Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies

Understand

We introduce a new adaptive and fully Bayesian grid-based method to model strong gravitational lenses with extended images.

• The primary goal of this method is to quantify the level of luminous and dark-mass substructure in massive galaxies, through their effect on highly-magnified arcs and Einstein rings.
• The method is adaptive on the source plane, where a Delaunay tessellation is defined according to the lens mapping of a regular grid onto the source plane.
• The Bayesian penalty function allows us to recover the best non-linear potential-model parameters and/or a grid-based potential correction and to objectively quantify the level of regularization for both the source and the potential.