Observation-driven simulations of strong-lensing galaxy clusters
Year: 2026
Type: article
Abstract: 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 ∼1% 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 SkyLens , 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 https://openscience.inaf.it/?page_id=724 .
Source: Astronomy and Astrophysics
Institutions: Osservatorio di Astrofisica e Scienza dello Spazio, National Institute for Astrophysics, Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Mila - Quebec Artificial Intelligence Institute, Université de Montréal, University of Bologna, University of Milan, Perimeter Institute, Flatiron Institute, Trottier Space Institute at McGill, McGill University, Heidelberg University, University of Salerno, Astronomical Observatory of Capodimonte, Istituto Nazionale di Fisica Nucleare, Gruppo Collegato di Salerno, Osservatorio Astronomico di Padova, SLAC National Accelerator Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, University of Ferrara
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,865
OpenAlex ID: W7170093140