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:

• Measurment of low charge pulses of photomultipliers: A PMT, upon detecting a photon, yields a charge following a Gaussian-like pattern, known as the "SPE distribution." A precise understanding of this distribution is important for accurate data reconstruction in IceCube. PMTs frequently generate low charge pulses that deviate from the typical Gaussian pattern, but differentiating this deviation from the background distribution remains a challenge. In this bachelor's thesis, the low charge region will be examined comprehensively through diverse measurement techniques.

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.
• Optimization of methods for determining the gain of a PMT: Upon detecting a photon, a PMT produces a charge that follows a Gaussian-like profile, known as the "SPE distribution". Typically, the determination of the PMT gain is adjusted using a sum of Gaussian functions. However, since the SPE distribution doesn't precisely match a Gaussian function, this method introduces a bias in the gain determination. This master's thesis explores alternative approaches to determining the PMT gain, considering both analytical methods using Machine Learning as well as innovative measurement techniques
• 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.