# Host galaxy and nuclear properties of IR-selected AGNs with and without
outflow signatures 111Released on December, 20st, 2023

Gabriel A. Oio Chinese Academy of Sciences South America Center for Astronomy
(CASSACA),
National Astronomical Observatories of China (NAOC),
CAS, 20A Datun Road, Beijing 100012, China Y. Sophia Dai (戴昱<EMAIL_ADDRESS>Y. Sophia Dai (戴昱) Chinese Academy of Sciences South America Center for
Astronomy (CASSACA),
National Astronomical Observatories of China (NAOC),
CAS, 20A Datun Road, Beijing 100012, China  C. G. Bornancini Instituto de
Astronomía Teórica y Experimental, (IATE, CONICET-UNC), Córdoba, Argentina
Observatorio Astronómico de Córdoba, Universidad Nacional de Córdoba, Laprida
854, X5000BGR, Córdoba, Argentina  Zi-Jian Li Chinese Academy of Sciences
South America Center for Astronomy (CASSACA),
National Astronomical Observatories of China (NAOC),
CAS, 20A Datun Road, Beijing 100012, China  National Astronomical
Observatories, Chinese Academy of Sciences, 20A Datun Road, 100012 Beijing,
China  School of Astronomy and Space Sciences, University of Chinese Academy
of Sciences, Beijing 100049, China

###### Abstract

Active galactic nucleus (AGN) driven outflows can have a significant impact on
the evolution of the host galaxy. In this work, we compare the properties of
galaxies that hosts AGNs with and without outflows. Our sample consists of 103
AGNs identified by mid-IR color-color selection, and confirmed with optical
spectroscopy at a redshift range of 0.3 $\lesssim$ z $\lesssim$ 0.9. We fit
the [O III] $\lambda$5007 line using spectra from the zCOSMOS survey to
identify and to study the occurrence of outflows. We find that ionized
outflows are present in $\sim$25% of our sample, with the largest incidence at
the highest [O III] and X-ray luminosity bins. The fastest outflows are found
in the more extended and massive galaxies. We do not observe a difference in
the star formation rate of AGNs with outflows compared to AGNs without
outflows. From visual inspection and non-parametric morphological studies, we
obtain that outflows are preferentially observed in galaxies with disk-type
and elliptical morphologies.

Galaxies: active – nuclei – emission lines – kinematics and dynamics –
Infrared: galaxies

††software: Astropy (Astropy Collaboration et al., 2013), Matplotlib (Hunter,
2007), ifscube (Ruschel-Dutra et al., 2021)

##  1 Introduction

Active galactic nuclei (AGN) are intrinsically linked with the origin and
evolution of galaxies. The relationships between black holes and various
parameters related to their host galaxies are well known, but differ by orders
of magnitude in their physical scale. Among them, we can name the relationship
between black hole mass and bulge mass (Magorrian et al., 1998; Wandel, 1999;
McLure & Dunlop, 2000; Häring & Rix, 2004; Graham & Scott, 2015; Ding et al.,
2020), between black hole mass and velocity dispersion (Ferrarese & Merritt,
2000; Gebhardt et al., 2000; Merritt & Ferrarese, 2001; Beifiori et al.,
2012), and even with the total mass of the host galaxy (Bandara et al., 2009).
The co-evolution of these different components is believed to be driven by
feedback (Silk & Rees, 1998; Di Matteo et al., 2005; Fabian, 2012; Harrison et
al., 2018; Veilleux et al., 2020). This feedback regulates the accumulation of
mass in galaxies by heating and/or removing the gas that would otherwise be
used to form stars, and by ejecting matter as mass is added to the central
black hole. This process, where warm ionized and cold molecular outflows are
being driven by an AGN accreting near the Eddington limit, is usually referred
to as “quasar mode” (see e.g., Fabian, 2012; Harrison et al., 2018).

There are numerous approaches in the literature to define outflows from the
emission lines. One way that has been used to find outflowing gas emission in
AGNs is by considering the line-of-sight velocity offset ($v_{off}$) of narrow
emission lines with respect to the systemic velocity of their host galaxies
(e.g., Boroson, 2005; Barbosa et al., 2009; Müller-Sánchez et al., 2011). This
approach is based on the idea that there is a stratified narrow line region
(NLR) where, in its innermost regions, high ionization lines such as [O III]
are blueshifted and more turbulent (broader) than the low-ionization lines
produced in the external emitting regions and also with respect to the stellar
absorption lines that are indicative of the systemic velocity of their host
galaxies. (e.g., Crenshaw & Kraemer, 2000; Ruiz et al., 2005; Komossa et al.,
2008; Bae & Woo, 2014; Schmidt et al., 2018; Trindade Falcão et al., 2021).
Given that the forbidden [O III] $\lambda$5007 emission lines cannot be
produced in the high-density, sub-parsec scales broad line regions (BLR) of
AGNs, they are usually considered to be a good tracer of the extended ionized
gas. Particularly in AGNs, the [O III] $\lambda$5007 line profile can show a
broad, blue-wing asymmetry, which is usually attributed to an outflowing gas.
Therefore, some works focus on the multi-component fitting of [O III]
$\lambda$5007 line and characterize the outflow by its blue-wing properties
(e.g., Komossa & Xu, 2007; Holt et al., 2008; Mullaney et al., 2013; Zakamska
& Greene, 2014; Carniani et al., 2015; Harrison et al., 2016; Schmidt et al.,
2018; Oio et al., 2019; Guolo-Pereira et al., 2021). In contrast, some authors
have chosen to characterize the outflow from its full line profile, using so-
called non-parametric definitions (e.g., Liu et al., 2013; Harrison et al.,
2014; Zakamska & Greene, 2014; Sun et al., 2017; Robleto-Orús et al., 2021).
Liu et al. (2013) measured the [O III] line width containing the central 80
per cent of flux (W80), in a sample of nearby, luminous, obscured, radio-quiet
quasars. In their work, they used this line width as an estimate of the
outflow velocity, and found that most objects showed a blue excess in their
line profiles, with a median outflow velocity of 760 km s-1. In a similar
manner, Harrison et al. (2014) employed the same approach to measure the line
width of the emission lines [O III] $\lambda$4959 and [O III] $\lambda$5007\.
Their objective was to demonstrate the prevalence of outflows in a sample of
16 luminous type II AGNs. Here, they found extended [O III] emission, with W80
ranging from 600 to 1500 km s-1.

Some results obtained in numerical simulations suggest that there is a stage
in the evolution of galaxies where violent interactions and mergers are more
frequent. In these evolutionary scenarios the first instances of mergers would
show galaxies with an obscured nuclei by large amounts of gas and dust. Then a
phase would follow where the AGN drives outflows that expel the surrounding
material and reveal an unobscured AGN (Di Matteo et al., 2005; Springel et
al., 2005; Hopkins et al., 2008a, b). The relationship between gas outflows or
inflows in AGNs and the obscuration measured by optical spectral line widths
or hard/soft X-ray emission is still not well understood. We might expect that
obscured AGNs selected in the X-rays, according to high neutral hydrogen
column densities or high hardness ratio values, calculated as
HR$=$H$-$S/H$+$S, where $H$ and $S$ are the count rates in the hard and soft
bands, to also have outflow signatures (Harrison et al., 2016). Ricci et al.
(2017) using the column density ($N_{\rm H}$) estimated from X-ray spectral
analysis showed opposite results. In the same way Harrison et al. (2016) found
no evidence that the highest ionized gas velocities are preferentially
associated with X-ray obscured AGN. Rojas et al. (2020) in a study of X-ray
selected (z$\sim$0.05) AGN taken from the BAT AGN Spectroscopic Survey (BASS)
found that the occurrence of outflow detections in unobscured type 1 and type
1.9 (with narrow lines except for a broad component seen in H$\alpha$) AGNs is
twice that of obscured (type 2) AGNs.

