Maximum parsimony analysis of the effect of the environment on the evolution of galaxies

Didier Fraix-Burnet,Mauro D’Onofrio,Paola Marziani

doi:10.1051/0004-6361/201935604

Abstract

Context. Galaxy evolution and the effect of the environment are most often studied using scaling relations or regression analyses around a given property. However, these approaches do not take into account the complexity of the physics of the galaxies and their diversity. Aims. We here investigate the effect of the cluster environment on the evolution of galaxies through multivariate, unsupervised classification and phylogenetic analyses applied to two relatively large samples from the Wide-field Nearby Galaxy-cluster Survey (WINGS), one of cluster members and one of field galaxies (2624 and 1476 objects, respectively). Methods. These samples are the largest ones ever analysed with a phylogenetic approach in astrophysics. To be able to use the maximum parsimony (cladistics) method, we first performed a pre-clustering in 300 clusters with a hierarchical clustering technique, before applying it to these pre-clusters. All these computations used seven parameters: B − V, log(Re), nV, ⟨μ⟩e, Hβ, D4000, and log(M*). Results. We have obtained a tree for the combined samples and do not find different evolutionary paths for cluster and field galaxies. However, the cluster galaxies seem to have accelerated evolution in the sense that they are statistically more diversified from a primitive common ancestor. The separate analyses show a hint of a slightly more regular evolution of the variables for the cluster galaxies, which may indicate they are more homogeneous compared to field galaxies in the sense that the groups of the latter appear to have more specific properties. On the tree for the cluster galaxies, there is a separate branch that gathers rejuvenated or stripped-off groups of galaxies. This branch is clearly visible on the colour-magnitude diagram, going back from the red sequence towards the blue one. On this diagram, the distribution and the evolutionary paths of galaxies are strikingly different for the two samples. Globally, we do not find any dominant variable able to explain either the groups or the tree structures. Rather, co-evolution appears everywhere, and could depend itself on environment or mass. Conclusions. This study is another demonstration that unsupervised machine learning is able to go beyond simple scaling relations by taking into account several properties together. The phylogenetic approach is invaluable in tracing the evolutionary scenarios and projecting them onto any bivariate diagram without any a priori modelling. Our WINGS galaxies are all at low redshift, and we now need to go to higher redshfits to find more primitive galaxies and complete the map of the evolutionary paths of present day galaxies.

Highlights

Correlations between physical properties of galaxies are helpful to extract key variables that may account for the variety in galaxy properties (Lagos et al 2016)
The scaling relations are always in 2D or 3D because they are most often identified through trial and error, more rarely from statistical tools such as principal component analysis (PCA) or the multivariate analyses that are explicitly aimed at minimising the number of variables needed to explain a large fraction of the variance in the p-dimensional space of variables
In other words, are they evolutionary disconnected, do they share some common ancestor from which their evolution diverged, or did they evolve but at a different pace due to their environment? Since we found that the latter is true, we decided to conduct analyses of the two sub-samples to look for more subtle differences in their evolution as separate populations and shed some light on the precise role of the environment in the statistical evolution of galaxies

Summary

Introduction

Correlations between physical properties of galaxies are helpful to extract key variables that may account for the variety in galaxy properties (Lagos et al 2016). The most fundamental of these correlations (called scaling relations) can effectively simplify the diversity of galaxy properties, they do not appear in any model or numerical simulation of galaxy formation and evolution. Lagos et al 2016) This may be due to several reasons, one being that the scaling relations often show variability within or across the line/plane, this scatter being often correlated to another variable; this shows the complexity of galaxy physics and evolution. Another reason can be that the scaling relations reveal some hidden (latent) variable that has not been or cannot be identified yet (Fraix-Burnet 2011)

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