A six-parameter space to describe galaxy diversification

TL;DR

This paper identifies a concise six-parameter space to effectively classify galaxy types and understand their evolution, using multivariate statistical methods on a large galaxy sample.

Contribution

It introduces a reduced six-parameter space for galaxy classification, improving objectivity and clarity in evolutionary analysis over previous high-dimensional approaches.

Findings

Six key parameters effectively classify galaxy types

Multivariate methods reveal clear evolutionary groupings

Reduced parameter set enhances analysis robustness

Abstract

Galaxy diversification proceeds by transforming events like accretion, interaction or mergers. These explain the formation and evolution of galaxies that can now be described with many observables. Multivariate analyses are the obvious tools to tackle the datasets and understand the differences between different kinds of objects. However, depending on the method used, redundancies, incompatibilities or subjective choices of the parameters can void the usefulness of such analyses. The behaviour of the available parameters should be analysed before an objective reduction of dimensionality and subsequent clustering analyses can be undertaken, especially in an evolutionary context. We study a sample of 424 early-type galaxies described by 25 parameters, ten of which are Lick indices, to identify the most structuring parameters and determine an evolutionary classification of these objects. Four independent statistical methods are used to investigate the discriminant properties of the observables and the partitioning of the 424 galaxies: Principal Component Analysis, K-means cluster analysis, Minimum Contradiction Analysis and Cladistics. (abridged)