The Universe is currently expanding at an ever-increasing rate. To understand the origin of the accelerated expansion due to dark energy, the amount of matter that is using gravity to hold the Universe together must be determined. The astronomical community is embarking on the largest and most ambitious astronomical survey to date: the Rubin Observatory Legacy Survey of Space and Time (LSST). One key goal of the survey is to construct the most detailed map of the matter distribution in our Universe ever created, thereby enabling cosmological studies of our Universe's expansion. However, 80% of the matter of the Universe is dark (invisible at any wavelength), so we must rely on indirect methods of detection. These investigators will use the distortion of galaxy images due to gravity to infer the matter density of the Universe. They will implement improvements that will enable higher resolution mass distribution maps, and they will extract dark matter and cosmological parameters from LSST data using these maps. The PI will co-direct the Tucson Initiative for Minority Engagement in Science and TEchnology Program (TIMESTEP) to promote the participation of University of Arizona undergraduate students in STEM careers. This grant will develop the KARMMA (Kappa Reconstruction for Mass Mapping Algorithm), a mass mapping algorithm designed to forward model the underlying density field subject to a homogeneous and isotropic lognormal prior. This forward modeling approach results in two key improvements over currently standard mass mapping techniques. 1. By forward modeling the convergence field, KARMMA avoids numerical biases in the mass reconstruction due to the interaction between survey boundaries and shear non-locality. 2. Forward modeling enables us to simultaneously sample both cosmological parameters and mass-map parameters, efficiently recovering the full information-content from the mass map for the purposes of constraining cosmology, while regularizing the clustering statistics on unresolved scales through physical cosmological priors. The current KARMMA implementation is restricted to fixed cosmologies. Further, its resolution is limited due to numerical considerations. Both of these deficiencies will be addressed by re-implementing the algorithm using spherical harmonics and enabling a slow/fast sampling of cosmological and mass-map parameters. These improvements to KARMMA will define a new standard in mass-mapping techniques in the era of LSST, while enabling cosmological analyses that utilize the full information of the observable shear field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.