Morfometryka -- A New Way of Establishing Morphological Classification of Galaxies (Ferrari+ 2015)
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by
JeanTate
arXiv:1509.05430, abstract (some formatting, and characters, messed up):
We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C1, Asymmetry A3, and Smoothness S3), and the new parameters entropy, H, and spirality σψ. The new parameters A3, S3 and H are better to discriminate galaxy classes than A1, S1 and G, respectively. The new parameter σψ captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from the Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify Baillard et al.(2011, 4478 galaxies), Nair & Abraham (2010, 14123 galaxies) and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index (Mi) and we show that it follows the human based T-type index very closely. We calculate morphometric index Mi for ∼780k galaxies from SDSS Legacy Survey - DR7. We discuss how Mi correlates with stellar population parameters obtained using the spectra available from SDSS-DR7.
Yes, Galaxy Zoo figures fairly prominently, e.g. in Section 2 "Related Work":
Dieleman et al. (2015) present a Neural Network machine to reproduce Galaxy Zoo classification. They work directly in pixel space, using a rotation invariant convolution that minimizes sensitiveness to changes in scale, rotation, translation and sampling of the image. The algorithm obtains an accuracy of 99% relative to the Galaxy Zoo human classification; however, since the human classification is also error prone, as discussed in Section 1, their algorithm reproduces also the errors in the human classification.
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