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Astrophysics Source Code Library

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Searching for codes credited to 'Moitinho, A'

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[ascl:1504.001] UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters

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.

[ascl:2101.016] pyUPMASK: Unsupervised clustering method for stellar clusters

pyUPMASK is an unsupervised clustering method for stellar clusters that builds upon the original UPMASK (ascl:1504.001) package. Its general approach makes it applicable to analyses that deal with binary classes of any kind, as long as the fundamental hypotheses are met. The core of the algorithm follows the method developed in UPMASK but introducing several key enhancements that make it not only more general, they also improve its performance.