Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology

Abstract Likelihood-free inference provides a rigorous approach to performing Bayesian analysis using forward simulations only. The main advantage of likelihood-free methods is their ability account for complex physical processes and observational effects in simulations. Here we explore the potential modeling cosmological redshift evolution cluster abundance combined with weak-lensing mass calibration. We use two complementary methods, namely Approximate Computation (ABC) Density-Estimation Likelihood-Free Inference (DELFI), develop an procedure parameters (? m , ? 8 ) scale survey sample. Adopting eROSITA-like selection function 10% scatter observable–mass relation flat ?CDM cosmology ? = 0.286 0.82, create synthetic catalog observable-selected Navarro–Frenk–White clusters area 50 deg 2 . stacked tangential shear profile number counts bins are used as summary statistics both methods. By series simulations, obtain convergent solutions posterior distribution from find that ABC recovers broader posteriors than DELFI, especially parameter. For source density n g 20 arcmin ?2 constraints on S 8 = ? ? mathvariant="normal">m / 0.3 0.836 ± 0.032 0.810 0.019 respectively. framework developed this study will be particularly powerful ongoing programs, such XMM–XXL eROSITA all-sky survey, combination wide-field surveys.