A new framework for understanding systematic errors in cluster lens modelling - II. Constraint selection

Abstract

Cluster lens models are affected by a variety of choices in the lens modelling process. We have begun a programme to develop a systematic error budget for cluster lens modelling. Here, we examine the selection of image constraints as a potential systematic effect. For constraining the mass model, we find that it is more important to have images be spatially distributed around the cluster than to have them distributed in redshift. We also find that some image sets appear to be more important than others in terms of how well they constrain the models; the 'important' image sets typically include an image that lies close to a lensing critical curve as well as an image that is relatively isolated from other images (providing constraints in a region that would otherwise lack lensing information). These conclusions can help guide observing programmes that seek follow-up data for candidate lensed images.