Mergers and interactions have long been considered a triggering mechanism for
AGN (Canalizo & Stockton, 2001; Hopkins et al., 2008c; Ramos Almeida et al.,
2011; Bessiere et al., 2012; Satyapal et al., 2014; Goulding et al., 2018;
Pierce et al., 2023). Because of the increase of nuclear star formation and
nuclear activity in mergers (e.g., Satyapal et al., 2014), it is reasonable to
consider that the incidence and properties of outflows may depend on the
morphology and/or environment of the galaxy. However, major mergers alone are
not sufficient to account for the entire AGN population (Draper & Ballantyne,
2012), as they appear to be associated mainly with ultra-luminous infrared
galaxies (ULIRGs) (Treister et al., 2012; Glikman et al., 2015; Barrows et
al., 2017, 2023). That is, only the most luminous AGN phases are associated
with major mergers, while less luminous AGNs appear to be driven by secular
processes (Treister et al., 2012).

Our goal is to test whether the host galaxy of AGNs behaves differently in the
case with and without outflow signatures. Therefore, in this work, we will
discuss and compare the properties of galaxies hosting mid-IR selected AGNs
with and without outflow signatures. This paper is structured as follows:
Section 2 summarizes the selection criteria for the sample used in this work
and how we select and measure outflows. In section 3 we describe some of the
AGN and host galaxy properties such as [O III] $\lambda$5007 luminosity, X-ray
emission, stellar-mass, star formation rate (SFR), rest-frame colors and
morphological properties. We discuss our results in section 4, and in section
5 we summarize our results.

Throughout this work we will use the AB magnitude system (Oke & Gunn, 1983)
and we will assume a $\Lambda$CDM cosmology with H0=70 km s-1Mpc-1,
$\Omega_{M}$=0.3, $\Omega_{\Lambda}$=0.7.

##  2 Sample selection

###  2.1 Parent Sample

Due to the diversity of the AGN population, no single AGN selection technique
is complete. A specific technique may correctly identify one population of
AGNs while missing others. For example, a selection based solely on X-ray or
optical emission may overlook AGNs that are obscured by interstellar gas or
dust. In contrast, MIR selection methods are more effective at detecting this
population because they are less affected by extinction (e.g., Lacy et al.,
2004; Stern et al., 2005; Donley et al., 2012; Dai et al., 2014). Thus we have
chosen to compare the host galaxies of AGNs selected in the IR to avoid other
selection biases. We derived a sample of mid-IR (MIR) selected and optically
confirmed AGNs from the work of Bornancini et al. (2022). Briefly, in that
study, they investigated the properties of host galaxies of AGNs selected
based on near-IR emission and MIR criteria, which were subsequently confirmed
spectroscopically. The different selection techniques have varying
completeness and reliability at selecting AGN (see e.g., Lacy & Sajina, 2020).
In this work, we will focus on the AGN sample selected using the criteria of
Lacy et al. (2004), which presents a high level of completeness (Messias et
al., 2014), and confirmed through optical spectroscopy using the mass-
excitation (MEx) diagram (Juneau et al., 2011, 2014) in order to obtain more
confidence in the sample. Bornancini et al. (2022) chose the AGN sample from
the COSMOS field (Scoville et al., 2007) using the COSMOS2015 catalog 222The
catalog can be downloaded from
ftp://ftp.iap.fr/pub/from_users/hjmcc/COSMOS2015/ (Laigle et al., 2016) and
the zCOSMOS redshift survey (Lilly et al., 2007, 2009). The COSMOS2015 catalog
provides photometric data across multiple wavelengths, ranging from UV to mid-
IR, as well as estimates of stellar mass obtained using LePhare (Arnouts et
al., 2002; Ilbert et al., 2006) following the method described in Ilbert et
al. (2013). For their analysis, Bornancini et al. (2022) utilized the Spitzer
IRAC and MIPS bands: 3.6, 4.5, 5.8, 8.0, 24, and 70µm (Sanders et al., 2007;
Le Floc’h et al., 2009; Ashby et al., 2013, 2015). The authors selected
sources within the spectroscopic redshift range of 0.3$\leq$$z_{\rm
sp}$$\leq$0.9 from the zCOSMOS DR3-bright catalog, ensuring reliable redshift
estimates. This catalog provides a confidence parameter that ranges from
insecure and probable redshift (Class 1 and 2, respectively), one broad AGN
redshift (Class 18), one line redshift (Class 9), and secure and very secure
redshift (Class 3 and 4, respectively). Each confident parameter is also
assigned a confidence decimal, which is derived from repeat observations and
by the consistency or otherwise with photometric redshifts. The confidence
decimal ranges from .1 (spectroscopic and photometric redshifts are not
consistent at the level of 0.04(1+z)), .3 (special case for Class 18 and 9,
consistent with photo-z only after the redshift is changed to the alternate
redshift), .4 (no photometric redshift available) to .5, (spectroscopic
redshift consistent within 0.04(1+z) of the photometric redshift). In the work
of Bornancini et al. (2022) they selected all sources with classes 3.x and
4.x, where x can take the values 1, 3, 4, and 5. Using the photometric data,
they performed a preselection of sources that satisfied the Lacy et al. (2004)
color-color criteria, employing the bands [4.5]-[8.0] vs [3.6]-[5.8]. Lacy et
al. (2004) identified the positions of QSOs detected in the Sloan Digital Sky
Survey (SDSS) on a log(S8.0/S4.5) vs. log(S5.8/S3.6) color-color diagram and
proposed the following relation to select AGN samples based on their MIR
colors:

$\displaystyle\rm log(S_{5.8}/S_{3.6})>0.1,$ $\displaystyle\rm
log(S_{8.0}/S_{4.5})>-0.2\wedge$ $\displaystyle\rm log(S_{8.0}/S_{4.5})\leq
0.8\times\rm log(S_{5.8}/S_{3.6})+0.5$ (1)

Here, $\wedge$ represents the logical AND operator (refer to Figure 1 from
Bornancini et al. 2022). A total of 490 AGN candidates with good redshift
estimates fall within the Lacy wedge and constitute the parent sample for this
study.

As noted by several authors (e.g., Sajina et al., 2005; Lacy et al., 2007;
Donley et al., 2012; Barrows et al., 2021), AGN IR-selection techniques can
suffer from contamination by galaxies classified as star-forming (SF) or
quiescent. Hence, it is advisable to apply a second selection criterion to
ensure that the chosen sample predominantly consists of AGNs. Optical
emission-line ratios can be used to diagnose the ionization mechanism, for
example, using the BPT diagram (Baldwin et al., 1981; Kewley et al., 2001;
Kauffmann et al., 2003). However, within the redshift range of the parent
sample, only the H$\beta$ and [O III] $\lambda$5007 lines fall within the
wavelength coverage of the zCOSMOS spectra. As an alternative to the BPT
diagram, the MEx diagram utilizes the host galaxy mass and the quotient [O
III] $\lambda$5007/H$\beta$ making it useful for selecting AGNs at
intermediate redshifts. To measure the signal-to-noise ratio (S/N) of these
emission lines, a single Gaussian profile fitting was performed on the
H$\beta$ and [O III] lines. Only spectra with both H$\beta$ and [O III] S/N
ratios greater than 3 were considered.

After applying the IR selection criteria of Lacy et al. (2004), 490 AGN
candidates were selected. The selection criterion according to the MEx diagram
yields a number of 122 AGNs and the restrictions on S/N on both [O III]
$\lambda$5007 and H$\beta$ spectral lines, resulted in a final number of AGNs
of 103.

We then select possible outflow candidates following the methods described in
Sec 2.2.2.

