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UPMASK (version 1.2)

Unsupervised Photometric Membership Assignment in Stellar Clusters

Description

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, dimensionality reduction, a clustering algorithm and a kernel density estimation.

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Version

Install

install.packages('UPMASK')

Monthly Downloads

41

Version

1.2

License

GPL (>= 3)

Maintainer

Alberto Krone-Martins

Last Published

February 1st, 2019

Functions in UPMASK (1.2)

getStarsAtHighestDensityRegion

Perform cut in the membership list based on the 2D space distribution
UPMASKdata

Run UPMASK in a data frame
meanThreeSigRej

Perform cuts in the data
takeErrorsIntoAccount

Take Errors Into Account for UPMASK analysis
UPMASKfile

Run UPMASK in a file
analyse_randomKde2d_AutoCalibrated

Perform analysis of random 2d distributions (auto calibrated)
analyse_randomKde2d

Perform analysis of random 2d distributions
analyse_randomKde2d_smart

Perform analysis of random 2d distributions
outerLoop

UPMASK outer loop
performCuts

Perform cuts in the data
kde2dForSubset

Compute the density based distance quantity using a 2D Kernel Density Estimation
UPMASK-package

Unsupervised Photometric Membership Assignment in Stellar Clusters
create_randomKde2d

Compute the density based distance quantity using a 2D Kernel Density Estimation
create_smartTable

Create a look up table
innerLoop

UPMASK inner loop