UPMASK (version 1.0)

UPMASK-package: 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, principal component analysis, a clustering algorithm and a kernel density estimation.

Details

Package:

UPMASK

Type:

Package

Version:

1.0

Date:

2014-09-11

License:

GPL (>= 3)

UPMASK-SimOC-Ex-Manual.jpgUPMASKexample

The two main functions in the UPMASK package are UPMASKfile and UPMASKdata. The later will run the UPMASK method on data inside a data frame, while the former will perform the analysis on a file (the later deals with loading a file inside a data frame, calling the UPMASKdata function and writing the results to an output file).

The package includes data from two simulated fields comprising simulated data from cluster and field stars -- to be used for demonstration. The analysis of these files using UPMASK lead to results presented in the figures above (from Krone-Martins&Moitinho, 2014), which show the spatial positions of the objects in the original datasets (in the left), the same objects but color coded by membership probability after the UPMASK analysis (in the center) and the color-magnitude diagram of all the stars also color coded by membership probability (in the right).

References

Krone-Martins, A. & Moitinho, A., Astronomy&Astrophysics, v.561, p.A57, 2014

See Also

UPMASKfile, UPMASKdata

Examples

## Not run: 
# #
# # Example of how to run UPMASK using data from a file
# # Note: serious analysis require larger nRuns, and see UPMASKfile documentation 
# # for the parametrization.
# # Write a string with the filename of the input and output files
# inputFileName <- system.file("extdata", 
#              "oc_12_500_1000_1.0_p019_0880_1_25km_120nR_withcolors.dat", package="UPMASK")
# outputFileName <- file.path(tempdir(), "RESULTS.dat")
# # Run UPMASK
# UPMASKfile(inputFileName, outputFileName, nRuns=5, starsPerClust_kmeans=25, 
#           verbose=TRUE, fileWithHeader=TRUE)          
# # Done, the results are written to the file outputFileName
# 
# #
# # Example of how to run UPMASK using data from a data frame 
# # Note: serious analysis require larger nRuns, and see UPMASKdata documentation 
# # for the parametrization.
# # Load the data into a data frame
# inputFileName <- system.file("extdata", 
#              "oc_12_5000_4000_4.0_p019_0900_1_15km_120nR_withcolors.dat", package="UPMASK")
# ocData <- read.table(inputFileName, header=TRUE)
# # Run UPMASK
# upmaskRes <- UPMASKdata(ocData, nRuns=5, starsPerClust_kmeans=25, verbose=TRUE)
# # Done, the results are in the data frame upmaskRes
# 
# # Clean the environment
# rm(list=c("inputFileName","outputFileName","ocData","upmaskRes"))
# ## End(Not run)