The SAMI survey: evidence for dynamical coupling of ionized gas and young stellar populations Free

ABSTRACT

We explore local and global dynamical differences between the kinematics of ionized gas and stars in a sample of galaxies from Data Release 3 of the SAMI Galaxy Survey. We find better agreement between local (i.e. comparing on a spaxel-to-spaxel basis) velocities and dispersion of gas and stars in younger systems as with previous work on the asymmetric drift in galaxies, suggesting that the dynamics of stars and ionized gas are initially coupled. The intrinsic scatter around the velocity and dispersion relations increases with increasing stellar age and mass, suggesting that subsequent mechanisms, such as internal processes, divergent star formation, and assembly histories, also play a role in setting and altering the dynamics of galaxies. The global (flux-weighted) dynamical support of older galaxies is hotter than in younger systems. We find that the ionized gas in galaxies is almost always dynamically colder than the stars with a steeper velocity gradient. In absolute terms, the local difference in velocity dispersion is more pronounced than the local difference in velocity, possibly reflecting inherent differences in the impact of turbulence, inflow and/or feedback on gas compared to stars. We suggest how these findings may be taken into account when comparing high and low redshift galaxy samples to infer dynamical evolution.

1 INTRODUCTION

Galaxies are complex structures composed of both dark and baryonic matter. The interaction of baryons in galaxies is thought to be regulated by a number of physical processes (e.g. gravitational interactions/collisions, gas inflow, outflow, and star formation; see e.g. Schaye et al. 2015; Taylor & Kobayashi 2015; Beckmann et al. 2017; Conselice et al. 2022) and these processes may apply to or involve multiple distinct ‘phases’ of the baryons (i.e. stars, ionized gas, dust, molecular gas, e.g. Tumlinson, Peeples & Werk 2017). Studying galaxies through observations of multiple baryonic phases allows for a more holistic picture and understanding of the physical mechanisms that have shaped galaxies throughout cosmic time.

Stars within galaxies are a compendium of successive generations. Some stars form within a galaxy, whereas others are accreted from neighbouring or merged systems (e.g. Ibata, Gilmore & Irwin 1994; Bell et al. 2008; D’Souza & Bell 2018; Helmi et al. 2018; Boecker et al. 2020; Casanueva et al. 2022; Remus & Forbes 2022). Over time, stellar orbits mix and evolve through interaction with external systems and secular star-to-star interactions. Enriched gas from previous stellar generations or externally accreted ‘pristine’ gas can dissipate and form new stars usually by (re)forming a disc, thereby rejuvenating the system and changing its dynamics (e.g. Dekel et al. 2009; Wright et al. 2021). Combined studies of ionized gas and stellar kinematics have shown that the gas is usually kinematically colder and faster rotating than the stars (e.g. Pizzella et al. 2004).

Direct studies of the dynamical evolution of galaxies are limited by the relative brightness of the different phases. While the ionized gas can be detected out to higher redshifts (z = 0.7−2.5, e.g. Stott et al. 2016; Förster Schreiber et al. 2018; Wisnioski et al. 2019; Tiley et al. 2021), measuring spectra of the faint stellar continuum in galaxies is challenging/prohibitively observationally costly beyond z ∼ 1 (e.g. van der Wel & van der Marel 2008; Belli, Newman & Ellis 2015; Mendel et al. 2015; Belli, Newman & Ellis 2017; Newman et al. 2018; Belli, Newman & Ellis 2019; Mendel et al. 2020). Furthermore, the most massive galaxies in the present day universe are quiescent. Since massive star-forming galaxies at high redshifts are often the progenitors of quiescent local galaxies (e.g. Guglielmo et al. 2015), understanding how coupled or otherwise the ionized gas is with the stars initially and over time is important for inferring the dynamical evolution of consistent galaxy populations across cosmic time (also see Straatman et al. 2022, for a study comparing dynamical mass estimates based on ionized gas and stars). It is thus desirable to further explore the validity of comparing ionized gas dynamics at high redshift with local stellar dynamics.

Recent findings tentatively suggest that the dynamical properties of stars of different ages in nearby galaxies may mirror that of the ionized gas across cosmic time. Using Schwarzschild orbital modelling (e.g. Schwarzschild 1979; van den Bosch et al. 2008; Zhu et al. 2020; Thater et al. 2022) of the lenticular galaxy NGC 3115, Poci et al. (2019) found that the stellar velocity dispersion as a function of population age steadily increases, mirroring that of the ionized gas in populations of galaxies across a broad range of redshifts (Kassin et al. 2012; Wisnioski et al. 2015; Übler et al. 2019). Older stars, like the ionized gas in high redshift galaxies, exhibit hotter dynamics (i.e. more turbulence or random motions) than their younger/local counterparts. This is surprising given the vastly different methods and samples used and does not imply the two are linked and much less causal. Indeed, this initially compelling picture has since been confounded by increased scatter when adding more systems into the local dynamically modelled sample (Poci et al. 2021), suggesting that other explanations and evolutionary mechanisms need to be considered.