###  2.2 Outflow Candidates

####  2.2.1 Emission line measurements

We are interested in measuring [O III] $\lambda$5007 as a tracer of possible
outflows in our galaxy sample. In AGNs, this line is produced in the low-
density narrow-line region, and any asymmetries are often attributed to
outflowing (or inflowing) gas, with some parts of this wind obscured by
surrounding dust. To measure the emission lines, we followed a two-step
procedure.

First, we shifted the spectra to the rest frame using the spectroscopic
redshift values tabulated in the zCOSMOS catalog (Lilly et al., 2007) and
fitted the continuum using the Penalized Pixel-Fitting code (ppxf Cappellari &
Emsellem 2004; Cappellari 2017). Since the power-law component and Fe II
emission are produced in the innermost regions of the nuclei, in type II AGNs,
this contamination will be enshrouded by the surrounding obscuring dust.
Therefore, we chose not to include these components in the continuum fitting
(see e.g., Oio et al., 2019). Next, the emission lines were fitted using the
ifscube package333https://github.com/danielrd6/ifscube/ (Ruschel-Dutra et al.,
2021). ifscube is a Python-based software package primarily developed to
perform analysis tasks in data cubes of integral field spectroscopy but has
great flexibility to work with single-slit or fiber spectra. This package
subtracts the stellar continuum obtained in the previous step and performs a
constrained multiple-component fitting on the pure emission line spectrum. In
the first iteration, Oxygen lines ([O III]$\lambda$4959,5007) and H$\beta$
were fitted with a single Gaussian component. The velocity from the Gaussian
fit of [O III] $\lambda$5007 was assumed as the systemic velocity, and this
value was reintroduced in ppxf to bring each spectra in our sample to a frame
of reference based on the observed wavelength of the peak of their [O III]
line. Then, a second fit to the emission lines was performed, this time
allowing up to two Gaussian components for the oxygen lines, while a single
Gaussian component was sufficient for H$\beta$ in all cases. We present
example fits for different cases of emission line profiles in Figure 1. We
show two prominent cases of [O III] asymmetric emission, one with a red wing
and one with a blue wing, as well as a typical example of a spectrum with a
symmetrical line profile (i.e., single Gaussian).

To analyze the kinematics of the emission lines we employed a non-parametric
scheme similar to the one utilized by Robleto-Orús et al. (2021) and described
in detail in Harrison et al. (2014). In this approach, we solely consider the
cumulative function of the synthetic line profile. Like Robleto-Orús et al.
(2021), we do not attribute any physical significance to the individual
Gaussian profiles. The non-parametric velocity definitions employed in this
study are the line width at 80% of the flux (W80) and the velocity offset
($\Delta v$), which is measured from the velocities corresponding to the 5th
and 95th percentiles of the overall emission-line profiles. Specifically,
$\Delta v$ is calculated as $\Delta v$ = ($v_{05}$ \+ $v_{95}$)/2. We chose to
work with these values because W80 and $\Delta v$ are more sensitive to
asymmetries in the base of the line profile compared to, for instance, the
full width at half maximum (FWHM). Both W80 and $\Delta v$ were measured from
the fitted line profile of [O III] $\lambda$5007.

Figure 1: Fit examples of different Gaussian configuration. Top panel: The
galaxy COSMOSID=810540 is one of the few objects in our sample with a red wing
asymmetry in its [OIII]$\lambda$5007 line. Middle panel: The object
COSMOSID=813133 shows one of the strongest blue asymmetry in our sample. It is
to be noted that for most of our sample with detected asymmetries in
[OIII]$\lambda$5007 the asymmetric component was not significantly detected in
[OIII]$\lambda$4959, nevertheless we will focus only on the former. Bottom
panel: COSMOSID=836868 shows the [OIII] emission line well fitted with a
single Gaussian. The flux density is in units of 10-17 erg s-1 cm-2 Å-1 in all
panels.

####  2.2.2 Line constrains: outflow selection

As mentioned in section 2.2.1, for our selection criteria we fitted the [O
III] lines with up to two Gaussian components, namely a “core” component and a
broader “asymmetric” component. As summarized in section 1, there are several
somewhat arbitrary ways to select optical emission line outflows. To decide
whether a second component was necessary or not we imposed the following
criteria:

  * •

The flux of both Gaussian components must be at least 1$\sigma$ greater than
the noise measured in the vicinity region of [O III] $\lambda$5007 (5030 $-$
5080 Å).

  * •

The velocity difference between the two components should be greater than the
sum of their respective velocity errors.

  * •

The line width W80 must be greater than 300 km s-1.

That is we require both Gaussian profiles to be detectable over the noise of
the spectra, and for the asymmetric component to be distinguishable from the
core component in terms of velocity shift. The third criteria is introduced to
ensure that we are in the presence of a broad asymmetrical profile, with the
imposed velocity cut being approximately twice the instrumental velocity
dispersion. Lastly, we required that the flux of both the [O III]
$\lambda$5007 and [O III] $\lambda$4959 lines to be at least 1$\sigma$ above
the noise of the vicinity of the line. By applying these criteria, we retained
a total of 91 AGNs, 26% (24) of which exhibited an asymmetric profile. Out of
the 24 AGNs, 21 displayed a blue asymmetry, while 3 objects showed a red wing
($\Delta$v $>$ 0 km s-1), indicating possible outflows and inflows,
respectively. Although we decided not to fit a possible Fe II emission
component, we took special care with objects exhibiting a red asymmetry. For
these three objects, we re-fitted the continuum by including an Fe II
template, but in no case was this component necessary according to our fitting
model.

If we apply stricter selection criteria, for instance, if we require that each
Gaussian component’s flux of [O III] $\lambda$5007 be greater than 2$\sigma$
and 3$\sigma$, we are left with 18 and 15 asymmetric profiles, respectively.
The different choice of S/N requirement in the Gaussian profile does not
significantly change our sample or the following conclusions. At 3$\sigma$ it
is dropped from the asymmetry sample the galaxy COSMOS ID = 810540, which is
the object with the highest red asymmetry ($\Delta$v = 118 km s-1, W80 = 573
km s-1). The most significant change in our sample arises from disregarding
the velocity difference between the two components. If we were to disregard
this criterion, the final sample of potential outflows would consist of 46
objects (instead of the 24 selected), representing 50% of the total.

We estimated the uncertainties for the fitted line parameters by performing
1000 Monte Carlo iterations for each spectrum. The errors for each Gaussian
parameter were then determined as the 1$\sigma$ dispersion obtained from the
1000 Monte Carlo runs.

In Figure 2, we present the relation between W80 and $\Delta v$. The majority
of objects with asymmetry exhibit negative values of velocity difference,
indicating potential outflows. The distribution of velocity dispersion and
velocity differences shows a wide range of values for objects with asymmetric
profiles, while it is narrower for objects with no discernible asymmetries. In
the following we will characterize the distributions by their median values
and adopt the error as the standard deviation from the mean. The distribution
of $\Delta v$, shown in the top panel of Figure 2, has a median value of $-$89
$\pm$ 100 km s-1 for AGNs with asymmetry, and a median value of 2 $\pm$ 14 km
s-1 for galaxies with no asymmetry. The median value of W80 is 634 $\pm$ 190
km s-1 for galaxies with asymmetric profiles, and a median of 567 $\pm$ 132 km
s-1 for objects with no asymmetry.

Our estimated outflow velocities align with those found in previous studies.
For example, Matzko et al. (2022), who examined a large sample of galaxy pairs
involving AGNs and SF galaxies, used W80 as a measure of outflow velocity,
taking it as 1.088$\times$FWHM. They reported a mean value of approximately
700 km s-1 for all AGN subsamples. Zakamska & Greene (2014) measured [O III]
$\lambda$4959 and [O III] $\lambda$5007 lines in a sample of obscured QSOs and
found a median value for W80 of 752 km s-1. From a sample of 16 type 2 AGNs
observed with Integral Field Unit (IFU), Harrison et al. (2014) found W80
values ranging between 600 and 1500 km s-1. In the case of highly luminous
type II QSOs (log(L${}_{\rm[O\,{\footnotesize III}]}$)$>$42.5), the velocity
widths are considerably higher, with W80 ranging from approximately 1000 to
5500 km s-1 (Zakamska et al., 2016; Storchi-Bergmann et al., 2018). As
depicted in Figure 2, there is a tendency for galaxies with larger blue
asymmetries to exhibit higher velocity dispersions for the ionized gas.
Henceforth, we disregard galaxies with red asymmetries and refer to AGNs with
outflows as those objects with $\Delta$v $<$ 0 km s-1.

