MSc thesis topics
FAQ - Informationen zur Masterarbeit in der Arbeitsgruppe von Prof. Ferber
| Topic: | GPU-accelerated track reconstruction using ACTS and Graph Neural Networks at Belle II and the FCC-ee |
| Summary: | Modern collider experiments like Belle II and the proposed FCC-ee generate large amounts of tracking data. Graph Neural Networks (GNNs) are increasingly used to find track candidates from raw hits. In this project, you will take the output of a GNN-based track finder and integrate it into ACTS (A Common Tracking Software), a modern C++ toolkit for track fitting. The focus is on implementing this connection and optimizing it for execution on GPUs. You will work in C++ and use accelerator frameworks like CUDA or SYCL to build a fast and scalable solution. The setup should be modular and suitable for both current (Belle II) and future (FCC-ee) detectors. For an example of GNN-based track finding, see the CAT project at Belle II. |
| You learn: | ACTS track reconstruction, GPU programming in C++ (CUDA/SYCL), integration of machine learning output, performance tuning and benchmarking, software development in particle physics |
| Prerequisites: | C++, 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). We love nerds: If you have very good C++ skills or experience with CUDA or SYCL, please reach out even if you have no prior courses in particle physics. |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 05.04.2025 |
| 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 |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Marc Neu |
| Last update: | 05.04.2025 |
| Topic: | Next generation AI for the upgrade of the Belle II 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 CDC-ML 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 cutting-edge real-time hardware, such as AMD Xilinx Versal AI Edge Series Gen 2 platforms. The CDC-ML team has 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 |
| Contact: | Prof. Dr. Torben Ferber |
| Last update: | 05.04.2025 |
| Topic: | First search for B0 → p anti-n ℓ ν at Belle II |
| Summary: | The persistent disagreement between inclusive and exclusive determinations of the Cabibbo-Kobayashi-Maskawa (CKM) matrix element |Vub| limits the possibility to overconstrain the CKM unitarity triangle in efforts to test the Standard Model (SM). A well-established strategy to determine |Vub| is to use measurements of semileptonic B-meson decays with b → uℓν transitions. These relatively abundant decays offer theoretically clean avenues to perform precise measurements of SM parameters, due to the factorization of the leptonic and hadronic final states. However, a major challenge for determinations of |Vub| is suppressing the CKM-favoured B → Xcℓν background, which exhibits a similar experimental signature and is O(100) times more abundant than B → Xuℓν decays. The signal extraction process is further complicated by the known resonant states comprising only a third of the total inclusive branching fraction, while non-resonant contributions of B → Xuℓν remaining poorly understood. For these reasons, the B → Xuℓν modelling uncertainty is hard to quantify and becomes dominant for both inclusive and exclusive studies. Decays involving baryons have remained largely unmeasured and could potentially constitute a sizeable component of the total inclusive B → Xuℓν branching. A first measurement of B0 → p anti-nℓν decays, a mode only accessible at Belle II, would provide much needed input to reduce modelling uncertainties in future B → Xuℓν studies, increase our current understanding of b → uℓν transitions, and shed light on the possible reasons for the infamous inclusive/exclusive puzzle. |
| You learn: | Python programming, machine learning, track reconstruction, algorithm optimization |
| 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"), ideally MSc course on modern methods of data analysis, good communication skills (english) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Raynette Van Tonder |
| Last update: | 05.04.2025 |
| Topic: | Correlation of Beam Background and Accelerator Parameters with Detector Performance in the Belle II Experiment |
| Summary: | The Belle II experiment at the SuperKEKB accelerator faces significant challenges due to high beam background originating from the extreme accelerator conditions. They affect key performance metrics such as tracking efficiency and calorimeter photon energy resolution. This project aims to investigate the impact of beam background on detector performance. We currently use two proxies to measure the level of beam background: out-of-time crystals (photons detected outside the expected timing window) and extra CDC hits (additional drift chamber hits not used by any tracking algorithm). The primary goal is to improve these proxies by applying anomaly detection techniques, specifically autoencoders, to identify anomalous beam conditions that correlate with performance degradation. Both simulation and real data from Belle II will be used for model training. Additionally, the project will incorporate time series data from the SuperKEKB accelerator, including thousands of sensor readings and accelerator parameters, to correlate operational conditions with fluctuations in detector performance. The first stage of this project involves developing a more precise method for measuring beam background and analyzing how accelerator conditions affect the performance of the Belle II detector. The final stage aims to identify key accelerator parameters from SuperKEKB data in order to define operational margins that ensure sufficient performance of the experiment. |
| 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Jonas Eppelt |
| Last update: | 05.04.2025 |
