UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters
Abstract: UPMASK, written in R, performs membership assignment in stellar clusters. It uses photometry and spatial positions, but can take into account other types of data. UPMASK takes into account arbitrary error models; the code is unsupervised, data-driven, physical-model-free and relies on as few assumptions as possible. The approach followed for membership assessment is based on an iterative process, principal component analysis, a clustering algorithm and a kernel density estimation.
Credit: Krone-Martins, Alberto; Moitinho, Andre
Site: https://cran.r-project.org/web/packages ... index.html
https://ui.adsabs.harvard.edu/abs/2014A%26A...561A..57K
Bibcode: 2015ascl.soft04001K
ID: ascl:1504.001