Figure 2: Relation between the velocity difference measured at the 5th and
95th percentiles ($\Delta$v) and the full line profile width at 80% of the
flux (W80). Diamond symbols indicate galaxies with asymmetrical profiles and
open circles represent the absence of an asymmetrical profile in [OIII] line,
on the bottom left is shown the median error for galaxies with asymmetrical
profiles. Top panel: $\Delta$v distribution in units of km s-1, shaded
histogram corresponds to galaxies with asymmetrical profiles, while blank
histogram corresponds to galaxies with symmetrical profiles. Right panel:
distribution of W80 in units of km s-1.

##  3 AGN and Host Galaxy Properties

###  3.1 [O III] $\lambda$5007 luminosity

We calculate the AGN luminosity from [OIII] to test if the AGNs with and
without outflow features have intrinsically different luminosities. The
luminosity for the [O III] $\lambda$5007 emission line was computed with the
standard formula:

$L_{\rm[O\,{\footnotesize III}]}=\frac{4\pi
d_{L}^{2}}{(1+z)}f_{\rm[O\,{\footnotesize III}]}$ (2)

where dL is the luminosity distance and f${}_{\rm[O\,{\footnotesize III}]}$ is
the [O III] $\lambda$5007 line flux. As in Bornancini et al. (2022) we have
not corrected the [O III] line flux for extinction. The distribution of the [O
III] $\lambda$5007 luminosity (L[O III]) for the sample of 91 galaxies is
shown in Figure 3. There is a tendency for galaxies with asymmetric profiles
towards higher [O III] $\lambda$5007 luminosity values, presenting a median
value of log(L[O III])=41.9 (erg s-1) against a median value of log(L[O
III])=41.1 (erg s-1) for the L[O III] of galaxies with no asymmetric profile.
The greater fraction of AGNs with asymmetrical profiles as a function of L[O
III] is evident in the top panel of Figure 3 where we show the incidence for
each type of galaxy per [OIII]$\lambda$5007 luminosity bin. In the Figure,
uncertainties on the incidence fraction are given by the binomial beta
distribution quantile technique with a 1$\sigma$ (68.3%) confidence interval
(Cameron, 2011), the error in luminosity value for each bin are given by the
standard deviation of the mean.

Figure 3: Distribution of the [O III] $\lambda$5007 luminosity. Top panel:
Fraction of AGNs with (closed diamonds) and without (open circles) asymmetric
profiles as a function of luminosity. Vertical error bars correspond to the
68.3% binomial confidence interval for a beta distribution (Cameron, 2011).
The position in the x-axis corresponds to the middle value in each bin and
their associated error is the 1$\sigma$ standard deviation.

Some authors find a good and even strong correlation between W80 and L[O III]
(e.g., Perna et al., 2017; Wylezalek et al., 2020; Scholtz et al., 2021;
Matzko et al., 2022), however we found no significant correlation between
these parameters. Considering the relation between W80 and L[O III] and
without making any difference on AGNs with and without signs of an outflow we
find a Spearman correlation coefficient Sp = 0.1 with a p_value = 0.3, if we
consider the sample with asymmetrical profiles Sp = 0.01 with p_value = 0.9,
and for the objects with symmetrical profiles Sp = $-$0.08 and p_value = 0.5.

###  3.2 X-ray properties

We investigate whether there is a relationship between the incidence of AGN
with and without outflows and X-ray luminosity and hardness ratio.

The COSMOS2015 catalog (Laigle et al., 2016) also provides information about
X-ray photometry. In their catalog they include fluxes and fluxes errors from
the previous Chandra COSMOS Survey (C-COSMOS) (Elvis et al., 2009; Puccetti et
al., 2009; Civano et al., 2012), X-ray detected sources from XMM-COSMOS
(Hasinger et al., 2007; Cappelluti et al., 2007; Brusa et al., 2010), the
matches with the NuSTAR Extragalactic Survey (Civano et al., 2015) and also
with the Chandra Cosmos-Legacy Survey (Civano et al., 2016; Marchesi et al.,
2016). The latter catalog is a 4.6 Ms Chandra program over 2.2 deg2 of the
COSMOS field, containing 4016 X-ray sources down to a flux limit off
2$\times$10-16erg s-1cm-2 in the 0.5$-$2 keV band. For our sample of 91 AGNs
we found 34 matches with the XMM-COSMOS catalog, 29 matches with the C-COSMOS,
5 matches with NuSTAR COSMOS survey and 42 with the Chandra COSMOS-Legacy
Survey. The catalog of Civano et al. (2016) encompasses all of the previous
X-ray matches so we decided to use the fluxes and hardness ratio provided by
this catalog.

In Figure 4, we present the distribution of the X-ray luminosity for the AGN
selected galaxies with and without outflow signatures calculated as:

$L_{X}=4\pi d^{2}_{L}f_{x}(1+z)^{\Gamma-2}~{}{\rm erg~{}s^{-1}}$ (3)

Here, dL is the luminosity distance in cm, fx is the X-ray flux in units of
erg s-1cm-2 in the hard-band and the photon index was assumed to be $\Gamma$ =
1.8 (Tozzi et al., 2006). In the top panel of Figure 4 we plot the fraction of
AGNs with (closed diamonds) and without (open circles) asymmetric profiles as
a function of the X-ray luminosity. We can see that the incidence is
practically constant except for the bins of the highest LX luminosity where we
see an increase in the fraction of AGNs with outflow. This result is in
agreement with the work of Harrison et al. (2016) where studying a sample of
89 AGN at z $\gtrsim$ 0.6, they find a larger fraction of AGNs with high wind
velocities (W80 $>$ 600 km s-1), for the objects with LX $>$ 1043 (erg s-1).
Later, Perna et al. (2017) confirmed these results in a larger sample of X-ray
AGNs ($\sim$ 500 SDSS/X-ray objects), with outflow signatures in their optical
spectra. They also found a higher fraction of AGNs with outflows at the
highest X-ray luminosity bins. As in the case with [O III] luminosity, we
found no significant correlation between the kinematic parameters and the
X-ray luminosity.

Figure 4: Distribution of the X-ray luminosity in the 5$-$10 KeV band. In the
top panel is shown the incidence of each class of line profile per luminosity
bin. Errors were estimated as in Figure 3.

We also want to compare whether there is a preference for AGN with outflows to
be found in X-ray obscured (i.e., high neutral hydrogen column densities or
high hardness ratio values) with the AGN without optical outflow signatures.
The hardness ratio (HR) is defined as:

$HR=\frac{H-S}{H+S},$ (4)

where S refers to the net count rate in the soft band 0.5$-$2 keV and H is the
count rate in the hard band 2$-$7 keV. So stated, HR provides an indication
about the flatness of an X-ray spectrum. In Figure 5 we plot the HR as a
function of the 2$-$10 keV luminosity for the objects in our sample with X-ray
emission. Several authors made use of the HR to set apart obscured from
unobscured sources in the X-rays at all redshifts (e.g., Gilli et al., 2009;
Treister et al., 2009; Marchesi et al., 2016). This is due to the the fact
that soft X-ray emission of obscured AGNs tend to be absorbed, while hard
X-ray are able to escape. We have taken as limiting value HR=$-$0.2 (dashed
horizontal line in the plot) which correspond for a source with a neutral
hydrogen column density, NH $>$ 1021.6 cm-2 (Gilli et al., 2009).

