The KMOS^3D Survey: Demographics and Properties of Galactic Outflows at z = 0.6–2.7^*

The vast majority of the galaxies (525 of 599, or 88%) are taken from the 5 yr KMOS^3D survey with the multi-IFU instrument KMOS at the VLT (Wisnioski et al. 2015, and in preparation). The survey strategy emphasized sensitive observations of individual sources and wide coverage of galaxy parameters in stellar mass, SFR, and colors in three redshift slices at z ∼ 0.9, z ∼ 1.5, and z ∼ 2.2 (with Hα observed in the YJ, H, and K bands, respectively). The KMOS^3D targets were drawn from the 3D-HST source catalog (Skelton et al. 2014; Momcheva et al. 2016), a near-IR grism survey with the Hubble Space Telescope (HST) in the CANDELS HST imaging survey fields (Grogin et al. 2011; Koekemoer et al. 2011), and with extensive X-ray to radio multi-wavelength data. The selection criteria for KMOS^3D were a K-band magnitude K_AB ≤ 23 mag, log(M_*/M_⊙) > 9.0, and a secure and sufficiently accurate redshift (either from the R ∼ 130 grism spectra or from R > 300 optical/near-IR slit spectra) to ensure sky line avoidance for the lines of interest.¹⁵ These criteria reduced biases toward brighter, bluer, and more intensely star-forming objects.

Up to the end of 2017 December, a total of 725 galaxies were observed in KMOS^3D for their Hα+[N ii] emission.¹⁶ These data have a median seeing-limited FWHM resolution of 05, are split roughly equally between the YJ, H, and K bands, and have typically ∼8 hr on-source integration times (with a median of 5, 8, and 9 hr in YJ, H, and K, respectively). The high Hα detection fraction of ≈79% is nearly constant across all bands; it increases to ≈91% among SFGs (defined as having a SFR relative to that of the MS at the source's redshift and stellar mass ΔMS = log(SFR/SFR_MS) > −0.85 dex) and is an appreciable ≈28% for quiescent galaxies with ΔMS < −0.85 dex.

We culled the objects for the present analysis among the detected sources with S/N per spectral channel at Hα of S/N > 3, and excluded cases where strong residuals from telluric lines affect the interval encompassing a few 1000 km s⁻¹ around the Hα+[N ii] complex (see Section 2.5.1 for more details). The latter criterion is particularly important, given the large velocity extent of the outflow emission and for optimizing the quality of the spectra. This yielded the set of 525 galaxies considered here (219 in YJ, 131 in H, 175 in K band), with median integration time of 8.2 hr (ranging from 1.2 to 28.8 hr).

We included 47 galaxies from the SINS/zC-SINF seeing-limited and AO-assisted surveys with the single-IFU instrument SINFONI at the VLT (Förster Schreiber et al. 2009, 2018; Mancini et al. 2011). The SINS/zC-SINF targets were drawn from a collection of parent z ∼ 1.5–2.5 samples selected on the basis of their K band, 4.5 μm, or optical magnitudes and/or colors, with accurate optical spectroscopic redshifts, and an expected observed integrated Hα flux ≥5 × 10⁻¹⁷ erg s⁻¹ cm⁻² (or equivalently a SFR ≳ 10 M_⊙ yr⁻¹). Of the 84 objects targeted for Hα in the initial seeing-limited observations (typical FWHM of 06), 74 were detected, and a representative subset of 35 were followed-up at high resolution with AO (median FWHM of 017) and with deep integrations (median integration time of 6.0 hr, ranging from 2 to 23 hr). As extensively discussed in the references provided, the SINS/zC-SINF galaxies probe well the z ∼ 2 SFG population over two orders of magnitude in stellar mass and SFR, although with biases in the low-mass regime toward bluer colors and higher specific SFRs, stemming from the parent surveys and the Hα flux detectability. We further included here two z ∼ 2 sources observed with SINFONI+AO that were not part of the original SINS/zC-SINF sample; one of them, J0901+1814, was in our previous G14 study (and will be discussed in more detail by R. L. Davies et al. 2019, in preparation).

For the study of outflows here, we kept the SINS/zC-SINF galaxies observed in AO and natural seeing that have S/N > 3 per spectral channel at Hα and no strong sky line residuals around Hα+[N ii], and that do not overlap with the KMOS^3D sample. The inclusion of these sources adds high-quality IFU data, especially toward lower masses at z ∼ 2, where the main KMOS^3D–based set has sparser sampling and resolves these smaller galaxies more poorly.

To further boost the numbers of high-mass but rarer galaxies, necessary for robust statistics in this regime, we also included objects from the following sets with seeing-limited near-IR spectroscopic data: (i) five log(M_*/M_⊙) ≳ 10.7, z = 1.4–2.3 galaxies, initially selected from their optical redshift, observed with the LUCI multi-object spectrograph (MOS) at the Large Binocular Telescope (LBT; G14; Newman et al. 2014; Wuyts et al. 2014), including EGS-13011166 observed in slit-mapping mode (Genzel et al. 2013); (ii) six emission line galaxies from the K-selected sample at log(M_*/M_⊙) ≳ 10.7 and 2 < z < 2.5 of Kriek et al. (2007, 2008), observed with the GNIRS spectrograph on the Gemini South telescope and with VLT/SINFONI (see also G14); (iii) the 16 of 25 star-forming compact massive galaxies with log(M_*/M_⊙) ≳ 10.7 and 2.0 < z < 2.5 from the 3D-HST survey presented by van Dokkum et al. (2015) and Barro et al. (2014b), observed with the multi-slit MOSFIRE or the single-slit NIRSPEC spectrographs on the Keck II telescope, and which are not part of the KMOS^3D, SINS/zC-SINF, and LUCI samples. Integration times for these data sets are typically between 1 and 4 hr. Further details of the selection and observations can be found in the references provided. These spectroscopic samples will hereafter be referred to as "LUCI," "K07," and "vD15/B14."

We followed the methodology applied in our previous work on outflows (Shapiro et al. 2009; Genzel et al. 2011, 2014; Newman et al. 2012a, 2012b; FS14). The fully reduced data cubes were first median subtracted to remove continuum emission, which is well detected in most of the more massive SFGs of our sample. The data were then 4σ-clipped blueward and redward of the Hα+[N ii] emission complex to remove telluric emission line shot noise. In some cases where a sky line was very close to the narrow (SF-dominated) Hα emission, we interpolated over one or up to at most four spectral channels. The cubes were then spatially smoothed with a Gaussian of FWHM between 3 and 4 pixels.

We fitted a single Gaussian line profile to the spectrum of each pixel to extract the smoothed velocity and velocity dispersion maps of the galaxy. A single Gaussian fit at the pixel level is mostly sensitive to the higher amplitude, narrower core of the line profile tracing emission from star-forming regions across galaxies, and, especially for the velocity of interest here, is little influenced by the broader and lower-amplitude outflow emission (see Förster Schreiber et al. 2018, Appendix C). The derived velocity field was then applied in reverse to the original data cube to remove large-scale velocity gradients. This technique minimizes the impact of velocity broadening due to orbital motions in the final extracted spectra, and at the same time improves the S/N for detecting faint features and line wings. The method has obvious limitations for the compact sources with strong but unresolved inner velocity gradients; in such cases, the unresolved velocity gradients result in increased central velocity dispersions.

