A six-parameter space to describe galaxy diversification

Abstract

Context. The diversification of galaxies is caused by transforming events such as accretion, interaction, or mergers. These explain the formation and evolution of galaxies, which can now be described by many observables. Multivariate analyses are the obvious tools to tackle the available 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 diminish the usefulness of these analyses. The behaviour of the available parameters should be analysed before any objective reduction in the dimensionality and any subsequent clustering analyses can be undertaken, especially in an evolutionary context. Aims. We study a sample of 424 early-type galaxies described by 25 parameters, 10 of which are Lick indices, to identify the most discriminant parameters and construct an evolutionary classification of these objects. Methods. 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. Results. The methods agree in terms of six parameters: central velocity dispersion, disc-to-bulge ratio, effective surface brightness, metallicity, and the line indices NaD and OIII. The partitioning found using these six parameters, when projected onto the fundamental plane, looks very similar to the partitioning obtained previously for a totally different sample and based only on the parameters of the fundamental plane. Two additional groups are identified here, and we are able to provide some more constraints on the assembly history of galaxies within each group thanks to the larger number of parameters. We also identify another "fundamental plane" with the absolute K magnitude, the linear diameter, and the Lick index Hβ. We confirm that the Mg b vs. velocity dispersion correlation is very probably an evolutionary correlation, in addition to several other scaling relations. Finally, combining the results of our two papers, we obtain a classification of galaxies that is based on the transforming processes that are at the origin of the different groups. Conclusions. By taking into account that galaxies are evolving complex objects and using appropriate tools, we are able to derive an explanatory classification of galaxies, based on the physical causes of the diverse properties of galaxies, as opposed to the descriptive classifications that are quite common in astrophysics.

Stellar populations in ω Centauri: a multivariate analysis

“…More details are given in Appendix A. We also apply the k-means algorithm to the data, in order to test the reproducibility of statistical analyses with k-means, and compare the results given by the two methods (see more details on the methods in Fraix-Burnet et al 2012). …”

Phylogenetic Analyses of Quasars and Galaxies

“…For instance, similarity techniques (like most statistical clustering and classification or phenetic tools) tend to find hyperspheres in the parameter space, while phylogenetic tools are able to detect evolutionary paths as can be shown on stellar evolutionary tracks (Fraix-Burnet, 2016). Many applications have been published on many kinds of astrophysical objects (Fraix-Burnet et al, 2009, 2010, 2012Cardone and Fraix-Burnet, 2013;Fraix-Burnet and Davoust, 2015;Jofre et al, 2017;Holt et al, 2017).…”

Multivariate approaches to classification in extragalactic astronomy

“…Multivariate k-means analyses of smaller sample of galaxies with the aim of discovering new classes of galaxies have been performed as a complement to other clustering methods by Fraix-Burnet et al (2010) with the four parameters of the fundamental plane, and by Fraix-Burnet et al (2012) with six parameters selected from 23 available. In the latter case, the selection of the parameters is made through different statistical tools, in order to find a parameter subspace in which a robust clustering of the data is present.…”