The SAMI Galaxy Survey: Towards an Optimal Classification of Galaxy Stellar Kinematics

Large galaxy samples from multi-object IFS surveys now allow for a statistical analysis of the z~0 galaxy population using resolved kinematics. However, the improvement in number statistics comes at a cost, with multi-object IFS surveys more severely impacted by the effect of seeing and lower signal-to-noise. We present an analysis of ~1800 galaxies from the SAMI Galaxy Survey and investigate the spread and overlap in the kinematic distributions of the spin parameter proxy $\lambda_{Re}$ as a function of stellar mass and ellipticity. For SAMI data, the distributions of galaxies identified as regular and non-regular rotators with $kinemetry$ show considerable overlap in the $\lambda_{Re}$-$\varepsilon_e$ diagram. In contrast, visually classified galaxies (obvious and non-obvious rotators) are better separated in $\lambda_{Re}$ space, with less overlap of both distributions. Then, we use a Bayesian mixture model to analyse the $\lambda_{Re}$-$\log(M_*/M_{\odot})$ distribution. As a function of mass, we investigate whether the data are best fit with a single kinematic distribution or with two. Below $\log(M_*/M_{\odot})$~10.5 a single beta distribution is sufficient to fit the complete $\lambda_{Re}$ distribution, whereas a second beta distribution is required above $\log(M_*/M_{\odot})$~10.5 to account for a population of low-$\lambda_{Re}$ galaxies, presenting the cleanest separation of the two populations. We apply the same analysis to mock-observations from cosmological simulations. The mixture model predicts a bimodal $\lambda_{Re}$ distribution for all simulations, albeit with different positions of the $\lambda_{Re}$ peaks and with different ratios of both populations. Our analysis validates the conclusions from previous, smaller IFS surveys, but also demonstrates the importance of using kinematic selection criteria that are dictated by the quality of the observed or simulated data.