Unsupervised Classification of Galaxies. I. ICA feature selection

TL;DR

This paper presents an objective, multivariate approach to classify a large galaxy dataset using ICA for feature extraction and K-means clustering, revealing physically meaningful groups aligned with traditional galaxy classes.

Contribution

It introduces a novel combination of ICA and K-means for galaxy classification, providing a more objective and comprehensive analysis than subjective methods.

Findings

Nine independent components describe key physical galaxy properties.

Ten galaxy groups correspond to traditional classifications.

Method effectively captures complex galaxy features.

Abstract

Subjective classification of galaxies can mislead us in the quest of the origin regarding formation and evolution of galaxies since this is necessarily limited to a few features. The human mind is not able to apprehend the complex correlations in a manyfold parameter space, and multivariate analyses are the best tools to understand the differences among various kinds of objects. In this series of papers, an objective classification of 362,923 galaxies from the Value Added Galaxy Catalogue (VAGC) is carried out with the help of two methods of multivariate analysis. First, Independent Component Analysis (ICA) is used to determine a set of derived independent components that are linear combinations of 47 observed features (viz. ionized lines, Lick indices, photometric and morphological properties, star formation rates etc.) of the galaxies. Subsequently, a K-means cluster analysis is applied on the nine independent components to obtain ten distinct and homogeneous groups. In this first paper, we describe the methods and the main results. It appears that the nine Independent Components represent a complete physical description of galaxies (velocity dispersion, ionisation, metallicity, surface brightness and structure). We find that our ten groups can be essentially placed into traditional and empirical classes (from colour-magnitude and emission-line diagnostic diagrams, early- vs late-types) despite the classical corresponding features (colour, line ratios and morphology) being not significantly correlated with the nine Independent Components. More detailed physical interpretation of the groups will be performed in subsequent papers.