Observation-driven simulations of strong-lensing galaxy clusters

Galaxy clusters are the most powerful strong lenses; they greatly magnify the flux of distant and faint sources. Strong lensing also allows for the reconstruction of their mass distribution with sim1% accuracy and enables the investigation of cosmological parameters. The number of known systems of this type is bound to increase in the coming years thanks to wide imaging surveys. Using simulations in this context is crucial to validate the analysis methods before they are applied to real data, and to train machine learning algorithms that can handle large volumes of images. In this work, we present a simulated set of 100 images of galaxy clusters that we produced with a novel code for simulating cluster-scale strong lenses. One of the main novelties of our approach, distinguishing it from other existing codes, is the use of empirical relations; these were derived from state-of-the-art observations for modelling characteristics such as morphology, colour, and spatial distribution of the cluster-member population. This allowed us to reliably reproduce the complexity of real observations. The simulations were partly carried out with the latest version of , a code that creates mock observations of strong lensing events in different systems and observational setups. The main improvements we introduce are the use of the message passage interface (MPI) paradigm---a standard for parallel programming that leverages the use of several processors to perform some given task---and the implementation of score-based diffusion models to augment the images of the background sources. Together, they lead to more efficient and realistic image simulations. We also present the validation of the code and simulations by comparing the properties of the mock clusters to those of real ones. We make the images, deflection maps, convergence maps, and catalogues of cluster members and background sources publicly available to the community at this . SkyLens