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Cosmological N-body simulations of galaxies operate at the level of "starparticles" with a mass resolution on the scale of thousands of solar masses.Turning these simulations into stellar mock catalogs requires "upsampling" thestar particles into individual stars following the same phase-space density. Inthis paper, we demonstrate that normalizing flows provide a viable upsamplingmethod that greatly improves on conventionally-used kernel smoothing algorithmssuch as EnBiD. We demonstrate our flow-based upsampling technique, dubbedGalaxyFlow, on a neighborhood of the Solar location in two simulated galaxies:Auriga 6 and h277. By eye, GalaxyFlow produces stellar distributions that aresmoother than EnBiD-based methods and more closely match the Gaia DR3 catalog.For a quantitative comparison of generative model performance, we introduce anovel multi-model classifier test. Using this classifier test, we show thatGalaxyFlow more accurately estimates the density of the underlying starparticles than previous methods.

GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs