SLITronomy: towards a fully wavelet-based strong lensing inversion
  technique
  Strong gravitational lensing provides a wealth of astrophysical information
on the baryonic and dark matter content of galaxies. It also serves as a
valuable cosmological probe by allowing us to measure the Hubble constant
independently of other methods. These applications all require the difficult
task of inverting the lens equation and simultaneously reconstructing the mass
profile of the lens along with the original light profile of the unlensed
source. As there is no reason for either the lens or the source to be simple,
we need methods that both invert the lens equation with a large number of
degrees of freedom and also enforce a well-controlled regularisation that
avoids the appearance of spurious structures. This can be beautifully
accomplished by representing signals in wavelet space. Building on the Sparse
Lens Inversion Technique (SLIT), in this work we present an improved
sparsity-based method that describes lensed sources using wavelets and
optimises over the parameters given an analytical lens mass profile. We apply
our technique on simulated HST and E-ELT data, as well as on real HST images of
lenses from the Sloan Lens ACS (SLACS) sample, assuming a lens model. We show
that wavelets allow us to reconstruct lensed sources containing detailed
substructures when using both present-day data and high-resolution images from
future thirty-meter-class telescopes. Wavelets moreover provide a much more
tractable solution in terms of quality and computation time compared to using a
source model that combines smooth analytical profiles and shapelets. Requiring
very little human interaction, our pixel-based technique fits into the effort
to devise automated modelling schemes. It can be incorporated in the standard
workflow of sampling analytical lens model parameters. The method, which we
call SLITronomy, is freely available as a new plug-in to the modelling software
Lenstronomy.
