Bachelor Thesis With Prof. Krämer in 2021
Simulating the gamma-ray sky with machine learning
The study of the gamma-ray sky from MeV to TeV energies permits to investigate different non-thermal processes, from cosmic-ray acceleration in Galactic and extragalactic sources to possible indirect signals of dark matter. In order to constrain our models for these processes, fast and accurate simulations of the signals expected in current and future detectors, such as Fermi-LAT or CTA, are fundamental. In this project we will use machine learning techniques to build a fast and efficient simulator for astrophysical and dark matter signals in the gamma-ray sky.
Two-zone cosmic-ray propagation nearby Galactic sources
Multi-wavelenght observations of the propagation of Galactic cosmic-rays have recently confirmed that the diffusion within few tens of parsec around their sources is inhibited by several orders of magnitude. To account for these observations, a two-zone propagation model needs to be introduced. In this project we will study the existing semi-analytical solutions for this two-zone diffusion model, and investigate further analitical, numerical or machine learning techniques to solve accurately and faster the propagation of different cosmic-ray species around their sources.
Searching for new physics at the LHC with machine learning
Current and future searches for new physics at the Large Hadron Collider (LHC) focus on complex signatures and more model-independent methods. Machine learning is a powerful tool for finding and classifying physics beyond the Standard Model. For example, deep neural networks can separate and identify subtle signatures of new physics from the background of Standard Model events. Moreover, methods of unsupervised deep learning allow the detection of anomalous signatures of new physics without having to make specific assumptions regarding the underlying theory. In this Bachelor thesis, you will explore neural network architectures for classification or unsupervised learning and their application to detect exotic signatures from new physics models at the LHC.
Sorting out Higgs decays
The Higgs boson has been discovered almost 8 years ago but the investigation of its detailed properties is still a fascinating endeavour. A specific Higgs-boson measurement is usually performed by looking at a specific set of particles the Higgs boson can decay into, a so-called final state. There are current efforts to define these final states in a mutually exclusive way, so that each Higgs event can be unambiguously classified. In this Bachelor thesis, you will use state-of-the-art simulation tools to perform a subset of this classification procedure. For this thesis project you can built on a Bachelor thesis which has been successfully carried out in summer term 2020.