Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach

TL;DR

This paper develops and evaluates machine learning methods, particularly Support Vector Machine and Random Forest, for automated binary morphological classification of galaxies from SDSS data, achieving over 96% accuracy.

Contribution

It introduces a photometry-based supervised machine learning approach for galaxy morphology classification, demonstrating high accuracy on SDSS DR9 data.

Findings

Support Vector Machine achieved 96.4% accuracy.

Random Forest achieved 95.5% accuracy.

Classified over 316,000 galaxies into E and L types.

Abstract

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 < z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.