Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics

Cosmological hydrodynamical simulations, while the current state-of-the art methodology for generating theoretical predictions for the large scale structures of the Universe, are among the most expensive simulation tools, requiring upwards of 100 millions CPU hours per simulation. N-body simulations, which exclusively model dark matter and its purely gravitational interactions, represent a less resource-intensive alternative, however, they do not model galaxies, and as such cannot directly be compared to observations. In this study, we use conditional score-based models to learn a mapping from N-body to hydrodynamical simulations, specifically from dark matter density fields to the observable distribution of galaxies. We demonstrate that our model is capable of generating galaxy fields statistically consistent with hydrodynamical simulations at a fraction of the computational cost, and demonstrate our emulator is significantly more accurate than traditional emulators over the non-linear scales between 1 $h\ \text{Mpc}^{-1}$ $\geq$ k $\geq$ 3.5 $h\ \text{Mpc}^{-1}$.