  Modeling strong gravitational lenses in order to quantify the distortions in
the images of background sources and to reconstruct the mass density in the
foreground lenses has traditionally been a difficult computational challenge.
As the quality of gravitational lens images increases, the task of fully
exploiting the information they contain becomes computationally and
algorithmically more difficult. In this work, we use a neural network based on
the Recurrent Inference Machine (RIM) to simultaneously reconstruct an
undistorted image of the background source and the lens mass density
distribution as pixelated maps. The method we present iteratively reconstructs
the model parameters (the source and density map pixels) by learning the
process of optimization of their likelihood given the data using the physical
model (a ray-tracing simulation), regularized by a prior implicitly learned by
the neural network through its training data. When compared to more traditional
parametric models, the proposed method is significantly more expressive and can
reconstruct complex mass distributions, which we demonstrate by using realistic
lensing galaxies taken from the cosmological hydrodynamic simulation
IllustrisTNG.
