Bachelorarbeiten

Masterarbeiten zu Software

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:24.02.2024
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:24.02.2024
Thema:Real-time anomaly detection for particle identification
Zusammenfassung: A new experiment at LUXE (Laser Und XFEL Experiment), but also particle physics experiments like Belle II, have to discriminate signal (photons) from background (neutrons). You will be using photon test-beam data to develop state-of-the-art deep learning algorithms for anomaly detection to reject non-photon events in calorimeters. The algorithms will be ultimately deployed on GPUs and FPGAs for offline and real-time analysis with inference times of a few 100 ns only.
Sie lernen kennen:machine learning, detector development, GPU and FPGA usage
Referent:Prof. Dr. Torben Ferber
Ansprechpartner:Prof. Dr. Torben Ferber
Letzte Änderung:24.02.2024
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 SHADOWS 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:24.02.2024
Thema:Development of a Machine Learning–based Bremsstrahlung Finder for the Belle II Experiment
Zusammenfassung: Electrons are subject to Bremsstrahlung when traversing the volume of the Belle II detector. The photon which is emitted in this process carries energy away from the electron and produces an additional signal in the electromagnetic calorimeter. The aim of this project is the development of a method to recover possible Bremsstrahlung photons for a given set of particle tracks using multivariate analysis techniques. The method is compared to an existing classical solution to the problem with the ultimate goal to outperform it.
Sie lernen kennen:Machine learning, data analysis, Python-programming, C++-programming
Referent:Prof. Dr. Torben Ferber
Ansprechpartner:Dr. Pablo Goldenzweig
Letzte Änderung:24.02.2024