Data-Driven Reconstruction of Gravitationally Lensed Galaxies using
  Recurrent Inference Machines
  We present a machine learning method for the reconstruction of the
undistorted images of background sources in strongly lensed systems. This
method treats the source as a pixelated image and utilizes the Recurrent
Inference Machine (RIM) to iteratively reconstruct the background source given
a lens model. Our architecture learns to minimize the likelihood of the model
parameters (source pixels) given the data using the physical forward model (ray
tracing simulations) while implicitly learning the prior of the source
structure from the training data. This results in better performance compared
to linear inversion methods, where the prior information is limited to the
2-point covariance of the source pixels approximated with a Gaussian form, and
often specified in a relatively arbitrary manner. We combine our source
reconstruction network with a convolutional neural network that predicts the
parameters of the mass distribution in the lensing galaxies directly from
telescope images, allowing a fully automated reconstruction of the background
source images and the foreground mass distribution.
