relensing: Reconstructing the mass profile of galaxy clusters from
  gravitational lensing
  In this work we present relensing, a package written in python whose goal is
to model galaxy clusters from gravitational lensing. With relensing we extent
the amount of software available, which provides the scientific community with
a wide range of models that help to compare and therefore validate the physical
results that rely on them. We implement a free-form approach which computes the
gravitational deflection potential on a adaptive irregular grid, from which one
can characterize the cluster and its properties as a gravitational lens. Here,
we use two alternative penalty functions to constrain strong lensing. We apply
relensing to two toy models, in order to explore under which conditions one can
get a better performance in the reconstruction. We find that by applying a
smoothing to the deflection potential enhances the capability of this approach
to recover the shape and size of the galaxy cluster's mass profile, as well as
its magnification map, which translates in a better estimation of the critical
and caustic curves. The power that the smoothing provides is also tested on the
simulated clusters Ares and Hera, for which our results represent an
improvement with respect to reconstructions that were carried out with methods
of the same nature than relensing. At the same time, the smoothing also
increases the stability of our implementation, and decreases the computation
time. On its current state, relensing is available upon request.
