Aims
How can we probe cosmic evolution with the least amount of necessary assumptions?
Since the 1980s, ideal observational cosmology has theoretically investigated the impact of models on our understanding of the universe in order to separate observation-based evidence from assumptions. By now, a multitude of high-quality data has become available and the goal to turn ideal observational cosmology into real observational cosmology has become feasible. My research contributes to reach this goal with a focus on inferring dark matter properties. The lean tools my colleagues and I have developed for strong gravitational lensing already provide an efficient, fully comprehensible data evaluation easy to automate for large data sets. Transferring the principle to other cosmological probes and joint data evaluations are the next goals.
What model extensions are physically reasonable?
Observation-based evidence is the maximum information all model fits to the data agree upon. This information is often subject to large uncertainties and constrained to the positions of the data points. With the increasing amount of available data, general constraints on the evolution of dark matter and its properties, for instance, are improving. Adding model assumptions tightens confidence bounds and explores regions devoid of data. But many of these models, like the famous dark matter halo mass profiles, are only based on heuristically inferred fitting functions. A second branch of my research therefore seeks to derive them from fundamental physical principles. In this way, necessary prerequisites for these functions to be applicable become explicit and allow us to gain a deeper understanding of underlying conservation laws or predict the future evolution of the structures under analysis. First results to derive the power-law mass densities typical for structures dominated by gravitational interactions have already been accomplished and the next steps are in preparation.
