For anyone interested in my research topics, here I provide a very small summary of ongoing and completed research projects:

Galaxy cluster characterization & potential reconstruction

Galaxy clusters are the largest known systems held together by their own gravity. Apart from small groups of a few ten galaxies, rich clusters such as the Coma cluster can contain up to thousands of galaxies and in addition contain lots of dark matter and plasma, which fills the space inbetween the galaxies. These giant self-gravitating systems are not only interesting on their own, but their individual and statistical properties as a sample offer versatile opportunities to explore key properties of our universe, of dark matter, dark energy and gravity.

A part of my research is concerned with the development of advanced methods to detect, characterize and map the mass distribution of galaxy clusters, aimed at further use in mass reconstructions and for cosmological parameter estimation. At the moment, I'm contributing to a project aiming at a reconstruction of cluster potentials from a combination of multi-wavelength observables. Joining all available observables in a single optimization approach allows to not only get the most complete information on cluster profiles, but also to learn about the validity of assumptions of each method, such as e.g. equilibrium conditions. An additional novelty and advantage of the approach newly developed in our group is its purely potential-based ansatz to quantify cluster matter, which avoids serious problems in defining the total mass of a cluster, which is strictly not possible.

The systematical characterization of cluster properties in a large sample is of key importance for current and near-future cosmological studies, since clusters are among the most promising observational probes of the geometry and structure growth in our universe, offering potentially decisive insights on the evolution of dark energy.

(co-authored work: Tchernin et al 2020, in prep)

Automated detection and modelling of strong gravitational lenses

I am main author of the strong gravitational lens detection code 'EasyCritics', which identifies the locations of strongly-lensing galaxy groups or clusters in optical wide-field surveys. EasyCritics performs an indirect detection of critical structures based on the optical luminosity of the lenses, using a so-called 'light-traces-mass' predictor. This allows to avoid several serious ambiguities in the recognition of arcs, given that lensed images are faint, noisy, and nearly impossible to detect and classify in a reliable way - especially in the seeing-limited ground-based surveys. In addition, by applying a predictive approach based on mass modelling, EasyCritics provides a first characterization of lens properties for all the detection candidates.

Gravitational lensing is the phenomenon of light deflection in inhomogeneous gravitational fields, which produces visible distortions in the images of distant background objects. The analysis of lensing phenomena reveals valuable information about the lenses, the lensed sources and the underlying geometry of spacetime. These informations have important implications for the nature of dark matter, dark energy and gravity.

However, the reliable detection of lenses in large survey material provides a considerable challenge up to this day. The combination of poor visibility and complex, almost arbitrary arc morphologies renders a reliable arc detection based on image classification approaches nearly impossible.

Our novel approach therefore focuses on optical properties of the lenses themselves, which enable an intriguingly reliable prediction for their lensing ability. The power of optical luminosity as a mass proxy and its opportunities for lens detection have been clearly underestimated in studies so far.

In recent works, we have shown that EasyCritics is capable of

by focusing purely on what we can learn from observables about the line-of-sight mass distribution itself.

The project is part of a larger effort to detect and characterize galaxy clusters with multiple observables. Currently, we are applying EasyCritics to data from the surveys CFHTLenS, KiDS and JPAS, with highly promising results so far. For more information, see also the publications Stapelberg et al 2019 and Carrasco et al 2018 (arXiV, in prep).

Making sense of the cosmic expansion + models for teaching cosmology

As a minor 'hobby' research topic on the side, I am creating concepts and illustrations to better understand unintuitive properties of expanding universes and to communicate cosmological and General Relativity contents (More details following soon).

Image processing for observational astronomy

As a part of my Bachelor studies, I developed methods for the efficient analysis of large image datasets. This involved algorithms for image processing, object recognition and calculations on image data, both on CPU and GPU. Some of these routines have become a basis for the EasyCritics lens detection code that I am the main author of. For a fast exploration of different parameters for gravitational lens models, to be fitted to known clusters during the calibration of the EasyCritics code, I have developed an efficient, parallelized Markov-Chain Monte Carlo algorithm that exploits adaptive grid techniques to sample different realizations of the gravitational lensing potential and to find the optimal lens parameters for a light-traces-mass lens model in fractions of the time required for conventional MCMC methods.

Detection of clustered regions in optical data with tree methods

As a small subproject during my Bachelor thesis, I extended a k-d tree-based detector for clustered regions in image data that is based on a simple overdensity criterion. Apart from several smaller improvements, I introduced the possibility to define a redshift selection criterion, improved the handling of noise and projection effects and added a routine that automatically creates image cutouts for reviewing the found candidates.

Smaller project practicals:

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