BSc thesis topics
FAQ - Informationen zur Bachelorarbeit in der Arbeitsgruppe von Prof. Ferber
| Topic: | Event classification in B- → μ- μ- π+ decay at Belle II |
| Summary: | The lepton number violating process B- → μ- μ- π+ is forbidden in the Standard Model. However, if neutrinos are their own antiparticles, processes that involve lepton-number violation can proceed via the production of on-shell Majorana neutrinos. The aim of this thesis is to optimize the selection criteria for the B- → μ- μ- π+ decay at Belle II using ML-based tools to suppress background processes. |
| You learn: | BSM B meson physics, python programming, statistical data analysis |
| Prerequisites: | Good proficiency in English is required. Solid knowledge of Python programming, for example from the lecture and all exercises in Rechnernutzung, is required. The exam in ModEx 1+2 must be completed. Prior experience in software development projects is an advantage. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Pablo Goldenzweig (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Dynamic reweighting of B-meson branching ratios at Belle II |
| Summary: | In experiments such as Belle II, we rely on large Monte Carlo simulations to model background processes. Producing these simulations takes months on distributed computing systems, so they cannot be regenerated frequently. At the same time, our knowledge of how particles decay improves over time as new measurements are made. Existing simulations that use this information do not automatically reflect these improvements and therefore become outdated. This leads to a mismatch between the decay information used in existing simulations and current experimental knowledge. In this project, you will develop a software framework that compares the decay information stored in simulated events with up to date decay information. Based on this comparison, the framework will apply corrections or reweightings to the outdated simulation output without rerunning the full simulation again. Using the rare decay B → K τ+ τ- as a primary use case, you will study the impact of these updates on signal sensitive physics observables. In such rare decay channels, even small mismodeling of dominant background branching fractions can fake a new physics signal or hide a real effect. |
| You learn: | python programming, information parsing, databases, template fitting, rare decay analysis |
| Prerequisites: | Good proficiency in English is required. Solid knowledge of Python programming, for example from the lecture and all exercises in Rechnernutzung, is required. The exam in ModEx 1+2 must be completed. Prior experience in software development projects is an advantage. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Lennard Damer (he/him) |
| Last update: | 23.07.2026 |
| Topic: | GNN-Based Track Finding on Remaining Hits After Conventional Reconstruction at Belle II |
| Summary: | Conventional tracking algorithms at Belle II are highly optimised for efficiency but may miss tracks in high-occupancy or low-momentum regions, or in events with complex topologies. These missed tracks can have a significant impact on physics analyses, particularly those involving secondary vertices or long-lived particles. This project investigates a hybrid approach in which graph neural network (GNN) based track finding is applied after the standard reconstruction, using the remaining, unassigned hits. You will integrate a GNN tracking pipeline into the Belle II reconstruction framework to operate as a second-stage algorithm. The focus will be on identifying tracks that evade conventional pattern recognition, improving overall reconstruction efficiency. Tasks include defining suitable input data from remaining hits, training and optimising the GNN for this use case, and evaluating performance with respect to track recovery rates and reconstruction quality. |
| You learn: | Hybrid tracking strategies, advanced GNN architectures for particle physics, integration of machine learning with conventional algorithms, reconstruction efficiency optimisation |
| Prerequisites: | Good proficiency in English is required. Solid knowledge of Python programming, for example from the lecture and all exercises in Rechnernutzung, is required. The exam in ModEx 1+2 must be completed. Prior experience in software development projects is an advantage. Basic familiarity with machine learning frameworks such as TensorFlow or PyTorch is beneficial. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Tristan Brandes (he/him) |
| Last update: | 23.07.2026 |
| 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 (he/him) |
| Contact: | Tristan Brandes (he/him) |
| Last update: | 23.07.2026 |
| 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 (he/him) |
| Contact: | Dr. Giacomo De Pietro (he/him) |
| Last update: | 23.07.2026 |