MSc thesis topics
FAQ - Informationen zur Masterarbeit in der Arbeitsgruppe von Prof. Ferber
| Topic: | Search for the charged lepton flavor violating B+ → K*+ τ+ ℓ- decay at Belle II |
| Summary: | The observation of charged lepton flavor violating B meson decays would constitute clear evidence for physics beyond the Standard Model. One such decay, B+ → K*+ τ+ ℓ-, has recently been studied in the Belle II ETP group. As evidenced by this thesis, the analysis is at an advanced stage. The goal of this MSc thesis is to complete the analysis. The remaining steps include data/MC comparison studies, a comprehensive demonstration of fit stability, the final fit to data (including discovery or limit setting), and the evaluation of all systematic uncertainties. |
| You learn: | BSM B meson physics, python programming, statistical data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", also see our BSc/MSc FAQ), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Dr. Pablo Goldenzweig (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Search for the lepton number violating B- → μ- μ- π+ decay at Belle II |
| Summary: | The lepton number violating decay B- → μ- μ- π+ is forbidden by lepton number conservation in the Standard Model. If neutrinos are Majorana particles, however, such processes could occur through the production of an on-shell neutrino. The observation of this decay would therefore constitute clear evidence for physics beyond the Standard Model. This thesis will search for the decay B- → μ- μ- π+ at the Belle II experiment. The project includes the development of event selection criteria, studies of signal and background processes, and the optimization of the analysis strategy. Simulated data samples will be used to evaluate the expected sensitivity of Belle II to this channel and to guide the analysis design. |
| You learn: | BSM B meson physics, python programming, statistical data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", also see our BSc/MSc FAQ), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Dr. Pablo Goldenzweig (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Search for Displaced Dark Higgs Decays from Dark Photon Higgs Strahlung at Belle II |
| Summary: | Belle II provides an opportunity to probe dark sector scenarios in which a dark photon couples to the Standard Model through kinetic mixing: Most searches concentrate on final states that are either fully visible or fully invisible. In this project you will study a different process in which a dark Higgs boson is emitted together with the dark photon. This mechanism is tied to the mass generation of the dark photon. If the dark Higgs boson is the lightest state in the dark sector, it is expected to have a long lifetime and to decay into Standard Model particles through mixing with the Higgs field. You will learn how to generate simulated samples with Monte Carlo tools and how to perform statistical analyses needed to evaluate the sensitivity of Belle II to this process. More details can be found in this paper. |
| You learn: | Python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", also see our BSc/MSc FAQ), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Dr. Giacomo De Pietro (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Differentiable Vertex Reconstruction using Machine Learning at Belle II |
| Summary: | This project explores a differentiable vertex fitting approach that formulates the reconstruction of particle decay vertices as an optimization problem, where the solution’s gradients are accessible via implicit differentiation and can be backpropagated through neural network architectures. The goal of this master’s thesis is to integrate this differentiable vertex fitting technique into the GNN-based track finding and fitting pipepline of Belle II. For an example of GNN-based track finding, see the CAT project at Belle II, for differentiable vertex fitting see this example. |
| You learn: | Differentiable programming for secondary vertex fitting, integration of optimization-based methods into ML architectures, interfacing with Belle II software, and evaluation of vertex reconstruction performance |
| Prerequisites: | Good proficiency in English is required. Solid knowledge of Python programming, for example from the lecture and all exercises in Rechnernutzung, is required (also see our BSc/MSc FAQ). 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. Detailed understanding of charged particle motion in magnetic fields is required. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Tristan Brandes (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Physics-Informed GNNs for Helical Track Reconstruction in the Belle II Drift Chamber |
| Summary: | Charged particle tracks in the Belle II detector follow helical trajectories in the uniform solenoidal magnetic field. While standard GNN-based tracking methods learn these patterns implicitly from simulated data, explicitly incorporating this known physical constraint can improve performance and training efficiency. This project investigates physics-informed GNN architectures that embed the helix model directly into the learning process. A particular challenge is the Belle II central drift chamber geometry, which uses tilted stereo wires to obtain z-coordinate information. However, this wire arrangement makes track-finding challenging since the exact hit position along a trajectory is only known after track parameter estimation. You will explore strategies such as helix-aware message-passing layers, and loss functions that penalise deviations from physically consistent trajectories. The aim is to produce a GNN that not only identifies track candidates with high efficiency but also outputs parameters consistent with the underlying helix model, even in the presence of background hits and geometric distortions from the stereo wires. Performance will be benchmarked against existing GNNs on simulated Belle II data, focusing on efficiency, fake rate suppression, and resolution in transverse and longitudinal parameters. For an example of GNN-based track finding, see the CAT project at Belle II. |