The vertical dashed line indicates a typical limit used in the X-rays to
separate AGNs from QSOs (Treister et al., 2009). On the right panel we show
the distribution of HR for objects with detected outflows (blue) and without
outflows (red). As it can be seen in the figure, the distribution of HR is
skewed towards softer spectra. Taking into account the low number of X-ray
detection in AGNs with outflow, their distribution seems to be bi-modal with a
mean value for HR=$-$0.09$\pm$0.28 with a slightly higher number of obscured
sources (55%). Inversely, 43% of AGNs without outflows have HR$\geq$-0.2
(i.e., obscured). While the obscured sources with outflows seem to be evenly
distributed in the areas corresponding to AGN and QSO, there seems to be a
preference for the unobscured sources with optical outflows to be located in
the AGN region, being 37% of them found in this area. In contrast, we can also
see in that figure that in general the most luminous sources with outflows
show a harder x-ray spectra than the most luminous sources without outflow.

Figure 5: Hardness Ratio as a function of the X-ray luminosity in the 2$-$10
KeV band. Errors in HR correspond to the 90% confidence limits from Civano et
al. (2015).

###  3.3 Stellar mass

Feedback is thought to be the main mechanism behind the co-evolution of the
AGN host galaxy and its supermassive black hole. This would be responsible for
regulating star formation and galaxy growth in AGNs. One of the main
methodologies to determine the stellar-mass and the SFR of galaxies is through
modeling the ultraviolet (UV) to infrared (IR) spectral energy distributions
(SEDs) of galaxies. Because the beam sizes of FIR/(sub)mm detectors are very
large, the fluxes from individual galaxies are sometimes difficult to measure
if they are close to other sources. This introduces heavy source confusion
(blending) which makes it difficult to correctly measure the SED and determine
the SFR. Liu et al. (2018) developed a new method called “super-deblending”
approach for obtaining prior-fitting multiband photometry for FIR/(sub)mm data
sets in the GOODS-North field. For this reason, in this work we use stellar
mass and SFR determinations obtained in the Super-deblended catalog in the
COSMOS fields by Jin et al. (2018). In the work of Jin et al. (2018) they
applied this method to 194428 galaxies in the COSMOS field, covering data from
Spitzer, Herschel, SCUBA2, AzTEC, MAMBO and NSF’s Karl G. Jansky VLA at 3 GHz
and 1.4 GHz. They use SED fitting techniques following the same approach as
the one presented in Liu et al. (2018); namely they include four SED
components in the fitting procedure: 1) a stellar component (Bruzual &
Charlot, 2003) with a Small Magellanic Cloud attenuation law; 2) a mid-
infrared AGN torus component (Mullaney et al., 2011); 3) a dust continuum
emission from the Magdis et al. (2012) library; 4) a power-law radio continuum
with an evolving qIR=2.35$\times$(1+z)-0.12+log(1.91) (Delhaize et al., 2017;
Magnelli et al., 2015). In this section we make use of their compiled values
for the stellar mass (Laigle et al., 2016; Muzzin et al., 2013) and SFRIR
computed from the integrated 8$-$1000$\mu$m infrared luminosities resulting
from the FIR+mm SED fitting, assuming a Chabrier IMF (Chabrier, 2003), and
excluding any AGN contamination as derived from the SED fitting. Figure 6
shows the median values of outflow velocity in bins of stellar mass on the
left panel, and the median star formation rate as a function of W80 on the
right. We observe a clear trend of higher velocities in [O III] line toward
higher galaxy masses with the outflows being detected in galaxies with stellar
masses higher than log(Mstar (M⊙))$=$9.4.

AGN driven outflows are often invoked to explain the quenching of star
formation by ejecting the interstellar medium (ISM) gas and preventing the
cooling and infall of intracluster medium (ICM) gas on larger scales (e.g., Di
Matteo et al., 2005; Croton et al., 2006; Choi et al., 2018; Cresci et al.,
2015; Förster Schreiber et al., 2019; Piana et al., 2022). We find a median
star formation rate of log(SFRIR (M⊙ yr-1))= 1.3 $\pm$ 0.3 for AGNs with
outflows, and log(SFRIR (M⊙ yr-1))= 1.2 $\pm$ 0.3, for AGNs without outflows.
We see on the right panel of Figure 6 that there is no impact on the star
formation rate throughout the velocity range studied. To continue
investigating any possible difference in the star formation among AGNs with
and without outflows, we now turn our attention to the specific star formation
rate (sSFR=$\frac{SFR}{M_{*}}$). We compare the distribution of the relative
sSFR in comparison to the mean sSFR of the whole sample in Figure 7. We have
divided the sample taking as limiting value W80 $=$ 750 km s-1 which is the
median W80 for AGNs with outflow. We can see that in both cases the relative
sSFR for AGNs with outflow is lower than for AGNs without outflow. The bigger
difference appears to be at higher velocities where their median values of
difference from the mean sSFR are -0.3 $\pm$ 0.2 and $-$0.02 $\pm$ 0.3
respectively. This result, together with the mass scaling relation, could hint
towards a feedback impact in the most massive galaxies. However, this
difference is not significant enough as to claim a quenching in the star
formation due to the outflow. Our results agree with those by Ward et al.
(2022), who studied three cosmological simulations with AGN feedback and found
that AGNs are preferentially found in galaxies with high gas fractions and
sSFR. According to this, the outflows observed in AGNs do not necessarily
imply the quenching of star formation, even if this negative feedback occurs
over long timescales.

Figure 6: Left: Relation between the [OIII] velocity (W80) and the stellar
mass. Right: Star formation obtained from FIR+mm SED fitting as function of
the gas velocity. We can see that higher mass galaxies present the higher
[OIII] velocities, while the overall SFR does not seems to be affected by the
presence of an outflow. Figure 7: Distribution of the relative sSFR depending
on outflow velocity of AGNs with outflow (blue) and without outflow (red),
with respect to the mean value of the whole sample. The median value of each
distribution is denoted with vertical dashed lines. High velocity outflow AGNs
tend to show lower sSFR than AGNs with symmetrical line profiles.

