The Astrophysical Journal, 893:26 (13pp), 2020 April 10 https://doi.org/10.3847/1538-4357/ab77ae © 2020. The American Astronomical Society. All rights reserved.

Spinning Bar and a -formation Inefficient Repertoire: Turbulence in NGC 7674

Diane M. Salim1,2 , Katherine Alatalo3,4,5 , Christoph Federrath1,2 , Brent Groves1,2 , and Lisa J. Kewley1,2 1 Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia; [email protected] 2 ARC Centre Of Excellence For All Sky Astrophysics in 3D (ASTRO3D), Australia 3 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 4 The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA 5 Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA Received 2019 July 3; revised 2020 February 3; accepted 2020 February 9; published 2020 April 9

Abstract The physics regulating (SF) in Hickson Compact Groups (HCG) has thus far been difficult to describe, due to their unique kinematic properties. In this study, we expand upon previous works to devise a more physically meaningful SF relation able to better encompass the physics of these unique systems. We combine CO(1–0) data from the Combined Array from Research in Millimeter Astronomy to trace the column density of molecular gas Sgas and deep Hα imaging taken on the Southern Astrophysical Research Telescope tracing SSFR to investigate SF efficiency across face-on HCG, NGC 7674. We find a lack of universality in SF, with two distinct sequences present in the Sgas–SSFR plane; one for inside and one for outside the nucleus. We devise an SF relation based on the multi-freefall nature of gas and the critical density, which itself is dependent on the virial parameter avir, the ratio of turbulent to gravitational energy. We find that our modified SF relation fits the data and describes the physics of this system well with the introduction of a virial parameter of about 5–10 across the . This avir leads to an order-of-magnitude reduction in SFR compared to avir » 1 systems. Unified Astronomy Thesaurus concepts: Star formation (1569); Star forming regions (1565); AGN host (2017); Galaxies (573); Hickson compact group (729)

1. Introduction Many studies have also proposed and observed supersonic random motions to play a key factor in regulating SF Star formation (SF) is complicated. The entangled, mutually (Zuckerman & Evans 1974; Zuckerman & Palmer 1974; dependent relationship between gas and SF is complex and nonlinear. Locally, the coalescence of gas via the influence of Larson 1981; Solomon et al. 1987; Falgarone et al. 1992; Ossenkopf & Mac Low 2002; Heyer & Brunt 2004; Roman- turbulence and gravity serve to regulate SF within the coldest ) phase of the interstellar medium (ISM)(Ferrière 2001; Elme- Duval et al. 2011; Schneider et al. 2011 . The two-fold role of green & Scalo 2004; Mac Low & Klessen 2004; McKee & turbulence can be seemingly contradictory. Turbulence may Ostriker 2007; Padoan et al. 2014). While the underlying serve as an SF suppressor, because turbulent kinetic energy stabilizes molecular clouds on large scales to prevent global physics that determine the column density of SF (SSFR) is still not known exactly and has been under heated debate for more collapse. However, these SF suppression effects are juxtaposed than two decades, observations have shown that SF regulation against the potential for turbulence to enhance SF. Turbulence follows patterns. Previous relations have suggested parameter- is supersonic and, thus, may induce local compressions in ( izations of SF including the column density of available gas shocks Elmegreen & Scalo 2004; Mac Low & Klessen 2004; McKee & Ostriker 2007). This in turn produces filaments and, (Sgas; Schmidt 1959; Kennicutt 1998; Bigiel et al. 2008; Leroy et al. 2008; Daddi et al. 2010; Schruba et al. 2011; Kennicutt & therefore, dense cores at the intersections of filaments, thereby Evans 2012; Renaud et al. 2012), such as the Kennicutt (1998, generating the initial conditions for SF (Schneider et al. 2012). hereafter K98) SF power-law relation: There have been extensive studies investigating relations between turbulence, molecular gas, and SF in MW clouds 1.4 SµSSFR gas ()1 (Bigiel et al. 2008, 2011; Lada et al. 2008; Heiderman et al. 2010; Wu et al. 2010). The influence of turbulence on the SF and the Krumholz et al. (2012, hereafter KDM12) relation, process is most apparent in the observations of giant molecular which correlate SSFR to the ratio between the mean column gas clouds (GMCs) in the MW, which are widely accepted to be ( density and the mean freefall time tff ()r0 of the cloud, which is supersonically turbulent structures Larson 1981; Elmegreen & the timescale required for a medium with negligible pressure Scalo 2004; Mac Low & Klessen 2004; McKee & Ostri- support to collapse. By doing so, the self-gravity of the system ker 2007; Hennebelle & Falgarone 2012; Padoan et al. 2014). is taken into account: GMCs do not collapse freely to form themselves, because the rate of gas conversion over a cloud’s freefall time would Sgas()r0 result in a star formation rate (SFR) two orders of magnitude S=SFR ff ,2() ( tff ()r0 more than that observed in the MW Zuckerman & Evans 1974; Krumholz & Tan 2007). While this could be due to stellar where ff is the efficiency of the gas. Calibrating for feedback, simulations have failed to produce this scenario (MW) clouds and Local Group galaxies, this efficiency (Hopkins et al. 2012; Tasker et al. 2015; Howard et al. 2016). is ≈1%. The other viable explanation is that the additional energy from