| You learn: | Physics-informed machine learning, detector geometry effects in track reconstruction, advanced GNN architecture design, integration of physical models into deep learning, high-precision parameter estimation in particle tracking |
| Prerequisites: | Good proficiency in English is required. Solid knowledge of Python programming, for example from the lecture and all exercises in Rechnernutzung, is required (also see our BSc/MSc FAQ). 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. Detailed understanding of charged particle motion in magnetic fields is required. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Tristan Brandes (he/him) |
| Last update: | 23.07.2026 |
| Topic: | FPGA-accelerated tracking using AMD Alveo V80 accelerator cards |
| Summary: | Real-time tracking in high-rate environments like Belle II or FCC-ee requires serious compute power, but maybe GPUs aren't always the answer. In this project, you'll explore an alternative: running core parts of the track reconstruction pipeline on AMD Alveo V80 FPGA cards, built for low-latency acceleration in data centers. Using high-level synthesis (HLS) and streaming I/O, you’ll implement modules such as hit filtering and candidate selection on the FPGA, in close collaboration with FPGA experts at ITIV. |
| You learn: | FPGA programming with HLS (e.g. Vitis), low-latency data processing, particle tracking logic, hardware/software co-design, performance tuning, resource optimization |
| Prerequisites: | C++, basic knowledge of digital logic and FPGAs, interest in pushing code into hardware, ideally some experience with HLS tools or RTL (e.g. from a lab or project), curiosity about data acquisition and real-time systems. ❤️ We love nerds: If you have very good C++ skills or experience with HLS, please reach out even if you have no prior courses in particle physics. |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Marc Neu (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Real-time track finding for the upgrade of the Belle II CDC track trigger |
| Summary: | The Belle II experiment relies on its Central Drift Chamber (CDC) for precise charged particle tracking and momentum measurement. As the experiment prepares for future upgrades and increased luminosity, improving the CDC track reconstruction algorithms becomes a crucial challenge. The candidate will work within the interdisciplinary real-time computing team at KIT ETP and ITIV to develop real-time machine learning-based tracking methods. Special attention will be given to handling background noise and missing hits, as well as optimizing inference on real-time hardware, such as AMD Xilinx Versal AI Edge Series Gen 2 platforms. You will have access to dedicated hardware at KIT for testing machine learning models on real-time architectures, ensuring that proposed solutions can be validated under experimental conditions. Close collaboration with electrical engineering students at ITIV will be necessary to co-design efficient hardware-software interfaces for real-time inference. |
| You learn: | Python programming, machine learning, track reconstruction, algorithm optimization |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), good knowledge in Python and/or C++ programming (e.g. lecture and all exercises in "Rechnernutzung", ideally experience in own software projects), ideally MSc course on modern methods of data analysis, good communication skills (english) |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Prof. Dr. Torben Ferber (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Search for dark photons and axionlike particles in single-photon events at Belle II |
| Summary: | Belle II is a world-leading experiment in the search for dark sector candidates in the GeV range, such as dark photons or axion-like particles (ALPs). Invisibly decaying or long-lived dark photons and ALPs give rise to events with a single, monochromatic photon in the event, a unique and very challenging experimental signature. You will join our team our team to work on this search, which is currently focused on on-shell dark photons, with the aim of extending it to so-called off-shell (heavy) dark photons and ALPs. You will learn how to use tools for generating simulated datasets with Monte Carlo techniques and for statistical analysis. |
| You learn: | Python programming, data analysis, statistics |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", also see our BSc/MSc FAQ), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Dr. Giacomo De Pietro (he/him) |
| Last update: | 23.07.2026 |
| Topic: | Search for B+ → ℓ+ ν γ at Belle II |
| Summary: | The radiative leptonic decay B+ -> ℓ+ ν γ yields important information for the theoretical predictions of non-leptonic B meson decays into light-meson pairs. The emission of the photon probes the first inverse moment λ_B of the light-cone distribution amplitude (LCDA) of the B meson. This parameter is a vital input to QCD factorisation schemes for the non-perturbative calculation of non-leptonic B meson decays. This project aims to observe the B+ → ℓ+ ν γ decay for the first time and set an improved limit on λ_B using modern machine learning analysis techniques. It will build upon the foundation established in . |
| You learn: | python programming, data analysis |
| Prerequisites: | introductory MSc course in particle physics (e.g. lecture and all exercises TP1), basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung", also see our BSc/MSc FAQ), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber (he/him) |
| Contact: | Dr. Pablo Goldenzweig (he/him) |
| Last update: | 23.07.2026 |