Map-based cosmology inference with lognormal cosmic shear maps

Most cosmic shear analyses to date have relied on summary statistics (e.g. ζ+ and ζ-). These types of analyses are necessarily suboptimal, as the use of summary statistics is lossy. In this paper, we forward-model the convergence field of the Universe as a lognormal random field conditioned on the observed shear data. This new map-based inference framework enables us to recover the joint posterior of the cosmological parameters and the convergence field of the Universe. Our analysis properly accounts for the covariance in the mass maps across tomographic bins, which significantly improves the fidelity of the maps relative to single-bin reconstructions. We verify that applying our inference pipeline to Gaussian random fields recovers posteriors that are in excellent agreement with their analytical counterparts. At the resolution of our maps - and to the extent that the convergence field can be described by the lognormal model - our map posteriors allow us to reconstruct all summary statistics (including non-Gaussian statistics).