The ‘asymmetric drift’ observed in the Milky Way (e.g. Golubov et al. 2013), M31 (e.g. Quirk et al. 2019), MaNGA (Shetty et al. 2020), and DiskMass (Martinsson et al. 2013) galaxies demonstrates that older stars tend to be on hotter orbits than young stars in present day systems. This is consistent with expectations from large hydrodynamical simulations (e.g. Quirk & Patel 2020) and simulations of the Milky Way (Bird et al. 2013). In the latter, the dynamics of Milky Way stars are shown to reflect the local conditions at the time of their formation. This tendency of older stars being on hotter orbits is consistent with the findings that older galaxies have larger intrinsic flattening (i.e. indicative of globally hotter orbits) than their younger counterparts (van de Sande et al. 2018).

While the mirroring of stellar and gas dynamics across cosmic time may reflect the conditions of the interstellar medium (ISM) at that redshift it could also be a by-product of longer opportunities for internal processes of dynamical heating (e.g. stellar migration, radial mixing, etc). For example, Okalidis et al. (2022) and Hayden et al. (2020) suggest, using simulations and observations respectively, that stellar migration triggered by interactions with non-axisymmetric structures like bars explain aspects of the distribution of ages, metallicities, and orbits of stars in the Solar neighborhood.

Older systems also have more time for externally triggered heating processes such as mergers and interactions to build up over time. Indeed, Minchev, Chiappini & Martig (2013) suggest that both stellar migration and interactions may play a role. Mackereth et al. (2018) find that a significant episode of past interaction is required to produce the bimodal α-element distribution observed in e.g. the Milky Way (Hayden et al. 2015) and other galaxies (e.g. Scott et al. 2021). In other words, a hybrid explanation where a mix of both initial conditions and latent dynamical heating may need to be invoked. Indeed, in eight Local Group galaxies, Leaman et al. (2017) find that the stellar velocity dispersion of local galaxies is largely set by the conditions at the redshift of formation with departure from this being dependent on stellar mass.

There is a myriad of observational evidence supporting the idea that the dynamics of gas and stars are initially coupled. Integral field spectroscopic surveys in particular have made it clear that there are dynamical and chemical evidence for a co-evolution of stars and gas. Using the MaNGA galaxy survey, Shetty et al. (2020) showed that the asymmetric drift exhibits more variations in stellar populations older than 1.5 Gyr compared to younger populations. In parallel, and also in MaNGA galaxies, Greener et al. (2022) find evidence for chemical co-evolution of the ionized gas and stars. Barat et al. (2019) compared rotational and central dispersion of gas and stars in the Sydney-AAO Multi-Integral field (SAMI) Galaxy Survey and find a steeper slope for the S0.5 − log₁₀M_* relationship in stars compared to gas kinematics. More recently, Oh et al. (2022) showed that the velocity dispersion of the ionized gas tends to be lower than that of the stars in SAMI galaxies, though caution that beam smearing may be playing a role in artificially enhancing differences. As the precursor to stars, cold gas kinematics can also be compared to stellar kinematics. Indeed, Quirk et al. (2019) find that the |${\rm H}{\rm \small I}$| and CO gas rotates slower when compared with the main sequence stars (∼7.5 Gyr) in the Andromeda galaxy.

In this work, we use SAMI data to explore how the dynamics of the ionized gas and stars compare locally within galaxies. We explore whether measured local and global dynamical differences are consistent with current thinking on the origin of asymmetric drift and the role of ‘latent’ dynamical heating and/or the ISM conditions at the time of formation.

The paper is divided as follows. Section 2 presents the data, whereas our sample selection can be found in Section 3. Section 4 details our data analysis and results. A discussion and our conclusions can be found in Section 5 and Section 6, respectively.

Throughout this paper, we assume a ΛCDM cosmology with Ω_m = 0.3, Ω_λ = 0.7 and H₀ = 70 km s⁻¹ Mpc⁻¹, and a Chabrier (2003) initial mass function.

2 DATA

This work uses spatially resolved spectroscopy and ancillary data from the SAMI Galaxy Survey (Croom et al. 2021, henceforth C21). Spectral cubes were observed with SAMI (Croom et al. 2012): a multi-object integral field instrument connected to the AAOmega spectrograph (Sharp et al. 2006) at the Anglo-Australian Telescope. SAMI had 13 hexabundles and 26 individual sky fibres deployable over a 1° field-of-view. Each hexabundle was a tightly packed bundle of 61 optical fibres over a 15 arcsec diameter with a 73 per cent fill-factor. The SAMI Galaxy Survey observed ∼3000 unique galaxies in the nearby universe (i.e. redshifts 0.004 < z < 0.095). The selection of the Galaxy And Mass Assembly (GAMA, Driver et al. 2011) and clusters samples are detailed in Bryant et al. (2015) and Owers et al. (2017), respectively. The data used in this work are part of the latest and final public SAMI Galaxy Survey data release (SAMI DR3, C21). Thus, all data used in this work are available through this public data release.

