Bachelorarbeiten zu Software
| Thema: | GNN-Based Track Finding on Remaining Hits After Conventional Reconstruction at Belle II |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | Hybrid tracking strategies, advanced GNN architectures for particle physics, integration of machine learning with conventional algorithms, reconstruction efficiency optimisation |
| Referent: | Prof. Dr. Torben Ferber (he/him) |
| Ansprechpartner: | Tristan Brandes (he/him) |
| Letzte Änderung: | 23.07.2026 |
| Thema: | GNN-Based Track Reconstruction in the Silicon Pixel Detector of Belle II |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | advanced track reconstruction techniques in particle physics, machine learning |
| Referent: | Prof. Dr. Torben Ferber (he/him) |
| Ansprechpartner: | Tristan Brandes (he/him) |
| Letzte Änderung: | 23.07.2026 |
| Thema: | Performance optimization for Machine Learning reconstruction algorithms |
| Zusammenfassung: | 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. |
| Sie lernen kennen: | advanced track reconstruction techniques in particle physics, advanced C++ optimization |
| Referent: | Prof. Dr. Torben Ferber (he/him) |
| Ansprechpartner: | Dr. Giacomo De Pietro (he/him) |
| Letzte Änderung: | 23.07.2026 |