1 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al. internal turbulence or magnetic fields regulate cloud dynamics HCGs have also been shown to exhibit SF suppression. to hinder the rapid conversion of gas into stars (Federrath & Alatalo et al. (2015a, hereafter A15) used the Combined Array Klessen 2012; Padoan et al. 2014). The force required to drive from Research in Millimeter Astronomy (CARMA) to observe the turbulence can originate from both within the cloud via and map CO(1–0) of galaxies belonging to 12 HCGs. The stellar feedback and outside the cloud from shear or sample studied was chosen to follow up a subset studied in ( ) interactions with neighboring clouds (Federrath et al. Lisenfeld et al. 2014 of CO-bright, warm H2-bright HCG 2016, 2017a). Theories of SF that incorporate turbulence have galaxies. The authors found that the global efficiencies of a also yielded good matches to observations for predictions of large portion of these galaxies are up to two orders of the initial mass function of stars (Padoan & Nordlund 2002; magnitude less than that predicted by the K98 SF power law Hennebelle & Chabrier 2011, 2013) as well as SFRs and and KDM12 single-freefall model for SF. efficiencies (Krumholz & McKee 2005; Hennebelle & In addition, HCG galaxies have been shown to exhibit signs of enhanced turbulence via strong shocks. Cluver et al. (2013) Chabrier 2011; Padoan & Nordlund 2011; Federrath & ʼ fi Klessen 2012, 2013; Federrath 2013a; Padoan et al. 2014; s study using the Spitzer Infrared Spectrograph nd that their Salim et al. 2015; Sharda et al. 2018). This in turn serves as an sample of HCG galaxies tend to exhibit warm hydrogen emission enhanced SF-powered photon-dominated regions, due indication that GMC evolution is dictated by gravity and to shocks caused by collisions with the clumpier inter-group turbulence, influenced by the environment of their host galaxy. medium. By using data from Cluver et al. (2013), A15 showed Furthermore, there have been extensive efforts to elucidate that the galaxies’ warm H luminosities are within a factor of what makes the Central Molecular Zone (CMZ), the central 2 fi three of the turbulent injected energy. The observed shocks and 500 pc of the MW, so inef cient in forming stars. Despite high high levels of turbulence may share the same origin, both being gas densities and large gas reservoirs, the CMZ exhibits SF that driven by the galaxy interactions. This additional turbulence is about an order-of-magnitude less active than expected could create an energy imbalance that leads to SF suppression. ( Johnstone et al. 2000; Longmore et al. 2013; Kruijssen et al. Similar systems with excess turbulence and inefficient SF have 2014), and many studies have suggested turbulence to play a been observed in the MW (Kauffmann et al. 2013), NGC 1266 central role in this supression (Rathborne et al. 2011, 2014; (Alatalo et al. 2015b), and 3C 326N (Guillard et al. 2015). Kruijssen et al. 2015; Federrath et al. 2016; Sormani et al. We study a face-on HCG from the sample in A15 with an 2018). Studying the effects of turbulence is therefore integral to inclination of 26°.7, namely NGC 7674. In this work, we isolate understanding galaxies’ evolutionary histories. a single HCG from A15 to investigate turbulent motions within Unfortunately, it is difficult to resolve turbulence on a galaxy using the spatially resolved CARMA CO(1−0) flux extragalactic scales and especially more so at high . maps from A15 and deep Hα imaging from the Southern To properly investigate the effects of turbulence, one would Astrophysical Research (SOAR) Telescope presented by require a resolution to at least the cloud scale of the order of 10 Eigenthaler et al. (2015). The molecular gas in NGC 7674 is pc. While studies of such scales are possible to investigate morphologically classified as a spiral and bar + ring, due to its regions of the MW (Rathborne et al. 2011, 2014; Federrath multiple components. It contains an et al. 2016) and in one exceptional case so far also for a high- (AGN), classified from its mid-IR spectrum (Cluver et al. 2013) redshift lensed galaxy (Sharda et al. 2018), this is beyond the and optical spectroscopy (Martínez et al. 2010). NGC 7674 is reach for many galaxies. Studies of turbulence and SF in interacting with its HCG companion HCG96c, exhibiting extragalactic systems therefore necessitate studying objects numerous tidal tails and stellar light bridging the two systems. with highly enhanced turbulent properties to maximize signal In Section 2, we describe our data and its preparations for to noise in order to compensate for lower spatial resolutions. analysis. In Section 3, we describe the direct results of the Many galaxies are found to exist within larger structures such observed data and the relationship between Sgas and SSFR in as groups or clusters, the members of which also significantly this galaxy. In Section 4, we describe our method of isolating impact neighboring galaxies’ intrinsic properties, thus serving the turbulent motions of the galaxies and present these results. as a great case study to investigate systems whose internal In Section 5, we use the inferred Sgas data to predict SSFR using turbulence is affected by a variety of physical processes. various SF relations, and compare them to the observed SSFR. To directly probe the enhanced turbulent features in unique In Section 6, we explore the implications of the SF models that best described the physics of NGC 7674. In section 7,we grouped galaxy environments, we explore the kinematics of a summarize our results. Hickson Compact Group (HCG) galaxy. Grouped galaxies like Throughout this study, we assume the Planck Collaboration HCGs make for ideal case studies to investigate the interactions et al. (2014) Hubble constant of 67.80 km s--11 Mpc . In all of between vigorous galaxy transition, SF quenching and the spatial images presented in this work, north is up and east turbulence, because we know that they are undergoing that is left. change. HCGs are defined as small groups of several (four or more) galaxies separated by projected distances of only a few tens of kiloparsecs (comparable to galaxy sizes) and relatively 2. Observations and Data Preparation isolated from other large structures (Hickson 1982, 1997; Bitsakis et al. 2014). HCGs are therefore some of the densest 2.1. Data Acquisition known galaxy systems. HCGs are similar in nature and To thoroughly probe the interplay between molecular gas, structure to the central regions of very dense galaxy clusters turbulence and SF in NGC 7674 on small scales, we need ( ) Hickson 1982, 1997 , exhibiting high densities but low spatially resolved images of both Sgas and SSFR, as well as intergalactic velocity dispersions (Hickson 1997). HCGs information regarding gas kinematics. For this reason, we appear to transform very rapidly from star-forming to quiescent (Johnson et al. 2007; Walker et al. 2010).

