The SAMI Galaxy Survey: a statistical approach to an optimal classification of stellar kinematics in galaxy surveys

Abstract

Large galaxy samples from multiobject integral field spectroscopic (IFS) surveys now allow for a statistical analysis of the z similar to 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 similar to 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 as a function of stellar mass and ellipticity epsilon(e). For SAMI data, the distributions of galaxies identified as regular and non-regular rotators with kinemetry show considerable overlap in the -epsilon(e) diagram. In contrast, visually classified galaxies (obvious and non-obvious rotators) are better separated in space, with less overlap of both distributions. Then, we use a Bayesian mixture model to analyse the observed -log(M-star/M-circle dot) 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-star/M-circle dot) similar to 10.5, a single beta distribution is sufficient to fit the complete distribution, whereas a second beta distribution is required above log(M-star/M-circle dot) similar to 10.5 to account for a population of low- 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 distributions, albeit with different positions of the 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.