From the velocity-shifted cube of each galaxy, we extracted a spectrum covering the Hα, [N ii], and [S ii] lines (and also [O i]λ6300 for galaxies for which the line falls within the observed spectral band and is not contaminated by telluric lines). The spectra were typically integrated over the galaxies, or over the "nuclear" regions for the more extended galaxies; the typical extraction region was ∼05–12 in diameter (∼2–5 kpc in radius). In the final selection of the galaxies, we rejected a few sources with very strong atmospheric contamination, such that only a small subset of the galaxies shows significant sky residuals in their Hα+[N ii] profiles.

The final spectra for each galaxy were normalized to the peak amplitude at Hα and interpolated onto a common velocity sampling of 30 km s⁻¹. The quality of the spectra extracted from the data cubes naturally varies, owing to the variations in line flux and on-source integration times. With integrated Hα fluxes ≳10⁻¹⁷ erg s⁻¹ cm⁻² (mean and median of 1.1 × 10⁻¹⁶ and 8.7 × 10⁻¹⁷ erg s⁻¹ cm⁻²) and the long integration times (mean and median of 8 hr), the average and median S/N per spectral channel for Hα is ∼15, with 56 galaxies having S/N > 30.

Our aim of deriving the physical properties of outflows relies on multi-component fitting (broad + narrow) to the Hα line and [N ii] and [S ii] doublets, as described in the next subsection. Although the overall S/N of the data is high, it is not sufficient to allow such fitting for all galaxies individually. We thus co-averaged spectra for different bins in global galaxy properties (with typically ∼10 galaxies per bin and up to ∼60 depending on our purposes) following two approaches. In one approach, we computed the averaged spectrum with a uniform weighting for all galaxies in a bin. In a few cases, the resulting spectrum was of insufficient quality for reliable fitting of all features of interest because of the contribution of an individual lower S/N spectrum, which was then excluded. While providing a fair estimate of the average and being least affected by outliers, this choice obviously does not optimize the S/N of the stacked spectrum. Thus, in the second approach, we averaged the spectra weighting by S/N or (S/N)² to obtain the highest quality stacked spectra. We also compared results by further splitting up the objects in a bin. We found that these various methods make little difference, indicating that the properties of the stacked spectra are robust.

Motivated by the earlier analyses of Ho et al. (1997), Genzel et al. (2011), and G14, we fitted multiple Gaussians to Hα, [N ii]λλ6548,6584, and whenever possible also [S ii]λλ6716,6731, with the following assumptions: (i) the systemic velocities and widths of the narrow component of all lines are identical, and likewise for the broad component, and (ii) the [N ii]λ6548/λ6584 flux ratio is 0.326 (Storey & Zeippen 2000). The free parameters in the fitting were thus the FWHM of the narrow and the broad components (FWHM_na, FWHM_br), the velocity shift between the broad and narrow component centroids (Δv_br), the [N ii]λ6584/Hα flux ratio in the narrow and broad components ([N ii]/Hα_na, [N ii]/Hα_br), and the broad-to-narrow Hα flux ratio F_br/F_na. In cases where we fitted the [S ii] lines as well, the additional free parameters were the following flux ratios: [S ii]λ6716+λ6731_na/Hα_na, [S ii]λ6716+λ6731_br/Hα_br, [S ii]λ6716/λ6731_na, and [S ii]λ6716/λ6731_br. All narrow and broad components were always fit simultaneously, using the Python Markov Chain Monte Carlo sampler emcee (Foreman-Mackey et al. 2013). We explored the parameter space with on average 1000 walkers, and 2000 burn-in and run steps. Uncertainties were taken as the 68% percentile (1σ) bounds of the marginalized posterior distributions.

The S/N of the spectra does not always justify a 10 parameter fit, in which case we applied strong priors to a subset of the parameters, or fixed their values, based on results obtained from the higher S/N stacks. In particular, we generally restricted the FWHM_na and FWHM_br to values below and above 400 km s⁻¹; in some cases we fixed the values to FWHM_br = 400 and 1000 km s⁻¹ for SF-driven and AGN-driven outflows, respectively. In all fits, we constrained the [S ii] doublet ratio to the theoretically allowed range of 0.4315 < [S ii]λ6716/λ6731 < 1.4484 (Sanders et al. 2016). Due to the small separation and weakness of the [S ii] lines, the amplitudes of the narrow and broad components can be insufficiently constrained, especially for the cases of strong higher-velocity nuclear outflows. To break this degeneracy, we used a prior on [S ii]λ6716/λ6731_na of 1.34 ± 0.03 obtained from a stack containing only sources without broad outflow emission. This ratio implies a local electron density within the H ii regions of n_e,na = ${76}_{-23}^{+24}$ cm⁻³, lower than recent estimates in the range 100–400 cm⁻³ from multi-slit rest-optical spectroscopy of z ∼ 1–2.5 SFGs (e.g., Masters et al. 2014; Steidel et al. 2014; Sanders et al. 2016; Kaasinen et al. 2017; Kashino et al. 2017). The difference may be due to contamination in single-component fits by broad emission from denser outflowing gas that is accounted for in our narrow+broad two-component fits (with n_e,br ∼ 380 cm⁻³ for SF-driven outflows, and ∼1000 cm⁻³ for AGN-driven outflows; see Sections 4.1 and 4.2). When investigating the broad-to-narrow flux ratio distribution over the entire (binned) stellar mass versus ΔMS plane, down to the weakest broad emission levels, we made the further simplifying assumption of a single broad line, comprising the sum of Hα and [N ii] (with the ratio denoted F_br/F(Hα)_na).

The assumption of a Gaussian line shape for the narrow component is justified in terms of the central limit theorem of many individual H ii regions contributing to the integrated profile where large-scale velocity gradients have been removed, and the fitting results indicate it is adequate (see also, e.g., Genzel et al. 2011). It is less obvious or potentially wrong for the broad component, which in some cases appears to exhibit a blue/red asymmetry, such that the inferred line widths serve as a first order description.

In practice, given the S/N of the stacked data and as shown by G14, a broad component can be detected if its integrated flux is at least 10% that of the narrow component, and its width at least twice that of the narrow component. The average S/N is comparable across the stellar mass and ΔMS ranges covered, such that the detectability of broad emission in terms of its relative flux fraction is roughly constant with these parameters. This assessment was verified quantitatively by adding broad Gaussian components of FWHM 400 and 1500 km s⁻¹ in Hα and [N ii] with varying amplitudes to stacked spectra in different mass bins (excluding those with strong detected broad components), and then analyzing the spectra as described above. In these stacks (of typically ∼10 galaxies each), the minimum detectable broad component, in the sense of a significant and correct extraction of its width and flux (at the ≥3σ level), is about 15%–20% of the narrow component in terms of flux ratio, more or less flat across the mass range sampled by our data and similar for both widths (see G14, Figure 3). Broad components with fluxes down to about 10% that of the narrow component are still detectable but the inferred properties from spectral fitting are uncertain.

For individual galaxy spectra, the lowest detectable broad component obviously typically corresponds to higher broad flux levels, although the high S/N tail extends to similar values as the stacks such that similar limits as derived above are applicable in those cases. Defining a single reliable criterion for individual spectra is not straightforward because the detectability depends on the S/N, as well as on the amplitude and width of the broad component, all of which can vary importantly among the objects. Since narrow + broad component fits are not possible for all individual galaxies, the identification relied largely on visual inspection. Following G14, we classified each galaxy as having a secure (unambiguous presence), a candidate (possible or marginal), or no detection of a broad component around the Hα+[N ii] complex. Weaker and/or lower velocity outflows may be missed; the outflow incidences based on this identification procedure may thus represent lower limits.

