Physical Cosmology

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Master Thesis Projects

  • Cosmological Models and Evidence
    In this project we aim to explore the number of well constrained cosmological parameters with the help
    of a combination of modern cosmological data sets. The notion of Bayesian evidence will be central here.
  • Covariance of Cluster of Galaxies as cosmological probes
    In this project we will exploit numerical simulations and analytic calculations to understand the cross-covariance of galaxy cluster counts with other cosmological probes. This is important in order to honestly assess the ability of the distribution of galaxy clusters to constrain cosmological models.
  • Clusters with AMICO
    We will exploit the cluster finder AMICO on the public data sets from the Dark Energy Survey - DES and produce and publish the cluster catalog.
  • Topological Classification of the Cosmic Web
    Usually cosmological information is extracted from overdense (clusters) or underdense (voids) regions of the cosmic web. However the structure of the cosmic web is much richer, not only consisting of clusters and voids, but also of filaments and sheets. Detecting and quantifying all these structures is an exercise in classifying the cosmic web topologically. In this project we want to explore the cosmic web with Betti numbers on different scales.
  • Axions and the Astrophysics of Galaxy Clusters
    We will explore the observational signatures of axion like particles in the context of the x-ray spectra of clusters of galaxies. We are interested in developing tools to distinguish axion-like signals from noise; following in particular the promising approach of using machine learning.
  • Real-Time Cosmology
    We will explore the ability of frequency comb spectrographs, such as the one on the Wendelstein telescope, to measure the time dependence of the redshift factor. This would allow, for example, to constrain intrinsically inhomogenous cosmological models.
  • Cosmic Voids
    The emptiest regions in the Universe may reveal key insights to our understanding of dark energy, dark matter, and other fundamental aspects of cosmology, but their composition and evolution has only begun to be investigated in detail. In this project we will identify voids in simulated and / or observational data, statistically analyze some of their properties, and develop physical models to establish connections to theory.
  • Machine learning and Cluster Finding
    We will use state of the art machine learning techniques to develop a galaxy cluster finder for up- and coming surveys, like for example the Dark Energy Survey - DES and the Euclid satellite.
  • Mark correlation of galaxies and halos
    Models of galaxy formation have to explain the large scale structure and the properties of the galaxies (luminosity, type, etc.). You will use mark correlation functions to investigate the interplay of spatial distribution and inner properties of galaxies. Theoretical as well as numerical approaches are possible.