  In this work we present relensing, a package written in python whose goal is
to model galaxy clusters from gravitational lensing. With relensing we extend
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 an 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, we are able to increase the
capability of this approach to recover the shape and size of the mass profile
of galaxy clusters, as well as its magnification map. This translates into 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 we get an rms on the lens plane of ~0.17 arcsec and ~0.16 arcsec,
respectively. Our results represent an improvement with respect to
reconstructions that were carried out with methods of the same nature as
relensing. At the same time, the smoothing also increases the stability of our
implementation, and decreases the computation time. In its current state,
relensing is available upon request.

Cosmology and Nongalactic Astrophysics