The size and homogeneous coverage over the physical properties explored enabled us to split the objects in several tens of bins sampling the parameter space. More specifically, in our finest grids, we split the full sample in about 60 bins containing each about 10 galaxies. To recover and visualize the mean trends in various pairs of galaxy properties, we found the non-parametric locally weighted polynomial regression method LOESS, as implemented by Cappellari et al. (2013),¹⁸ particularly useful. We assigned the properties derived from the binned data to the individual galaxies, and accounted for Poisson uncertainties. We typically employed a second-order polynomial and a 0.5 fraction of points in the local approximation, and validated the recovered trends against the input binned distributions.

Figure 3 illustrates the previous steps, showing the incidence of a broad outflow emission component as a function of stellar mass and MS offset. We determined the presence of the broad component from the spectra of individual galaxies, distinguishing between secure and candidate cases as explained in the previous subsection (left panel of Figure 3). We defined seven bins in log(M_*) of width between 0.3 and 0.5 dex, with ∼100 galaxies in each of the central five bins and about 30 in the lowest and highest mass bins. For every mass bin, we then defined ΔMS intervals containing ∼10 galaxies each, with typical width of 0.15 dex but varying from ∼0.05 dex close to the MS at intermediate masses and up to ∼1–3 dex for the lowest ΔMS bin below the MS. We then computed the fraction of galaxies with a broad outflow component (and its Poisson uncertainty) for each of the 61 resulting bins, assigning a weight of 1 and 0.5 to secure and candidate detections, respectively (middle panel of Figure 3). Specifically, this fraction is defined as f_out = (N_secure + 0.5 × N_candidate)/N_total, where N_total is the number of galaxies in the bin.¹⁹ These fractions are used as input for the two-dimensional LOESS smoothing, with the resulting distribution highlighting the main underlying trends (right panel of Figure 3). Because of the modest number of galaxies in each bin, the relative uncertainties can be large, especially for low incidence bins (exceeding ∼50% for f_out ≲ 0.20), but are taken into account in the LOESS smoothing. We note that all trends in outflow incidence presented in this work remain qualitatively the same if we exclude the candidates (or weight them as secure detections), which reflects the fairly similar distributions of candidate and secure cases in the galaxy parameters explored.

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Because the strength of the broad outflow emission varies with galaxy properties, and because most of the data were obtained in natural seeing conditions, it is not possible to distinguish between SF- and AGN-driven outflows in all galaxies based on the above spatial and spectral characteristics. Instead, we used as a discriminant the narrow component [N ii]/Hα flux ratio²¹ together with diagnostics from ancillary data from X-ray to mid-IR and radio wavelengths. Specifically, galaxies with [N ii]/Hα_na > 0.45 and/or X-ray, mid-IR, or radio properties indicative of an AGN are identified as hosting an AGN. These different diagnostics are known to select partly different subsets of the full AGN population, notably because of the variability and phenomenology of AGN activity (e.g., Juneau et al. 2013; Coil et al. 2015; Azadi et al. 2017; Padovani et al. 2017), motivating our approach of using complementary indicators. When present, the broad Hα+[N ii] component is then attributed to an AGN-driven outflow if the galaxy has an AGN, or to SF-driven outflows otherwise.

For the AGN identification from the ancillary data, we followed a similar approach as described by G14 with updated source catalogs, based on the combination of the following diagnostics: (1) X-ray detection and X-ray-based properties (Xue et al. 2011; Symeonidis et al. 2014); (2) mid-IR Spitzer/IRAC 5.8–3.6 μm versus 8–4.5 μm colors (Donley et al. 2012); (3) 1.4 GHz "radio excess threshold" comparing the measured flux to that expected from the SFR (e.g., Appleton et al. 2004; Delvecchio et al. 2017); (4) detection in wide-field VLBI observations; and (5) a match with optically or X-ray variable sources. The list of surveys and catalogs used is given in the Appendix. Since the sensitivity, instruments used, and availability of the observations vary considerably between the different extragalactic fields relevant to our full sample, the identification from ancillary data is not uniform and likely misses a higher proportion of AGN in shallower fields.

Ideally, AGN identification from rest-optical line emission would require at least one pair of ratios such as the classical "BPT" diagnostic [O iii]λ5007/Hβ versus [N ii]λ6584/Hα (e.g., Baldwin et al. 1981; Veilleux & Osterbrock 1987; Kauffmann et al. 2003; Kewley et al. 2006, 2013). Measurements of all four lines are available only for a small number of objects in our sample (discussed by Kriek et al. 2007; Newman et al. 2014, FS14, and G14), so we relied on [N ii]/Hα for most galaxies. A ratio above ∼0.45 indicates a contribution by a non-stellar excitation source, which we attributed to AGN activity. This [N ii]/Hα cut may miss some AGN in low-metallicity hosts toward lower stellar masses (Kewley et al. 2013). In addition, nebular line emission from star-forming regions within the apertures used to extract the spectra may lower the overall line ratio sufficiently (a well-known effect; e.g., Ho et al. 1997; FS14; Coil et al. 2015); weaker AGN in actively star-forming systems can be difficult to detect, especially in seeing-limited data of distant galaxies.

Among the full sample, 574 galaxies have relevant data in the X-ray, mid-IR, or radio ranges, and 70 of them (12%) satisfy at least one of the corresponding AGN criteria. X-ray-identified AGN dominate, with 54 sources. From the [N ii]/Hα_na ratio, 119 of all 599 galaxies (20%) are identified as having an AGN. In addition, six objects show evidence for very broad emission (FWHM ≳ 3000 km s⁻¹) in Hα but not in the [N ii] and [S ii] forbidden lines and for a bright, central point-like source in their HST rest-UV/optical imaging. The latter properties are consistent with Type 1 AGN dominated by emission from the unobscured BLR in the close vicinity of the nucleus; five of the six BLR sources are also identified as AGN from the ancillary data. In total, there are thus 152 AGN (25%) in the full sample, of which 146 are associated with obscured Type 2 AGN (24%); 38 objects fulfill both the X-ray/mid-IR/radio and [N ii]/Hα_na sets of criteria. Not all AGN identified from the ancillary data exhibit AGN signatures in their rest-optical spectra; this is the case for 32 sources in our sample. Conversely, 81 AGN are identified solely from their elevated [N ii]/Hα_na ratio.

The global fractions of AGN in our full sample are consistent with those of AGN surveys at z ∼ 1–3 using similar AGN indicators (e.g., Reddy et al. 2005; Daddi et al. 2007; Kriek et al. 2007; Brusa et al. 2009; Xue et al. 2010; Aird et al. 2012; Bongiorno et al. 2012; Hainline et al. 2012; Juneau et al. 2013; Coil et al. 2015; Padovani et al. 2017; Wang et al. 2017). With the average S/N ∼ 15 per spectral channel of our galaxy spectra (Section 2.5.1), [N ii]/Hα ratios of 0.45 are measured with an uncertainty lower than 10% (and ratios down to ∼0.1 are determined with a 3σ significance); measurement uncertainties are thus unlikely to dominate the partial cross-identification between diagnostics. The AGN fraction in flux limited surveys increases as a function of galaxy stellar mass, and different diagnostics select partly complementary subsets of AGN (see references provided previously). Our use of combined diagnostics and the fact that our sample is weighted toward high-mass galaxies explains that our fractions tend to lie at the higher end of ranges reported in the literature when considering the full mass range. The non-uniform depth of the ancillary data between the various fields is also reflected in the corresponding AGN fractions. In particular, the fraction of X-ray-identified AGN decreases by a factor of 2.6 between the deepest GOODS-South field and the shallower COSMOS field (from 13% to 5%), emphasizing the importance of using complementary techniques.

