# Improving galaxy morphology with machine learning

## TL;DR

This study applies machine learning techniques to classify galaxy morphologies into elliptical and spiral types using various morphological parameters, achieving high accuracy with supervised methods and exploring clustering approaches.

## Contribution

The paper demonstrates effective galaxy classification using machine learning on morphological parameters, with high accuracy and comparison of supervised and unsupervised methods.

## Key findings

- Supervised models achieved over 97% accuracy.
- Unsupervised clustering methods identified galaxy groups.
- Morphological parameters effectively distinguish galaxy types.

## Abstract

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA). Except concentration, all parameters performed a image segmentation pre-processing. For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo. With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects balanced dataset, we applied K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.