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We use the SAMI (Sydney-AAO Multi-object Integral field spectrograph) galaxy survey to study the the kinematic morphology–density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We build a logistic regression model to quantitatively study the dependence of kinematic morphology (whether a galaxy is a fast or slow rotator) on a wide range of parameters, without resorting to binning the data. Our model uses a combination of stellar mass, star formation rate (SFR), r -band half-light radius, and a binary variable based on whether the galaxy's observed ellipticity (ϵ) is less than 0.4. We show that, at fixed mass, size, SFR, and ϵ, a galaxy's local environmental surface density (log10(Σ5/Mpc−2)) gives no further information about whether a galaxy is a slow rotator, that is, the observed kinematic–morphology density relation can be entirely explained by the well-known correlations between environment and other quantities. We show how our model can be applied to different galaxy surveys to predict the fraction of slow rotators which would be observed and discuss its implications for the formation pathways of slow rotators. [ABSTRACT FROM AUTHOR]

It is well established that there are at least two main channels to form lenticular (or S0) galaxies. The first, which we name 'faded spiral' scenario, includes quenching events that led to consumption or removal of gas from a spiral progenitor. The second, which we call 'merger' scenario, includes merger-like events and interactions between galaxies. Each scenario leaves characteristic signatures in the newly formed lenticular galaxy. However, the conditions that trigger one mechanism over another are still unknown. This paper is the third of a series aimed at understanding the role of the environment in the formation of lenticular galaxies. In this study, we combine the kinematics, morphology, and properties of the stellar populations of 329 S0s from the SAMI and MaNGA surveys in order to highlight the role of the environment in the process. We divide the S0s into two classes (A and B) according to their global properties, that we can associate to the products of a faded spiral scenario (class A) or a merger scenario (class B). We then study how the various classes are distributed within different environments. Our study reveals that the 'faded spiral' pathway is the most efficient channel to produce S0s, and it becomes more efficient as the mass of the group or cluster or local density of galaxies increase. The merger pathway is also a viable channel, and its efficiency becomes higher with decreasing local density or environment mass. [ABSTRACT FROM AUTHOR]

Large galaxy samples from multiobject integral field spectroscopic (IFS) surveys now allow for a statistical analysis of the z ∼ 0 galaxy population using resolved kinematic measurements. However, the improvement in number statistics comes at a cost, with multiobject IFS survey more severely impacted by the effect of seeing and lower signal-to-noise ratio. We present an analysis of ∼1800 galaxies from the SAMI Galaxy Survey taking into account these effects. We investigate the spread and overlap in the kinematic distributions of the spin parameter proxy |$\lambda _{R_{\rm {e}}}$| as a function of stellar mass and ellipticity ε e. For SAMI data, the distributions of galaxies identified as regular and non-regular rotators with kinemetry show considerable overlap in the |$\lambda _{R_{\rm {e}}}$| – ε e diagram. In contrast, visually classified galaxies (obvious and non-obvious rotators) are better separated in |$\lambda _{R_{\rm {e}}}$| space, with less overlap of both distributions. Then, we use a Bayesian mixture model to analyse the observed |$\lambda _{R_{\rm {e}}}$| –log (M ⋆/M⊙) distribution. By allowing the mixture probability to vary as a function of mass, we investigate whether the data are best fit with a single kinematic distribution or with two. Below log (M ⋆/M⊙) ∼ 10.5, a single beta distribution is sufficient to fit the complete |$\lambda _{R_{\rm {e}}}$| distribution, whereas a second beta distribution is required above log (M ⋆/M⊙) ∼ 10.5 to account for a population of low- |$\lambda _{R_{\rm {e}}}$| galaxies. While the Bayesian mixture model presents the cleanest separation of the two kinematic populations, we find the unique information provided by visual classification of galaxy kinematic maps should not be disregarded in future studies. Applied to mock-observations from different cosmological simulations, the mixture model also predicts bimodal |$\lambda _{R_{\rm {e}}}$| distributions, albeit with different positions of the |$\lambda _{R_{\rm {e}}}$| peaks. Our analysis validates the conclusions from previous, smaller IFS surveys, but also demonstrates the importance of using selection criteria for identifying different kinematic classes that are dictated by the quality and resolution of the observed or simulated data. [ABSTRACT FROM AUTHOR]