The AGN among our sample span a wide range of bolometric AGN luminosities log(L_AGN/[erg s⁻¹]) ∼ 42.5–47. We estimated the L_AGN for the X-ray identified AGN from the published X-ray fluxes (corrected for H absorption in most cases; see references in Appendix), applying the bolometric correction to the derived hard 2–10 keV luminosity of Rosario et al. (2012). For the AGN identified from [N ii]/Hα_na, we inferred the [N ii]λ6584 AGN luminosity from the narrow component Hα flux and [N ii]/Hα_na ratio. Since the apertures used to extract the spectra encompass regions at least a few kpc in diameter, we accounted for a likely contribution by star-forming regions to the narrow [N ii] emission based on the (evolving) mass–metallicity relation as parametrized by Genzel et al. (2015), and the conversion from log(O/H) to [N ii]/Hα of Pettini & Pagel (2004).²² The [N ii] luminosity was scaled to the bolometric luminosity assuming a fiducial [N ii]λ6584/[O iii]λ5007 ∼ 0.75 (the mean and median for bright Seyfert 2 galaxies in the SDSS survey) and a bolometric conversion based on Netzer (2009). For AGN hosts satisfying both the X-ray and [N ii]/Hα_na criteria, the respective log(L_AGN) estimates are in broad agreement, with a median difference of ∼0.5 dex and a scatter of ∼0.8 dex.

All these estimates have large uncertainties but are sufficient for an order-of-magnitude assessment. The distributions of X-ray and [N ii]-based log(L_AGN) largely overlap, with median values of 44.7 and 45.2, respectively. Given the large uncertainties, this difference is not significant but we note that the very deep X-ray data in the GOODS-South field probe AGN down to the lowest luminosities, and our strict [N ii]/Hα_na cut would miss weaker AGN in galaxies toward lower masses and metallicities, and with stronger outshining from star-forming regions. The specific AGN luminosity together with the black hole to galaxy stellar mass ratio M_BH/M_* provides a measure of the Eddington ratio, log(λ_Edd) = log(L_AGN)–38.1–log(M_BH). Assuming a universal M_BH/M_* = 0.0014 (Häring & Rix 2004), the AGN in our sample have a broad distribution spanning ∼4.5 dex and peaking at log(λ_Edd) ∼ −1, with no obvious differentiation between the subsets identified from X-ray and [N ii]/Hα_na. This peak value is broadly consistent with the break in the Eddington ratio distribution of X-ray-selected AGN at z ∼ 1–3 (e.g., Aird et al. 2012, 2018; Hickox et al. 2014; Bernhard et al. 2018). In summary, the AGN identified in our sample span a broad range from low luminosity, very sub-Eddington AGNs (log(λ_Edd) ∼ −3.5) to luminous QSOs with high Eddington ratios (with 10 objects, or 7%, at log(λ_Edd) ≳ 0), with distribution peaks around the typical values as inferred from X-ray-selected surveys.

With the above AGN identification, the broad emission component is attributed to AGN-driven winds in 103 of the 190 galaxies with outflow signatures, and to SF-driven winds in the other 87 galaxies. There are 43 AGN in which no outflow signature is detected. Given the shortcomings noted previously for our [N ii]/Hα_na plus ancillary data-based classification, it is possible that in some objects the dominant driver of the outflow is misidentified. It is also possible that AGN- and SF-driven outflows coexist in the same source, as best seen in a few of the SINS/zC-SINF targets with high-resolution AO data (FS14). However, the spectral differences seen in Figure 5, and the distinction in the various trends and physical properties of the AGN- and SF-driven outflows discussed later, suggest that "cross-contamination" is not important.

The incidence of AGN and AGN-driven outflows, denoted f_AGN and f_AGNout, is shown in Figure 6, in the log(M_*) versus ΔMS plane, and also as a function of log(M_*) for wider ΔMS bins below, around, and above the MS. The incidence of AGN and AGN-driven nuclear outflows appears to be solely a function of mass, with almost two orders of magnitude increase between log(M_*/M_⊙) ∼ 9 and >11. The correlation with stellar mass is very strong (ρ = 0.84 for AGN and ρ = 0.81 for AGN-driven outflows for the binned data, both significant at ≈6.5σ). AGN and AGN-driven winds are not correlated with ΔMS (ρ = −0.10 and 0.04, respectively). At the highest masses, up to ∼80%–100% of the galaxies harbor an AGN, and most (∼60%–75%) drive a prominent outflow. This is equally true above, on, and below the MS. We note that because our AGN identification procedure includes the [N ii]/Hα_na criterion in addition to the X-ray/mid-IR/radio indicators, our resulting f_AGN are higher than reported by G14. Counting only AGN identified based on the ancillary indicators, the f_AGN for the present sample are ∼2–3 times lower, and at most ∼40%–55% at the highest masses, similar to the fractions in G14.

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Other near-IR studies also report high fractions ∼50%–75% of AGN-driven outflows among X-ray-selected QSOs and more moderate luminosity AGNs based on [O iii]λ5007 and/or Hα+[N ii] kinematic signatures (e.g., Brusa et al. 2015b; Harrison et al. 2016). On the other hand, Leung et al. (2017) found a lower fraction of 19% among z ∼ 2 AGNs identified from X-ray, IR, and rest-optical indicators, observed as part of the MOSDEF survey. Since AGNs identified by various diagnostics form a subset of the entire galaxy population, part of the differences in reported fractions may be attributed to the different sample selection. Our main goal of characterizing the role of outflows among the overall galaxy population motivated our analysis of a sample selected irrespective of the nuclear activity of the galaxies. In our sample and over all masses (excluding BLRs), outflows are detected in 103 of the 146 AGNs for a global fraction of 71%, or 60% when weighting outflow candidates by ×0.5. There is no obvious distinction between AGNs identified through different diagnostics. For instance, 37 of the 49 AGN sources detected in hard X-ray emission (irrespective of their [N ii]/Hα_na ratio) exhibit an outflow signature, and 90 of the 119 AGN galaxies identified based on the [N ii]/Hα_na criterion (irrespective of their X-ray properties) do, for nearly equal unweighted fractions of 76% or ∼60% when downweighting candidates. The stacked spectra of these subsets of AGNs with outflows are very similar to each other and to the stack from all AGN-driven outflows.

The dependence of AGN or AGN-driven outflow incidences is at least quadratic in stellar mass, and possibly exponential, with a sharp onset around log(M_*/M_⊙) ∼ 10.7–10.9. This threshold coincides with the Schechter mass, independently of redshift across the z = 0.6–2.7 range our data sample. Again, our results are in excellent agreement with and further strengthen the conclusions of G14. Since the Schechter mass corresponds to the transition above which the likelihood of quenching rises strongly at all redshifts z ≲ 2–3, thus limiting galaxy growth (Peng et al. 2010b; Ilbert et al. 2013; Muzzin et al. 2013), the threshold for the onset of prominent AGN-driven nuclear outflows appears to be concomitant to quenching. This point is discussed further in Section 4.

