# Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study

## TL;DR

This study compares traditional machine learning and deep learning methods, including a novel non-parametric approach, for galaxy morphological classification, achieving over 94.5% accuracy on large SDSS datasets.

## Contribution

It introduces a new non-parametric feature extraction system, CyMorph, and demonstrates deep learning's superior performance in galaxy morphology classification.

## Key findings

- Deep learning models achieve 99% accuracy for two-class classification.
- Traditional machine learning models reach over 94.5% accuracy.
- A large galaxy catalog with morphological classifications is provided.

## Abstract

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.