2 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al. choose to utilize interferometric CO(1–0) data taken with 2.3. Inferring SSFR CARMA6 from A15 and partner this with deep Hα imaging The column density of SF was traced by Hα+[N II] fluxes taken with the 4.1 m SOAR Telescope form Eigenthaler et al. ( ) measured by the SOAR telescope. Following Eigenthaler et al. 2015 . (2015), we apply two corrections to better isolate the flux CARMA was an interferometric array of 15 radio dishes fi α fl × × speci cally originating from H . First, the narrowband uxes each of dimensions of 6 10.4 m or 9 6.1 m located in the must be corrected for the contamination of the nitrogen ( ) Eastern Sierras in California Bock et al. 2006 . The galaxy was [N II]ll6548, 6583 lines. A Hα/(Hα+[N II]) ratio of ’ observed using CARMA s D-array with baselines between 11 0.74±0.13 was applied, which is based on integrated [N II]/ and 50 m. Alatalo et al. (2013) describes in detail the procedure Hα measurements of 58 galaxies from the SINGS survey to observe and reduce the data for NGC 7674. This method (Kennicutt et al. 2003, 2009). resulted in the spatially resolved moment and channel maps Second, we correct for dust extinction of Hα flux by taking analyzed in this study. We use the zeroth moment map of this advantage of the relation between Hα extinction and stellar galaxy to infer the distribution of Sgas across the galaxy, and we mass (Garn & Best 2010; Kashino et al. 2013). We use the use the channel map and the velocity spectrum within each Garn & Best (2010) relation that estimates the extinction in Hα, pixel to infer the kinematic properties of the galaxy. These AHa as: maps have an FWHM of 2 5 and a spatial scale of 0 4 per AXXX=+0.91 0.77 + 0.1123 - 0.09 ,() 4 pixel. Ha The SOAR Telescope is a 4.1 meter aperture telescope XMM=- where log10()*  10 with an uncertainty quoted as located on Cerro Pachón in Chile. The Goodman 0.28 mag. Eigenthaler et al. (2015) reports NGC 7674 to have a Spectograph was used in imaging mode to measure Hα + MM = α fl stellar mass of log10()*  11.34 0.14. The H ux [N II] narrowband fluxes, with a spatial scale of 0 15 per pixel, was then corrected for extinction using the Cardelli et al. with an average seeing of 0 8 during the night of observation. (1989) reddening curve. Due to the known presence of Hα- Full details of the data acquisition and reduction techniques enhancing shocks in this galaxy, the Hα measurements are an ( ) used for these data are available in Eigenthaler et al. 2015 . upper limit. However, we confirm that this method adequately corrects for the dust extinction of the galaxy by comparing the final total extinction-corrected SFRs to that obtained by tracing 2.2. Inferring Sgas SSFR by 70 μm emission as taken by the Herschel Photo- We estimate the column density of molecular hydrogen H2, detecting Array Camera and Spectrometer (PACS) instrument traced by the carbon monoxide CO(1–0) transition line and first presented in Bitsakis et al. (2014).70mm emission can observed by CARMA. We determine the column density of be a good tracer for SF because it does not suffer from molecular hydrogen in each line of sight (LOS) by: extinction. However, due to the PACS instrument’s larger beam size of 5 8 (compared to the SOAR Goodman Spectro- MSvKXM-2 =D ()[]···total H2 in LOS g cmCO CO H2 , graph’s08 and CARMA’S25), the 70 mm-inferred SSFR ()3 map is incapable of resolving individual H II regions so a lot of information regarding the internal SF structure within NGC -1 where SCO Dv is the CO(1–0) flux in Jykms , K is the Kelvin 7674 would not be inferrable and thus inappropriate for our per Jansky factor of 15.02 as quoted in Alatalo et al. (2015a). pixel-by-pixel study. We, thus, use the global values of The assumed XCO CO-to-H2 factor is 70 mm-inferred SSFR to ensure that the extinction correction 20--- 2 1 1 2 ´ 10 cm() K km , which is the value presented in applied to the Hα imaging-inferred SSFR is adept enough to ( ) Bolatto et al. 2013 . The mass of a single molecular hydrogen report an accurate measurement of SSFR. Bitsakis et al. (2014) -24 −1 atom MH2 is 1.66´ 10 g. While we acknowledge that the reports a total SFR of 17.4 M yr for NGC 7674, while the centers of galaxies with strong bars tend to exhibit a smaller total SFR in the Hα imaging-inferred SSFR map within the ( −1 gas-to-dust conversion factor than that in the disk Bolatto et al. contours of signal-dominated pixels is 15.7 M yr . These 2013; Sandstrom et al. 2013), Alatalo et al. (2015a) showed total SFRs derived using these two methods agree within that the gas-to-dust ratio of NGC 7674 fell within the range for ≈10%. We can, therefore, trust that the extinction correction nearby galaxies of solar metallicities and exhibited little to no applied adequately corrects for dust attenuation in NGC 7674 dependence on SF suppression. This indicates that it is such that we can trust the SSFR values used in the subsequent acceptable to apply a standard Galactic conversion factor for analysis of this study. 12 inferring the Sgas content. Detailed observations of CO Having implemented these corrections, the final corrected 13 18 isotopologues such as CO and C O and dense gas such as Hα flux (FHa) in each LOS was converted to a luminosity HCN, HCO+, and CS are required to confirm whether the through the relation system requires a different conversion factor. -1 2 LDFHaa[]erg s= 4L p H , () 5 We obtain our final Sgas values by converting the total H2 -2 mass in each pixel into units of M pc then multiplying by where DL is the luminosity distance of NGC 7674 in cm. This 1.36, which incorporates the correction factor to account for luminosity was converted into an SFR following the calibration ( ) Helium. The final spatial distribution of Sgas across the galaxy presented in Calzetti et al. 2007 and reviewed in Kennicutt & is shown in the middle panel of Figure 1. Evans (2012), SFR[]ML yr---242=´ 5.3 10 []() erg s1 . 6 6 http://www.mmarray.org  Ha

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Figure 1. HCG NGC 7674. Left panel: i-band image from the (HST; Armus et al. 2009). Middle panel: Sgas maps inferred from the CARMA CO(1–0) observations (Alatalo et al. 2015a). Right panel: SSFR maps inferred from deep Hα imaging taken with the 4.1 m SOAR telescope (Eigenthaler et al. 2015). In every panel, the outermost contour indicates the boundary at which we separate the noise-dominated pixels to the signal-dominated pixels we use in analysis. The dotted green rectangle indicates the boundary of the bar of NGC 7674, as visually determined by the HST i-band image. In the middle and right panels, the central green circle represents the boundary of the central nucleus, as determined by the deep Hα imaging. In the middle and right panels, the top-right pink circle indicates the FWHM of the beam of the instruments that took the observations. The FHWM of each respective instrument is written above the beam size.

We use the IDL routine HASTROM7 to match the pixel scale of (2015), we can infer that the AGN emission is highly obscured the SFR map to that of the CARMA pixel grid. This routine by dust clouds forming stars. Furthermore, from the HST i- applies a transformation to an image so that its astrometry is band image depicted in Figure 1, it is evident that the very fi identical with that in a reference header. We then convert our central nucleus exhibits an independent spiral structure ner than the resolution of the Hα map. Therefore, in Hα, the central SFR into a column density of SF, SSFR by dividing SFR by the −1 AGN can be treated as a point source the size of the beam of galactocentric scale of g of 0.586 kpc arcsec , as recorded on the instrument used to capture the Hα emission. Thus, to the NASA/IPAC Extragalactic Database,8 which yields mitigate AGN influence on our SF study, we have disregarded the pixels that fall within the area of the central beam in Hα --21SFR S=SFR []M yr kpc ,7() from further analysis. gw· where w is the pixel width of the CARMA grid of 0 4. The 2.4. Phenomenological Features and Signal-to-noise Cuts final spatial distribution of SSFR across the galaxy is shown in the middle panel of Figure 1. Using this image, we visually We define the noise-dominated pixels as those that have a determine the boundaries of the nucleus. The nucleus is signal-to-noise ratio of less than 5 in the intensity-weighted ( ) demarcated as a circle in the middle and right panels of mean velocity of CO along the LOS moment 1 observations. We define the lower threshold in S and S as the standard Figure 1. gas SFR deviation of the noise-dominated Sgas and SSFR pixels, respectively. These thresholds are shown in the KS plane as 2.3.1. Influence of the AGN on Hα as an SFR Tracer the vertical and horizontal dotted purple relations in Figure 2. We acknowledge the undeniable presence of an AGN in the Any pixels that fall below these thresholds in either Sgas or center of NGC 7674 that has been proven through infrared SSFR are disregarded in subsequent analyses in the rest of this Cluver et al. (2013) and optical Martínez et al. (2010) study. Thus, to be considered signal-dominated and, therefore, observations. Hα emission is known to be enhanced by the adept for analysis, a pixel must exhibit higher values of Sgas influence of AGNs (Kewley et al. 2002, 2006; Kewley & and SSFR than the lower thresholds defined in both domains. Dopita 2003; Rich et al. 2011) and so has traditionally been The boundary between noise-dominated and signal-dominated treated with caution as a tracer for SF in AGN-driven galaxies. pixels are shown spatially as the thickest, outermost contour in This is especially an issue when the beam size of the every panel of Figure 1. Pixels that fall within this contour are observation instrument covers a greater physical area than that considered signal-dominated in both Sgas and SSFR, while of the central nucleus of the galaxy, because the central anything outside is considered noise-dominated and is, thus, concentration of Hα emission (and thus inferred SFR) will be omitted from analysis. Any subsequent figures following spread out via beam smearing and thus incorrectly attributed to Figure 1 showing the spatial distribution of Sgas across NGC SF in the disk. 7674 will only show the data that falls within the boundary of However, given that NGC 7674 is a lines of sights that are not noise-dominated. exhibiting particularly strong SF activity Eigenthaler et al. In order to investigate whether these regions have properties distinctly differing from the rest of the galaxy, we isolate the 7 https://idlastro.gsfc.nasa.gov/ftp/pro/astrom/hastrom.pro regions in which the galactic bar and nucleus lie. In the left- 8 http://ned.ipac.caltech.edu/ most panel of Figure 1, we show the optical i band image from