The incidence of outflows driven by SF, f_SFout (i.e., in galaxies without indication of AGN activity from either the [N ii]/Hα_na or the ancillary data indicators) is shown in Figure 7 (left panel). The f_SFout does not depend on stellar mass (ρ = −0.01) but increases upward with ΔMS at all masses, with ρ = 0.50 (3.9σ) in the binned data. About 25%–30% of "starbursting outliers" above the MS (ΔMS ≳ 0.6 dex) drive a SF-driven outflow detected in rest-optical line emission. Physically, one would expect that the incidence of (detectable) SF-driven outflows also depends on the SFR surface density, Σ_SFR (e.g., Heckman 2002; Kornei et al. 2012; N12b; G14). Figure 7 (right panel) shows the distribution in the log(M_*) versus log(Σ_SFR) plane, where the surface density is taken as half the total SFR uniformly distributed within R_e. The incidence increases with Σ_SFR, with significant fractions ≳10% above ∼0.5–1 M_⊙ yr⁻¹ kpc⁻², corresponding to the observed threshold above which the broad emission signature becomes strong (e.g., N12b; Davies et al. 2019).

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Galactic winds identified from interstellar absorption features in rest-frame UV spectra of SFGs at z ∼ 0.5–3 are more prevalent (≳50%; e.g., Weiner et al. 2009; Steidel et al. 2010; Kornei et al. 2012; Martin et al. 2012; Rubin et al. 2014). Our lower detection rate could be due to S/N limitations in individual spectra (since the broad SF-driven outflow signature typically has a modest amplitude and velocity width; Figure 5), different sample selection, or possibly reflects the different outflow phase probed by each technique. The Hα+[N ii] emission line technique is sensitive to the emission measure and thus probes preferentially ongoing ejection of denser gas. The rest-UV absorption line technique integrates over the line of sight and down to more tenuous material, and would thus more easily detect outflows even of low duty cycle at any given time. These factors may also explain differences in outflow incidence trends with SF properties, which are found to be typically weak or absent based on rest-UV interstellar absorption tracers (e.g., Weiner et al. 2009; Kornei et al. 2012; Martin et al. 2012; Rubin et al. 2014). Observations of sizeable samples targeting both interstellar absorption and nebular line tracers of outflows in the same galaxies would be valuable to understand these differences.

We investigated correlations between larger sets of parameters to include spectral properties and additional galaxy parameters. We quantified trends between pairs of properties with the Spearman rank correlation coefficient and examined all properties simultaneously through a Principal Component Analysis (PCA). Because the coverage in galaxy parameters of the sample varies somewhat with redshift and some trends may not be monotonic, we validated the main trends by inspecting the 2D distributions in properties (similarly to Figure 3) and controlling for specific variables where appropriate. In particular, the low-mass coverage is different for the galaxies at 0.6 < z < 1.2, 1.2 < z < 1.8, 1.8 < z < 2.7, leading to some spurious correlations with redshift. In G14, we found that the broad outflow emission incidence and spectral properties did not depend significantly on redshift when splitting the sample in two z intervals. With the larger sample analyzed here, no significant redshift dependence at fixed galaxy property is found either (see also Table 1). For the trend analysis below, we thus marginalized over redshift.

Table 1.  Derived Physical Properties of SF- and AGN-driven Outflows

Stack	$\langle z\rangle$	$\langle$log(M*/M⊙)$\rangle$	$\langle$ΔMS $\rangle$	L(Hα)0,SF	$\langle \mathrm{SFR}\rangle$	$\langle$Re$\rangle$	Fbr/Fna(Ha)	L(Hα)0,br	vout	$\langle$vc$\rangle$	vout/vc	Re/vout	ne,br	Mout(H ii+He)	${\dot{M}}_{\mathrm{out}}$	η	Pout/Prad	${\dot{E}}_{\mathrm{out}}/{L}_{\mathrm{SFR}}$
				(erg s−1)	(M⊙ yr−1)	(kpc)		(erg s−1)	(km s−1)	(km s−1)		(yr)	(cm−3)	(M⊙)	(M⊙ yr−1)
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)
SF-driven Outflows
Best spectra (33, secure detections)	1.84	10.58	0.180	9.9E+42	47.3	3.4	0.68	6.75E+42	450	220	2.0	7.7E+06	${380}_{-167}^{+249}$	5.77E+07	7.5	0.16	1.0	7.8E–04
z < 1.7 (35, incl. candidates)	0.92	10.21	0.350	6.2E+42	29.3	3.1	0.63	3.88E+42	460	200	2.3	6.7E+06	380	3.31E+07	4.9	0.17	1.1	8.7E–04
z > 1.7 (49, incl. candidates)	2.27	10.55	0.160	2.0E+43	94.0	3.2	0.88	1.74E+43	460	190	2.4	6.9E+06	380	1.48E+08	21.5	0.23	1.5	1.2E–03
log(M*/M⊙) < 10 (18, incl. candidates)	1.00	9.71	0.410	3.2E+41	1.5	2.5	0.54	1.70E+41	260	119	2.2	9.6E+06	380	1.45E+06	0.2	0.10	0.4	1.7E–04
log(M*/M⊙) = 10–10.3 (12, incl. candidates)	2.20	10.17	0.180	8.2E+42	39.0	2.9	0.76	6.22E+42	490	139	3.5	6.0E+06	380	5.32E+07	8.9	0.23	0.6	4.5E–04
log(M*/M⊙) = 10.3–10.6 (18, incl. candidates)	2.20	10.49	0.100	1.1E+43	51.0	3.1	0.74	7.93E+42	400	241	1.7	7.7E+06	380	6.77E+07	8.8	0.17	0.3	2.3E–04
log(M*/M⊙) = 10.6–10.9 (19, incl. candidates)	2.00	10.74	0.210	1.8E+43	88.0	4.1	0.83	1.53E+43	410	264	1.6	1.0E+07	380	1.31E+08	13.0	0.15	0.7	5.1E–04
log(M*/M⊙) > 10.9 (11, incl. candidates)	2.20	11.05	0.270	5.3E+43	253.0	4.4	0.39	2.07E+43	430	304	1.4	1.0E+07	380	1.77E+08	17.1	0.07	0.3	2.1E–04
AGN-driven Outflows
Best spectra (30, secure detections)	1.60	11.00	0.160	2.0E+43	97.0	2.9	0.86	1.75E+43	2300	284	8.1	1.3E+06	1000	5.69E+07	44.7	0.46	15.4	5.9E–02
z < 1.7 (62, incl. candidates)	1.31	10.95	0.150	8.8E+42	42.0	3.8	0.68	6.00E+42	1990	283	7.0	1.9E+06	${915}_{-339}^{+605}$	1.95E+07	10.2	0.24	7.0	2.3E–02
z > 1.7 (36, incl. candidates)	2.32	11.05	−0.050	3.1E+43	148.0	2.9	0.68	2.11E+43	1000	286	3.5	2.9E+06	${1229}_{-357}^{+579}$	6.86E+07	23.4	0.16	2.3	3.8E–03
[N ii]/Hαna = 0.45–0.75 (54, incl. candidates)	1.61	11.00	0.010	1.7E+43	83.0	3.3	0.31	5.40E+42	1400	309	4.5	2.4E+06	${659}_{-474}^{+1811}$	1.75E+07	7.3	0.09	1.8	4.2E–03
[N ii]/Hαna > 0.75 (35, incl. candidates)	1.03	11.03	−0.110	9.0E+42	43.0	3.1	0.52	4.70E+42	900	274	3.3	3.5E+06	>3375	1.53E+07	4.4	0.10	1.3	2.0E–03
"Strong Outflows" (8)	1.60	10.97	0.630	5.3E+43	253.0	3.1	1.34	7.11E+43	1500	301	5.0	2.1E+06	1000	2.31E+08	111.7	0.44	9.6	2.4E–02

