The SAMI galaxy survey: gas velocity dispersions in low-z star-forming galaxies and the drivers of turbulence

We infer the intrinsic ionized gas kinematics for 383 star-forming galaxies across a range of integrated star formation rates (SFR ∈ [10−3, 102] M⊙ yr−1) at z ≲ 0.1 using a consistent 3D forward-modelling technique. The total sample is a combination of galaxies from the Sydney-AAO Multiobject Integral field Spectrograph (SAMI) Galaxy survey and DYnamics of Newly Assembled Massive Objects survey. For typical low-z galaxies taken from the SAMI Galaxy Survey, we find the vertical velocity dispersion (σv,z) to be positively correlated with measures of SFR, stellar mass, H I gas mass, and rotational velocity. The greatest correlation is with SFR surface density (ΣSFR). Using the total sample, we find σv,z increases slowly as a function of integrated SFR in the range SFR ∈ [10−3, 1]  M⊙ yr−1 from 17 +- 3 to 24 +- 5 km s−1 followed by a steeper increase up to σv,z ∼80 km s−1 for SFR ≳ 1 M⊙ yr−1. This is consistent with recent theoretical models that suggest a σv,z floor driven by star formation feedback processes with an upturn in σv,z at higher SFR driven by gravitational transport of gas through the disc.The SAMI Galaxy Survey is based on observations made at the AAT. The SAMI was developed jointly by the University of Sydney and the Australian Astronomical Observatory. The SAMI input catalogue is based on data taken from the SDSS, the GAMA Survey, and the VST ATLAS Survey. The SAMI Galaxy Survey is supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions, through project number CE170100013, the Australian Research Council Centre of Excellence for All-sky Astrophysics, through project number CE110001020, and other participating institutions. The SAMI Galaxy Survey website is http://sami-survey.org/. DBF and KG acknowledge support from the Australian Research Council Discovery Program grant DP160102235. DBF acknowledges support from Australian Research Council Future Fellowship FT170100376. LC is the recipient of an Australian Research Council Future Fellowship (FT180100066) funded by the Australian Government. MRK acknowledges support from Australian Research Council Future Fellowship FT180100375, and from a Humboldt Research Award from the Alexander von Humboldt Foundation. JJB acknowledges support of an Australian Research Council Future Fellowship (FT180100231). CF acknowledges funding provided by the Australian Research Council (Discovery Projects DP170100603 and Future Fellowship FT180100495), and the Australia-Germany Joint Research Cooperation Scheme (UADAAD). BG is the recipient of an Australian Research Council Future Fellowship (FT140101202). MSO acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140100255). JvdS is funded under JBH’s ARC Laureate Fellowship (FL140100278)

The SAMI Galaxy Survey: Bayesian Inference for Gas Disk Kinematics using a Hierarchical Gaussian Mixture Model

We present a novel Bayesian method, referred to as Blobby3D, to infer gas kinematics that mitigates the effects of beam smearing for observations using Integral Field Spectroscopy (IFS). The method is robust for regularly rotating galaxies despite substructure in the gas distribution. Modelling the gas substructure within the disk is achieved by using a hierarchical Gaussian mixture model. To account for beam smearing effects, we construct a modelled cube that is then convolved per wavelength slice by the seeing, before calculating the likelihood function. We show that our method can model complex gas substructure including clumps and spiral arms. We also show that kinematic asymmetries can be observed after beam smearing for regularly rotating galaxies with asymmetries only introduced in the spatial distribution of the gas. We present findings for our method applied to a sample of 20 star-forming galaxies from the SAMI Galaxy Survey. We estimate the global H$\alpha$ gas velocity dispersion for our sample to be in the range $\bar{\sigma}_v \sim$[7, 30] km s$^{-1}$. The relative difference between our approach and estimates using the single Gaussian component fits per spaxel is $\Delta \bar{\sigma}_v / \bar{\sigma}_v = - 0.29 \pm 0.18$ for the H$\alpha$ flux-weighted mean velocity dispersion.Comment: 23 pages, 12 figures, accepted for MNRA

The SAMI Galaxy Survey: Bayesian inference for gas disc kinematics using a hierarchical Gaussian mixture model

We present a novel Bayesian method, referred to as BLOBBY3D, to infer gas kinematics that mitigates the effects of beam smearing for observations using integral field spectroscopy. The method is robust for regularly rotating galaxies despite substructure in the gas distribution. Modelling the gas substructure within the disc is achieved by using a hierarchical Gaussian mixture model. To account for beam smearing effects, we construct a modelled cube that is then convolved per wavelength slice by the seeing, before calculating the likelihood function. We show that our method can model complex gas substructure including clumps and spiral arms. We also show that kinematic asymmetries can be observed after beam smearing for regularly rotating galaxies with asymmetries only introduced in the spatial distribution of the gas. We present findings for our method applied to a sample of 20 star-forming galaxies from the SAMI Galaxy Survey. We estimate the global H α gas velocity dispersion for our sample to be in the range ¯σv ∼[7, 30] km s−1. The relative difference between our approach and estimates using the single Gaussian component fits per spaxel is σ¯v/σ¯v = −0.29 ± 0.18 for the H α flux-weighted mean velocity dispersion.The SAMI Galaxy Survey is supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013, the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and other participating institutions. BJB acknowledges funding from New Zealand taxpayers via the Marsden Fund of the Royal Society of New Zealand. JBH is supported by an ARC Laureate Fellowship that funds JvdS and an ARC Federation Fellowship that funded the SAMI prototype. EDT acknowledges the support of the Australian Research Council (ARC) through grant DP160100723. JJB acknowledges support of an Australian Research Council Future Fellowship (FT180100231). CF acknowledges funding provided by the Australian Research Council (Discovery Projects DP170100603 and Future Fellowship FT180100495), and the Australia-Germany Joint Research Cooperation Scheme (UA-DAAD). BG is the recipient of an Australian Research Council Future Fellowship (FT140101202). Support for AMM is provided by NASA through Hubble Fellowship grant #HST-HF2-51377 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. MSO acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140100255). NS acknowledges support of a University of Sydney Postdoctoral Research Fellowship

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.Comment: 30 pages and 17 figures, accepted for publication in MNRAS. Abstract abridged for Arxiv. The key figures of the paper are: 3, 7, 8, and 1