Orateur
Description
Weak gravitational lensing provides a powerful probe of dark matter distribution in the Universe. Mass mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, are critical for extracting higher-order statistics to constrain cosmological parameters. This study evaluates the impact of different mass mapping algorithms—Kaiser-Squires, inpainting Kaiser-Squires, and MCALens—on cosmological inference using weak lensing peak counts. Using simulated data from cosmo-SLICS, we compute peak counts and perform Bayesian analysis via MCMC to estimate matter density, amplitude of matter fluctuations, and dark energy equation of state. Results show MCALens significantly improves constraints on cosmological parameters by up to 157% compared to Kaiser-Squires, while inpainting provides limited gains. These findings highlight the importance of advanced mass mapping techniques, such as MCALens, in enhancing the precision of cosmological inference from weak lensing data.
