pyUPMASK: an improved unsupervised clustering algorithm

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