Note. Properties are reported for subsets of galaxies and their co-averaged spectra, as listed in Column 1, where the number of galaxies included is given in parenthesis. Galaxy properties include the following, corresponding to the median value for the galaxies entering each stacked spectrum: redshift (Column 2), stellar mass log(M_*), MS offset ΔMS, SFR, and effective radius R_e (Columns 3, 4, 6, and 7), the total extinction-corrected Hα luminosity (Column 5), and the circular velocity derived from the measured kinematics (Column 11). Outflow properties include the broad-to-narrow Hα flux ratio and implied intrinsic Hα luminosity of the outflowing ionized gas (Columns 8 and 9), the outflow velocity from the broad component line width (Column 10), the ratio of outflow to circular velocities (Column 12), the time for the outflow to travel a distance of R_e at constant velocity (Column 13), the local electron densities derived from the [S ii]λ6716/λ6731 flux ratio in the broad component (Column 14), the mass and mass ejection rate of the outflowing ionized gas including H and He (Columns 15 and 16), the mass loading η = ${\dot{M}}_{\mathrm{out}}$/SFR, the outflow to stellar radiation momentum ratio, and the outflow kinetic energy to stellar luminosity ratio (Columns 17, 18, and 19). For n_e,br, the fitting results are listed if the [S ii] ratio in the broad component was measured at >3σ, with uncertainties corresponding to the 68% confidence intervals if the ratio is >1σ away from the high-density limit (adopting the calculations by Sanders et al. 2016). If the [S ii] ratio was confidently measured but within 1σ of the high-density regime, the lower 68% confidence interval is given as limit. If the fitted FWHM of the broad component exceeded 1500 km s⁻¹, roughly twice the [S ii] line separation, the fits become questionable and we then list the best-determined estimate from the successful fits for either SF-driven or AGN-driven outflow stacks (see text). For all calculations of mass outflow, momentum, and energy rates, we used a common n_e,br = 380 cm⁻³ for the SF-driven outflows and 1000 cm⁻³ for the AGN-driven outflows (see Sections 4.1 and 4.2).

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In a first step, we considered variations in the binned log(M_*)–ΔMS plane of the incidences f_SFout, f_AGNout, f_AGN, the [N ii]/Hα_na and F_br/F(Hα)_na flux ratios, and the broad component velocity width FWHM_br. The spectral properties were derived from fits to the Hα+[N ii] complex in the unweighted stacks, with a single component for the broad emission in order to characterize the emission across all bins, as described in Section 2.5.3. Figure 8 (left six panels) shows the fractions and spectral properties plotted at the average log(M_*) and ΔMS values of the galaxies in each bin (using the median instead makes little difference in the distributions and derived trends). In addition to the trends for the incidences discussed in the previous subsection, [N ii]/Hα_na and F_br/F(Hα)_na correlate most strongly with log(M_*) (ρ ∼ 0.76 at 5.9σ, and ρ ∼ 0.47 at 3.6σ) and little with ΔMS (ρ < 0.15). The FWHM_br correlates primarily though moderately with log(M_*) (ρ ∼ 0.4 at ∼3σ).

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These trends, and the strong associations among subsets of properties, are most clearly visualized in the results from the PCA shown in the rightmost panel of Figure 8. For these data and eight parameters, 71% of the total sample variance is explained by two principal components (PCs), with PC 1 and 2 strongly coupled to log(M_*) and ΔMS, respectively. The most striking feature of the loading plot is the nearly orthogonal separation between f_SFout on one hand, and f_AGNout, f_AGN and spectral characteristics on the other, implying in particular that SF- and AGN-driven outflows are strongly decoupled from each other in their incidence and emission properties. The association between f_AGN, [N ii]/Hα_na, and log(M_*) is not surprising, given the AGN identification procedure of Section 3.2.1 and the galaxy stellar mass–metallicity relationship (e.g., Erb et al. 2006; Queyrel et al. 2009; Wuyts et al. 2014, 2016a; Zahid et al. 2014; Sanders et al. 2015, 2018; Kashino et al. 2017). To some extent, that with FWHM_br and F_br/F(Hα)_na may be influenced by a more important contribution to the line profile from steep but unresolved inner velocity gradients in the more massive and denser galaxies (or residuals stemming from uncertainties in the velocity field used to remove the orbital motions). However, tests using model data cubes of massive rotating disks with a bulge, subjected to the typical seeing and noise level of the data, and analyzed in the same way indicate that such effects could only account for a line FWHM of ∼400 km s⁻¹ (G14). This value is well below the FWHM ≳ 1000 km s⁻¹ measured in the high-mass bins.

Focusing on the incidences, we searched for additional significant trends with a larger set of galaxy parameters. We considered the stellar mass surface density Σ_* within R_e, the absolute and specific SFs rates SFR and sSFR, and Σ_SFR within R_e. We also included the structural parameters ΔMR_e, R_e, the projected major-to-minor axis ratio q and the Sérsic index ${n}_{{\rm{S}}\acute{{\rm{e}}}\mathrm{rsic}}$ from the HST H-band imaging (van der Wel et al. 2012; Lang et al. 2014). We computed the incidences rebinning the full sample in each of these galaxy properties paired with stellar mass following the same procedure as described in 2.5.2, with about 60 bins of ∼10 galaxies each. We also carried out a PCA using individual galaxy parameters, and replacing fractions for the incidence with categorical variables representing the absence or presence of an outflow or AGN with 0 and 1, respectively, and a candidate outflow with 0.5.

This more comprehensive exploration shows that f_AGNout and f_AGN correlate roughly equally with Σ_*, and Σ_{*,1 kpc} (ρ ∼ 0.8 at the ∼6σ level) as with log(M_*), and also depend significantly on ${n}_{{\rm{S}}\acute{{\rm{e}}}\mathrm{rsic}}$ and ΔMR_e, which are related to compactness (ρ ∼ 0.4–0.5 at the ∼3–4σ level). The f_SFout correlates most tightly and in similar measure with ΔMS and log(sSFR) (ρ ∼ 0.5 at the ∼4σ level), and is somewhat more loosely coupled to SFR and Σ_SFR (ρ ∼ 0.4 at the ∼3σ level). The PCA indicates that the first PC is largely a measure of central stellar mass concentration (largest positive loading from Σ_*, largest negative loading from ΔMR_e), the second PC is related to SF activity and intensity (large positive loadings from all of ΔMS, log(SFR), log(sSFR), log(Σ_SFR)), and the third PC is most closely connected to size properties (largest loading from R_e). These three PCs account for 71% of the total variance. There is no significant correlation of outflow incidence with q for our sample, suggesting that inclination may not play an important role in terms of the detectability of ionized gas outflows in z ∼ 0.6–2.7 galaxies.