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Figure 2. Left panel: we show the molecular gas surface density from CO(1–0) of every LOS column in NGC 7674ʼs CARMA field compared to the SSFR for the same column. Each LOS column is represented as a single circular data point in the left panel of the diagram. Green circles indicate pixels that fall within the bounds of the galactic nucleus, and outlined circles highlight pixels that make up the bar outside the nucleus (see Figure 1 for the parts of the galaxy defined as nucleus and -1 bar, respectively). Both of these regions are defined in Section 2.4. The overlaid dark pink circles are the median SSFR within Sgas bins of 0.1 dex of Mpc . The vertical error bars extending above and below these points are the standard deviations of SSFR within each Sgas bin. The Kennicutt (1998, K98) and Bigiel et al. (2008, hereafter B08) relations are also displayed on the same axes as the solid and dashed pink lines, respectively. The lower thresholds in Sgas and SSFR, as described in Section 2.4, are also shown in Figure 2 as the dotted purple vertical and horizontal dashed lines, respectively. Any values in either Sgas or SSFR that are lower than these thresholds we disregard and show in a fainter gray. Note that because the pixel size of the CARMA grid (0 4) is smaller than that of the PSF (2 5), adjacent pixels are correlated. Right panel: we show the contours that represent the two-dimensional probability density within the Kennicutt–Schmidt framework of the same data as shown in the left-hand panel. Darker green areas, therefore, show regions of the KS plane that the LOS columns of NGC 7674 are more likely to occupy. the HST, observed as part of the Great Observatories All-sky that two distinct SFR sequences exist that intersect at about 2.3- 2 LIRG Survey (Armus et al. 2009). Using this image, we 10M pc . The nuclear region (green data points) in the left visually determine the boundaries of the bar. A rectangle panel of Figure 2 exhibits a much steeper Sgas−SSFR slope denotes the bar in each panel of Figure 1 and every subsequent than the bulk of the galaxy. However, the low-Sgas region, image of NGC 7674 in this study. This galaxy is also predicted which contains the outskirts of the galaxy, is inefficient. We are to host compact nuclear gas. We visually determine the confident in the physicality of these two distinct sequences fi boundary of the nucleus via the nal SSFR map. In each panel because the beam size of the Hα observation instrument is of Figure 1 and every subsequent image of NGC 7674 in this much smaller in area than the nuclear region defined in study, the nucleus is demarcated by a circle. We show the Section 2.3, and we have disregarded any pixels falling within entire bar for visualization purposes, but for subsequent the central beam in Hα, as described in Section 2.3.1. analysis, pixels of the bar that fall within the boundary of the Therefore, the steep SF relation within the nuclear region fi nucleus are classi ed as only a nucleus pixel. cannot merely be the systematic effect of beam smearing due to Hα-enhanced AGN emission. The right-hand panel of Figure 2 illustrates that almost all 3. Results: Interaction between Gas and SF lines of sight in this galaxy lie within the low-Sgas region. In 3.1. Behavior in the KS Plane this region, the median SSFR in each Sgas bin consistently lies about 0.3–0.8 dex and 0.1–0.3 dex below the K98 and B08 SF While A15 demonstrate that this galaxy does not globally relations, respectively. deviate significantly from the K98 relation, we find that on NGC 7674 is a barred galaxy. We see that the pixels that resolved scales, the bulk of NCG 7674 is very inefficient at make up NGC 7674ʼs bar outside of the nucleus follow an forming stars, as highlighted in Figure 2. Because the global fi value of CO of a galaxy is a weighted average by luminosity, especially SF inef cient sequence. As a population, barred more luminous areas such as the galaxy’s central region galaxies have been found to exhibit higher gas concentrations ( contribute a more significant influence in calculating the Sakamoto et al. 1999; Jogee et al. 2005; Sheth et al. 2005; Regan et al. 2006) and higher central SFRs (de Jong et al. galaxy’s global Sgas and SSFR properties. We, therefore, infer that the reason that the global value of NGC 7674 lies so close 1984; Hawarden et al. 1986; Devereux 1987; Puxley et al. to the K98 relation is because most of the lines of sight around 1988; Ho et al. 1997) than most galaxies. Recent studies such the central region lie close to the K98 relation. This is apparent as Chown et al. (2019) and Lin et al. (2017) find evidence that when we consider the nuclear region (green data points) labeled the presence of bar or tidal interaction is a necessary condition lines of sights in Figure 2. for central SF enhancement. Therefore, NGC 7674ʼs bar is In this work, we take advantage of the spatially resolved likely to be driving gas from the outskirts of the galaxy into the measurement of Sgas and SSFR to identify and analyze regions nucleus. This would serve as a reasonable explanation for our fi of inefficient SF. For any Sgas, the corresponding SSFR can vary nding that the center of the galaxy exhibits a vastly different by more than an order of magnitude. Furthermore, it is apparent SF behavior than the areas of the outskirts. This result

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Figure 4. Same as Figure 3 but with integrated 2×2CO(1–0) spectra overlaid Figure 3. Average depletion time (pink color gradient) of every 2×2 LOS on the integrated intensity map (grayscale). Each spectrum was constructed by × fi columns overlaid on top of the corresponding Sgas map (grayscale). Only pixels summing every pixel in the 2 2 sub-grid in each channel. We t each ( ) above the Sgas and SSFR noise thresholds are shown. spectrum with Gaussian function as in Equation 9 and color each spectrum from red to blue in accordance with the difference between the velocity of the Gaussian peak and the galaxy’s average velocity, with red indicating redshifted exemplifies the limitations of inferring SF properties of and blue indicating blueshifted velocities. galaxies from a single global data point (as in K98) across the galaxy. We need spatially resolved studies to investigate galaxy characteristics capable of reducing SSFR in the bulk of the galaxy.