SAMI data are reduced using the 2dfdr pipeline, which performs the usual reduction steps: bias subtraction, wavelength calibration using CuAr arc frames and sky subtraction. Spectral extraction and reduction are detailed in Sharp & Birchall (2010) and Hopkins et al. (2013), respectively. Flux calibration and telluric absorption corrections are performed using standard stars. Each field is nominally observed with a 7-dither (minimum 6) pattern to deal with gaps between fibres within the hexabundle. Data are then combined and mapped onto a grid with flux and covariance carefully propagated as described in Sharp et al. (2015). Further detail on the data reduction can be found in Allen et al. (2015) and Sharp et al. (2015), with the latest improvements described in C21.

Production of the stellar kinematic maps is described in van de Sande et al. (2017b) with updates outlined in C21. Briefly, the line-of-sight velocity distribution is parametrized as a Gaussian using the penalized pixel-fitting (pPXF; Cappellari & Emsellem 2004; Cappellari 2017) algorithm. A selection of templates are broadened and shifted through convolution to determine the most likely recession velocity (V_rec) and velocity dispersion (σ) for each spaxel. This is done in two steps as described in van de Sande et al. (2017b) to mitigate uncertainties associated with possible template mismatches, especially in the lower signal-to-noise spectra. The stellar kinematic position angles PA_{kin, stars} is determined using the fit_kinematic_pa code, which is based on the method described in Krajnović et al. (2006, their Appendix C).

Gas kinematics and Hα emission line flux maps are measured using lzifu (Ho et al. 2016) as described in Green et al. (2018), Scott et al. (2018) and C21. Briefly and as described in Owers et al. (2019), the continuum is first fitted to Voronoi-binned spaxels using single stellar population (SSP) templates from Vazdekis et al. (2010) and González Delgado et al. (2005). This continuum is subtracted from individual spaxels to leave ‘pure’ emission line spectra, which are then fitted using lzifu. Although lzifu fits up to three independent velocity components, we only use the one-component fit in this work. As for the stars, the kinematic position angle of the ionized gas (PA_{kin, gas}) is determined using the fit_kinematic_pa code following Krajnović et al. (2006, their Appendix C).

Global star formation rate (SFR) estimates are measured on elliptical 1R_e aperture spectra using the extinction-corrected Hα flux and use the Kennicutt, Tamblyn & Congdon (1994) corrected to a Chabrier (2003) stellar initial mass function (Medling et al. 2018, C21, for more detail).

The light-weighted stellar population ages and metallicities ([Z/H]) within 1R_e are derived as described in Vaughan et al. (2022). We give a brief summary here. First, the SAMI blue and red arm spectra are joined together and convolved with a Gaussian kernel (of variable width) such that the spectral resolution is a constant value at all wavelengths (following van de Sande et al. 2017a). We use pPXF (Cappellari & Emsellem 2004; Cappellari 2017) to fit the MILES SSP models of (Vazdekis et al. 2015) to the spectrum of each galaxy extracted within 1R_e. The templates range in metallicity from −2.21 dex to 0.4 dex, in age from 30 Myr to 14 Gyr and in [α/Fe] abundance from 0.0 to +0.4 dex. The templates use the isochrones from the ‘Bag of Stellar Tracks and Isochrone’ models (BaSTI; Pietrinferni et al. 2004, 2006). During the fit, we also include gas emission line templates corresponding to the Balmer series (Hα, Hβ, and Hγ) as well as the atomic species [|${\rm N}{\rm \small {II}}$|], [|${\rm O}{\rm \small {III}}$|], [|${\rm S}{\rm \small {II}}$|], and [|${\rm O}{\rm \small I}$|]. We use a multiplicative Legendre polynomial of the order of 10 to correct for small differences in the shape of the observed and template spectra. The final stellar population parameters for each spectrum are calculated following McDermid et al. (2015).

As described in D’Eugenio et al. (2021) and C21, photometric values such as effective radii, position angles and ellipticities are obtained by applying the Multi-Gaussian Expansion technique (MGE Emsellem, Monnet & Bacon 1994) on r-band Sloan Digital Sky Survey and Very Large Telescope Survey Telescope images.

Stellar masses are estimated following equation (8) of Taylor et al. (2011) using i-band absolute magnitudes and (g − i) colours.

As described in (Cortese et al. 2016), morphologies for all SAMI galaxies are based on careful compilation of visual classifications from multiple team members. A small fraction have uncertain visual morphological types (∼5 per cent).

