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The kinematic morphology–density relation of galaxies is normally attributed to a changing distribution of galaxy stellar masses with the local environment. However, earlier studies were largely focused on slow rotators; the dynamical properties of the overall population in relation to environment have received less attention. We use the SAMI Galaxy Survey to investigate the dynamical properties of ∼1800 early and late-type galaxies with log (M ⋆/ M ⊙) > 9.5 as a function of mean environmental overdensity (Σ5) and their rank within a group or cluster. By classifying galaxies into fast and slow rotators, at fixed stellar mass above log (M ⋆/ M ⊙) > 10.5, we detect a higher fraction (∼3.4σ) of slow rotators for group and cluster centrals and satellites as compared to isolated-central galaxies. We find similar results when using Σ5 as a tracer for environment. Focusing on the fast-rotator population, we also detect a significant correlation between galaxy kinematics and their stellar mass as well as the environment they are in. Specifically, by using inclination-corrected or intrinsic |$\lambda _{R_{\rm {e}}}$| values, we find that, at fixed mass, satellite galaxies on average have the lowest |$\lambda _{\, R_{\rm {e}},\rm {intr}}$|⁠ , isolated-central galaxies have the highest |$\lambda _{\, R_{\rm {e}},\rm {intr}}$|⁠ , and group and cluster centrals lie in between. Similarly, galaxies in high-density environments have lower mean |$\lambda _{\, R_{\rm {e}},\rm {intr}}$| values as compared to galaxies at low environmental density. However, at fixed Σ5, the mean |$\lambda _{\, R_{\rm {e}},\rm {intr}}$| differences for low and high-mass galaxies are of similar magnitude as when varying Σ5 (⁠|$\Delta \lambda _{\, R_{\rm {e}},\rm {intr}} \sim 0.05$|⁠ , with σrandom = 0.025, and σ syst < 0.03). Our results demonstrate that after stellar mass, environment plays a significant role in the creation of slow rotators, while for fast rotators we also detect an independent, albeit smaller, impact of mass and environment on their kinematic properties. [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]