3.2. Spatial Distribution of Depletion Times suggested that SF is not only a function of the molecular gas density. Other factors that have been found to control SF rate ʼ − NGC 7674 s lack of a universal Sgas SSFR relation is further include turbulence (Krumholz & McKee 2005; emphasized by the spatial distribution of depletion time, tdep, Federrath 2010, 2013b; Hennebelle & Chabrier 2011; Padoan across the galaxy. The depletion time is the rate at which fi & Nordlund 2011; Federrath & Klessen 2012; Salim et al. GMCs form stars and is de ned as the ratio between the ) fi ( molecular gas surface density and SF rate: 2015 , magnetic elds Federrath 2015; Krumholz & Feder- rath 2019), and freefall time, which is the timescale required for Sgas a medium with negligible pressure support to collapse tdep = .8() ( ) SSFR Krumholz et al. 2012 . This is the time that is required to turn the available gas into 4. Turbulent stars, also known as the timescale of SF. In Figure 3, the We investigate properties of the velocity distribution of the grayscale map represents the S values of the pixel, which is gas molecular gas to determine whether turbulent forces could play also presented in Figure 1. The overlaid pink dots indicate the a significant role in inhibiting SF in this system. We first × × average tdep per 2 2 pixels or 0.8 0.8 arcsecs. Without measure the total velocity dispersion (second moment) across external influences, one would expect that areas with the the galaxy using CARMA data. We increase signal-to-noise by densest gas collapse the fastest and, therefore, have the shortest creating spectra of 2×2 binned pixels (rather than individual depletion times. While this is true of the nucleus, there is no pixels). clear spatial correlation of these two factors in the rest of the These spectra are shown in Figure 4, overlaid on the galaxy. Aside from the central nuclear region, the densest gas is corresponding spatial area in Sgas. To each spectrum, we fita located in the outer regions of the galaxy. These areas, Gaussian F as a function of velocity v: ’ however, also host regions of some of the galaxy s longest ⎡ ⎤ 1 ⎛ vA- ⎞2 depletion times of >4Gyr. This further suggests that despite FA=-exp ⎢ ⎜ 1 ⎟ ⎥,9() 0 ⎢ ⎝ ⎠ ⎥ the abundance of dense gas, a competing factor must be ⎣ 2 A2 ⎦ suppressing SF. This supports many previous studies that have

6 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al.

Figure 5. Left panels: the uncorrected velocity dispersion (moment2) map, the values of which are derived from the standard deviation of the Gaussian for each spectrum shown in Figure 4. Middle panels: velocity gradients across the galaxy. Bottom panels: the gradient-corrected velocity dispersions of the galaxy (velocity gradient subtracted from fitted Gaussian standard deviations). where A0 is the height of the Gaussian peak, A1 is the velocity beam smearing. Beam smearing is caused when the velocity at which the peak occurs, and A2 = s is the standard deviation field changes on spatial scales smaller than the spatial of the Gaussian distribution. Where there is a spectrum shown resolution of the observation. The line-of-sight velocity on Figure 4, a Gaussian was successfully fit. From the standard dispersion is enhanced by beam smearing because rotational deviation of the fitted Gaussian A2, we can infer the observed velocities can mimic dispersion. This leads to an artificial velocity dispersion, sobs across the galaxy. sobs includes increase in the measured velocity dispersion if there is a steep contributions from all observed dispersions, including beam velocity gradient across neighboring pixels. We thus only smearing as well as the actual dispersion of the gas. The spatial regard pixels in which the observed velocity dispersion is at distribution of sobs is shown in the right-hand panel of Figure 5. least twice that of the velocity gradient (sobs> 2v grad), resulting The corresponding FWHM of each spectrum is in pixels where beam smearing is not the dominant contribution FWHMobs= 2 2ln2 s obs. We color each spectrum in to sobs (Varidel et al. 2016; Federrath et al. 2017b; Zhou et al. Figure 4 based on the difference between the velocity of the 2017). To obtain the final rotation-corrected velocity dispersion Gaussian peak and the galaxy’s average velocity. Red spectra values across the galaxy, we subtract the velocity gradient from therefore indicate areas that are redshifted, and blue spectra the observed FWHM in quadrature from every pixel: indicate regions that are blueshifted relative to the average 2 2 velocity. We see there is a systematic red-to-blue gradient FWHMcorrected =- FWHMobs vgrad () 11 running across the galaxy, indicating the rotation of the galaxy. FWHMcorrected Because the gradient is smooth, we approximate the extent of scorrected = .12() 22ln2 the rotation of the galaxy via the velocity gradient vgrad, which describes how much the molecular gas velocities between The beam-smearing-corrected second-moment maps are shown adjacent pixels differ. This observed gradient is present in sobs in the right-hand panel of Figure 5. The velocity dispersion is and must be corrected for in order to isolate the turbulent not isotropic across this galaxy, indicating that gravitational ( ) ( ) unordered motions see Federrath 2016 . To determine vgrad torques do not seem to act globally in this system. In particular, in each pixel, we utilize the CARMA CO(1–0) observation’s we find that the velocity dispersion is enhanced in the areas of average velocity (first moment) map. We estimate the local the galaxy that are directly behind the direction of motion of velocity gradient for a given pixel at coordinates (i, j) as the the galaxy’s bar. This indicates that the bar is likely driving vector sum magnitude of the difference in velocity between turbulent motions. In the following, we utilize these inferred adjacent pixels, as in Varidel et al. (2016): velocity dispersions to explore the validity of a turbulence- regulated SF relation (Salim et al. 2015) and compare them to vijgrad (), SSFR predicted by SF models that only account for gravity =+--+[(vi1, j ) vi ( 1, j )]2 [( vi , j +-- 1 ) vi ( , j 1 )]2 (Kennicutt 1998; Krumholz et al. 2012). ()10 5. Comparing Observed to Predicted SFRs (see also Zhou et al. 2017 and Federrath et al. 2017b.) If a pixel To determine whether turbulence influences the SF effi- fi has a neighbor that is unde ned, the gradient in that direction is ciency within NGC 7674, we use the Sgas and inferred velocity disregarded. The velocity gradient contributes to sobs through dispersion derived in Section 4 as inputs to predict SSFR via