3 SAMPLE SELECTION

Our target selection stringently keeps only those galaxies with the highest quality gas and stellar kinematic maps. Stellar kinematic map spaxels with V_{stars, error} > 30 km s⁻¹, σ_{stars, error} > 0.1σ + 25 km s⁻¹ and signal-to-noise ratio <3 are discarded. Gas kinematic maps spaxels with F_Hα/F_{Hα, error} < 5 are discarded. We then only keep those galaxies where both the ionized gas and stellar kinematic maps have a minimum 85 per cent fill factor within an elliptical aperture with circularized radius corresponding to 1R_e. As a result, all galaxies from the full SAMI sample of 3068 galaxies without significant and spatially extended emission lines are effectively removed. We select galaxies with log₁₀(M_*/M_Sun) > 9.5 and require that the full width at half maximum (FWHM) seeing value for the cube not exceed half the effective radius (i.e. R_e > 2 × FWHM).¹

The above selection leaves 188 unique galaxies and an additional 22 duplicate observations. For targets with duplicates, we select the instance with the better seeing. Fig. 1 shows how the distributions in log₁₀(M_*/M_Sun), R_e, specific SFR (sSFR, i.e. the SFR per unit mass), and visual morphology of galaxies selected for this work compare with the full SAMI sample. Our requirement for high spatial resolution necessarily biases the sample towards high R_e values and the requirement for reliable gas and stellar kinematics biases the sample towards star forming and intermediate to later galaxy types (eSp). Fig. 2 shows the SFR versus stellar mass of SAMI galaxies. We note that the full SAMI sample is not a volume limited survey and that it includes many small and low-mass filler targets that do not pass our selection criteria. The star-forming main sequence line is fit to the distribution of galaxies within the full SAMI sample with available SFRs as in Fraser-McKelvie et al. (2021), using the functional form introduced by Leslie et al. (2020). Over the stellar mass range 8 < log₁₀(M_*/M_Sun) < 11.5, the best-fitting main sequence line was found to be:

While the bulk of our selected galaxies lie along the star-forming main sequence, there are a considerable number that scatter to lower SFRs (Fig. 2), consistent with at least some galaxies in our sample containing gas ionized by other ionizing mechanisms than young massive hot stars.

4 ANALYSIS AND RESULTS

Example gas and stellar velocity (V), dispersion (σ), and (V/σ) maps for SAMI106717, a typical galaxy in our final sample, are shown in Fig. 3. For this galaxy, and as we will show below, for most galaxies in our sample, the ionized gas is visibly dynamically colder than the stars (i.e. (V/σ)_gas > (V/σ)_stars).

We measure and subtract the systemic velocity (V_sys) for the gas and stars separately by performing a flux weighted mean of the velocity map within 1.5 arcsec (3 × 3 pixels).

To quantify the local dynamical differences between the ionized gas and stars, we define two parameters. We purposely avoid the central regions to ensure that differences are not driven by beam smearing, whose effect is most pronounced in the centre. First, we take the mean absolute velocity difference between gas and stars |$\left\langle \Delta V\right\rangle _{R_e}$| within an elliptical annulus of 0.9 − 1R_e around the SAMI cube centre, as:

where V_{gas, i} and V_{stars, i} are the V_sys-subtracted recession velocity measured in the i^th spaxel for the ionized gas and the stellar kinematic maps, respectively. R_i is the circularized radius of the i^th pixel.

Similarly, the mean difference in velocity dispersion between the ionized gas and the stars, |$\left\langle \Delta \sigma \right\rangle _{R_e}$|⁠, is computed as follows:

where σ_{gas, i} and σ_{stars, i} is the velocity dispersion measured in the i^th spaxel for the ionized gas and the stellar kinematic maps, respectively.

To study the global nature (rotation vs pressure) of the dynamical support, we compute (V/σ) for the ionized gas and stars within an elliptical 1R_e aperture following Cappellari et al. (2007):

where X stands for either ‘gas’ or ‘stars’. F_i is the mean continuum or Hα flux for the stars or gas, respectively, in the i^th spaxel. We compute Δ(V/σ), the simple arithmetic difference between the ionized gas and stars (V/σ), as follows:

When relevant, we use the Spearman correlation coefficient (ρ) and associated p-value to determine the significance of trends. We consider values of 0.01 ≤ p ≤ 0.05 as weak trends, with p < 0.01 being the threshold to call a trend statistically significant.

Throughout, we refer to <ΔV > and <Δσ > as measures of the local difference between the dynamics of the ionized gas and stars; and to Δ(V/σ) as a measure of global dynamical differences. In all cases, the uncertainties are propagated by neglecting covariance which is unavailable for the velocity and dispersion maps. We check the impact of ignoring covariance on our conclusions by repeating the analysis using every other spaxel. This effectively ensures that none of the spaxels used in the calculation are correlated (i.e. no covariance). This exercise shows only minimal absolute differences of order ≲ 1 km s⁻¹ in the binned means and standard deviations of |$\left\langle \Delta V\right\rangle _{R_e}$| and |$\left\langle \Delta \sigma \right\rangle _{R_e}$| compared to those listed in Table 1 and thus ignoring covariance does not affect our conclusions.

