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.
  • 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 Large-Scale Structure
    We will explore the cosmological observational signatures of axion like particles in the context of the cosmic microwave background and large-scale structure.
  • 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 applied to challenges in photometric redshift estimation for large photometric surveys.
    Using the latest tools from machine learning you will be working on cutting edge research which will be critical for a range of collaborations, including Euclid and the Dark Energy Survey. Each student will tackle as a different problem, for example: data augmentation, probability distribution function calibration, dealing with massive data streams.
  • Generalised anomaly detection for small and large datasets.
    You will use and develop cutting edge tools for machine learning based anomaly detection. Anomalous data is data which does not "look" like other data in a data set, in a very generalised sense. These data will consitist of many things, including instrument noise, but also potentially brand new classes of objects never before known or studied! Students will be able to choose between applying anomaly detection routines to the latest GAIAv2 data release, consisting of 1.7 billion sources, the Sloan Digital Sky Survey, conisting of 300 million observed sources, radio data, exo-planet data, or stella light curves. All with the idea of finding rare objects, and then charaterising and understanding them.
  • Exploring high dimensional parameter space.
    This project will benchmark, and develop routines to explore high dimensional parameter space, which is a general problem encoutered when fitting data to a model. In particular the algorithms will be used to develop the brand new idea of using correlation functions to estimate redshift distributions, which unlike other methods, do not rely on training data with spectroscopic redshifts.