Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey. Kelly, B., C. and McKay, T., A. The Astronomical Journal, 127:625-645, 2004.
Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey [pdf]Paper  Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey [link]Website  abstract   bibtex   
We describe application of the ``shapelet'' linear decomposition of \ngalaxy images to morphological classification using images of ~3000\ngalaxies from the Sloan Digital Sky Survey. After decomposing the\ngalaxies, we perform a principal component analysis to reduce the number\nof dimensions of the shapelet space to nine. We find that each of these\nnine principal components contains unique morphological information and\ngive a description of each principal component's contribution to a\ngalaxy's morphology. We find that galaxies of differing Hubble type\nseparate cleanly in the shapelet space. We apply a Gaussian mixture\nmodel to the nine-dimensional space spanned by the principal components\nand use the results as a basis for classification. Using the mixture\nmodel, we separate galaxies into seven classes and give a description of\neach class' physical and morphological properties. We find that several\nof the mixture model classes correlate well with the traditional Hubble\ntypes both in their morphology and their physical parameters (e.g.,\ncolor, velocity dispersions, etc.). In addition, we find an additional\nclass of late-type morphology but with high velocity dispersions and\nvery blue color; most of these galaxies exhibit poststarburst activity.\nThis method provides an objective and quantitative alternative to\ntraditional and subjective visual classification.
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 title = {Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey},
 type = {article},
 year = {2004},
 identifiers = {[object Object]},
 keywords = {Galaxies: Fundamental Parameters,Galaxies: Statistics,Methods: Data Analysis,Methods: Statistical,Techniques: Image Processing},
 pages = {625-645},
 volume = {127},
 websites = {http://iopscience.iop.org/article/10.1086/380934/pdf,http://adsabs.harvard.edu/abs/2004AJ....127..625K},
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 abstract = {We describe application of the ``shapelet'' linear decomposition of \ngalaxy images to morphological classification using images of ~3000\ngalaxies from the Sloan Digital Sky Survey. After decomposing the\ngalaxies, we perform a principal component analysis to reduce the number\nof dimensions of the shapelet space to nine. We find that each of these\nnine principal components contains unique morphological information and\ngive a description of each principal component's contribution to a\ngalaxy's morphology. We find that galaxies of differing Hubble type\nseparate cleanly in the shapelet space. We apply a Gaussian mixture\nmodel to the nine-dimensional space spanned by the principal components\nand use the results as a basis for classification. Using the mixture\nmodel, we separate galaxies into seven classes and give a description of\neach class' physical and morphological properties. We find that several\nof the mixture model classes correlate well with the traditional Hubble\ntypes both in their morphology and their physical parameters (e.g.,\ncolor, velocity dispersions, etc.). In addition, we find an additional\nclass of late-type morphology but with high velocity dispersions and\nvery blue color; most of these galaxies exhibit poststarburst activity.\nThis method provides an objective and quantitative alternative to\ntraditional and subjective visual classification.},
 bibtype = {article},
 author = {Kelly, Brandon C and McKay, Timothy A},
 journal = {The Astronomical Journal}
}
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