The SAMI Galaxy Survey: a statistical approach to an optimal
  classification of stellar kinematics in galaxy surveys
  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 survey more severely impacted by the effect of seeing and
lower S/N. 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 \textsc{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 observed
$\lambda_{Re}$-$\log(M_*/M_{\odot})$ distribution. Below
$\log(M_{\star}/M_{\odot})\sim10.5$, a single beta distribution is sufficient
to fit the complete $\lambda_{Re}$ distribution, whereas a second beta
distribution is required above $\log(M_{\star}/M_{\odot})\sim10.5$ to account
for a population of low-$\lambda_{Re}$ 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 kinematic maps
should not be disregarded in future studies. Applied to mock-observations from
different cosmological simulations, the mixture model also predicts bimodal
$\lambda_{Re}$ distributions, albeit with different positions of the
$\lambda_{Re}$ peaks. 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.