| 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 05.04.2025 |
| Topic: | Search for long-lived dark photons decaying into visible final states at Belle II |
| Summary: | Among all the dark sector searches, the Belle II experiment has a unique sensitivity to a weakly coupled dark photon whose decays have a displaced vertex. You will start a new search for long-lived dark photons decaying into a pair of muons, pions, kaons and, more challenging, electrons, with the aim of exploring previously unexplored parameters’ space. You will learn how to characterise the detector performance for the reconstruction of displaced vertices and use tools 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 05.04.2025 |
| Topic: | Search for B+→l nu gamma at Belle II |
| Summary: | The radiative leptonic decay B+ -> l+nu gamma 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+nu gamma decay for the first time and set an improved limit on λ_B using modern machine learning analysis techniques. |
| 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 05.04.2025 |
| Topic: | Search for Bs→φπ0 decays at Belle |
| Summary: | The Belle experiment, which concluded in 2011, collected a unique sample of Υ(5S) decays to Bs meson pairs in the clean e+e- collision environment. This dataset has yet to be fully exploited. The rare decay Bs→φπ0 is strongly suppressed in the Standard Model and has yet to be observed. However, in a theoretical analysis motivated by the Kπ CP-puzzle, models with modified or additional Z bosons allow for an increase of the branching fraction by an order of magnitude without inconsistencies with other measurements. The Kπ CP-puzzle consists of an unexpectedly large direct CP asymmetry in the decays B± → K±π0 and B0 → K±π∓. These decays are dominated by isospin-conserving processes, but have a small contribution from isospin-violating penguin processes as well. In the isospin-violating decay Bs→φπ0 the penguin processes dominate, which means that potential NP contributions can have a much larger relative effect. If these contributions exist, an observation of the Bs→φπ0 decay may be possible with the Belle Υ(5S) dataset. |
| 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 05.04.2025 |
| Topic: | Bs→K+ l- nu with Y(5S) using machine learning-based Full Event Interpretation |
| Summary: | There are two conceptually different methods to determine the CKM matrix element Vub, a factor in the amplitude of transitions from a b-quark to a u-quark and a W boson. As quarks cannot be detected directly, one has to handle the hadronisation to infer Vub, even in the simplest case, where the W boson decays into a charged lepton and a neutrino. One method, which is easier to calculate but is experimentally more challenging, takes into account all possible final states that typically occur during the hadronisation process (inclusive measurement). The other involves calculating the hadronisation effects and searching for only a single final state, e.g., B0->pi-l+nu (exclusive measurement). Both approaches have been pursued and the result is a tantalizing 3sigma discrepancy between the measurements of the same quantity Vub. By measuring Bs->K-l+nu we can shed light on the possible reasons or sources for this, as we replace the spectator d quark in B0->pi-l+nu with an s quark. Since the calculation of the hadronisation in the exclusive measurement relies on Lattice QCD, the kaon in the final state simplifies the calculation significantly. A result close to the existing exclusive measurement would strengthen the trust in that measurement and could hint to new particles beyond the Standard Model. A result closer to the inclusive measurement would more likely be interpreted as a hint that the calculations for the exclusive measurement are unreliable and that the inclusive measurement gives the real value of Vub. This will constitute a first measurement of the rare Bs→K-l+nu decay by employing a modern machine learning-based algorithm to reconstruct the full Y(5S) event for the first time. |
| You learn: | machine learning, 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"), ideally MSc course on modern methods of data analysis |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Pablo Goldenzweig |
| Last update: | 05.04.2025 |
| Topic: | GPU support for the Grid |
| Summary: | GPUs play an increasingly important role in High Energy Physics (HEP). Therefore, KIT provides GPUs accessible to various HEP collaborations via the distributed computing infrastructure, the so-called Grid. Currently, there are only initial concepts. You would contribute to implementing the GPU support in the Grid on a collaboration-wide scale. It can be complementary to the PhD or MSc thesis. |
| You learn: | Batch System HTCondor, GPU usage, Grid infrastructure |
| Prerequisites: | interest in large scale computing, |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 05.04.2025 |
| Topic: | Reinforcement Learning for Dynamic Resource Management |
| Summary: | KIT develops concepts and software to integrate opportunistic computing resources transparently. The decision of how many resources of which type has to be provided for efficient usage is complex. Reinforcement learning, where an artificial neural network learns itself, can increase resource efficiency and reduce resource specific parameter tuning. The implementation and study of reinforcement learning can complement the PhD or MSc thesis. |
| You learn: | Batch System HTCondor, bash, python, machine learning, Grid infrastructure |
| Prerequisites: | interest in large scale computing, basic knowledge in Python programming (e.g. lecture and all exercises in "Rechnernutzung"), basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 05.04.2025 |