  We present KaRMMa 2.0, an updated version of the mass map reconstruction code
introduced in Fiedorowicz et al. (2022). KaRMMa is a full-sky Bayesian
algorithm for reconstructing weak lensing mass maps from shear data. It
forward-models the convergence field as a realization of a lognormal field. The
corresponding shear map is calculated using the standard Kaiser-Squires
transformation, and compared to observations at the field level. The posterior
distribution of maps given the shear data is sampled using Hamiltonian Monte
Carlo chains. Our work improves on the original algorithm by making it
numerically efficient, enabling full-sky reconstructions at $\approx$ 7 arcmin
resolution with modest computational resources. These gains are made with no
loss in accuracy or precision relative to KaRMMa 1.0. We compare the KaRMMa 2.0
posteriors against simulations across a variety of summary statistics
(one-point function, two-point functions, and peak/void counts) to demonstrate
our updated algorithm provides an accurate reconstruction of the convergence
field at mildly non-linear scales. Unsurprisingly, the lognormal model fails as
we approach non-linear scales ($\ell \gtrsim 200$), which in turn biases the
map posteriors. These biases are at the 2% level in the recovered power
spectrum, and at the 5% to 15% level for other statistics, depending on the
resolution.