###  3.4 Quiescent and star forming galaxies

With the help of color-color diagrams, we can study whether AGNs with and
without outflows are found in galaxies with different stellar populations.
Rest-frame color-color diagrams have been largely used to separate populations
of quiescent and star-forming galaxies (e.g., Chester & Roberts, 1964; Hogg et
al., 2003; Bell et al., 2004; Baldry et al., 2006; Arnouts et al., 2007;
Williams et al., 2009; Arnouts et al., 2013; Ilbert et al., 2013; Bouquin et
al., 2015; Moutard et al., 2016a, b; Pacifici et al., 2016; Foltz et al.,
2018; Xu et al., 2020). Williams et al. (2009) introduced a selection method
using dust corrected optical colors, and considering the optical and near-IR
emission of galaxies at redshifts z $\leq$ 2, the rest-frame U$-$V versus
V$-$J color$-$color (hereafter UVJ) diagram. The rest-frame NUV $-$ r+ versus
r+ $-$ J diagram introduced by Ilbert et al. (2010), was presented as an
alternative to the UVJ diagram of Williams et al. (2009), as NUV $-$ r+ is
more sensitive to the history of star formation activity (e.g., Martin et al.,
2007; Arnouts et al., 2007), while the r-band is more sensitive to the amount
of stellar mass, formed over the course of a galaxy’s history. Figure 8 shows
the rest-frame MNUV $-$ Mr vs Mr $-$ MJ color-color diagrams for the AGNs with
and without asymmetric component (blue and red symbols, respectively). The
solid line separate regions occupied by quiescent and star-forming galaxies as
defined by Ilbert et al. (2013), where galaxies with MNUV $-$ Mr $>$
3(Mr$-$MJ)+1 and MNUV $-$ Mr $>$ 3.1 are considered as quiescent. The blending
between dusty star-forming galaxies and quiescent galaxies is avoided with
this selection given that dust absorption would shift star-forming galaxies
along a diagonal axes from the bottom left to the top right of Fig. 8. With
the increased fraction of red galaxies observed across time (Faber et al.,
2007), it has been postulated an evolutionary path for galaxy populations from
blue to red. In the color-magnitude diagram there is a region in between the
blue and red galaxy populations, called Green Valley (GV) which would be
inhabited by a transition population called Green Valley galaxies (Wyder et
al., 2007; Salim et al., 2007; Salim, 2014). The boundaries used to define
this region vary among authors, with the limiting values being e.g.,
4$<M_{NUV}-M_{r}<$4.5 (Martin et al., 2007), 3.5$<M_{NUV}-M_{r}<$4.5 (Salim et
al., 2009), 3.2$<M_{NUV}-M_{r}<$5 (Mendez et al., 2011) or by a linear
relation considering other bands as in McNab et al. (2021) with
2(M${}_{V}-M_{J}$)$+$1.1 $\leq$ (M${}_{NUV}-M_{V}$) $\leq$ 2($M_{V}-M_{J}$)
$+$ 1.6. In this work as in Davidzon et al. (2017), we consider a $\pm$ 0.5
mag. shift from the limit defined by Ilbert et al. (2013), shown in Fig. 8
with dashed green lines. In top and right panels we also include the
corresponding color distributions for each sample. We find that 59% of the
AGNs with outflows and 74% of AGNs with symmetrical [O III] profiles are
located in the star-forming region, while 41% of the galaxies with outflows
and 24% without outflows reside in the green valley region, and only two
objects can be found in the quiescent zone (both without outflow). As it can
be seen, we find an excess of 15% for galaxies with star-formation and no
evidence of outflows in their optical spectra. On the other hand, galaxies
with outflows are 17% more likely to be found in the GV than AGN with no
outflows. Regardless, the color distributions of AGNs with and without
outflows are indistinguishable. For the Mr $-$ Mj colors for AGNs with
outflows and without outflows we obtain a median value of 0.8 $\pm$ 0.2 and
0.8 $\pm$ 0.3, while for the MNUV $-$ Mr colors we get a median of 2.6 $\pm$
0.7 and 2.6 $\pm$ 0.9 respectively. In Fig. 8 we also show the objects with
detected X-ray emission as filled points, and dashed histograms. For the AGNs
with no outflows and X-ray emission we find the following percentages: (SF,
GV, quiescent)$=$(77, 19, 4)%. For the sample of AGNs with outflows we find
(SF, GV, quiescent)$=$(58, 42, 0)%. When constraining our sample to objects
with X-ray emission we obtain more than double AGNs with outflow in the green
valley region, with respect to AGN without outflow, while the large majority
of AGN without outflows and X-ray emission can be found in the SF region.

Figure 8: Rest-frame (MNUV $-$ Mr) vs. (Mr $-$ MJ) color-color diagram for AGN
host galaxies with outflows (blue) and without outflow signatures (red). Solid
black line mark regions which separates quiescent (upper-left corner) and
star-forming galaxies (Ilbert et al., 2013). Dashed green lines encompass the
transitional region Green Valley. Filled circles represent AGNs with detected
X-ray emission. Color distributions are included in the upper and right panels
for the objects with and without outflows (dashed lines, blue and red
respectively) and for objects with X-ray emission (dashed histograms).

###  3.5 Morphology

####  3.5.1 Sérsic profiles

In order to address whether the occurrence of outflows is related to their
host galaxy morphology, we perform three independent analysis derived from
observer-frame optical data. First, we study the host galaxy morphological
properties as given by the Sérsic index, making use of the data provided by
the Advanced Camera for Surveys General Catalog (ACS-GC) (Griffith et al.,
2012). This catalog used publicly available data obtained with the Advanced
Camera for Surveys (ACS) instrument on the Hubble Space Telescope to construct
a photometric and morphological database. The imaging data used to construct
the ACS-GC was collected from four surveys covering 469,501 galaxies: the All-
wavelength Extended Groth Strip International Survey (AEGIS, Davis et al.,
2007), the Great Observatories Origins Deep Survey (GOODS, Dickinson et al.,
2003; Giavalisco et al., 2004), the Cosmological Evolutionary Survey (COSMOS,
Scoville et al., 2007), and the Galaxy Evolution from Morphologies and SEDs
(GEMs, Rix et al., 2004; Caldwell et al., 2008). The data used in this work
correspond to the COSMOS survey which covers an area of 1.8 deg2 in the F814W
filter, with a limiting AB magnitude of 26.0. At the mean redshift of our
sample ($z$ = 0.59) this broadband filter images covers the spectral region
corresponding to H$\beta$ \+ [O III]$\lambda\lambda$4959,5007, which is
closest to the [O III] feature we used to find the outflow features. In
Griffith et al. (2012) they employed an automated fitting method called
GALAPAGOS (Häußler et al., 2011; Häussler et al., 2007) to measure structural
parameters such as the Sérsic index (Sérsic, 1963). With a search radius of 5
arcsec, we find a total of 100 objects from our initial sample (of 103
objects) present in this catalog.

As in Bornancini et al. (2022), we also exclude objects with $n$ = 0.2 and $n$
= 8 that are likely to correspond to erroneous fits or systematics, therefore
we constrain the Sérsic index to be in the range 0.2 $<$ $n$ $<$ 8\. With this
restriction and after applying the line signal cut described in Section 2.2.2,
we are left with 75 objects. We separate the galaxies into two main classes:
(1) late type galaxies, spirals or disc dominated with 0.2 $<$ $n$ $<$ 2.5;
(2) early-types or elliptical galaxies, with 2.5 $<$ $n$ $<$ 8 (Griffith et
al., 2012). According to their Sérsic index values and without making any
distinction regarding their [O III] profiles, we find a percentage of 45 $\pm$
5% disc dominated or late type galaxies and 55 $\pm$ 5% corresponding to early
type galaxies. While for the 13 galaxies with [O III] asymmetrical profiles
and valid Sérsic indexes we obtain that 23${}_{-7}^{+14}$% of them correspond
to late-types/spiral galaxies, 77${}_{-14}^{+7}$% early-types or elliptical
galaxies. Among the 62 galaxies with symmetrical line profiles we find an even
distribution of late type or disc dominated galaxies (50 $\pm$ 6%) and early
types or bulge dominated galaxies (50 $\pm$ 6%). We estimated the
uncertainties for the computed fractions (percentages) according to a Bayesian
approach using a 68.3% (1$\sigma$) confidence interval, as explained in
Cameron (2011).

####  3.5.2 Non-parametric morphology

Continuing with our morphological analysis, we use in second place
quantitative measures of the distribution of light. By doing so, we can avoid
a-priory assumptions about the distribution of the light. Here we use three
morphological parameters that are commonly used in non-parametric methods for
galaxy classification, the Asymmetry index (A) (Abraham et al., 1996;
Conselice et al., 2000), the Gini coefficient (G) (Abraham et al., 2003) and
the moment of the brightest 20% of galaxy flux (M20) (Lotz et al., 2004,
2008). The Asymmetry index of a galaxy is determined by rotating the image by
180∘ and then subtracting it from the original image, and then adding up the
absolute value of the differences in intensity at each pixel location. This
total value is then compared to the original flux of the galaxy. The Gini
coefficient, which was initially proposed as an economic indicator to assess
wealth distribution within a population, has also been employed in
astrophysics to quantify the inequality in the distribution of light across
pixels in a galaxy. A value of Gini=1 would imply that all the light is
concentrated in a single pixel, whereas a Gini coefficient of 0 indicates that
the light is uniformly distributed across all pixels. M20 is a measure of the
concentration of light in a galaxy. It is defined as the second order moment
of the brightest 20% of a galaxy’s pixels. It is a useful tool to distinguish
between normal galaxies and non-symmetric objects, and to identify galaxies
that have recently undergone a merger. We use the morphological parameters
from the catalog presented by Cassata et al. (2007). This catalog provides
information on non-parametric diagnostics of galaxy structure derived from
images from the Hubble Space Telescope ACS, for 232022 galaxies up to a
limiting magnitude IAB = 25.

