Masterarbeiten zu Software
| Thema: | Track reconstructing using Transformers at Belle II |
| Zusammenfassung: | Traditional methods for track reconstruction in High Energy Physics experiments often face scalability issues with detector occupancy. Inspired by the success of Transformer models in natural language processing, we're exploring the feasibility of training a Transformer to translate detector signals into track parameters. You will develop and train a Transformer to find tracks in the Belle II experiment using simulated and real data taking at the SuperKEKB collider in Japan. You will evaluate the feasibility and benefits of deploying the Transformer-based track reconstruction in terms of processing speed, track finding efficiency, and track parameter resolution. |
| Sie lernen kennen: | python-programming, machine Learning |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Lea Reuter |
| Letzte Änderung: | 21.04.2024 |
| Thema: | Neuromorphic Computing with Spiking Neural Networks for Energy Efficient Waveform Analysis |
| Zusammenfassung: | Waveform analysis is a technique to extract information from signals, such as amplitude and shape. It is widely used in particle physics experiments, for example in the readout of calorimeters. However, waveform analysis is computationally intensive and energy consuming, which limits its scalability and applicability. Spiking neural networks (SNNs) are a type of artificial neural networks that mimic the behavior of biological neurons, which communicate through spikes or pulses. SNNs have the potential to perform waveform analysis with high accuracy and low energy consumption, as they can exploit the temporal dynamics and sparsity of signals. The objective of this project is to investigate the feasibility and performance of using SNNs for waveform analysis in particle physics. You will evaluate and compare the accuracy and energy efficiency of SNN models with conventional methods on simulated and real data sets for CsI(Tl)-crystals similar to those used, for example, by the Belle II experiment. The results of the model evaluation will be analyzed to identify the strengths and weaknesses of SNN models for waveform analysis in particle physics. You will discuss factors that affect the accuracy and energy efficiency of SNN models will suggest possible improvements and future directions. |
| Sie lernen kennen: | python-programming, machine learning, sustainable computing |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Dr. Jan Kieseler |
| Letzte Änderung: | 20.04.2024 |
| Thema: | Ultrafast machine learning with quantized graph neural networks |
| Zusammenfassung: | The electromagnetic calorimeter (ECL) is a subdetector of Belle II that measures the energy and time of photons and neutral hadrons, and is used to identify electrons. The ECL reconstruction is computationally intensive and time-consuming and needs specialized algorithms to be used for trigger decisions. One possible way to achieve this goal is to use QKeras, a quantization extension to Keras that provides drop-in replacement for some of the Keras layers. QKeras allows the creation of deep quantized neural networks that can be deployed on field-programmable gate arrays (FPGAs). The proposed research project aims to use QKeras to optimize the existing ECL reconstruction algorithm for Belle II and prepare the implementation on FPGAs. You will design and train deep quantized graph neural network using QKeras that can perform ECL reconstruction with high accuracy and efficiency. You will then compare the performance of the QKeras-based ECL reconstruction with the current one in terms of energy and time resolution, particle identification, and physics observables. As last step, you will evaluate the feasibility and benefits of deploying the QKeras-based ECL reconstruction on FPGAs in terms of processing speed, power consumption, and resource utilization. |
| Sie lernen kennen: | python-programming, machine Learning, QKeras, FPGA |
| Referent: | Prof. Dr. Torben Ferber |
| Ansprechpartner: | Isabel Haide |
| Letzte Änderung: | 20.04.2024 |
| Thema: | Detector studies for future beam dump experiments |
| Zusammenfassung: | Light dark sector particles can be searched for at high intensity beam dump experiments like LUXE (Laser Und XFEL Experiment) at DESY, or SHiP at CERN. Axionlike particles would be detected by reconstructing its decay products: Two photons with a distinct angular and energy correlation. During the MSc thesis you will design and optimize possible detector options to measure energy, time, and direction of the decay photons using state-of-the-art simulation software. |
| Sie lernen kennen: | Particle physics (ALPs), GEANT4, data analysis, Python-programming, C++-programming |
| Referent: | Prof. Dr. Torben Ferber, Prof. Dr. Markus Klute |
| Ansprechpartner: | Prof. Dr. Torben Ferber |
| Letzte Änderung: | 13.03.2024 |