Lensed: a code for the forward reconstruction of lenses and sources from
  strong lensing observations
  Robust modelling of strong lensing systems is fundamental to exploit the
information they contain about the distribution of matter in galaxies and
clusters. In this work, we present Lensed, a new code which performs forward
parametric modelling of strong lenses. Lensed takes advantage of a massively
parallel ray-tracing kernel to perform the necessary calculations on a modern
graphics processing unit (GPU). This makes the precise rendering of the
background lensed sources much faster, and allows the simultaneous optimisation
of tens of parameters for the selected model. With a single run, the code is
able to obtain the full posterior probability distribution for the lens light,
the mass distribution and the background source at the same time. Lensed is
first tested on mock images which reproduce realistic space-based observations
of lensing systems. In this way, we show that it is able to recover unbiased
estimates of the lens parameters, even when the sources do not follow exactly
the assumed model. Then, we apply it to a subsample of the SLACS lenses, in
order to demonstrate its use on real data. The results generally agree with the
literature, and highlight the flexibility and robustness of the algorithm.