The trends between f_AGNout and f_AGN with central stellar mass density and galaxy size are consistent with the elevated AGN fraction among compact SFGs in log(M_*/M_⊙) ≳ 10 samples (e.g., Barro et al. 2014a; Rangel et al. 2014; Kocevski et al. 2017; Wisnioski et al. 2018). Most interestingly, perhaps, the anticorrelation with R_e and the correlation with Σ_* in our sample are clearly stronger in the mid- to low-mass regime and significantly weaker above the Schechter mass. Figure 9 plots the incidences as a function of R_e, normalized to the mean redshift evolution in size for SFGs (∝(1+z)^−0.75; van der Wel et al. 2014), for two mass intervals below and one above log(M_*/M_⊙) = 10.75. Given the typical uncertainties, the anticorrelation with relative size of f_AGNout and f_AGN is only significant below the Schechter mass. In contrast, f_SFout shows no significant trend in all mass ranges.

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In summary, the formal analysis of the new sample of 599 galaxies robustly confirms the main trends reported in our earlier N12, FS14, and G14 work based on much smaller samples. The incidence of AGN-driven outflows (and AGN) depends primarily and strongly on galaxy stellar mass and central surface density, while that of SF-driven outflows depends primarily on SF properties. They are almost completely decoupled from each other in the log(M_*)–ΔMS plane. Over the best sampled region of parameter space, the outflow velocities and the broad-to-narrow flux ratio are dominated by the mass trend. After accounting for the cosmic evolution in galaxy properties (such as the MS zero-point), the incidence of outflows and AGN, and the outflow emission properties, do not significantly depend on redshift.

Figure 11 presents the mass dependence of the inferred outflow mass loading factor and of the ratio of outflow velocity to galaxy circular velocity. Except for the lowest mass bin, v_out is approximately constant while v_c increases from ∼120 km s⁻¹ at the lowest mass to ∼305 km s⁻¹ at the highest mass. Consequently, v_out/v_c decreases with mass, from values >2 in the two lower mass bins to ≲1.5 in the two higher mass bins. Given the uncertainties in these estimates, this trend is tentative. However, it would be consistent with SF-driven winds in the lower mass objects escaping the galaxies more easily and perhaps penetrating into the outer halo while the higher mass SFGs can only drive fountains, as has been proposed theoretically for some time (e.g., Dekel & Silk 1986; Murray et al. 2005; Oppenheimer & Davé 2008; Übler et al. 2014).

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In momentum- or energy-driven winds, one would expect that the outflow mass loading factor depends on galaxy mass as follows:

$$\begin{eqnarray}\begin{array}{rcl}{\dot{M}}_{\mathrm{out}}{v}_{\mathrm{out}} & = & {L}_{\mathrm{SFR}}/c\propto \mathrm{SFR}\sim {M}_{* }\ \mathrm{on}\ \mathrm{the}\ \mathrm{MS},\\ \mathrm{or}\ {\dot{M}}_{\mathrm{out}}{{v}^{2}}_{\mathrm{out}} & \propto & {L}_{\mathrm{SFR}}\propto \mathrm{SFR},\ \mathrm{such}\ \mathrm{that}\\ \eta & = & \displaystyle \frac{{\dot{M}}_{\mathrm{out}}}{\mathrm{SFR}}\propto {v}_{\mathrm{out}}^{-1}(\mathrm{momentum})\ \mathrm{or}\ {v}_{\mathrm{out}}^{-2}\ (\mathrm{energy}).\end{array}\end{eqnarray} \tag{ 3 }$$

If v_out = a × v_c (e.g., Oppenheimer & Davé 2006) and v_c ∝ (M_*/m_d)^1/3 (e.g., Mo et al. 1998), where M_d is the fraction of the central galaxy relative to that of the entire halo, then η ∝ ${M}_{* }^{\alpha }$, with α in the range −1/3 to −2/3.

Over the roughly 1.5 dex range in average log(M_*) for the stacked spectra, this scaling would predict a drop in mass loading by a factor of ∼3 for momentum-driven winds and ∼10 for energy-driven winds. Assuming a constant local electron density in the outflowing ionized gas of n_e,br = 380 cm⁻³, the mass loading factor inferred empirically from our observations is fairly constant across this mass range, with a slope in logarithmic units of −0.1 ± 0.2 (Figure 11). This n_e,br value is most robustly constrained from the best-fit [S ii]λ6731/λ6716_br = 1.07 ± 0.13 for the stack of the 33 highest S/N spectra of galaxies with secure SF-driven outflow detection (Table 1 and Figure 5). We note that a nearly identical value of 1.08 ± 0.14 is obtained without prior on the narrow [S ii] ratio.²⁴ This measurement represents the first somewhat significant (∼2.5σ away from the low-density limit) determination of the local electron density in the broad component in the literature. For comparison, the lower S/N stack of 14 SFGs of N12b yielded a 2σ limit of <1200 cm⁻³, with a ratio consistent with the low-density limit. Since η is inversely proportional to n_e,br, a variation with mass could still occur if the electron density of the outflowing ionized gas changes substantially with stellar mass. Because the [S ii] lines are weak, S/N considerations hamper a reliable narrow+broad component decomposition in the stacked spectra for five mass bins, but splitting in two mass bins does not indicate that n_e,br changes significantly with mass.

Our finding of a roughly constant, and fairly low, mass loading factor over the log(M_*) range probed is surprising in view of the slope of the observed relationships between galaxy metallicity, M_*, and SFR, as well as the mass dependence of M_*/M_halo from abundance matching studies up to log(M_*/M_⊙) ∼ 10.7, which appear to require a power-law slope of α ∼ −0.35 to −0.8, with η ∼ 0.3–1 or higher around log(M_*/M_⊙) ∼ 10 (Lilly & Carollo 2016; Davé et al. 2017). In their analysis of outflow properties in stacked slit spectra of ∼125 non-AGN SFGs at z ∼ 2 from the MOSDEF survey, Freeman et al. (2019) found a positive correlation between η and log(M_*), which also is discrepant with theoretical expectations. Since Hα+[N ii] observations probe ∼10⁴ K ionized gas, it is conceivable that an important part of the expelled mass is contained in other outflow phases. Nonetheless, the indication of lower v_out/v_c at higher masses from our data could be consistent with more massive galaxies having higher baryon mass fractions and metallicities by retaining their baryons more efficiently than lower mass galaxies, at least up to log(M_*/M_⊙) ∼ 10.7.

Keeping in mind the substantial uncertainties of the above estimates and the simplicity of our model, resulting in systematic uncertainties of the mass ejection, momentum, and energy rates by at least ±0.3 dex, the inferred outflow rates are typically ∼10 M_⊙ yr⁻¹ (and in the range ∼0.2–20 M_⊙ yr⁻¹) and the mass loading factors are η ∼ 0.1–0.2 with our adopted n_e,br = 380 cm⁻³. These mass loading factors are ∼3–10 times lower than in our earlier work (Genzel et al. 2011; N12a, N12b; FS14; G14; see also Erb et al. 2006), owing to the higher n_e,br estimate based on the present data. As noted previously, prior [S ii] ratio measurements for the broad component were poorly constrained and prior estimates for the mass loading factors assumed n_e,br ∼ 50–80 cm⁻³. Our new results imply that the outflowing gas has on average both a higher local electron density and a higher [N ii]/Hα ratio than the narrow emission component (Figures 5 and 10). These findings suggest that the broad component-emitting gas may be compressed by shocks.