7 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al. the K98, KDM12 and Salim et al. (2015)(hereafter SFK15) SF and the logarithmic density variance ss is defined as derived by relations. We compare these predictions to the observed SSFR Padoan & Nordlund (2011) and Molina et al. (2012): from the deep Hα imaging to determine which, if any, of these ⎛ b ⎞ models can best describe the small-scale physics taking place in s2 =+ln⎜ 1 b22 ⎟.17() NGC 7674. s ⎝ b + 1⎠ 2 ss is itself parameterized by the turbulence driving parameter 5.1. Predictions from Previous Literature b, which represents the degree of compressive or solenoidal This investigation has shown that areas of NGC 7674 behave turbulence in the system (Federrath et al. 2008, 2010), the sonic quite differently, and are washed out when only the global Mach number , and the ratio between thermal and magnetic values are used. It is thus crucial to understand how small-scale pressure β. SFK15 then derived their SF correlator by interactions effect galaxy evolution, rather than relying on a integrating over all densities for the entire PDF. The new global average. correlator is denoted as the maximum ormulti-freefall gas Descriptions of the K98 and KDM12 SF models were consumption rate, (Sgast) multi- ff : presented in Section 1.InSFK15, the SF relation is derived ⎛ ⎞ ¥ Sgas Sgas()r ⎛ 3 ⎞ accounting for the sonic Mach number of turbulence. The ⎜ ⎟ = 0 exp ⎜⎟spsds().18 ( )  (i.e., the ratio between the absolute velocity ⎝ ⎠ ò ⎝ ⎠ Mach number ttmulti--¥ ff ff ()r0 2 SFK15 of the gas and the local sound speed, cs) is By computing the integral and calibrating to MW clouds and  = scorrected cs.13() the Small Magellanic Cloud, the SFK15 model is reduced to ⎛ ⎞ In this study, we assume a constant sound speed across the Sgas S=SFR 0.45% ´⎜ ⎟ () 19 galaxy. To estimate the most viable value, we consider that all ⎝ t ⎠multi- ff SFK15 H2 gas is most likely to lie within the temperature range of – S ()r ⎛ b ⎞38 10 100 K, outside of which the gas will no longer be molecular =´0.45%gas 0 ´+⎜ 1 b22 ⎟ .20() ( ) ⎝ ⎠ under typical ISM conditions Ferrière 2001 . This range is tff ()r0 b + 1 supported by observations. Molecular clouds in Galactic spiral ∼ SFK15 had assumed a natural mixed turbulence scenario of arms have been observed to have temperatures between 10 b=0.4. However, because of the likely presence of the bar in and 50 K, whereas those in the Galactic center can reach NGC 7674, we expect that the gas in this galaxy may be subject temperatures up to 100 K (Ginsburg et al. 2016). The local to very strong shear forces (Federrath 2010; Federrath & sound speed of gas is related to the temperature by Klessen 2012; Federrath et al. 2016). A solenoidal driving scenario of b=0.3 is therefore a more reasonable choice in the ⎛ ⎞12 case of NGC 7674 and, therefore, the value we assume in ⎜ kTB ⎟ cs = ⎜ ⎟ ,14() making our SFK15 SF predictions in this study. As in SFK15, ⎝ mpmH ⎠ for simplicity, we assume the absence of magnetic fields, leading to a limit of plasma b ¥. We are able to measure where kB is the Boltzmann constant, mH is the mass of a Mach numbers across the galaxy from our gradient-corrected hydrogen atom, and mp is the mean particle weight. Under second-moment map shown in Figure 5, which represent as accurately as possible the velocity dispersion of the molecu- standard cosmic abundances, typical values of mp are 2.3 for molecular gas and 0.6 for ionized gas (Kauffmann et al. 2008). lar gas. Thus, we have that temperatures of T=10K and T=100K The results of the SSFR predictions derived from the K98  −1 and KDM12 SF relations, as well as that of SFK15, are directly correspond to molecular sounds speeds of 0.2 km s and 0.6 α  −1 compared to SSFR measurements inferred by deep H imaging km s , respectively. In our calculation of Mach numbers and in Figure 6. The best least-squares linear fit to the median subsequent analyses, we therefore choose a sound speed of predicted S values within each observed S bin is shown  −1 SFR SFR cs =0.4 0.2 km s . This estimate is appropriate for dense, as the blue solid lines. We apply a robust linear fit to the cold star-forming phases of the ISM of temperatures of median SSFR values within each 0.2 dex Sgas bin instead of T=10–100 K. taking every individual point into account in order to minimize The SFK15 relation uses the fact that Sgas and the density ρ the influence of outlier lines of sight. We see that none of the ( ) closely follow a log-normal probability density function PDF previous SF relations accurately capture the observed SSFR in in both simulations and observations (Krumholz et al. 2012; NGC 7674. While the data points lie closer to the one-to-one Federrath 2013a): line when the K98 and KDM12 relations are applied, the best- fit lines exhibit slopes that are quite shallow, with power-law ⎛ 2 ⎞ ( ) 1 ()ss- 0 slopes of 0.54 and 0.55, respectively, with 1 being the ideal . psds() =-exp ⎜ ⎟ds,15() This indicates that the K98 and KDM12 relations are fair at 2 ⎝ 2s2 ⎠ 2pss s predicting intermediate SF where gravity may be the primary driver of SF, but they fail at doing so at extremely low- or high- where the density variable s is expressed as the logarithm of the SSFR regimes, when other influences of SF cannot be ignored. density normalized to the mean density This may also explain why A15 found that NGC 7674 globally sits near these relations. While the total values for Sgas and s = ln()rr0 , () 16 SSFR may be driven by the gravity of the dense nucleus, this information is insufficient to predict and explain the

8 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al.