In Figure 9 top panel, we plot Asymmetry versus Gini coefficient for the
sample of AGNs with symmetrical [O III] profiles (red circles) and for AGNs
with outflows (blue diamonds). We include dividing lines between regions of
predominantly irregulars, spirals, and elliptical morphological types taken
from Capak et al. (2007). The solid line between irregular and spiral galaxies
is defined as log10(Asymmetry)=2.353$\times$log10(Gini)+0.353, while the solid
line between spiral and elliptical galaxies is defined as
log10(Asymmetry)=5.5$\times$log10(Gini)+0.825. We also include a horizontal
dashed line to separate the region where mayor mergers are expected, defined
by log(Asymmetry) $>$ $-$0.46 (Conselice, 2003) and a vertical dashed line at
log(Gini)= $-$0.3 which separates late-type from early-type galaxies according
to Abraham et al. (2007). In Fig. 9, bottom panel, we show the Gini parameter
against M20. Here, we include dividing lines between the regions of mergers,
Sb/Sc/Irr and E/S0/Sa galaxy types taken from Lotz et al. (2008), according to
the following definitions, Mergers: G $>$ $-$0.14$\times$M20 + 0.33;
Early(E/S0/Sa): G $\leq$ $-$0.14$\times$M20 + 0.33, and G $>$ 0.14$\times$M20
+ 0.80; Late(Sb/Ir): G $\leq$ $-$0.14$\times$M20 + 0.33, and G $\leq$
0.14$\times$M20 + 0.80. We note in both panels of Fig. 9, that AGNs with
outflow signatures are preferentially located in the region corresponding to
early-type/elliptical galaxies, with 95${}_{-9}^{+1}$% of them found in the
area defined by Abraham et al. (2007) and 86${}_{-11}^{+4}$% inside the areas
defined by Capak et al. (2007) and Lotz et al. (2008). While for the AGNs with
no asymmetric component in [O III], 55${}_{-4}^{+7}$% are early type galaxies
according to Abraham et al. (2007) and Capak et al. (2007), and
41${}_{-5}^{+6}$% according to the limits of Lotz et al. (2008). We do not
obtain any galaxies with outflows in the region corresponding to mergers, and
14${}_{-2}^{+5}$%, 7${}_{-1}^{+4}$%, and 4${}_{-1}^{+4}$% of AGN without
outflows are mergers in accordance to Conselice (2003), Capak et al. (2007)
and Lotz et al. (2008) respectively.

Figure 9: Top: Asymmetry parameter versus Gini coefficient for AGNs with
outflow signatures (blue) and symmetrical [O III] profiles (red). The unfilled
circles represent AGNs without X-ray detection. Solid lines shows regions of
predominantly irregular, spiral, and elliptical types from Capak et al.
(2007). Dashed line at log(Asymmetry) = $-$0.46 (Conselice, 2003) shows the
dividing line above which objects are expected to be major mergers. Vertical
dotted line at log(Gini)= $-$0.3 separates late and early-type galaxies
according to Abraham et al. (2007). Bottom: Gini coefficient vs M20, symbols
are the same as in the top panel. Solid lines show regions of mergers,
Sb/Sc/Irr and E/S0/Sa galaxy types taken from Lotz et al. (2008).

####  3.5.3 Visual Classification

In this section we apply a visual classification criterion to estimate the
morphology of the AGN host galaxies. We classify the galaxies in our sample
into three visual classes: elliptical (bulge dominated), spiral (disk
dominated) and irregular/merger. This classification aims at describing the
dominant morphology of the host galaxy. The objects were examined
independently by the authors using the HST ACS F814W band images. To assign a
galaxy to each category we requested a simple majority of votes. In Figure 10,
we show the fraction of galaxies with and without outflows (blue and red
symbols respectively) in each morphological type. We compute the error in each
class according to a Bayesian approach using a 68.3% (1$\sigma$) confidence
interval, as explained in Cameron (2011). We obtain that AGNs with outflows
are found preferentially in galaxies with elliptical (38${}_{-9}^{+11}$%) and
disk (43${}_{-9}^{+10}$%) morphology with a slightly higher incidence than
AGNs with no outflows in the elliptical class type. Conversely, the fraction
of AGNs in merger (or irregular) galaxies with outflows (19${}_{-5}^{+11}$%)
is lower than the AGNs in irregular host galaxies with no outflows
(27${}_{-4}^{+6}$%).

Figure 10: Fraction of galaxies with (without) outflows in blue (red) for each
morphological class from visual classification. The error bars correspond to
the 68.3% binomial confidence interval for a beta distribution (Cameron,
2011).

By comparing with the non-parametric morphological classification, we find
that the separation limit of Capak et al. (2007) recovers a 97% of the
elliptical galaxies, while the separation of log(Gini) $>$ $-$0.3 of Abraham
et al. (2007) recovers 90% of them. In the case of visually classified spiral
(late types) galaxies, we see that they present a large scatter in their Gini
index values, with $\sim$60% of them found in the region for early-types. This
result is in agreement with the works of Abraham et al. (2007) and Kartaltepe
et al. (2010). A possible explanation is that spiral galaxies with a prominent
bulge or significant AGN contribution will have a greater Gini concentration
parameter and a lower M20 thus leading to a miss-classification.

##  4 Discussion

###  4.1 Outflow incidence

We can see from Figures 3 and 4 a higher incidence of AGNs containing outflow
at increasing [O III] and X-ray luminosity. This is consistent with previous
works, where a higher fraction of galaxies with outflows are found at higher
luminosities (e.g., Perna et al., 2017; Avery et al., 2021; Matzko et al.,
2022). We would expect that the correlation reported between bolometric
luminosity and outflow velocity (Veilleux et al., 2013; Spoon et al., 2013;
Fiore et al., 2017), to be also present with the [O III] $\lambda$5007
luminosity. However, we do not find a clear trend between the outflow velocity
and the total [O III] line luminosity nor with the X-ray luminosity. This
could be partly explained because of the low [O III] line luminosity of our
objects, while the trend is more noticeable at increased luminosity ranges.
Matzko et al. (2022) studied the merger impact on the outflow properties,
derived from the [O III] line profile, at a similar luminosity range than our
sample. They report that a noticeable increase in the average outflow velocity
is evident only in the most luminous [O III] $\lambda$5007 bin. To have a
broader perspective of the place our objects occupy within the context of the
overall type 2 AGN population, in terms of their [O III] luminosities and
velocities, we show in Figure 11 a comparison with previous works. In this
Figure, we compare our sources with the 2920 type 2 QSOs at a redshift range
of 0.4 $\lesssim$ z $\lesssim$ 0.65, and [O III] luminosities in the range of
log(L[O III])$\sim$41$-$44 erg s-1, from Yuan et al. (2016) (grey symbols);
the $\sim$560 luminous (log(L[O III]) $\gtrsim$41.6 erg s-1) from Reyes et al.
(2008) (green crosses); and nine luminous (log(L[O III]) $>$ 42 erg s-1) type
2 QSOs with redshifts 0.1 $<$ z $<$ 0.5 from Storchi-Bergmann et al. (2018)
(orange squares). We compute the median values of velocity in luminosity bins
for our objects with outflows (large blue symbols), and without outflows
(large red symbols), and also combining all the data-sets plotted here (black
symbols). We can see that, although with large dispersion, our objects seem to
follow the overall W80 $-$ luminosity trend.