The local and global kinematic differences within 1R_e with respect to age and stellar mass are shown in Fig. 4. The amplitude of local differences between the velocity maps (⁠|$\left\langle \Delta V\right\rangle _{\rm R_e}$|⁠) is less scattered in younger stellar populations (see Table 1). Younger systems (∼1 Gyr) have a mean |$\left\langle \Delta V\right\rangle _{R_e} \sim 21$| km s⁻¹, while older (∼9 Gyr) systems have mean |$\left\langle \Delta V\right\rangle _{R_e} \sim 43$| km s⁻¹. The scatter away from this trend increases with stellar age with the standard deviation tripling from 11 km s⁻¹ for ages ∼1 Gyr to 34 km s⁻¹ for ages ∼9 Gyr. Similarly, low mass systems (log₁₀(M_*/M_Sun) ∼ 9.9) have a mean |$\left\langle \Delta V\right\rangle _{R_e} \sim 23$| km s⁻¹, whereas high mass (log₁₀(M_*/M_Sun) ∼ 11.0) systems have mean |$\left\langle \Delta V\right\rangle _{R_e} = 39$| km s⁻¹. The scatter away from this trend more than doubles over the range of stellar masses probed [i.e. the standard deviation is 16 km s⁻¹ for log₁₀(M_*/M_Sun) ∼ 9.9 and 37 km s⁻¹ for log₁₀(M_*/M_Sun) ∼ 11.0].

The lower panels of Fig. 4 indicate that the dynamics of the ionized gas is usually colder than the stars in the majority of systems (i.e. all but a handful of targets lie above the dashed line). This is also shown in Fig. 6, which exhibits a steep correlation between |$(V/\sigma)_{\rm stars, R_e}$| and |$(V/\sigma)_{\rm gas, R_e}$|⁠, where the bulk of the data points lie above the one-to-one line. The trend is also steeper than the one-to-one, with older and/or lower SFR and/or higher [|${\rm N}{\rm \small {II}}$|]/Hα galaxies having lower and more similar |$(V/\sigma)_{\rm gas, R_e}$| and |$(V/\sigma)_{\rm stars, R_e}$|⁠. The latter suggests a different mechanism (other than star-formation) is responsible for ionizing the gas in those galaxies.

As per recent SAMI work by Ristea et al. (2022) and similarly to other studies (e.g. Davis et al. 2011; Lagos et al. 2015; Bryant et al. 2019; Casanueva et al. 2022), we define misaligned galaxies as those where the kinematic position angle of the gas and stars differ by more than 30 degrees (i.e. |PA_{kin, gas} − PA_{kin, stars}| > 30). We find a total of 10 misaligned galaxies amongst the 188 galaxies in our final sample (namely, SAMI IDs 202 399, 208 652, 238 922, 279 905, 321 059, 39 057, 517 278, 536 994, 560 238, and 618 993).

Much of the increase in the scatter in |$\left\langle \Delta V\right\rangle _{R_e}$| as a function of mass and age corresponds to a higher proportion of kinematically misaligned galaxies (yellow outlined symbols in Fig. 4). In other words, higher scatter tends to correspond to more massive and older galaxies, with all misaligned galaxies having flux weighted stellar ages >3 Gyr.

Fig. 5 shows how the local dispersion differs more than the local velocity in most galaxies (except for kinematically misaligned systems) and that this does not seem to be linked with stellar population age or SFR, but possibly weakly with stellar mass.

Following Varidel et al. (2016) and Oh et al. (2022), we compute the projected mean velocity gradient within an elliptical 1R_e aperture for both the ionized gas and stellar kinematics as follows:

where W_pix is the pixel scale (i.e. 0.5 arcsec/pix for SAMI) and |$N_{\rm pix, R_e}$| is the number of pixels within 1R_e. |$\nabla V_{\rm gas, R_e}$| and |$\nabla V_{\rm stars, R_e}$| reflect the spatial change within the effective radius of the velocity of galaxies. The velocity gradient has been used as a proxy for the impact of beam smearing on gas and stellar velocity dispersion. In Appendix  A, we discuss how our results are robust against the impact of beam smearing and that trends with ∇V at least partly reflect real physical processes.

Fig. 7 shows that the projected gas and stars velocity gradients best agree in low mass and younger systems. Conversely, in high mass and older systems, the gas velocity gradient is on average steeper than the stellar velocity gradient.

5 DISCUSSION

The main driver for this work is to better understand how gas and stars in galaxies correlate, and test whether one may safely assume that the two phases are coupled, at least initially, when forming stars. In other words, can we infer dynamical evolution of galaxies using high and low redshift surveys by comparing the dynamics of ionized gas in massive star forming galaxies to their local often quiescent (and without ionized gas) descendants? In what follows, we explore how this may be done along with stating significant caveats to this approach.

Despite our simplistic approach, our results are relevant to a number of topics in the literature, including asymmetric drift, dynamical heating, evolution of ISM dynamics, as well as our main goal, which is to test possible methodologies for quantifying the dynamical evolution of galaxies. As such, our results are relevant to both local group and high redshift studies. This discussion focuses on contrasting a selection of relevant previous work from the high and low redshift literature as well as local group studies to contextualize our findings.

