Thesis Topics (BSc, MSc, PhD)

How to analyze experimental data in such a way that we learn as much as possible about the underlying physics? Does this bring us closer to the discovery of new particles or new phenomena?

These are questions that my team and I work on. We develop methods for data analysis and apply these to the CMS experiment to learn more about the fundamental building blocks of matter. Moreover, we develop methods for the data analysis at the Einstein Telescope. For this, we often use methods from the area of deep learning. You can read more on the page Research. Possible thesis topics:

Method-driven topics (CMS and ET):

  • CMS - Improved measurement of particle properties in the detector: How can we use deep learning to better determine the properties of the interacting particles from the detector raw data?
  • CMS - Fast simulations: How can we use deep learning to accelerate the complex simulation of the CMS detector?
  • ET - Likelihood-free inference: How can we use deep learning to estimate physics parameter directly from the data?
  • ET - Experimental design: How can we use deep learning to position support detectors, so that the noise is better suppressed?

Physics-driven topics (CMS):

  • Differential measurements of the Higgs boson: Measurements in the decay channel H → γγ enable particularly good precision.
  • Search for rare Higgs-boson processes: Processes that are strongly suppressed help us to search for physics beyond the Standard Model.
  • Interpretation in effective field theories (EFT): Using the framework of EFTs, we can learn something about new particles with masses that are much larger than what we can produce directly at the LHC.
  • If you are interested in the physics of the top quark or the search for new particles, feel free to contact me with your suggestions for thesis topics!

If you are interested, please write me an .