  Chemical tagging has great promise as a technique to unveil our Galaxy's
history. Grouping stars based on their similar chemistry can establish details
of the star formation and merger history of the Milky Way. With precise
measurements of stellar chemistry, chemical tagging may be able to group
together stars born from the same gas cloud, regardless of their current
positions and kinematics. Successfully tagging these birth clusters requires
high quality chemical space information and a good cluster-finding algorithm.
To test the feasibility of chemical tagging on data from current and upcoming
spectroscopic surveys, we construct a realistic set of synthetic clusters,
creating both observed spectra and derived chemical abundances for each star.
We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to
group stars based on their spectra or abundances; these groups are matched to
input clusters and are found to be highly homogeneous and complete. The
percentage of clusters with more than 10 members recovered is 40% when tagging
on abundances with uncertainties achievable with current techniques. Based on
our fiducial model for the Milky Way, we predict recovering over 600 clusters
with at least 10 observed members and 70% membership homogeneity in a sample
similar to the APOGEE survey. Tagging larger surveys like the GALAH survey and
the future Milky Way Mapper in SDSS V could recover tens of thousands of
clusters at high homogeneity. Access to so many unique co-eval clusters will
transform how we understand the star formation history and chemical evolution
of our Galaxy.
