Masterarbeiten zu Software
| Thema: | Physics-Informed GNNs for Helical Track Reconstruction in the Belle II Drift Chamber |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | 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 |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Lea Reuter |
| Letzte Änderung: | 11.08.2025 |
| Thema: | Domain Adaptation for Robust GNN-Based Track Reconstruction at Belle II |
| Zusammenfassung: | Graph Neural Networks (GNNs) for charged particle track reconstruction at Belle II can show performance degradation when applied to data with input feature distributions or background conditions different from those seen during training. Such differences can arise from detector conditions, background levels, or other simulation-to-data discrepancies. This project investigates domain adaptation techniques to make GNN-based reconstruction more robust and less sensitive to these variations. The approach will augment the training dataset with tracks reconstructed using the standard Belle II baseline reconstruction algorithm, blending them with simulated truth-level training data. You will develop strategies for incorporating this additional domain-specific data into the GNN training process, aiming to improve generalisation and stability across different running conditions. The work will include designing suitable preprocessing and feature harmonisation methods, implementing domain adaptation loss functions or training schedules, and evaluating performance under varied detector and background scenarios. Results will be validated both on simulated and reconstructed data to quantify gains in reconstruction efficiency and resilience. For an example of GNN-based track finding, see the CAT project at Belle II. |
| Sie lernen kennen: | Domain adaptation in machine learning, robust GNN training techniques, detector simulation and reconstruction workflows, advanced data augmentation strategies, integration of ML methods into high-energy physics reconstruction software |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Lea Reuter |
| Letzte Änderung: | 11.08.2025 |
| Thema: | GPU-accelerated track reconstruction using ACTS and Graph Neural Networks at Belle II and the FCC-ee |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | ACTS track reconstruction, GPU programming in C++ (CUDA/SYCL), integration of machine learning output, performance tuning and benchmarking, software development in particle physics |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Dr. Giacomo De Pietro |
| Letzte Änderung: | 12.06.2025 |
| Thema: | FPGA-accelerated tracking using AMD Alveo V80 accelerator cards |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | FPGA programming with HLS (e.g. Vitis), low-latency data processing, particle tracking logic, hardware/software co-design, performance tuning, resource optimization |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Marc Neu |
| Letzte Änderung: | 12.06.2025 |
| Thema: | Next generation AI for the upgrade of the Belle II track trigger |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | Python programming, machine learning, track reconstruction, algorithm optimization |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Prof. Dr. Torben Ferber |
| Letzte Änderung: | 12.06.2025 |