Mapping the Milky Way with SDSS-V: Galactic Genesis Selection Function and Mock Catalog

Robust chemodynamic mapping of the Milky Way requires accounting for the selection function of the survey used, the probability that a given star is included in the final catalog as a function of its observable properties. We derive the empirical selection function of the SDSS-V Galactic Genesis survey, the largest Milky Way Mapper program targeting red giant stars for Galactic structure studies. Treating the Galactic Genesis sample as a subsample of its 2MASS parent catalog, we model the selection function as a function of sky position, apparent H magnitude, and G-H color using a Bayesian binomial estimator, computing both a mean and maximum a posteriori (MAP) version. The resulting selection function reflects the survey's targeting strategy and observing history, including the transition from the pencil-beam SDSS-IV footprint to the fuller sky coverage of SDSS-V and the prioritization of the Magellanic Clouds. Using this selection function with the FIRE-2 Milky Way-mass simulations, we construct mock Galactic Genesis catalogs built on existing mock Gaia DR3 catalogs, injecting realistic photometric noise and applying the selection function via a per-star Bernoulli accept-reject procedure. We demonstrate the mock catalogs' utility with an example application to azimuthal metallicity variations in the disk. Furthermore, we release a Python package allowing users to recompute the selection function for their own samples and science cases. The package, selection function and mock catalogs will be updated and made available as the survey continues, extending the tools needed to reach the full scientific potential of SDSS-V Galactic Genesis.