Bachelorarbeiten zu Software
| Thema: | Slow Control Software for the AI-Based Calorimeter Trigger CaloClusterNet in Belle II |
| Zusammenfassung: | The AI-based calorimeter trigger at Belle II requires precise control over hardware parameters to ensure stable and efficient operation. A robust slow control system is essential for configuring trigger settings, monitoring system health, and responding to environmental changes in real time. In this project, you will develop slow control software in C/C++ to manage and optimize the AI-based calorimeter trigger. This includes implementing configuration interfaces, integrating hardware monitoring tools, and designing automated response mechanisms to maintain stable trigger performance. Your work will directly contribute to the reliable operation of this state-of-the-art trigger system in a high-energy physics experiment. |
| Sie lernen kennen: | FPGA slow control |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Marc Neu |
| Letzte Änderung: | 04.06.2025 |
| Thema: | Data Quality Monitoring for the AI-Based Calorimeter Trigger CaloClusterNet in Belle II |
| Zusammenfassung: | Ensuring high data quality is critical for the AI-based calorimeter trigger at Belle II, as it will play a key role in real-time event selection. A dedicated data quality monitoring system is needed to evaluate trigger performance, detect anomalies, and optimize trigger efficiency under varying conditions. In this project, you will implement data quality monitoring information for the calorimeter trigger to the Belle II data quality monitoring framework MiraBelle. Your work will involve designing monitoring tools, implementing visualization dashboards, and analyzing trigger performance metrics. You will focus on detecting inefficiencies, identifying systematic biases, and ensuring that the AI-based trigger operates at peak performance throughout data-taking periods. |
| Sie lernen kennen: | FPGA slow control |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Isabel Haide |
| Letzte Änderung: | 04.06.2025 |
| Thema: | GNN-Based Track Reconstruction in the Silicon Pixel Detector of Belle II |
| Zusammenfassung: | The Belle II Pixel Detector (PXD), located closest to the interaction point, presents unique challenges due to its small pixels and extreme background conditions. Efficient track reconstruction in this environment is crucial for precise vertex determination in Belle II. This project will extend the CATFinder ) to incorporate information from the high-resolution PXD. You will develop a GNN-based approach that integrates PXD data into the existing tracking framework, optimizing it for the high-occupancy conditions near the beam pipe. Your work will focus on improving robustness against background hits, refining pattern recognition, and enhancing overall tracking efficiency in the inner detectors of Belle II. |
| Sie lernen kennen: | advanced track reconstruction techniques in particle physics, machine learning |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Lea Reuter |
| Letzte Änderung: | 04.06.2025 |
| 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: | 04.06.2025 |
| 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: | 04.06.2025 |