Topics for graduate theses
Theses are possible at any time in the areas of hardware, simulation and analysis. Some current topics are listed below as examples. For more information and further topics for theses please contact Alexander Kappes (e-mail), IKP room 224.
Current topics for bachelor theses:
- Sensor simulation with machine learning: The segmentation of the photosensitive area of the new generation of sensors for future extensions of the IceCube neutrino detector requires new ways for their efficient implementation in the simulation environment of the experiment. One promising possibility is the use of neural networks (NN). In this bachelor thesis, an existing NN architecture is trained using events from a detailed but slow Geant 4 simulation of the mDOM (multi-photomultiplier digital module) sensor developed in Münster and then its performance is characterized in comparison to the Geant 4 simulation.
- Measuring the internal reflections of a PMT: Photomultipliers (PMTs) are the photosensitive heart of neutrino telescopes such as IceCube. In order to be able to simulate these as accurately as possible, the optical properties of the internal components are to be measured as part of this Bachelor's thesis and compared with older measurements and simulations in Geant 4.
Current topics for master theses:
- Improving supernova detection in IceCube Upgrade using machine learning: Neutrinos from core-collapse supernovae typically possess energies in the ballpark of tens of MeVs, rendering them impossible to detect using IceCube's standard reconstruction methods. Currently, IceCube identifies potential supernova neutrino bursts by monitoring for a collective increase in the rates across all optical modules. The forthcoming integration of mDOMs in the IceCube Upgrade heralds a promising shift for detecting these events. Given the configuration of 24 photomultipliers per module, the low-energy neutrino events can produce hits across multiple photomultipliers in a single module in a very short time. This master's study will employ machine learning to efficiently discern supernova neutrinos from background noise, focusing on the analysis of hit patterns and correlations across the array of photomultipliers and optical modules.
- Investigation of the performance of real-time reconstruction in IceCube: IceCube has an extensive infrastructure for notifying other experiments in real time when interesting events are detected (real-time alerts). These pipelines are optimized for efficiency and speed and therefore use special reconstruction algorithms. The aim of the program is to find an electromagnetic counterpart to the neutrinos with other telescopes. In this master thesis the performance of the different algorithms will be analyzed, compared and finally improved. In addition, it will be investigated how the uncertainty in the directional reconstruction affects the sensitivity for certain source classes. The work will be carried out in close cooperation with the IceCube group in Bochum, including stays there.
- Seismic Noise Prediction for the Einstein Telescope: This Master's thesis project aims to enhance the Einstein Telescope (ET) Project by developing machine and deep learning algorithms for predicting seismic noise, crucial in gravitational wave analysis. The focus is on forecasting the arrival of seismic waves: P-waves (primary waves that are the fastest and travel through solids, liquids, and gases), S-waves (secondary waves that are slower and only move through solids), and surface waves (which travel along the Earth's surface and cause most of the damage during earthquakes). The candidate will also model the real-time 3D movement of the Earth. This research is key to filtering seismic interference in gravitational wave data. The project aims to significantly enhance the accuracy of gravitational wave detection and contribute to astrophysical research.