Figure 11: W80 as a function of [O III] luminosity. Blue filled diamonds and
red open dots refer to the objects used in this work with and without outflows
respectively; grey circles and green crosses shows type 2 Quasars from Yuan et
al. (2016) and Reyes et al. (2008); orange squares represent type 2 QSOs from
Storchi-Bergmann et al. (2018). Large symbols represent the median velocity in
bins of [O III] luminosity for our sample (red and blue), and for the
combination of all the objects in the four works (black). Error-bars
correspond to the standard deviation in each bin.

###  4.2 Compaction related to outflows

We revisited the MNUV $-$ Mr vs Mr $-$ Mj color-color diagram as a function of
the morphology of their host-galaxy given by the visual classification in
Figure 12. We can see that galaxies classified as irregular/mergers show bluer
colors with median values of (Mr $-$ Mj) = 0.72 and (MNUV $-$ Mr) = 2.44, and
most galaxies with outflow and merger signatures are found in the SF region
(80%). Spiral galaxies are mostly located in the zone demarcated for SF with
18% of them in the Green Valley. We see that AGNs with outflow and early type
morphology, are evenly located in the GV and SF regions. Noticeably we find 12
(41%) early type objects with “blue” colors, populating the demarcated area
for star-forming galaxies. These objects might belong to the population of
galaxies known as blue ellipticals, where the star formation is being driven
by secular gas accretion processes (Lazar et al., 2023).

Figure 12: Rest-frame (MNUV $-$ Mr) vs. (Mr $-$ MJ) color-color diagram for
galaxies with different morphological types as indicated in the insert. Solid
black line mark regions which separates quiescent (upper-left corner) and
star-forming galaxies (Ilbert et al., 2013). Dashed green lines encompass the
transitional region know as Green Valley. Filled diamonds represent AGNs with
outflow emission. Color distributions are included in the upper and right
panels for the objects with different morphological types.

The gas compaction in massive galaxies could also trigger the AGN activity
(Chang et al., 2017a, b). In the compaction scenario (Tacchella et al., 2016),
galaxies live through one or more blue nugget phases which a minimum in gas
depletion time and a maximum in gas fraction are reached. In Figure 13 we plot
the relation between the outflow velocity (W80) and the half-light radius
(R50). We see a clear positive trend of faster outflow velocity with larger
R50. Meaning that the more extended (and more massive) galaxies, displays the
strongest outflows. From the distribution of R50, on the top panel of Fig. 13,
we obtain that outflows are preferentially found in AGN with the most compact
light distributions. The median half-light radius for galaxies with outflows
is R50 = 1.8 $\pm$ 1.1 kpc and for galaxies with no outflows R50 = 2.7 $\pm$
1.3 kpc. When segregating according to their morphology, this effect is more
noticeable in elliptical galaxies with outflows where their median R50 is 1.5
$\pm$ 0.6 kpc, while elliptical galaxies with no outflows have a median R50 =
2.0 $\pm$ 0.6 kpc. That is, elliptical galaxies with outflows present a half-
light radius of $\sim$80% the size of AGN of the same morphological class with
no outflows. This is consistent with the scenario proposed by Chang et al.
(2017b) where obscured AGNs are most likely found in star-forming galaxies
that have undergone a process of dynamical contraction. In this scenario, a
galaxy’s core becomes more compact due to an episode of intense gas inflow,
therefore forming a massive bulge with a high gas fraction and star-formation
(Zolotov et al., 2015). The inflow of gas can also sustain accretion onto the
central supermassive black hole, which can trigger the formation of an active
galactic nucleus (AGN) with moderately sub-Eddington luminosities. This in
turn would favor the formation of the observed AGN-driven outflows.

To further explore how the link between outflows and AGNs is driven, and their
impact on their host galaxy, we will need a larger sample of AGNs. This will
be achieved thanks to wider AGN surveys, such as those that will be performed
by the James Webb Space Telescope (JWST).

Figure 13: Relation between the outflow velocity (W80) and the half-light
radius.

##  5 Summary

In this paper, we have studied the galaxy host properties of a sample of AGNs
with and without outflow signatures selected on the basis of the MIR color-
color diagram proposed by Lacy et al. (2004) and the line diagnostic diagram
which relates the [O III]/H$\beta$ line ratio and the stellar mass known as
mass-excitation diagram (Juneau et al., 2011, 2014). We summarize the main
results as follows:

  * •

The outflow incidence increases with [O III] luminosity. Despite this, we do
not see a significant trend relating the outflow velocity and the line
luminosity.

  * •

We also observe a slight trend of higher outflow incidence towards higher
X-ray luminosity, with the caveat of having a low statistical significance.
The hardness ratio distribution are practically identical for AGNs with and
without outflow, together with the previous we can not claim an influence of
the inner disc or corona in the triggering of the outflow.

  * •

We do not see a significant difference in the host stellar mass distribution
nor their star formation rates, between AGNs with outflow signatures and those
without them. There is a trend of increasing gas velocities with higher host
galaxy mass.

  * •

By inspecting the MNUV $-$ Mr vs. Mr $-$ Mj color-color diagram, we find that
in the Green Valley region there is a higher fraction of AGNs with outflows.
The majority of AGNs without outflow ($\sim$75%) are found in the star forming
region.

  * •

Morphological analysis from Sérsic index and non-parametric measurements
results in a majority of AGNs with outflows found in galaxies with early type,
bulge dominated morphology. From visual inspection we obtain similar fractions
($\sim$ 40%) of AGN with outflows residing galaxies with disk and elliptical
morphological types. We conclude that automated processes to classify the
morphological type of a galaxy, fail to correctly identify almost half of the
late type galaxies.

  * •

It is favored a scenario of dynamical compaction which brings gas into the
central part of the galaxy. This would supply the gas necessary to be accreted
into the central black-hole, triggering the AGN and the observed outflows.

From our results we can not infer a significant impact on the host galaxy
given by the outflow. The feedback claimed by theoretical works is not so
evident in our studied sample. AGN feedback if and when present must likely be
a local phenomenon, and not galaxy-wide. On the other hand, large-scale
properties of the galaxy such as its mass and morphology, might contribute on
the likelihood of observing an AGN-driven outflow.

##  6 Acknowledgments

This work is sponsored by the National Key R&D Program of China for grant No.
2022YFA1605300, the National Nature Science Foundation of China (NSFC) grants
No. 12273051 and 11933003. This work was partially supported by the Consejo
Nacional de Investigaciones Científicas y Técnicas (CONICET) and the
Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba
(SeCyT). Based on data products from observations made with ESO Telescopes at
the La Silla Paranal Observatory under ESO programme ID 179.A-2005 and on data
products produced by TERAPIX and the Cambridge Astronomy Survey Unit on behalf
of the UltraVISTA consortium. Based on zCOSMOS observations carried out using
the Very Large Telescope at the ESO Paranal Observatory under Programme ID:
LP175.A-0839 (zCOSMOS).

##  Appendix A Individual spectral fits

In this Section, we present the spectra with multiple component Gaussian
fitting for the sample of 25 galaxies with [O III] $\lambda$5007 asymmetric
profile chosen by the criteria defined in section 2.2.2.

Figure 14: Multiple Gaussian decomposition of the
[OIII]$\lambda\lambda$4959,5007 emission lines of the galaxies with
asymmetrical [OIII]$\lambda$5007 line profile. In black is shown the observed
spectra with its corresponding error, in orange is the stellar continuum
determined by ppxf each Gaussian component is shown in slashed green lines,
and the full synthetic spectra is plotted in red. The residuals are presented
at the bottom panel in cyan. The flux is in units of 10-17 erg s-1 cm-2 Å-1.
Figure 15: Cont. of Fig. 14 Figure 16: Cont. of Fig. 14

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