As a first caveat, in galaxies with a complex mix of stellar populations, the stellar dynamics do not just represent those of the youngest stars. Hence, we do not necessarily expect an exact one-to-one correspondence between the ionized gas and stars in any of our systems. This is because on the one hand, newborn stars ionize and heat up the gas. On the other hand, the physics of gas and stars fundamentally differ, with gas being collisional and stars being collisionless, which affects the heating mechanisms of the two dynamical tracers. Furthermore, the ionizing source of the gas can vary between systems and even spatially across a single galaxy. Ionizing sources can include hot young and/or evolved stars, low-ionization nuclear emission-line region (LINER), shocks and active galactic nuclei (AGN).

Using simulations of the Milky Way, Bird et al. (2013) show that the dynamics of the stars could reflect the ISM conditions of their surroundings at the time of their formation. Assuming massive galaxies are more dynamically ‘evolved’, the trends we see in the mean |$\left\langle \Delta V \right\rangle _{R_e}$| and |$\left\langle \Delta \sigma \right\rangle _{R_e}$| values (i.e. large purple symbols in Fig. 4) are consistent with a scenario in which gas and stars are initially partly coupled or in which heating mechanisms follow an overall trend across cosmic time. In other words, in young and low mass galaxies, the dynamics of the gas and stars could be more similar if the gas from which stars form is being ionized by (possibly those same) recently formed stars with possible subsequent heating enhancing differences over time. This suggests that the stars may continue to carry some of the kinematic information of the gas from which they formed and subsequently ionized. It is noteworthy that Poci et al. (2019, for a single galaxy) found an offset of about ∼10 km s⁻¹ in the stellar dispersion compared to the ionized gas data from Wisnioski et al. (2015) at the same epoch, whereas Poci et al. (2021) found significant scatter in this offset when comparing with different systems.

In almost all cases, galaxies show colder gas dynamics than the stars, with the difference being more pronounced in high mass and older systems. Our finding that galaxies with younger stellar populations (or higher star formation rate, see Fig. 6) have more pronounced differences in dynamical support, with a steeper than one-to-one relationship between dynamical support of the ionized gas and stars, is consistent with a scenario wherein galaxy evolution becomes increasingly driven by dry mergers just as the global star formation drastically reduces (e.g. Madau & Dickinson 2014), thereby heating the stars through orbital mixing.

The fact that the most massive and older galaxies tend to scatter more than their lower mass and younger counterparts (top panel of Fig. 4 and bottom panel of Fig. 5) is consistent with the findings of Leaman et al. (2017) and with scenarios in which stars experience varying degrees of dynamical ‘latent’ heating (i.e. dynamical heating after formation) depending on the galaxies’ individual merging and formation histories. This interpretation that dynamical heating could be the cause of the increased scatter is corroborated by the fact that the most highly scattered points tend to be those with misaligned gas and stars kinematics, which is primarily associated with external gas accretion processes as recently argued by e.g. Ristea et al. (2022).

An important caveat and consideration is that we use luminosity-weighted ages of mixed stellar populations, which are inherently uncertain and may overrepresent the brighter younger populations as the mass-to-light ratio plateaus at older ages (e.g. Bruzual & Charlot 2003), hence leading to a higher scatter in age. It may be possible to compare the local dynamical differences between gas and various stellar populations using orbital dynamical modelling for a subset of the galaxies in our sample (e.g. Poci et al. 2019; Santucci et al. 2022) and test the impact of using light-weighted ages on our conclusions; however, this is beyond the scope of this work.

Comparing the dynamics of the ionized gas and stars locally (i.e. on a spaxel-by-spaxel basis) ensures that radial differences in asymmetric drift are implicitly accounted for (Shetty et al. 2020). The increased scatter in |$\left\langle \Delta V\right\rangle _{\rm R_e}$| at older ages is consistent with the MaNGA results of Shetty et al. (2020) that the asymmetric drift of stellar populations beyond 1.5 Gyr varies greatly, while the asymmetric drift of stellar populations younger than 1.5 Gyr is fairly stable with radius at or above 0 km s⁻¹. This is again consistent with and likely reflects a broad range of possible galaxy formation and evolutionary pathways that diverge slowly over time.

The measured local offsets (median|$\left\langle \Delta V\right\rangle _{\rm R_e}=+20\pm 1$| km s⁻¹ and median |$\left\langle \Delta \sigma \right\rangle _{\rm R_e}=-40\pm 1$| km s⁻¹) are of comparable order of magnitude to those found in Quirk et al. (2019) for different gas phases in Andromeda. Namely, Quirk et al. (2019) find that the |${\rm H}{\rm \small I}$| and CO gas rotates faster by up to 18 km s⁻¹ than the younger asymptotic giant branch stars (∼2 Gyr). The difference increases to 63 and 37 km s⁻¹ for |${\rm H}{\rm \small I}$| and CO gas, respectively, when compared with red giant branch stars (∼4 Gyr).

