UPMASK: Unsupervised Photometric Membership Assignment in St

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Ada Coda
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UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters

Post by Ada Coda » Sun Apr 05, 2015 6:49 pm

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
Last edited by Ada Coda on Mon Jan 25, 2021 7:37 am, edited 1 time in total.
Reason: Updated code entry.

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