UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters

An implementation of the UPMASK method for performing membership assignment in stellar clusters in R. It is prepared to use photometry and spatial positions, but it can take into account other types of data. The method is able to take into account arbitrary error models, and it 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.

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AuthorAlberto Krone-Martins, Andre Moitinho
Date of publication2017-04-03 20:19:18 UTC
MaintainerAlberto Krone-Martins <algol@sim.ul.pt>
LicenseGPL (>= 3)

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