  Understanding the large-scale structure of the Universe and unravelling the
mysteries of dark matter are fundamental challenges in contemporary cosmology.
Reconstruction of the cosmological matter distribution from lensing
observables, referred to as 'mass-mapping' is an important aspect of this
quest. Mass-mapping is an ill-posed problem, meaning there is inherent
uncertainty in any convergence map reconstruction. The demand for fast and
efficient reconstruction techniques is rising as we prepare for upcoming
surveys. We present a novel approach which utilises deep learning, in
particular a conditional Generative Adversarial Network (cGAN), to approximate
samples from a Bayesian posterior distribution, meaning they can be interpreted
in a statistically robust manner. By combining data-driven priors with recent
regularisation techniques, we introduce an approach that facilitates the swift
generation of high-fidelity, mass maps. Furthermore, to validate the
effectiveness of our approach, we train the model on mock COSMOS-style data,
generated using Colombia Lensing's kappaTNG mock weak lensing suite. These
preliminary results showcase compelling convergence map reconstructions and
ongoing refinement efforts are underway to enhance the robustness of our method
further.
