The Gaia mission has provided distance and velocity measurements of over a billion stars in the Milky Way, making it the largest stellar catalog at hand. Simultaneously, recent developments in cosmological simulations have made it possible to track stars and dark matter in realistic Milky Way-like galaxies. In this talk, I will demonstrate how using cutting-edge simulations and Gaia data in tandem has enabled me to start building the first local map of the cold dark matter phase space distribution in our Galaxy. Doing so led me to discover Nyx, a stream of stars in the solar neighborhood that I identified using machine learning methods. Nyx, potentially the result of a prograde merger, is crucial in understanding the formation of the disk of the Milky Way as it might have been associated with an accreted dark disk. Finally, I will summarize how future surveys will help fully map out the phase space distribution of dark matter in the Milky Way, and show that such an empirical map will have extensive ramifications both for our understanding of the formation of the Milky Way, and for direct dark matter searches.