Figure 6. Comparison between SSFR predicted using SF relations of K98 (left panel), KDM12 (middle panel), and SFK15 (right panel) to that inferred from deep Hα imaging. The overlaid dark pink points are the average predicted SSFR for 0.2 dex bins, with errors bars that show the standard deviation of each bin. The blue solid line is the best least-squares linear fit to the median predicted SSFR values within each observed SSFR bin. The pink solid line is the the one-to-one correlation (when the model fits the observations perfectly). The resultant slopes and offsets between the predicted and observed SSFR for each of the K98, KDM12, and SFK15 SF models are tabulated in Table 1. inefficiencies of SF outside of the nuclear region. The SFK15 twice the kinetic energy to the gravitational prediction, which takes into account the internal turbulence energy, avir= 2EE kin∣ grav∣. within the small-scale structure of the system, exhibits a power- This scrit was first defined in Krumholz & McKee (2005) and law slope of 0.75, which is closer to unity but still significantly is based on comparing the Jeans length to the sonic length. off. While the correlation between observed and predicted SSFR Federrath & Klessen (2012) extended this derivation by is better in SFK15 compared to K98 and KDM12, the SFK including magnetic fields in the description, finding scrit by relation overestimates SSFR by almost an order of magnitude comparing the magnetic Jeans length with the magneto-sonic overall. In the following, we address this discrepancy by scale. (For the definition and relevance to the sonic length, see extending the SFK15 relation to incorporate the virial Federrath & Klessen 2012 and Federrath 2016). We assume the parameter (ratio of kinetic to gravitational energy), which had Federrath & Klessen (2012) fudge factor value of fi previously been left out of the SFK relation. Here however, fx =0.17 0.02. This value was determined by tting to a because of the strong tidal interactions experienced by this set of 34 numerical simulations of molecular clouds with HCG galaxy, we need to take the virial ratio of NGC 7674 into resulting SF rates over more than two orders of magnitude. To account, as explained in the following. incorporate this into our model, we take the (Sgast) multi- ff parameterization of SFK15 shown in Equation (18) and 5.2. Extension to Turbulence-regulated SFR: Considering Only integrate from scrit instead of -¥: Gas Denser Than the Critical Density ⎛ ⎞ ¥ Sgas Sgas()r ⎛ 3 ⎞ ⎜ ⎟ 0 ⎜⎟ The SFK15 SF relation had integrated over the entire density ⎝ ⎠ = ò exp ⎝ s22⎠ ps() ds ( ) ttmulti- ff ff ()r0 scrit 2 PDF from -¥ to ¥, suggesting that clouds of any density EXTENDED have the capability of forming stars. This assumption may be the cause of the overestimated S predictions in NGC 7674. ⎡ ⎛ ⎞⎤ SFR S ()r ⎛ ⎞ s2 - s gas 0 ⎜⎟3 2 1 ⎢ ⎜ s crit ⎟⎥ We explore a possible remedy by only considering gas dense = exp ss ⎢1erf+ ⎜ ⎟⎥ ()23 t ()r ⎝ 8 ⎠ 2 2 enough to form stars. To test this, we integrate only from the ff 0 ⎣ ⎝ 2ss ⎠⎦ minimum threshold density at which gas is able to collapse into stars. This minimum density is called the critical density, scrit, ⎡ ⎛ ⎞⎤ 2 ⎛ S ⎞ defined by 1 ⎢ ⎜ ss - scrit ⎟⎥⎜ gas ⎟ =+⎢1erf⎜ ⎟⎥ .24() 2 2 ⎝ t ⎠multi- ff ⎣ ⎝ 2ss ⎠⎦ ⎡⎛ 2 ⎞ ⎤ SFK15 ⎢ p 2 2⎥ scrit = ln ⎜ ⎟ favir  ,21() fi ⎣⎝ 5 ⎠ x ⎦ Assuming the same SF ef ciency as in SFK15, we derive an extended SF relation that is more physically descriptive, fi “ ” ( where fx is a xed fudge factor of order unity introduced in ⎛ S ⎞ ⎜ gas ⎟ Krumholz & McKee (2005); see Federrath & Klessen (2012) S=SFR 0.45% ´ () 25 ⎝ t ⎠ multi- ff for details), and avir is the virial parameter, which is the ratio of EXTENDED

9 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al.

Figure 7. Same as the right-hand panel of Figure 6, but for the extended comparison between SSFR predicted using this work’s extended turbulence-regulated SF model (Equation (26)) for assumed avir of 1, 5, and 10 to the observed SSFR inferred from deep Hα imaging. The resultant slopes and offsets between the predicted and observed SSFR for each of the avir=1, 5, and 10 scenarios for this SF model are tabulated in Table 1.

⎡ ⎛ ⎞⎤ 2 ⎛ ⎞ Table 1 1 ⎢ ⎜ s - scrit ⎟⎥ Sgas =´+0.45% 1erfs ⎜ ⎟ .26() Offset and Slope of the Best Least-Squares Linear Fit to the Median Predicted ⎢ ⎜ ⎟⎥⎝ ⎠ S Values within Each Observed S Bin for the Three Previously Derived 2 ⎣ ⎝ 2s2 ⎠⎦ t multi- ff SFR SFR s SFK15 SF models by K98, KDM12, and SFK15, as Well as the Extended Turbulence- regulated SF Relation Derived in This Work, for Assumed avir of 1, 5, and 10 Note that for the limiting case of a =0, the extended SF vir SF Prediction Offset Slope relation reduces exactly to the previous SFK15 relation K98 0.15 0.55 (Equation (18)). In practice, the predicted SSFR for systems KDM12 −0.02 0.56 with avir <»1 is nearly identical to the previous SFK15 relation. Thus, the new relation only makes a significant SFK15 0.78 0.76 This work, a = 1 0.54 0.75 difference for systems with a > 1. vir vir This work, a = 5 0.30 0.78 Similar to the method in Section 5, we now use the CO(1–0) vir This work, avir = 10 0.16 0.80 observations of Sgas and inferred velocity dispersion derived in Section 4 as inputs in our newly derived extended turbulence- Note. A perfect correlation between predicted and observed SSFR would result regulated SF relation. We compare these predicted values to the in an offset of 0 and a slope of 1. SSFR from deep Hα imaging to investigate whether this new SF relation better describes the internal physics of NCG 7674.

5.3. Applying Extended Turbulence-regulated SF Relation to NGC 7674 closer to the observed SSFR, with increasing avir. This is We apply the extended turbulence-regulated SF relation reflected in Figure 7, where we see a decrease in offset of the fi derived in Section 5.2 to predict SSFR across NGC 7674 and best robust linear t to the data in the logarithmic investigate how these compare to observations. In the absence S-SSFR()observed SFR () predicted plane with increasing a of of a direct measurement of avir, we assume fixed, uniform vir but a nearly constant slope. The resultant slopes and S values of avir across the galaxy in order to investigate how avir offsets between the predicted and observed SFR for each of the impacts SSFR. Figure 7 shows how these predictions compare SF models tested in this study are tabulated in Table 1. to observations for assumed avir values of 1, 5, and 10. The avir values that best predict S in NGC 7674 are a 10. SFR vir 6. Discussion For the same values of Sgas and , Equation (26) results in the SFK15 SF prediction multiplied by a factor dependent on It is plausible that this system possesses sufficient excess avir. Since the critical density scrit is directly proportional to kinetic energy that avir of 10 or more is viable. The HCG avir, systems with higher values of avir would, in turn, have environment of NGC 7674 exhibits H2 shocks (Cluver et al. higher values of scrit and, consequentially, the inclusion of a 2013), indicative of excessive kinetic energy. Environments smaller fraction of the density PDF capable of forming stars. with conditions thought to be similar to that of HCGs, such as This ultimately results in a prediction of more suppressed SF at the CMZ of the MW, have also been found to exhibit higher higher values of avir. Using Equation (26), predicted SSFR get virial parameters (Kruijssen et al. 2015; Federrath et al. 2016).

