Morfometryka -- A New Way of Establishing Morphological Classification of Galaxies
Fabricio Ferrari (1), Reinaldo Ramos de Carvalho (2), Marina Trevisan, (2, 3) ((1) FURG, (2) INPE, (3) IAP)

TL;DR
This paper introduces an advanced morphometric system with new parameters for automatic galaxy classification, achieving over 90% accuracy and closely matching human T-type indices across large galaxy samples.
Contribution
It develops a novel set of morphological parameters and a classification method that significantly improves automatic galaxy classification accuracy.
Findings
Achieved over 90% classification accuracy.
Defined a morphometric index closely matching T-type.
Applied to ~780,000 galaxies from SDSS-DR7.
Abstract
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 , Asymmetry , and Smoothness ), and the new parameters entropy, , and spirality . The new parameters , and are better to discriminate galaxy classes than , and , respectively. The new parameter 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…
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