Poster abstract details

Dark Matter Distribution: A Fully Automated Multiple-Image Finder ALgorithm (MIFAL) and Strong-Lensing Analysis of CLASH clusters
Carrasco, M., Zitrin, A., Bartelmann, M., Seidel, G.


We present an innovative tool for automatically finding sets of multiple images in strong lensing (SL) clusters. We combine an arc-finding algorithm with photometric redshift measurements, along with a parametric mass model, to physically match together multiple-image systems in an automated ("blind'') manner. We obtain
accordingly a robust assessment of the likelihood of each arc to belong to one of the multiple-image systems, as well as the redshift of the different systems. These are then used to automatically constrain and refine the lens model to obtain an accurate mass distribution and profile, via a Monte-Carlo Markov Chain (MCMC) with Metropolis-Hastings algorithm. We apply this procedure to perform a case-study SL analysis of CLASH galaxy clusters. Our method and results constitute another step towards fully automating SL analyses as a standard tool for studying cluster mass distributions, and is
optimized also for constraining the geometry of the universe or the cosmological parameters from SL clusters.