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 Prof. Alexander Kappes (e-mail), IKP room 224. For topics related to the Einstein Teleskiop you can also contact Dr. Waleed Esmail (E-Mail), IKP Raum 115.

Current topics for bachelor theses:

Waveform Generation for the Einstein Telescope Using CBS-GPT: The Einstein Telescope (ET), as a next-generation gravitational wave (GW) observatory, will push the boundaries of GW detection, capturing signals from extreme mass-ratio inspirals, highly eccentric binaries, and other challenging astrophysical systems. Accurate and efficient waveform generation is a cornerstone of gravitational wave data analysis. It is crucial for signal detection, parameter estimation, and multi-messenger astronomy, where GWs are often correlated with electromagnetic or neutrino counterparts. Traditional numerical methods for waveform generation are computationally expensive, particularly for systems with extreme parameters or long inspirals. These challenges are amplified for ET, given its sensitivity to low-frequency signals and the complexity of its noise characteristics. This project aims to utilize the CBS-GPT framework (Compact Binary Systems
Waveform Generation with Generative Pre-trained Transformer), a state-of-the-art generative model, to efficiently generate GW waveforms tailored to the ET. By leveraging CBS-GPT, this project will enable a scalable solution for the vast parameter spaces expected in ET's observations.

[1] Ruijun Shi, et al., Compact binary systems waveform generation with a generative pretrained transformer, Phys. Rev. D 109, 084017, 2024
[2] Tianyu Zhao, et al., GWAI: Artificial intelligence platform for enhanced gravitational wave data analysis, SoftwareX, Volume 28, 101930, 2024
Rapid Sky Localization of gravitational waves through Model Quantization: This project aims to develop and implement a deep learning model for the rapid sky
localization of gravitational wave (GW) events. Accurate and swift localization is crucial for enabling timely electromagnetic (EM) follow-up observations in multi-
messenger astronomy. Traditional Bayesian inference methods, while precise, can be computationally intensive and slow, often taking from seconds to days to
produce localization maps. In contrast, deep learning approaches have demonstrated the potential to generate accurate sky localization inferences within
milliseconds to seconds, significantly enhancing the responsiveness of follow-up observation strategies. The key innovation will be the application of model
quantization to optimize the model for real-time inference. The project aims to train a neural network to process GW strain data and to predict the probable sky location of the GW event, with model quantization to reduce the computation time and memory requirements.

[1] Alex Kolmus, et al., Fast sky localization of gravitational waves using deep learning seeded importance sampling, Phys. Rev. D 106, 023032, 2022

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.
Multi-Messenger Observations of Core-Collapse Supernovae with the Einstein Telescope and IceCube-Gen2: Core-collapse supernovae (CCSNe) are among the most energetic astrophysical phenomena, producing gravitational waves (GWs), neutrinos, and electromagnetic signals. These events provide a unique opportunity for multi-messenger astrophysics, offering insights into the physics of stellar collapse and the formation of neutron stars or black holes. This master project aims to pursue a feasibility study that integrates simulated data from ET and IceCube-Gen2 for multi-messenger analyses of CCSNe. Detection of CCSNe with current observatories like Advanced LIGO (aLIGO), and IceCube is challenging, primarily due to the limitations in their sensitivity and detection methodologies. ET's unprecedented sensitivity, especially in the low-frequency range, will extend the GW detection horizon for CCSNe by an order of magnitude. In addition, the integration of multi-PMT optical modules (mDOMs) in IceCube-Gen2 will enhance sensitivity to MeV-scale neutrinos, allowing individual events to be detected and reconstructed. The projects aims to develop methods to correlate gravitational wave signals from the ET with low-energy neutrino bursts from IceCube-Gen2, focusing on enhancing detection and correlation accuracy.

[1] Timo Peter Butz, Study On The Detection Of Gravitational Waves From Core-Collapse Supernovae With The Einstein Telescope, Master thesis RWTH Aachen, 2024
[2] Jade Powell, et al., Determining the core-collapse supernova explosion mechanism with current and future gravitational-wave observatories, Phys. Rev. D 109, 063019, 2024