10 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al. The CMZ has also been observed to have a low efficiency the SFK15 SF model and this work’s Equation (26), this will of SF. result in a reduction in the amount of SSFR predicted to be in Federrath et al. (2016) found that the virial parameter within any system when compared to assuming complete lack of the MW cloud nicknamed “the Brick” exhibits a value of about magnetic fields. Numerical simulations have also supported the 4. The fact that the objects in our study are experiencing presence of SF suppression as an effect of introducing magnetic gravitational torque via their companion galaxies makes it fields (Padoan & Nordlund 2011; Federrath & Klessen 2012; possible that these environments could exhibit more extreme Federrath 2015; Krumholz & Federrath 2019). avir than the clouds in the CMZ. It is, thus, reasonable to believe that if an MW cloud can exhibit a virial parameter of 4, these effects may be amplified in an HCG environment to 7. Conclusions induce high virial parameters of 10 or more. In this study, we present and compare S inferred from Furthermore, there is strong evidence that the turbulence in gas CO(1–0) and S inferred from deep Hα imaging of the face- this system is induced by the presence of a bar. We see, in the SFR on HCG galaxy NGC 7674. We measure velocity dispersions spatial distribution of inferred velocity dispersions shown in the across the galaxies via the CO(1–0) channel maps and piece right-most panel of Figure 5, that the top left and bottom right together this information to explore the effects and roles of quadrants of the center region of the galaxy exhibit the highest- fi fl turbulence in shaping the kinematics and SF inef ciency of this velocity dispersions. Just as a solid moving through a uid will galaxy. In doing so, we find the following: create vortices behind it, the rotation of the galaxy’s bar through the ISM will disturb the gas that it has already passed, 1. NGC 7674 does not exhibit a straightforward relation inducing heightened turbulent motions just behind the bar. between Sgas and SSFR, with any value of Sgas correlating Another competing factor that may be enhancing the velocities to a SSFR range of up to an entire order of magnitude on of molecular gas in this system is the torquing of gas into the the KS plane. We find that this galaxy exhibits two center bulge due to the existing concentration of mass and, distinct SF sequences in the KS plane; one for the nucleus therefore, gravitational forces (Forbes et al. 2012, 2014; of the galaxy and one for outlying regions. Krumholz et al. 2018). However, if gravity was the primary 2. We find that spatially, the densest regions of Sgas do not driver of motion in this system, the spatial distribution of necessarily correspond to regions of the shortest depletion regions of high-velocity dispersions would look more isotropic times; instead, the exact opposite is often the case in across all four quadrants of the galaxy plane. This further many sections of this galaxy outside the nucleus. suggests that disruption from the bar and, therefore, strong 3. The velocity dispersion across NGC 7674 is non- shear and solenoidal driving scenarios are significant sources of isotropic and enhanced in areas directly behind the kinetic motions in this galaxy. These enhanced kinematic direction of motion of where we believe the galaxy’s bar properties, coupled with strong torques from neighboring lies. This suggests that the bar is a key physical feature galaxies, makes it highly likely that high avir of 10 or more is driving turbulence within this galaxy. The interplay plausible within this system. between turbulence induced by the bar, accretion of gas into the galaxy’s dense nucleus, and competing forces from the torques induced by neighboring galaxies in the 6.1. Limitations and Caveats group all contribute to the likelihood that this galaxy In this study, we choose to explore how the SF predictions experiences extreme, enhanced kinematic properties. using the extended turbulence-regulated SF relation 4. We apply the K98, KDM12, and SFK15 SF relations to ( ( )) Equation 26 behaves with increase of avir uniformly across predict SSFR and compare them to observations from the galaxy. We choose this method instead of individually deep Hα imaging. We find that none of these relations fi estimating a value of avir because we do not have suf cient adeptly predict the observed SSFR. While the global resolution to accurately measure this quantity, as this would values of NGC 7674 sit near the K98 and KDM12 SF require a full energetics analysis of this system, which is relations, when isolating individual lines of sights, it is beyond the scope of this study. clear that these models do not result in a linear correlation Moreover, we acknowledge that the resolution of the data between the predicted and the observed SSFR. The SFK15 used in this study has been too coarse to resolve the GMC relation represents a more linear correlation between the scales needed to isolate individual regions of distinct kinematic model and observed SSFR than that predicted by K98 properties. In this study, we compromised high resolutions for and KDM12. However, the SFK15 model systematically ( ) reliability of the parameters we input into Equation 26 , but overestimates the SSFR by almost an order of magnitude subsequent studies should strive at least for GMC-scaled on this galaxy. We attribute this overestimation resolutions to scrutinize the legitimacy of this description of to SFK15ʼs assumption that gases of all densities have SF. For this, we would need data that could resolve at least the potential to form stars. GMCs, which scale around a few tens of each 5. We extend upon the theoretical SF relation by SFK15 to (Krumholz 2017). formulate a new SF relation, Equation (26).Wedosoby Finally, we emphasize that the simplistic assumption that integrating the density PDF from the critical density scrit magnetic fields are absent in this system is definitely to formulate an SF relation dependent on Sgas, , and unrealistic. Magnetic fields have been observed in almost all the virial parameter avir. We did this in response to the extragalactic sources (Federrath & Klessen 2012; Krum- finding that the previously formulated SF relations holz 2017). In the presence of magnetic fields, the magnetic by K98, KDM12, and SFK15 did not adequately describe pressure Pmag is greater than the thermal pressure Pth. Thus, the the small-scale physics of this system. plasma β, which is a ratio of these two values b = PPth mag will 6. We find that Equation (26) describes the SF across NGC be small in the presence of strong magnetic fields. In both 7674 best in the assumption that the galaxy exhibits high

11 The Astrophysical Journal, 893:26 (13pp), 2020 April 10 Salim et al.

avir values of 10. Such high values of avir are plausible Federrath, C., Glover, S. C. O., Klessen, R. S., & Schmidt, W. 2008, PhST, in NGC 7674 due to the presence of a bar that is likely to 132, 014025 be inducing turbulence behind its direction of motion, as Federrath, C., & Klessen, R. S. 2012, ApJ, 761, 156 Federrath, C., & Klessen, R. S. 2013, ApJ, 763, 51 well as the external torques being exerted by the Federrath, C., Rathborne, J. M., Longmore, S. N., et al. 2016, ApJ, 832, 143 neighboring galaxies in the HCG. It is very likely that Federrath, C., Rathborne, J. M., Longmore, S. N., et al. 2017a, in IAU Symp. these physical features are injecting excess kinetic energy 322, The Multi-Messenger Astrophysics of the Galactic Centre, ed. R. M. Crocker, S. N. Longmore, & G. V. Bicknell (Cambridge: into this system, which may cause high values of avir. Cambridge Univ. Press), 123 We anticipate that the extension of SFK15ʼs turbulence- Federrath, C., Salim, D. M., Medling, A. 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