Searching for Dwarf Galaxies in Gaia DR2 Phase-space Data Using Wavelet Transforms

We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in Gaia DR2 data. Our algorithm detects overdensities in 4D position-proper-motion space, making it the first search to explicitly use velocity information to search for dwarf galaxy candidates. We optimize our algorithm and quantify its performance by searching for mock dwarfs injected into Gaia DR2 data and for known Milky Way satellite galaxies. Comparing our results with previous photometric searches, we find that our search is sensitive to undiscovered systems at Galactic latitudes divide b divide > 20 degrees and with half-light radii larger than the 50% detection efficiency threshold for Pan-STARRS1 (PS1) at (i) absolute magnitudes of -7 < M ( V ) < -3 and distances of 32 kpc < D < 64 kpc, and (ii) M ( V ) < -4 and 64 kpc < D < 128 kpc. Based on these results, we predict that our search is expected to discover 5 +/- 2 new satellite galaxies: four in the PS1 footprint and one outside the Dark Energy Survey and PS1 footprints. We apply our algorithm to the Gaia DR2 data set and recover similar to 830 high-significance candidates, out of which we identify a gold standard list of similar to 200 candidates based on cross-matching with potential candidates identified in a preliminary search using Gaia EDR3 data. All of our candidate lists are publicly distributed for future follow-up studies. We show that improvements in astrometric measurements provided by Gaia EDR3 increase the sensitivity of this technique; we plan to continue to refine our candidate list using future data releases.

Automated summary

This study introduces a wavelet-based algorithm to identify dwarf galaxy candidates in the Milky Way, optimizing the algorithm's performance in Gaia DR2 data. It predicts the discovery of new satellite galaxies and shows increased sensitivity of the technique with improvements in astrometric measurements provided by Gaia EDR3.