Morphology-spin connection in the SAMI Galaxy Survey

Abstract

The spin parameter $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ is a proxy for the specific stellar angular momentum of galaxies and is a useful metric for classifying kinematic morphology. This study aims to quantify the relative importance of galaxy properties in explaining $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$, using data from the Sydney-AAO Multi-object Integral-field spectrograph (SAMI) Galaxy Survey. We apply partial correlation analysis and partial least squares regression to assess the relative contributions of different parameters in explaining $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$. We find that morphology indicators, bulge-to-total ratio within one effective radius ($B/T_\rm{e}$) and ellipticity ($\varepsilon_\rm{e}$), show the strongest correlations with $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ and play a leading role in the regression analysis. This result statistically confirms the established fast-rotator sequence, in which fast-rotating early-type galaxies form a continuous structural and kinematic sequence with spiral galaxies, with $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ decreasing as bulge prominence increases. The light-weighted age and stellar mass also exhibit significant correlations, but their contributions are secondary to the morphology indicators in multivariate analyses. We also examine whether the observed trends in $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ can be reproduced using galaxy properties alone. The morphology indicators (${B/T_\rm{e}}$, $\varepsilon_\rm{e}$) reproduce the overall distribution of observed $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ with a scatter of about 0.12, while the inclusion of Age_LW and $M_{\star }$ provides only modest additional improvement. However, these relations do not reproduce the slow-rotator regime well. Overall, our results show that photometric structural parameters best explain $\hat{I}{»}_{{\mathrm{R}}_{\mathrm{e}}}$ and suggest that statistical inference of galaxy spin from non-IFS observables may become feasible with improved models and a broader set of parameters.

15 pages, 6 figures, accepted for publication in MNRAS