MSc Project: Inter-galactic machine vision

This is a tentative proposal, as presented in 2021/22. The motivations and idea remain relevant, but the objectives have to be revised following recent developments. Please contact me to discuss options.


The universe is full of matter that we cannot see directly, the so-called dark matter. However, dark matter can be observed indirectly because it deflects light from distant galaxies, resulting in distorted images. Good models for the distortion exists, so that we can simulate the distorted image given an undistorted image and the mass distribution of the dark matter. However, inverting this model to obtain a description of the dark matter given a distorted image is hard. The purpose of this project is to solve this inverse problem by means of machine learning.

A simulator for gravitational lensing has been developed at the department in 2022, and we are able to generate large synthetic datasets, which can be used to train machine learning models to reconstruct the lens mass. However, there are many open problems.

We propose two research questions which are suitable for an MSc thesis. A project can focus on either one or both, and there are variations available.


The candidate will work together with astro physicists to develop tools to simulate and visualise known theories of phsyics.

Research method

Different research methods may be used depending on the angle taken. Mathematical modelling is deductive. Machine learning is evaluated with hypothetic-deductive methods, using either synthetic or empirical data. User-centric tool development is also possible, and may take more of a design approach.


Final deliverables and dissemination


Hans Georg Schaathun /