Classification and evolution of galaxies according to the dynamical state of host clusters and galaxy luminosities

Abstract

We analyze the dependence of galaxy evolution on cluster dynamical state and galaxy luminosities for a sample of 146 galaxy clusters from the Yang SDSS catalog. Clusters were split according to their velocity distribution in Gaussians (G) and Non-Gaussians (NG), and further divided by luminosity regime. We performed a classification in the Age-SSFR plane providing three classes: star-forming (SF), passive (PAS), and intermediate (GV -green valley). We show that galaxies evolve in the same way in G and NG systems, but also suggest that their formation histories leads to different mixtures of galactic types and infall patterns. Separating the GV into star-forming and passive components, we find more bright galaxies in the passive mode of NG than in G systems. We also find more intermediate faint galaxies in the star-forming component of NG than in G systems. Our results suggest the GV as the stage where the transition from types Sab and Scd to S0 must be taking place, but the conversion between morphological types is independent of the dynamical stage of the clusters. Analyzing the velocity dispersion profiles, we find that objects recently infalling in clusters have a different composition between G and NG systems. While all galaxy types infall onto G systems, Sab and Scd dominate the infall onto NG systems. Finally, we find that faint Scd in the outskirts of NG systems present higher asymmetries relative to the mean asymmetry of field galaxies, suggesting environmental effects acting on these objects.

Late growth of early-type galaxies in low-z massive clusters

“…Also, Nascimento et al ( 2019 ) find that the passive population in systems with Gaussian velocity distribution is the only family with lower velocity dispersion in massive clusters. Morell et al ( 2020 ) find a similar result showing that ellipticals and lenticulars have the most isotropic orbits.…”

Unveiling the internal structure of Hercules supercluster

“…In general lines, the algorithm searches for optimized clusters from models encompassing variable shapes, orientations and volumes. For a detailed description of the application of M in galaxy classification, we refer the reader to recent works of Morell et al (2020) and Lourenço et al (2020).…”

The impact of disturbed galaxy clusters on the kinematics of active galactic nuclei

“…The VDPs we produce in this work are functions of the projected radius, ( ), originally devised by Bergond et al (2006) for analysing the kinematics of stellar systems but have since been extended to the large-scale structures of galaxy groups and clusters by a variety of authors (e.g. Hou et al 2009Hou et al , 2012Pimbblet et al 2014;Bilton & Pimbblet 2018;Morell et al 2020). These VDPs are calculated through cluster galaxy radial velocities at fixed incremental bins of radius, with each bin weighted against a Gaussian window function that is driven exponentially by the square of the difference in radius for each ℎ galaxy.…”

“…Tests for determining the degree of sub-structuring, such as that of Dressler & Shectman (1988), can be used as proxies for delineating between 'merging' and 'non-merging' cluster environments. Analysing the cluster galaxy kinematics of these opposing cluster dynamical states via velocity dispersion profiles (VDPs) and rotational profiles can provide an insight into how cluster galaxies, and their sub-populations, respond kinematically to their environment as a function of radius (Hou et al 2009(Hou et al , 2012Bilton & Pimbblet 2018;Bilton et al 2019;Morell et al 2020). In addition, VDPs themselves can independently act as proxies for determining a merging environment if they depict a rising profile as one increases the clustocentric radius within the virial regions, vice versa for nonmerging environments (see Menci & Fusco-Femiano 1996;Hou et al 2009;Bilton & Pimbblet 2018).…”