hybrid-Lenstool: A self-consistent algorithm to model galaxy clusters
  with strong- and weak-lensing simultaneously
  We present a new galaxy cluster lens modeling approach, hybrid-Lenstool, that
is implemented in the publicly available modeling software Lenstool.
hybrid-Lenstool combines a parametric approach to model the core of the
cluster, and a non-parametric (free-form) approach to model the outskirts.
hybrid-Lenstool optimizes both strong- and weak-lensing constraints
simultaneously (Joint-Fit), providing a self-consistent reconstruction of the
cluster mass distribution on all scales. In order to demonstrate the
capabilities of the new algorithm, we tested it on a simulated cluster.
hybrid-Lenstool yields more accurate reconstructed mass distributions than the
former Sequential-Fit approach where the parametric and the non-parametric
models are optimized successively. Indeed, we show with the simulated cluster
that the mass density profile reconstructed with a Sequential-Fit deviates form
the input by $2-3\sigma$ at all scales while the Joint-Fit gives a profile that
is within $1-1.5\sigma$ of the true value. This gain in accuracy is
consequential for recovering mass distributions exploiting cluster lensing and
therefore for all applications of clusters as cosmological probes. Finally we
found that the Joint-Fit approach yields shallower slope of the inner density
profile than the Sequential-Fit approach, thus revealing possible biases in
previous lensing studies.
