pyUPMASK: an improved unsupervised clustering algorithm

TL;DR

pyUPMASK is an improved, faster, and more versatile unsupervised clustering algorithm for stellar clusters, demonstrating superior performance over the original UPMASK across multiple synthetic tests.

Contribution

The paper introduces pyUPMASK, an enhanced Python implementation of UPMASK with key improvements that increase its generality and significantly boost its performance.

Findings

pyUPMASK outperforms UPMASK on all statistical metrics.

pyUPMASK is many times faster than UPMASK.

pyUPMASK effectively handles contaminated synthetic clusters.

Abstract

Aims. We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. Its general approach 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 introducing 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.