VERIFICATION OF MACHINE LEARNING METHODS FOR BINARY MORPHOLOGICAL CLASSIFICATION OF GALAXIES FROM SDSS

Abstract

We present a study on the verifica-tion of Machine Learning methods to be applied forbinary morphological classification of galaxies. Withthis aim we used the sample of 60561 galaxies from theSDSSDR9 survey with a redshift of 0 . 02 < z < 0 . 06 andabsolute magnitudes of − 24 m < M r < − 19 . 4 m . Weapplied the following classification methods using owncode in Python to predict correctly the morphology ofLate and Early galaxies: Naive Bayes, Random Forest,Support Vector Machines, Logistic Regression, and k-Nearest Neighbor algorithm. To study the classifier, weused absolute magnitudes M u ,M g ,M r ,M i ,M z , colorindices M u − M r ,M g − M i ,M u − M g ,M r − M z , andinverse concentration index to the center R50/R90.We compared these new results with previous onemade with the KNIME Analytics Platform 3.5.3. Itturned out that Random Forest and Support VectorMachine Classifiers provide a highest accuracy, asin the previous study, but with help our code inPython we increased an accuracy from 92.9 % ofcorrectly classified (96% – E and 84% – L ) to 94,6%(96,9% – E and 89,7 % – L ). The accuracy of theremaining methods also grew by 88% to 93%. So,using these classifiers and the data on color indices,absolute magnitudes, inverse concentration index ofgalaxies with visual morphological types, we were ableto classify 60561 galaxies from the SDSSDR9 withunknown morphological types and found 22301 E and38260 L types among them