Master's Thesis in the Working Group of Prof. Husemann

The research in the working group of Prof. Husemann at the Institute of Experimental Particle Physics (ETP) of KIT is focused on two main topics, data analysis and particle detectors For the CMS experiment at the CERN Large Hadron Collider, we analyze the data and we build silicon tracking detectors. Such detectors are also very relevant for medicine, where they are employed, e.g. in radiotherapy. On this page you will find suggestions for thesis topics. Some of the topics are offered jointly with other working groups, from ETP or from the KIT Institute for Data Processing an Electronics (IPE).

Data Analysis at the CERN Large Hadron Collider

Topic:Search for Beyond the Standard Model Higgs Bosons
Summary:Together with the research groups Klute/Quast/Wolf at ETP we search for the production of two Higgs bosons within the next-to-minimal supersymmetric standard model (NMSSM). Specifically, the decays of the two Higgs bosons into a pair of tau leptons and a pair of bottom quarks are of great interest to us. This analysis offers possibilities for contributions of Master students in object reconstruction, machine learning, and interesting analysis of CMS data in general.
What you will learn:BSM Higgs physics, data analysis, Python programming, C++ programming machine learning
Advisor:Prof. Ulrich Husemann, Prof. Markus Klute
Contact:Prof. Ulrich Husemann
Last change:29/01/2024
Topic:Search for Dark Matter in Association with Heavy Quarks
Summary:Deciphering the nature of dark matter (DM) is one of the greatest scientific challenges of our time. At particle accelerators, DM can be produced and studied in a controlled laboratory environment. We are conducting a search for DM using the CMS experiment at the Large Hadron Collider (CERN), studying final states with top and bottom quarks and missing energy. In your Master's thesis, you can improve this search with the most recent CMS data and new analysis methods.
What you will learn:Physics beyond the standard model, data analysis, machine learning, Python programming, C++ programming, ROOT, statistical methods and numerical optimization
Advisor:Prof. Ulrich Husemann
Contact:Prof. Ulrich Husemann;
Last change:26/01/2024
Topic:Machine Learning in Particle Physics
Summary:Multivariate methods of data analysis have been used in particle physics for many years. Current machine learning techniques such as Convolutional Networks (CNN), Graph Neural Networks (GNNs), Normalizing Flows (NFs) and Transformers have recently also entered particle physics. Increased attention is also currently being paid to methods for the systematic investigation of machine learning techniques, collectively known as "Explainable AI/ML". Try out one of the many new possibilities this opens up in your Master's thesis.
What you will learn:Collider physics, machine learning, Python programming
Advisor:Prof. Ulrich Husemann
Contact:Prof. Ulrich Husemann
Last change:26/01/2024
Topic:Associated ttX Production
Summary:In the associated production of Higgs bosons and top quark-antiquark pairs, the top Higgs-Yukawa coupling can be directly measured. We observed this process for the first time in 2018 and have recently published updated analysis. To further understand the process and discover possible traces of new physics, we are also investigating the main background processes that look almost like the signal in the detector. These are mainly the associated production of top quarks with additional bottom quarks or Z bosons. Be part of the team investigating top-Yukawa coupling with the full data set from LHC Run 2, determining key background processes with the highest accuracy.
What you will learn:Top-quark and Higgs-boson physics, data analysis, machine learning, Python programming, C++ programming, ROOT
Advisor:Prof. Ulrich Husemann
Contact:Prof. Ulrich Husemann
Last change:26/10/2023

Silicon Detectors for Particle Physics and Medicine

Topic:Resistive Silicon Detector R&D for Phase 3 CMS and Future Colliders
Summary:Over the past ten years, silicon timing detectors with gain layers have developed from research and development (R&D) projects to full subsystems that will be installed in large scale collider experiments such as the CMS and ATLAS experiments at CERN. This technology also led to the idea of ‘4D’ sensors capable of high spatial and temporal resolution. At KIT, we collaborate with the University of Torino on studying the Resistive Silicon Detectors (RSDs), a novel type of ‘4D’ sensor. As a master’s student you will gain hands-on experience with state-of-the-art silicon sensors. You will be studying new geometries, writing reconstruction software, and assisting with irradiation testing.
What you will learn:Data taking and data analysis; detector assembly
Advisor:Prof. Markus Klute, Prof. Ulrich Husemann
Contact:Dr. Brendan Regnery, Dr. Alexander Dierlamm
Last change:06/05/2024
Topic:Development of an Ion Beam Monitor using HV-CMOS Chips
Summary:In a joint project with IPE, we are developing a beam monitor for therapeutic ion beams. Our beam monitor is based on silicon sensor technology, while conventional beam monitors are constructed from wire chambers and ionization chambers. The new technology comes with new challenges, including the readout concept, interconnection technology of a matrix of 13x13 chips and thermal management at minimal mass. In this thesis, concepts are developed and tested to meet these high requirements. Subsequently, you will participate in laboratory and beam tests.
What you will learn:Data taking and data analysis; detector assembly; FEM simulations
Advisor:Prof. Ulrich Husemann
Contact:Dr. Alexander Dierlamm
Last change:06/05/2024