MIFAL: fully automated Multiple-Image Finder ALgorithm for strong-lens
  modelling -- proof of concept
  We outline a simple procedure designed for \emph{automatically} finding sets
of multiple images in strong lensing (SL) clusters. We show that by combining
(a) an arc-finding (or source extracting) program, (b) photometric redshift
measurements, and (c) a preliminary light-traces-mass lens model,
multiple-image systems can be identified in a fully automated (`blind') manner.
The presented procedure yields an assessment of the likelihood of each arc to
belong to one of the multiple-image systems, as well as the preferred redshift
for the different systems. These could be then used to automatically constrain
and refine the initial lens model for an accurate mass distribution. We apply
this procedure to \emph{Cluster Lensing And Supernova with Hubble} observations
of three galaxy clusters, MACS J0329.6-0211, MACS J1720.2+3536, and MACS
J1931.8-2635, comparing the results to published SL analyses where multiple
images were verified by eye on a particular basis. In the first cluster all
originally identified systems are recovered by the automated procedure, and in
the second and third clusters about half are recovered. Other known systems are
not picked up, in part due to a crude choice of parameters, ambiguous
photometric redshifts, or inaccuracy of the initial lens model. On top of real
systems recovered, some false images are also mistakenly identified by the
procedure, depending on the thresholds used. While further improvements to the
procedure and a more thorough scrutinisation of its performance are warranted,
the work constitutes another important step toward fully automatising SL
analyses for studying mass distributions of large cluster samples.
