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We report the results of a systematic search for ultra-faint Milky Waysatellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1(PS1). Together, DES and PS1 provide multi-band photometry inoptical/near-infrared wavelengths over ~80% of the sky. Our search forsatellite galaxies targets ~25,000 deg$^2$ of the high-Galactic-latitude skyreaching a 10$\sigma$ point-source depth of $\gtrsim$ 22.5 mag in the $g$ and$r$ bands. While satellite galaxy searches have been performed independently onDES and PS1 before, this is the first time that a self-consistent search isperformed across both data sets. We do not detect any new high-significancesatellite galaxy candidates, while recovering the majority of satellitespreviously detected in surveys of comparable depth. We characterize thesensitivity of our search using a large set of simulated satellites injectedinto the survey data. We use these simulations to derive both analytic andmachine-learning models that accurately predict the detectability of Milky Waysatellites as a function of their distance, size, luminosity, and location onthe sky. To demonstrate the utility of this observational selection function,we calculate the luminosity function of Milky Way satellite galaxies, assumingthat the known population of satellite galaxies is representative of theunderlying distribution. We provide access to our observational selectionfunction to facilitate comparisons with cosmological models of galaxy formationand evolution.

Milky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1