Physical Cosmology
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Master Thesis Projects

  • Note that besides the projects below there are also opportunities for Master Thesis projects at the Max-Planck Institutes. For example in the High Energy Group of MPE here.
  • 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.
  • 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.
  • Cosmic Voids and eRosita  
    eROSITA will perform the first imaging all-sky survey in the soft energy X-Ray band. Its unprecedented spectral and angular resolution provide us with the unique opportunity of creating a complete mapping of cosmic voids in our cosmos. Voids are typically studied in the distribution of matter. However, to test our theoretical findings with observations, we need to rely on luminous tracers. Recent studies have focussed on how bridge the gap between theory and observations, suggesting that this can easily modelled via the tracer bias. Given this context, eRosita cluster and AGN catalogues represent the exciting next generation of data that will allow us to trace a realistically complete sample of voids, and guide us towards a better understanding of their statistical properties.
  • 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.
  • Deep Learning the Weak Lensing Mass of Galaxy Clusters
    In this project we will explore state of the art learning algorithm to estimate the mass of a galaxy cluster from imaging data of background galaxies. Initially the project will be developed on synthetic catalogs of galaxies but there might be scope to apply this to real observational data.