zusammenfassung: | This thesis project centers on optimizing the performance of Machine Learning (ML) reconstruction algorithms, particularly focusing on Python algorithms and their integration with C++ interfaces for the high-level trigger at Belle I running on a large computing cluster. As a bachelor student, your primary objective will be to streamline the execution of ML reconstruction algorithms on CPUs and GPUs. You will explore techniques such as algorithm parallelization, memory management, and code optimization to achieve optimal performance in both Python and C++ environments. Through rigorous benchmarking and profiling, you will evaluate the impact of your optimizations on the reconstruction speed and resource utilization. By the end of your thesis, you will have contributed to the development of robust and efficient ML reconstruction pipelines, essential for high-level trigger systems in particle physics experiments. |
| Sie lernen kennen: | advanced track reconstruction techniques in particle physics, advanced C++ optimization |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Dr. Giacomo De Pietro |
| Letzte Änderung: | 02.02.2025 |