Model-independent characterisation and model selection of gravitational lenses

• Applicant: Dr. Jennifer Wagner
• Subject Area: Astrophysics and Astronomy
• Term: from 2014 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 258217013

So far, information about the nature, distribution and agglomeration of dark matter in gravitationally bound objects was drawn from observations of distorted images of galaxies in the strong gravitational lensing regime by fitting gravitational lens models to a given observation. Yet, model fitting is a tedious (manual) fine-tuning work and it is not unique because the same set of multiple images can be produced by many different lens models. To forgo these problems, we develop a model-independent approach to characterise the lens which is based on observations of multiply-imaged sources and general properties of the gravitational lensing formalism only. Our approach yields a simple set of equations to calculate (ratios of) derivatives of the lensing potential at the critical curve, which determines its position and orientation angle in the sky from the observed shape, orientation, and relative distances of close-by multiple images. Additionally given time-delays between the multiple images determine the local surface mass density and shear and the magnifications of the images due to the lensing effect. Proof-of-principle results show that our set of equations has an accuracy and precision high enough to be useful for the reconstruction of the central parts of galaxy clusters. As our approach does not require manual ad-hoc addition of masses, or time-consuming parameter optimisation algorithms, it is a promising tool to fully automatically analyse a large amount of multiple images to describe local dark matter distributions in galaxy clusters. The goal of this proposal is to turn our protoype into a working system and to test the improvements in precision and accuracy that it achieves when being integrated into a mesh-free free-form global cluster-potential-reconstruction algorithm.

• DFG Programme: Research Grants