Improving the open cluster census. I. Comparison of clustering
  algorithms applied to Gaia DR2 data
  The census of open clusters in the Milky Way is in a never-before seen state
of flux. Recent works have reported hundreds of new open clusters thanks to the
incredible astrometric quality of the Gaia satellite, but other works have also
reported that many open clusters discovered in the pre Gaia era may be
associations. We aim to conduct a comparison of clustering algorithms used to
detect open clusters, attempting to statistically quantify their strengths and
weaknesses by deriving the sensitivity, specificity, and precision of each as
well as their true positive rate against a larger sample. We selected DBSCAN,
HDBSCAN, and Gaussian mixture models for further study, owing to their speed
and appropriateness for use with Gaia data. We developed a preprocessing
pipeline for Gaia data and developed the algorithms further for the specific
application to open clusters. We derived detection rates for all 1385 open
clusters in the fields in our study as well as more detailed performance
statistics for 100 of these open clusters. DBSCAN was sensitive to 50% to 62%
of the true positive open clusters in our sample, with generally very good
specificity and precision. HDBSCAN traded precision for a higher sensitivity of
up to 82%, especially across different distances and scales of open clusters.
Gaussian mixture models were slow and only sensitive to 33% of open clusters in
our sample, which tended to be larger objects. Additionally, we report on 41
new open cluster candidates detected by HDBSCAN, three of which are closer than
500 pc. When used with additional post-processing to mitigate its false
positives, we have found that HDBSCAN is the most sensitive and effective
algorithm for recovering open clusters in Gaia data. Our results suggest that
many more new and already reported open clusters have yet to be detected in
Gaia data.
