Towards a universal dynamical scaling relation for galaxies of all types


L. Cortese, L. M. R. Fogarty, I.-T. Ho, K. Bekki, J. Bland-Hawthorn, M. Colless, W. Couch, S. M. Croom, K. Glazebrook, J. Mould, N. Scott, R. Sharp, C. Tonini, J. T. Allen, J. Bloom, J. J. Bryant, M. Cluver, R. L. Davies, M. Drinkwater, M. Goodwin, A. Green, L. J. Kewley, I. S. Kostantopoulos, J. S. Lawrence, S. Mahajan, A. M. Medling, M. Owers, S. N. Richards, S. M. Sweet, O. I. Wong


We take advantage of the first data from the Sydney-AAO Multi-object Integral field (SAMI) Galaxy Survey to investigate the relation between the kinematics of gas and stars, and stellar mass in a comprehensive sample of nearby galaxies. We find that all 235 objects in our sample, regardless of their morphology, lie on a tight relation linking stellar mass ($M_{*}$) to internal velocity quantified by the $S_{0.5}$ parameter, which combines the contribution of both dispersion ($\sigma$) and rotational velocity ($V_{rot}$) to the dynamical support of a galaxy ($S_{0.5}=\sqrt{0.5V_{rot}^{2}+\sigma^{2}}$). Our results are independent of the baryonic component from which $\sigma$ and $V_{rot}$ are estimated, as the $S_{0.5}$ of stars and gas agree remarkably well. This represents a significant improvement compared to the canonical $M_{*}$ vs. $V_{rot}$ and $M_{*}$ vs. $\sigma$ relations. Not only is no sample pruning necessary, but also stellar and gas kinematics can be used simultaneously, as the effect of asymmetric drift is taken into account once $V_{rot}$ and $\sigma$ are combined. Our findings illustrate how the combination of dispersion and rotational velocities for both gas and stars can provide us with a single dynamical scaling relation valid for galaxies of all morphologies across at least the stellar mass range 8.5$<log(M_{*}/M_{\odot})<$11. Such relation appears to be more general and at least as tight as any other dynamical scaling relation, representing a unique tool for investigating the link between galaxy kinematics and baryonic content, and a less biased comparison with theoretical models.

Publication Date: 
November 2014
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