The finding that the dispersion of the ionised gas and stars (median |$\left\langle \Delta \sigma \right\rangle _{\rm R_e}$| is −40 km s⁻¹) differs more (in absolute terms) than the velocities (median |$\left\langle \Delta V\right\rangle _{\rm R_e}$| is +20 km s⁻¹; see Figs 4 and 5) may be a result of inherent differences in the collisional properties of gas and stars and the impact of inflow and/or non star-forming feedback activity mainly on gas dynamics/heating (as described in e.g. Wellons et al. 2020). Part of this difference may also be explained by beam smearing effects (see Appendix  A).

Also using SAMI data, Oh et al. (2022) found that gas emission not directly associated with star-formation (e.g. AGN/LINER) is associated with higher gas dispersion. This finding could explain why, in galaxies with older stellar populations (i.e. those more likely to harbour AGN or LINER-like emission at the expense of star formation), the measured (V/σ)_gas is lower (see lower panel of Fig. 4). If the reason that the ionized gas dynamics are hotter in galaxies with older stellar populations than younger ones is because the gas is, on average, ionized by different sources, they should also exhibit higher [|${\rm N}{\rm \small {II}}$|]/Hα, which is confirmed in the bottom panel of Fig. 6. The higher gas dispersion of the non star-forming gas could be associated with outflows in the case of AGN and shocks, but could also be associated with the diffuse ionized gas, that shows LINER-like emission and has both a higher dispersion and slower velocity structure when extra-planar (see eg, Levy et al. 2019; Belfiore et al. 2022; Micheva et al. 2022).

Another important dimension of this work is the observed difference in the velocity gradients (∇V) of the ionized gas and stars. Our results indicate that in most galaxies, the stellar velocity gradient is steeper than the gas velocity gradient in those systems. This is true at nearly all masses, except in high mass galaxies where it is the ionized gas gradient that is steeper. An important caveat of using ∇V as defined here is that the ‘physical’ pixel scale (i.e. kpc per pixel) changes with redshift and hence across the sample and this is not directly taken into account. While ∇V_gas has been used as a beam smearing proxy (Varidel et al. 2016; Oh et al. 2022), Fig. 7 shows that there are statistically significant differences in the gradient of gas and stars that also reflect differences in the dynamics of gas and stars with stellar age and stellar mass discussed previously.

In Appendix  A, we investigate the possible impact of beam smearing on our results using two separate proxies. Oh et al. (2022) suggested that gas velocity dispersion in SAMI is more impacted by beam smearing than stellar dispersion owing to steep gas velocity gradients using a different sample selection and Δσ parameter definition. In the appendix, we argue that our stringent selection for the best resolved targets and different parameter definitions mitigate the impact of beam smearing and discuss that some of the trends seen with various beam smearing proxies are at least partly attributed to the real physical effects discussed.

As a sanity check, we also investigate possible trends with the uncertainties in the kinematics scale with age or stellar mass (not shown). As expected, we do find a shallow trend with stellar mass, but this trend does not explain the magnitude of the scatter shown in e.g. Fig. 4, and there is no trend of uncertainty with age.

6 SUMMARY AND CONCLUSIONS

In this work, we explore the difference in local and global dynamics of the ionized gas and stars. We select a sample of 188 SAMI galaxies with high quality kinematic maps and optimal spatial resolution. Local differences (<ΔV > and <Δσ >) are computed for a 1R_e elliptical annulus on a spaxel-by-spaxel basis, whereas global differences (Δ(V/σ)) are computed using flux weighting within a 1R_e aperture.

We find that our findings are broadly consistent with observational and theoretical expectations from asymmetric drift (e.g. Quirk & Patel 2020) and detailed modelling of the stellar dynamics and populations in local galaxies (e.g. Poci et al. 2019, 2021).

Our main conclusions are summarized as follows:

• The local dynamics of the ionized gas better mimic those of the stars in galaxies with younger light-weighted stellar population ages or lower mass than in older and higher mass systems. This is consistent with the dynamics of ionized gas and stars being initially coupled (Figs 4, 5).

• The two- to three-fold increase in scatter in local dynamical differences between gas and stars and the increased divergence of the gas and stellar velocity gradients with stellar mass and age are consistent with dynamical heating playing a key role and resulting from a broad range of formation and assembly histories among galaxies (Figs 4, 7).

• The global dynamics of the ionized gas are typically colder than those of the stars in the star-formation dominated galaxies. In galaxies with AGN/LINER-like emission, the global dynamics are more similar (Figs 4, 6).

• Older galaxies have hotter stellar and ionized gas dynamics on average than younger galaxies, and we argue that this difference is consistent with being driven by different merging and/or accretion histories and/or ionizing mechanisms (Fig. 6).

• In absolute terms, the median local difference in the velocity dispersion of the ionized gas and stars is greater than the difference in velocity regardless of stellar age (i.e. |$|\left\langle \Delta \sigma \right\rangle _{\rm R_e, median}| -|\left\langle \Delta V\right\rangle _{\rm R_e, median}| = 20$| km s⁻¹; Fig. 5). This may reflect inherent differences in the collisional properties of gas and stars or the impact of turbulence, inflow and/or non star-forming feedback on gas dynamics.

