Bayesian mass mapping with weak lensing data using karmma: Validation with simulations and application to Dark Energy Survey year 3 data

We update the field-level inference code karmma to enable tomographic forward-modeling of shear maps. Our code assumes a log-normal prior on the convergence field, and properly accounts for the cross-covariance in the lensing signal across tomographic source bins. We use mock weak lensing data from $N$-body simulations to validate our mass-mapping forward model by comparing our posterior maps to the input convergence fields. We find that karmma produces more accurate reconstructions than traditional mass-mapping algorithms. Moreover, the karmma posteriors reproduce all statistical properties of the input density field we tested—1- and 2-point functions, and the peak and void number counts—with $\approx 10\%$ accuracy. Our posteriors exhibit a small bias that increases with decreasing source redshift, but these biases are small compared to the statistical uncertainties of current [Dark Energy Survey (DES)] cosmic shear surveys. Finally, we apply karmma to DES year 3 weak lensing data, and verify that the 2-point shear correlation function ${\xi }_{+}$ is well fit by the correlation function of the reconstructed convergence field. This is a nontrivial test that traditional mass mapping algorithms fail.