BSc thesis topics
FAQ - Informationen zur Bachelorarbeit in der Arbeitsgruppe von Prof. Ferber
| Topic: | Performance of Particle Identification for Long-Lived Particle Decays at Belle II |
| Summary: | Belle II is equipped with state-of-the-art particle identification (PID) systems to distinguish between electrons, muons, pions, kaons, and protons. While PID performance for prompt particles is well understood, decays from long-lived particles (LLPs), such as neutral kaons (K0-short and K0-long) or lambda-baryons, or hypothetical dark Higgs bosons or other beyond-the-standard-model (BSM) particles, pose unique challenges due to their displaced vertices. In this project, you will study PID performance for displaced tracks using simulated and real collision data from the Belle I experiment. These studies are crucial for ongoing BSM LLP searches, where accurate PID affects both signal efficiency and background rejection. You'll evaluate how PID efficiency and misidentification rates vary with displacement, momentum, and particle type. |
| You learn: | data analysis |
| Prerequisites: | good proficiency in English is required, good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 23.04.2025 |
| Topic: | Slow Control Software for the AI-Based Calorimeter Trigger CaloClusterNet in Belle II |
| Summary: | 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. |
| You learn: | FPGA slow control |
| Prerequisites: | good proficiency in English is required, good knowledge in C/C++ is required, exam in ModEx1+2 is a plus, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Marc Neu |
| Last update: | 20.03.2025 |
| Topic: | Analyzing Cosmic Ray Data Collected with the Belle II Detector |
| Summary: | The Belle II detector provides a unique opportunity to study cosmic rays using dedicated runs without colliding beams. These data sets are crucial for evaluating detector performance, testing new trigger algorithms, and refining event reconstruction techniques. In this project, you will analyze real cosmic ray data to assess the efficiency of a new state-of-the-art AI-based calorimeter trigger. Your work will involve studying the trigger response, identifying potential inefficiencies, and evaluating how the trigger efficiency varies as a function of the cosmic ray angle and energy. |
| You learn: | data analysis |
| Prerequisites: | good proficiency in English is required, good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Prof. Dr. Torben Ferber |
| Last update: | 20.03.2025 |
| Topic: | Data Quality Monitoring for the AI-Based Calorimeter Trigger CaloClusterNet in Belle II |
| Summary: | 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. |
| You learn: | FPGA slow control |
| Prerequisites: | good proficiency in English is required, good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Isabel Haide |
| Last update: | 20.03.2025 |
| Topic: | Combining Graph Neural Networks for Track Reconstruction in Very High Backgrounds |
| Summary: | Graph Neural Networks (GNNs) have proven to be powerful tools for track reconstruction in high-energy physics experiments, particularly in environments with complex event topologies. However, in events with extremely high background levels, distinguishing signal tracks from noise remains a significant challenge. This project aims to enhance track reconstruction by combining two different types of GNN architectures. You will first apply interaction networks to filter background noise. The cleaned events will be used to train variants of the CATFinder developed in our group. Your work will focus on optimizing this hybrid approach, improving robustness against background noise, and enhancing tracking efficiency. |
| You learn: | advanced track reconstruction techniques in particle physics, machine learning |
| Prerequisites: | good proficiency in English is required, good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Lea Reuter |
| Last update: | 20.03.2025 |
| Topic: | GNN-Based Track Reconstruction in the Silicon Pixel Detector of Belle II |
| Summary: | 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. |
| You learn: | advanced track reconstruction techniques in particle physics, machine learning |
| Prerequisites: | good proficiency in English is required, good knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Lea Reuter |
| Last update: | 20.03.2025 |
| Topic: | Automated process monitoring with workflow management software towards sustainable computing |
| Summary: | 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. |
| You learn: | workflow management software (luigi), monitoring and accounting of computing resources, databases |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Jonas Eppelt |
| Last update: | 02.02.2025 |
| Topic: | Performance optimization for Machine Learning reconstruction algorithms |
| Summary: | 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. |
| You learn: | advanced track reconstruction techniques in particle physics, advanced C++ optimization |
| Prerequisites: | good proficiency in English is required, good knowledge in Python and C++ programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 02.02.2025 |
| Topic: | Performance study of NN-Training with data streaming from remote storage |
| Summary: | In High Energy Physics, datasets of several hundreds of GB are used for machine learning. Often, these datasets are stored at remote storage servers. Sometimes it is not possible to keep them on the computing machine. It could be an option to stream the data from the remote storage. It is possible to use a cache that keeps the data near the computing resource. That reduces the network traffic and the access latency for the files. The performance during streaming with and without cache should be studied. |
| You learn: | concepts and usage of distributed computing, transfer protocol, and storage/caching software XRootD |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 02.02.2025 |
| Topic: | Multi-core support for Belle II Grid |
| Summary: | Belle II uses a distributed computing infrastructure called Grid. Currently, Belle II supports only one CPU core per application in the Grid. The use of multiple CPU cores would speedup applications and reduce memory footprint. You can improve the Belle II Grid computing by configuring, implementing, and testing the support for multiple CPU cores in the Belle II Grid. |
| You learn: | usage and configuration of the workflow management system DIRAC, Python, bash, Grid computing |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 02.02.2025 |