The modest mass loading factors, along with the ratios of outflow to stellar radiation momentum rates ∼0.1–1 and energy rates ∼10⁻⁴–10⁻³, can be easily accounted for by single scattering, photon pressure in momentum-driven winds (e.g., Murray et al. 2005; Hopkins et al. 2011). However, these low mass loading factors would not seem to be sufficient to account for the low galaxy formation efficiencies inferred at low masses from abundance matching studies (Behroozi et al. 2013a, 2013b; Moster et al. 2013, 2018). We stress again that all our derived outflow properties pertain to the ∼10⁴ K ionized gas phase; any additional contribution from very hot ionized plasma or cold atomic and molecular material in the outflows would increase the mass outflow rates and mass loading factors. The total mass loading factors could be much higher if the warm ionized phase of the SF-driven outflows carries only a small fraction of the total mass (as well as momentum and energy) of the winds, as inferred for local starbursts driving winds where the neutral and cold molecular phases dominate the mass outflow rates and the hot phase dominates the energetics (e.g., Veilleux et al. 2005; Heckman & Thompson 2017; and references therein).

We now turn to AGN-driven outflows. One of the distinctive spectral features of the galaxies hosting AGN-driven winds is the high excitation implied by their elevated [N ii]/Hα ratios. The good statistics afforded by our sample at high stellar masses (with a total of 119 [N ii]-strong sources) enabled us to investigate properties as a function of [N ii]/Hα_na ratio. Above the mass threshold where AGN-driven outflows (and AGN) become common (i.e., at log(M_*/M_⊙) ≳ 10.6), [N ii]-strong sources with a wide range of [N ii]/Hα_na ratios (0.45–5.6) are stochastically distributed in log(M_*) versus ΔMS space. Figure 12 shows the stacked spectra of the objects split in five bins of [N ii]/Hα_na ratio. It is apparent that as the narrow [N ii]/Hα ratio increases, so does the broad [N ii]/Hα. Overall, the [N ii]/Hα_br line ratio is typically between 1 and 2, indicating that the outflowing component likely is dominated by shocks. Here again, a reliable broad+narrow profile decomposition for [S ii] is hampered by the combination of the weakness of the [S ii] lines and S/N, and is further complicated by the strong blending resulting from the faster winds. Splitting instead the AGN-driven outflows in two bins of low and high [N ii]/Hα_na ratio or redshift, or considering the stack of 30 of the highest S/N spectra (Figure 5, bottom), yields higher local electron densities n_e,br ∼ 1000 cm⁻³ for the broad emission compared to the narrow emission, and also compared to the densities obtained for the SF-driven outflows. Comparably high electron densities ∼1000 cm⁻³ have been inferred from rest-optical line ratios tracing warm ionized gas in integrated spectra of local AGNs (e.g., Perna et al. 2017). Despite the uncertainties, the trends in line excitation suggest that compressing shocks are common in the outflowing components of AGN-driven outflows (e.g., Kewley et al. 2010, 2013).

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In addition to the line excitation properties, a key difference for the AGN-driven outflows is their higher velocities compared to the SF-driven outflows. The v_out/v_c ratios are ≳3.5 for the various stacks considered (see Table 1) and up to about 8 for the "best stack," systematically above the range of ∼1.5–3.5 for the SF-driven winds. Thus, in contrast to the SF-driven outflows, the AGN-launched outflows can in principle escape the galaxy and even its halo. In a collision with gas coming in from outside the halo, the shocked wind will attain a post-shock temperature of ∼5.5 × 10⁷ K, which has a very long cooling time, and will thus remain as a hot atmosphere that could prevent gas from entering the galaxy (e.g., Bower et al. 2017).

Nine objects further stand out in their spectral properties by their particularly strong broad emission component, indicative of more powerful outflows (we refer to them as "strong outflows"). The stack of the eight objects whose spectra encompass the [O i]λ6300 emission line is plotted in Figure 12 (bottom right), from which a high F_br/F_na = 1.3 is derived. In the stack, as well as in some of the individual spectra, [O i] is well detected. The total [N ii]λ6584/Hα, [S ii]λ6716+6731/Hα, and [O i]λ6300/Hα ratios (0.7, 0.25, 0.05, respectively) are consistent with a mixture of stellar photoionization, hard radiation from the AGN, and shocks contributing to the line excitation (e.g., Kewley et al. 2006; Allen et al. 2008; Rich et al. 2010, 2011; see also G14). These nine "strong outflows" sources are systematically more actively star-forming²⁵ and more compact than the other galaxies hosting an AGN-driven outflows in the same log(M_*/M_⊙) > 10.7 range, with median ΔMS = +0.63 dex and ΔMR_e = −0.25 dex compared to −0.01 and −0.11 dex, respectively. They also appear to have a more elevated nuclear accretion activity, with a median log(L_AGN/[erg s⁻¹]) ∼ 45.7 and log(λ_Edd) ∼ −0.5 compared to ∼45.4 and −1.0 for the other similarly massive objects with an AGN-driven outflow. Possibly, both SF and AGN activity contribute importantly to driving the observed winds in these "strong outflows" systems. Despite their above-average outflow mass ejection rates (∼110 M_⊙ yr⁻¹), and momentum and energy injection rates, the higher average SFR implies η ∼ 0.45, ${\dot{p}}_{\mathrm{out}}$/${\dot{p}}_{\mathrm{rad}}$ ∼ 10, and ${\dot{E}}_{\mathrm{out}}$/L_SFR ∼ 0.02. The latter estimates are comparable to the other AGN-driven outflows (Table 1), which are eight times more frequent in our sample. These properties are consistent with enhanced outflows being associated with increased AGN and SF activity in absolute terms, but the scarcity of these objects suggests that this phase is rather short-lived and does not dominate in a population- and time-averaged sense.

Summarizing, for the majority of the AGN-driven outflows, and adopting a common n_e,br ∼ 1000 cm⁻³, the inferred mass outflow rates and mass loading factors are in the ranges ∼5–45 M_⊙ yr⁻¹ and ∼0.1–0.5, respectively. These estimates are comparable or somewhat higher than those of the SF-driven outflows, but because of the larger wind velocities, they carry ∼10 times greater momentum and ∼50 times greater energy. If the density in the outflow were lower, the differences would be even larger. Overall, the AGN-driven outflows have typical inferred ${\dot{p}}_{\mathrm{out}}$/${\dot{p}}_{\mathrm{rad}}$ ∼ 1–15, and ${\dot{E}}_{\mathrm{out}}$/L_SFR ∼ 0.002–0.06, supporting that the AGN is required to power the observed winds. The ratio of AGN to stellar bolometric luminosities estimated for these galaxies spans a very wide range, but is typically L_AGN/L_SF ∼ 1, such that the momentum and energy ratios remain roughly the same when referred to the AGN properties. The high ratios suggest the AGN-driven outflows may be energy-driven (e.g., Fabian 2012; Faucher-Giguère & Quataert 2012; Zubovas & King 2012).