SDSS galaxies morphological classification

For the analysis we used absolute magnitudes Mu, Mg, Mr, Mi, Mz; color indices Mu-Mr, Mg-Mi, Mu-Mg, Mr-Mz; and the inverse concentration index to the center R50/R90. We determined the ability of each method to predict the morphological type, and verified various dependencies of the method's accuracy on redshifts, human labeling, morphological shape, and overlap of different morphological types for galaxies with the same color indices.

We find that the morphology based on the supervised machine learning methods trained over photometric parameters demonstrates significantly less bias than the morphology based on citizen-science classifiers. The support-vector machine and random forest methods with Scikit-learn software machine learning library in Python provide the highest accuracy for the binary galaxy morphological classification. Specifically, the success rate is 96.4% for support-vector machine (96.1% early E and 96.9% late L types) and 95.5% for random forest (96.7% early E and 92.8% late L types).

Applying the support-vector machine for the sample of 316 031 galaxies from the SDSS DR9 at z<0.1 with unknown morphological types, we found 139659 E and 176372 L types among them.