A Bayesian approach to strong lensing modelling of galaxy clusters

Abstract. In this paper, we explore how well, one can recover the mass distribution in strong lensing cluster cores where different set of multiple images with different redshifts have been identified. To be able to quantify the uncertainty in the mass reconstruction, we have used a Bayesian Monte Carlo Markov Chain (MCMC) sampler (“Bayesys”). In particular, such optimization method allows to avoid local minima in the likelihood distributions which can be frequent in large parameter spaces modelling. To illustrate the power of the MCMC technique, we have simulated three clusters of galaxies with a set of underlying galaxy-scale subhalos and a clusterscale halo modelled with a Pseudo-Isothermal Elliptical Mass…