More generally, while younger stellar populations may initially better reflect the dynamical properties of the gas from which they formed, our findings suggest there would be inherent risks associated with comparing ionized gas kinematics at high redshifts with the stellar dynamics of local systems to infer the dynamical evolution of (now) quiescent galaxies. Considering sufficiently large samples and limiting the investigation to star-forming ionized gas can help mitigate the inherent scatter and confounding factors identified in this work.

ACKNOWLEDGEMENTS

The authors thank S. L. Martell for helpful comments and discussions.

CF is the recipient of an Australian Research Council Future Fellowship (project number FT210100168) funded by the Australian Government. MSO acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140100255). SKY acknowledges support from the Korean National Research Foundation (2020R1A2C3003769, 2022R1A6A1A03053472). JvdS acknowledges support of an Australian Research Council Discovery Early Career Research Award (project number DE200100461) funded by the Australian Government. SMS acknowledges funding from the Australian Research Council (DE220100003). SB acknowledges funding support from the Australian Research Council through a Future Fellowship (FT140101166). JJB acknowledges support of an Australian Research Council Future Fellowship (FT180100231). FDE acknowledges funding through the ERC Advanced grant 695671 ‘QUENCH’, the H2020 ERC Consolidator Grant 683184 and support by the Science and Technology Facilities Council (STFC).

Part of this research was conducted by the Australian Research Council Centre of Excellence for All Sky Astrophysics in three Dimensions (ASTRO 3D), through project number CE170100013.

The SAMI Galaxy Survey is based on observations made at the Anglo-Australian Telescope. The Sydney-AAO Multi-object Integral field spectrograph (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 Sloan Digital Sky Survey, the GAMA Survey and the VST ATLAS Survey. The SAMI Galaxy Survey website is http://sami-survey.org/. The SAMI Galaxy Survey is supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in three 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.

Based on data acquired at the Anglo-Australian Telescope under programs A/2013B/012 and A/2016B/16. The authors acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay their respects to elders past and present.

GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/.

This work makes use of colour scales chosen from van der Velden (2020).

DATA AVAILABILITY

All SAMI DR3 data (Croom et al. 2021) used in this work are publicly available through Data Central (datacentral.org.au). SAMI DR3 data tables used in this work are: EmissionLine1CompDR3, MGEPhotomUnregDR3, InputCatGAMADR3, InputCatClustersDR3, and VisualMorphologyDR3. The Hα flux and signal-to-noise maps are based on ‘SAMI DR3 1-component line emission map: Hα’. The [|${\rm N}{\rm \small {II}}$|] flux maps are taken from ‘SAMI DR3 1-component line emission map: [NII](6583Å)’. Kinematic maps used for the ionized gas are the ‘SAMI DR3 1-component ionized gas velocity map’ and ‘SAMI DR3 1-component ionized gas dispersion map’. Stellar kinematic maps used are ‘SAMI DR3 Stellar Velocity map (two moments) default cube’ and ‘SAMI DR3 Stellar Velocity dispersion map (2 moments) default cube’. Stellar ages were contributed by co-author SV based on the publicly available SAMI DR3 spectra as described in Section 2.

Footnotes

REFERENCES

APPENDIX A: BEAM SMEARING

In this section, we explore the possible impact of beam smearing (or atmospheric seeing) on our results and conclusions. We investigate two separate proxies for the impact of beam smearing.

First, we look for possible trends between the spatial resolution defined as the ratio of the size of the galaxy and the atmospheric seeing (R_e/HWHM). In Fig. A1, we find a negative trend between |$\left\langle \Delta \sigma \right\rangle _{R_e}$| and the spatial resolution (R_e/HWHM) and a weak trend with (⁠|$\Delta (V/\sigma)_{R_e}/(V/\sigma)_{\rm stars, R_e}$|⁠). Importantly, these trends go contrary to what would be expected should beam smearing be the driver i.e. the dynamical differences between gas and stars are more pronounced in galaxies with higher and thus more favourable R_e/HWHM. We suggest that given our strict selection, these trends are instead dominated by real correlations between the physical size of galaxies (R_e) and dynamical differences between ionized gas and stars.

Finally, following Varidel et al. (2016) and Oh et al. (2022), we look for trends between the gas and stellar velocity gradients and our local and global kinematic measurements. Fig. A2 shows significant trends in |$\left\langle \Delta V\right\rangle _{R_e}$| with velocity gradients, which can be attributed to real differences between the stellar and ionized gas velocity gradients (see Fig. 7). The correlation between |$\left\langle \Delta \sigma \right\rangle _{R_e}$| and velocity gradients is somewhat weaker, and less pronounced than those found in Oh et al. (2022) for the dispersion ratio. This apparent discrepancy can be attributed to our markedly different choice of parameter definition and our more stringent sample selection. Given the results of previous tests in this Appendix, we instead attribute the bulk of the (admittedly sometimes weak) trends between dynamical differences and velocity gradients to real physical differences in the dynamics of the gas and stars already discussed in this work.