SIMBAD references

Aims. We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. The general approach of this method makes it plausible to be applied to analyses that deal with binary classes of any kind as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository.
Methods. The core of the algorithm follows the method developed in UPMASK but introduces several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably.
Results. We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters affected by varying degrees of contamination by field stars. To assess the performance, we employed six different statistical metrics that measure the accuracy of probabilistic classification.
Conclusions. Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster.