11institutetext: 1 Dipartimento di Fisica e Astronomia, Università di Firenze,
Via G. Sansone 1, 50019 Sesto Fiorentino, Firenze, Italy
2 INAF - Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze,
Italy
3 Max-Planck-Institut für Extraterrestrische Physik (MPE), Giessenbachstraße
1, D-85748 Garching, Germany
4 Centro de Astrobiología (CAB), CSIC–INTA, Cra. de Ajalvir Km. 4, 28850 –
Torrejón de Ardoz, Madrid, Spain
5 European Southern Observatory, Karl-Schwarzschild-Strasse 2, Garching bei
München, Germany
6 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD
21218, USA
7 INAF - Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5,
I-35122 Padova, Italy
8 Dipartimento di Fisica e Astronomia “Augusto Righi”, Università di Bologna,
Via P. Gobetti 93/2, 40129, Bologna, Italy
9 INAF - Osservatorio di Astrofisica e Scienza dello Spazio, Via Gobetti 93/3,
40129 Bologna, Italy
10 Dipartimento di Fisica, Università di Trieste, Sezione di Astronomia, Via
G.B. Tiepolo 11, I-34131 Trieste, Italy
11 INAF - Osservatorio Astronomico di Trieste, Via G. Tiepolo 11, I-34143
Trieste, Italy
12 Scuola Normale Superiore, Piazza dei Cavalieri 7, I-56126 Pisa, Italy
13 Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029,
Blindern 0315, Oslo, Norway
14 Department of Physics & Astronomy, University College London, Gower Street,
London, WC1E 6BT, UK
15 ESA, European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n,
28692 Villanueva de la Cañada, Madrid, Spain
16 IFPU - Institute for fundamental physics of the Universe, Via Beirut 2,
34014 Trieste, Italy
17 School of Mathematics, Statistics and Physics, Newcastle University,
Newcastle upon Tyne, NE1 7RU, UK
18 School of Physics and Astronomy, Tel-Aviv University, Tel Aviv 69978,
Israel
19 INAF - Osservatorio Astronomico di Roma, Via di Frascati 33, 00040,
Monteporzio Catone, Rome, Italy
20 School of Physics and Astronomy, University of Southampton, Highfield SO17
1BJ, UK
21 Kavli Institute for Cosmology, University of Cambridge, Madingley Road,
Cambridge, CB3 0HA, UK
22 Cavendish Laboratory - Astrophysics Group, University of Cambridge, 19 JJ
Thompson Avenue, Cambridge, CB3 0HE, UK
23 INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, Via A.
Corti 12, 20133 Milano, Italy

# SUPER VIII

Fast and Furious at $z\sim 2$: obscured type-2 active nuclei host faster
ionised winds than type-1 systems

G. Tozzi 112233<EMAIL_ADDRESS>G. Cresci 22 M. Perna 44 V. Mainieri 55 F.
Mannucci 22 A. Marconi 1122 D. Kakkad 66 A. Marasco 77
M. Brusa 8899 E. Bertola 22 M. Bischetti 10101111 S. Carniani 1212 C. Cicone
1313 C. Circosta 14141515 F. Fiore 11111616 C. Feruglio 11111616 C. M.
Harrison 1717 I. Lamperti 44 H. Netzer 1818 E. Piconcelli 1919 A. Puglisi 2020
J. Scholtz 21212222 G. Vietri 2323
C. Vignali 8899 and G. Zamorani 99

(Received XX; accepted XX)

We present spatially resolved VLT/SINFONI spectroscopy with adaptive optics of
type-2 active galactic nuclei (AGN) from the SINFONI Survey for Unveiling the
Physics and Effect of Radiative feedback (SUPER), which targeted X-ray bright
($L_{\rm 2-10\leavevmode\nobreak\ keV}\gtrsim 10^{42}$ erg s-1) AGN at the
Cosmic Noon ($z\sim 2$). Our analysis of the rest-frame optical spectra
unveils ionised outflows in all seven examined targets, as traced via [O
iii]$\lambda$5007 line emission, moving at $v\gtrsim 600$ km s-1. In six
objects these outflows are clearly spatially resolved and extend on 2–4 kpc
scales, whereas marginally resolved in the remaining one. Interestingly, these
SUPER type-2 AGN are all heavily obscured sources ($N_{\rm H}\gtrsim 10^{23}$
cm-2) and host faster ionised outflows than their type-1 counterparts within
the same range of bolometric luminosity ($L_{\rm bol}\sim 10^{44.8-46.5}$ erg
s-1). SUPER has hence provided observational evidence that the type-1/type-2
dichotomy at $z\sim 2$ might not be driven simply by projection effects, but
might reflect two distinct obscuring life stages of active galaxies, as
predicted by evolutionary models. Within this picture, SUPER type-2 AGN might
be undergoing the ‘blow-out’ phase, where the large amount of obscuring
material efficiently accelerates large-scale outflows via radiation pressure
on dust, eventually unveiling the central active nucleus and signal the start
of the bright, unobscured type-1 AGN phase. Moreover, the overall population
of ionised outflows detected in SUPER has velocities comparable with the
escape speed of their dark matter halos, and in general high enough to reach
30–50 kpc distances from the centre. These outflows are hence likely to sweep
away the gas (at least) out of the baryonic disk and/or to heat the host gas
reservoir, thus reducing and possibly quenching star formation.

###### Key Words.:

galaxies: active - galxies: evolution - galaxies: high-redshift - quasars:
emission lines - techniques: imaging spectroscopy

##  1 Introduction

A key question in galaxy evolution is how active galactic nuclei (AGN)
interact with their host galaxy and shape its physical properties, from gas
content to star formation (SF) and chemical enrichment. This so-called AGN
feedback typically acts via massive outflows (e.g. King 2005; Fabian 2012;
Costa et al. 2014), powered on sub-pc scales by the radiative output of
accreting supermassive black holes (BH), and then accelerated up to galaxy
scales of $\sim$1–10 kpc (e.g. King & Pounds 2015). These winds are considered
the main responsible for quenching SF within their host, by heating or
expelling the gas reservoir from which new stars form (i.e. ‘preventive’
versus ‘ejective’ feedback), and settling the observed scaling relations
between BH mass and host galaxy properties (e.g. Ferrarese & Merritt 2000;
Marconi & Hunt 2003; Kormendy & Ho 2013; Marasco et al. 2021).

To date we have plenty of observational evidence of AGN-driven outflows from
low (e.g. Feruglio et al. 2010; Harrison et al. 2014; Woo et al. 2016) to high
redshift (z$>$1; e.g. Maiolino et al. 2012; Förster Schreiber et al. 2014;
Carniani et al. 2023), in different gas phases (e.g. Cicone et al. 2018).
While mm/sub-mm interferometric observations are optimally suited to
investigate the cold molecular ($T$$<$100 K) component of AGN-driven outflows
(typically via CO line emission; Cicone et al. 2014; Morganti et al. 2015;
Chartas et al. 2020), optical/near-IR Integral Field Spectroscopy (IFS)
enables detailed spatially resolved studies of the warmer ionised component of
outflows. To trace ionised outflows, the most commonly adopted tracer is the
optical [O iii]$\lambda$5007 line emission (e.g. Cano-Díaz et al. 2012;
Venturi et al. 2018; Marshall et al. 2023), a forbidden line transition which
can originate only from low-density regions ($n_{\rm e}<10^{6}$ cm-3), thus
excluding any potential contamination from high-density AGN Broad Line Region
(BLR). Therefore, [O iii]$\lambda$5007 is an excellent tracer of ionised gas
extended on 1–10 kpc scales.

Thanks to high-quality IFS observations, obtained with ground-based adaptive
optics (AO) assisted facilities or from space with the James Webb Space
Telescope, AGN-driven ionised outflows and feedback mechanisms have been
deeply investigated at z$>$1 (e.g. Förster Schreiber et al. 2014; Perna et al.
2015b, 2023; Wylezalek et al. 2022; Cresci et al. 2023; Marshall et al. 2023;
Vayner et al. 2023), with the primary focus on the so-called Cosmic Noon
(z$\sim$2), where both SF and BH accretion histories reach the peak of their
activity (Madau & Dickinson 2014). This makes the redshift range z$\sim$1–3
the golden epoch of AGN feedback, where its effects are expected to be
maximised.

Nonetheless, the high-redshift search has primarily focused so far on brighter
distant sources ($L_{\rm bol}>10^{46}$ erg s-1; e.g. Bischetti et al. 2017;
Perrotta et al. 2019), easier to detect and to examine, which has led to a
biased census of high-redshift AGN-driven outflows. In addition to this, the
lack of direct observational evidence confirming (or disproving) key
theoretical predictions prevents us from a clear and complete picture of AGN
feedback and outflows, with several questions still open. For instance, how do
AGN-driven outflows really affect their host galaxy? Do they quench SF
activity efficiently? Are they representative of a particular life stage of
galaxies’ evolution?

One of these theoretical scenarios still waiting for observational proof
predicts that AGN galaxies experience a first dust-enshrouded life stage (e.g.
Hopkins et al. 2006; Menci et al. 2008), where the central nucleus is obscured
by dust and gas, which fuel both supermassive BH growth and SF efficiently.
This sets the stage to the short-lived (a few tens of Myr) ‘blow-out’ phase
(e.g. Hopkins et al. 2008), during which powerful AGN-driven winds sweep away
obscuring material and make the central AGN visible and unobscured. Such an
evolutionary scenario was proposed to explain the observed dichotomy between
red and blue quasars (e.g. Brusa et al. 2010; Klindt et al. 2019; Perrotta et
al. 2019; Fawcett et al. 2023), with the former representing the brief
obscured, transitional phase featured by powerful winds accelerated via
radiation pressure on dust (e.g. King & Pounds 2015; Costa et al. 2018). For
this reason, X-ray bright (easier to detect) but optically obscured AGN at
$z=1-3$ have been deeply investigated for years, as optimal candidates
undergoing the blow-out phase (e.g. Brusa et al. 2015, 2016; Cresci et al.
2015, 2023; Perna et al. 2015a, b; Veilleux et al. 2023), hence as the most
promising targets to catch AGN feedback and outflows in action.

To draw a coherent picture of AGN feedback at the Cosmic Noon, it is
fundamental to conduct systematic and unbiased searches for outflows in
statistically large samples. The KMOS AGN Survey at High redshift (KASHz;
Harrison et al. 2016) has provided spatially-resolved information for hundreds
of X-ray selected AGN, revealing ionised gas velocities likely indicative of
outflows in about 50% of the examined sample (Harrison et al. 2016). However,
KASHz employed seeing-limited observations with a typical spatial resolution
of 4–9 kpc at $z>1$ (i.e. $>$0.5′′), which is not sufficient to trace outflows
and their properties in detail. To reach higher spatial resolution, we can
exploit more time-consuming AO-assisted observations of smaller samples, yet
still representative of the AGN population at $z\sim 2$.

Our completed Large Programme SUPER (Survey for Unveiling the Physics and
Effect of Radiative feedback; PI: V. Mainieri, ID: 196.A-0377), carried out
with the near-IR spectrograph SINFONI (Eisenhauer et al. 2003) at the Very
Large Telescope (VLT) of the European Southern Observatory (ESO), has
collected AO-assisted IFS data of a representative sample of X-ray AGN
($L_{\rm 2-10\leavevmode\nobreak\ keV}\gtrsim 10^{42}$ erg s-1) at z$\sim$2,
spanning a wide range of bolometric luminosity ($L_{\rm bol}=10^{44-48}$ erg
s-1). Presented by Circosta et al. (2018), the SUPER survey offers us the
extraordinary possibility of investigating AGN-driven ionised outflows in a
few tens of AGN at the Cosmic Noon, selected in an unbiased way with respect
to the chance of hosting outflows. Thanks to SINFONI AO system, we reached
exquisite angular resolutions of 0.2′′–0.5′′, capable of probing spatial
scales down to 2–4 kpc at z$\sim$2.

In such a perspective, this paper continues the series of publications
dedicated to SUPER111Link to the SUPER webpage http://www.super-survey.org/.,
by presenting SINFONI observations of the type-2 AGN subsample. In particular,
this work complements the analysis carried out by Kakkad et al. (2020) of the
ionised outflows in the SUPER type-1 AGN (see also Vietri et al. 2020), by
extending the study of ionised outflows to the SUPER type-2 systems (see also
Lamperti et al. 2021). We still employ [O iii]$\lambda$5007 line emission to
trace large-scale ionised outflows, and then compare the inferred properties
of the type-2 ionised outflows with those of the type-1 counterparts, aiming
at uncovering possible differences between these two AGN populations.

This paper is organised as follows. In Sect. 2, we introduce the SUPER type-2
AGN subsample examined in this work, and present our SINFONI observations as
well as data reduction strategy. In Sect. 3, we describe the fitting procedure
adopted to derive ionised gas properties from SINFONI data. The inferred
results on ionised outflows are then presented in Sect. 4, whereas in Sect. 5
they are studied as a function of host galaxy properties and compared with
those previously obtained for the SUPER type-1 AGN (Kakkad et al. 2020).
Further discussion on our main findings as well as comparison with results
from the literature is found in Sect. 6. We finally draw our conclusions in
Sect. 7. A $\Lambda$CDM flat cosmology with $\Omega_{\rm m,0}=0.3$,
$\Omega_{\rm\Lambda,0}=0.7$ and $H_{\rm 0}=70$ km s-1 Mpc-1 is adopted
throughout this work.

##  2 Sample and data description

The sample presented in this work is part of the SUPER survey (ID: 196.A-0377;
PI: V. Mainieri), which acquired near-IR IFS observations of 33222Compared to
the original 39-AGN SUPER sample, six objects were not observed eventually.
This was a consequence of time lost because of bad weather conditions, since
part of the survey was executed in visitor mode rather than in service.
blindly selected X-ray AGN at $z\sim 2$ (see Circosta et al. 2018 for details
on sample selection and properties). The total 33-AGN observed sample consists
of: 21 type-1 AGN, deeply investigated in Kakkad et al. (2020) and Kakkad et
al. (2023); and 12 type-2 AGN, whose SINFONI observations are presented and
analysed in this work. All together these three papers investigate ionised
outflows with an unprecedented spatial resolution of $\sim$ kpc in an unbiased
sample of AGN at cosmic noon, using [O iii] line emission as outflow tracer,
and search for links between outflow properties (e.g. velocity, mass rate) and
fundamental AGN/host galaxy parameters (e.g. bolometric luminosity, stellar
mass, star formation rate). In the following subsections, we first introduce
the observed type-2 AGN subsample (Sect. 2.1), then we briefly describe the
observations, the data reduction, and the final type-2 AGN sample (i.e. seven
objects) analysed in this paper (Sect. 2.2).

Table 1: Main properties of the 12 observed type-2 AGN from SUPER. From left,
columns report: sky field and target ID; coordinates of the optical
counterpart of the target (J2000); spectroscopic redshift $z$; galaxy stellar
mass M∗, star formation rate (SFR), AGN bolometric luminosity Lbol, all with
1$\sigma$ uncertainties, derived from SED fitting (Circosta et al. 2018); 2–10
keV X-ray luminosity $L_{\rm 2-10\leavevmode\nobreak\ keV}$ and hydrogen
column density NH with 90% confidence level errors, measured from archival
X-ray data (Circosta et al. 2018); whether the object is detected in SINFONI
observations, hence included in the final analysed sample (see Sect. 2.2). The
redshift of the detected objects is inferred from rest-frame optical SINFONI
spectra (Sect. 3.1), whereas for the others from archival rest-frame UV
spectra (marked with †).

Field & Target ID | RA | DEC |  $z$ | log M∗ | SFR | log $L_{\rm bol}$ | log $L_{\rm 2-10\leavevmode\nobreak\ keV}$ | log $N_{\rm H}$ | Analysed
---|---|---|---|---|---|---|---|---|---
| (hh:mm:ss) | (dd:mm:ss) |  | [M⊙] | [M⊙ yr-1] | [erg s-1] | [erg s-1] | [cm-2] |
CDF-S XID36 | 03:31:50.77 |  $-$27:47:03.41 | 2.255 | $10.68\pm 0.07$ | $184\pm 9$ | $45.70\pm 0.06$ | $43.84^{+0.31}_{-0.63}$ | $>24.1$ | yes
CDF-S XID57 | 03:31:54.40 |  $-$27:56:49.70 | 2.298 † | $10.49\pm 0.11$ | $<34$ | $44.26\pm 0.18$ | $44.04^{+0.17}_{-0.24}$ | $23.30^{+0.32}_{-0.39}$ | no
CDF-S XID419 | 03:32:23.44 |  $-$27:42:54.97 | 2.143 | $10.89\pm 0.02$ | $42\pm 4$ | $45.54\pm 0.05$ | $43.84^{+0.29}_{-0.44}$ | $24.28^{+0.19}_{-0.31}$ | yes
CDF-S XID427 | 03:32:24.20 |  $-$27:42:57.51 | 2.303 † | $10.87\pm 0.08$ | $<72$ | $44.60\pm 0.13$ | $43.20^{+0.06}_{-0.06}$ | $22.43^{+0.24}_{-0.34}$ | no
CDF-S XID522 | 03:32:28.50 |  $-$27:46:57.99 | 2.309 † | $10.42\pm 0.02$ | $492\pm 25$ | $45.02\pm 0.02$ | $43.51^{+0.76}_{-0.87}$ | $>22.5$ | no
CDF-S XID614 | 03:32:33.02 |  $-$27:42:00.33 | 2.453 | $10.78\pm 0.08$ | $247\pm 12$ | $44.97\pm 0.13$ | $43.61^{+0.18}_{-0.18}$ | $24.25^{+0.19}_{-0.18}$ | yes
COSMOS cid_1057 | 09:59:15.00 | +02:06:39.65 | 2.210 | $10.84\pm 0.07$ | $85\pm 4$ | $45.91\pm 0.06$ | $44.53^{+0.26}_{-0.30}$ | $23.98^{+0.24}_{-0.28}$ | yes
COSMOS cid_451 | 10:00:00.61 | +02:15:31.06 | 2.444 | $11.21\pm 0.05$ | $<125$ | $46.44\pm 0.07$ | $45.18^{+0.23}_{-0.19}$ | $23.87^{+0.19}_{-0.15}$ | yes
COSMOS cid_2682 | 10:00:08.81 | +02:06:37.66 | 2.437 | $11.03\pm 0.04$ | $<93$ | $45.48\pm 0.10$ | $44.30^{+0.96}_{-0.27}$ | $23.92^{+1.01}_{-0.20}$ | yes
COSMOS cid_1143 | 10:00:08.84 | +02:15:27.99 | 2.443 | $10.40\pm 0.17$ | $108\pm 18$ | $44.85\pm 0.12$ | $44.83^{+0.43}_{-0.36}$ | $24.01^{+0.77}_{-0.29}$ | yes
COSMOS cid_971 | 10:00:59.45 | +02:19:57.44 | 2.473 † | $10.60\pm 0.12$ | $<96$ | $44.71\pm 0.24$ | $43.87^{+0.36}_{-0.38}$ | $<23.68$ | no
COSMOS cid_1253 | 10:01:30.57 | +02:18:42.57 | 2.147 † | $10.99\pm 0.25$ | $280\pm 194$ | $45.08\pm 0.18$ | $43.92^{+0.29}_{-0.31}$ | $23.22^{+0.47}_{-0.39}$ | no

###  2.1 Observed SUPER type-2 AGN subsample

SUPER targeted 12 type-2 AGN in total: six sources are from the Chandra Deep
Field-South (CDF-S; Luo et al. 2017); whereas the other six are from the
COSMOS-Legacy survey (Civano et al. 2016). Before being observed with SINFONI,
they were identified as type-2 systems based on the absence of BLR emission in
the archival rest-frame UV spectra (Circosta et al. 2018). Their type-2
spectral classification was then confirmed by SINFONI observations, showing no
BLR components in rest-frame optical Balmer hydrogen lines (H$\alpha$ and
H$\beta$), typically less extincted than UV emission lines. In Table 1, we
list some main properties of the 12 observed type-2 AGN, including
spectroscopic redshift $z$; bolometric luminosity $L_{\rm bol}$, stellar mass
$M_{*}$, and SFR, all derived from Spectral Energy Distribution (SED) fitting
(Circosta et al. 2018); 2–10 keV X-ray luminosity $L_{\rm
2-10\leavevmode\nobreak\ keV}$ and hydrogen column density $N_{\rm H}$
inferred from archival X-ray spectra (Circosta et al. 2018). Redshift values
have been obtained from rest-frame optical SINFONI spectra (see Sect. 3.1),
except for sources being undetected or marginally detected (five in total) in
our SINFONI data (see Sect. 2.2). For these sources, the redshift has been
measured from archival rest-frame UV spectra (marked with †). Depending on the
quality of [O iii] detection in SINFONI H-band spectra, the last column shows
what objects are included in the final type-2 AGN sample (i.e. seven objects)
analysed in this work (details in Sect. 2.2).

###  2.2 Observations and data reduction

Table 2: SINFONI observations of the 12 SUPER type-2 AGN. For each target, we
list the observing mode (AO/noAO) and the following parameters for each
adopted grating (H, K, HK): absolute (AB) magnitude measured from SINFONI
integrated spectra accounting for the corresponding filter transmission curve,
total on-source exposure time $t_{\rm exp}$, spatial resolution $\theta_{\rm
PSF}$ (computed as PSF FWHM), and detected emission lines for the objects with
[O iii] detected at S/N$>$2\. For these sources, we indicate marginal
detections (S/N$<$2) of H$\alpha$ with a ‘†’ symbol. The AB magnitudes marked
with ‘∗’ have required a correction factor ($\sim 2$) to match the previous
synthetic measurements (Circosta et al. 2018).

Target | Obs mode |  H band |  K band
---|---|---|---
|  | mag (AB) | $t_{\rm exp}$ [hr] |  $\theta_{\rm PSF}$ [′′] | Detected lines | mag (AB) | $t_{\rm exp}$ [hr] |  $\theta_{\rm PSF}$ [′′] | Detected lines
|  | ($\theta_{\rm PSF}$ [kpc]) | ($\theta_{\rm PSF}$ [kpc])
XID36 | AO | 21.45 | 5.3 | $0.29\times 0.26$ | H$\beta$, [O iii] | 21.13 | 2.0 | $0.15\times 0.15$ | H$\alpha$, [N ii], [S ii]
($2.4\times 2.1$) | $1.2\times 1.2$
XID57 | AO | - | 3.0 | $0.32\times 0.30$ | - | - | 1.0 | $0.33\times 0.27$ | -
($2.6\times 2.5$) | ($2.7\times 2.2$)
XID419 | AO | 22.69* | 0.8 | $0.47\times 0.52$ | [O iii] | 21.85* | 1.0 | $0.24\times 0.22$ | H$\alpha$, [N ii]
($3.9\times 4.3$) | ($2.0\times 1.8$)
XID427 | AO | 22.72 | 1.0 | $0.23\times 0.20$ | - | 21.90* | 1.0 | $0.28\times 0.20$ | -
($1.9\times 1.6$) | ($2.3\times 1.6$)
XID522 | AO | - | 1.0 | $0.25\times 0.20$ | - | - | 1.0 | $0.25\times 0.19$ | -
($2.0\times 1.6$) | ($2.0\times 1.6$)
XID614 | AO | 22.41* | 11.5 | $0.38\times 0.37$ | H$\beta$, [O iii] | 21.79 | 3.5 | $0.27\times 0.28$ | H$\alpha$, [N ii]
($3.1\times 3.0$) | ($2.2\times 2.3$)
cid_1057 | AO | 21.80* | 2.0 | $0.32\times 0.30$ | H$\beta$, [O iii] | 20.91 | 0.7 | $0.46\times 0.43$ | -
($2.6\times 2.5$) | ($3.8\times 3.6$)
cid_451 | AO | 21.73 | 5.0 | $0.28\times 0.30$ | [O iii] | 21.69* | 2.0 | $0.28\times 0.27$ | H$\alpha$, [N ii]
($2.3\times 2.4$) | ($2.3\times 2.2$)
cid_2682 | AO | 21.58 | 5.6 | $0.33\times 0.28$ | [O iii] | 21.01* | 1.8 | $0.28\times 0.25$ | H$\alpha$, [N ii]
($2.7\times 2.3$) | ($2.3\times 2.0$)
cid_1143 | AO | 22.44 | 5.5 | $0.41\times 0.38$ | H$\beta$, [O iii] | 22.66 | 2.0 | $0.30\times 0.30$ | H$\alpha$ †
($3.3\times 3.1$) | ($2.4\times 2.4$)
cid_1253 | noAO | 22.45 | 4.0 | $0.68\times 0.63$ | - | 21.61 | 1.0 | $0.66\times 0.61$ | -
($5.6\times 5.2$) | ($5.5\times 5.1$)
|  |  HK band
|  | mag (AB) |  $t_{\rm exp}$ [hr] |  $\theta_{\rm PSF}$ [′′] | Detected lines
|  |  |  | ($\theta_{\rm PSF}$ [kpc]) |
cid_971 | noAO | - | 1.8 | $0.42\times 0.39$ | -
($3.4\times 3.2$)

SUPER targets were observed with SINFONI between November 2015 and December
2018. All observations were planned to be carried out in AO mode using a laser
guide star (LGS), covering both H band, targeting H$\beta$ and [O
iii]$\lambda\lambda$4959,5007 emission lines, and K band, targeting H$\alpha$,
[N ii]$\lambda\lambda$6549,83, and [S ii]$\lambda\lambda$6716,31.

Due to the absence of suitable stars close to the targets, observations of ten
out of 12 type-2 AGN were performed in AO-assisted mode with no tip-tilt star
(i.e. in Seeing Enhancer mode; Davies et al. 2008), using both H and K
gratings ($R\sim$3000 and $R\sim$4000, respectively); the remaining two
objects were observed with no-AO (cid_1253 with H and K gratings; cid_971 with
the HK grating, $R\sim$1500) in order to optimize the time available during
the visitor-mode observing run, reduced because of bad weather conditions. For
AO-assisted observations, we adopted a plate scale of $3^{\prime\prime}\times
3^{\prime\prime}$ with a spatial sampling of $0.05^{\prime\prime}\times
0.1^{\prime\prime}$, then re-sampled to $0.05^{\prime\prime}\times
0.05^{\prime\prime}$ in the final datacube. For the two targets observed in
seeing-limited mode, we instead selected the largest field of view (FoV;
$8^{\prime\prime}\times 8^{\prime\prime}$), corresponding to a pixel scale of
$0.25^{\prime\prime}\times 0.25^{\prime\prime}$. Before or after each
observing block, a dedicated bright star was observed to estimate the Point
Spread Function (PSF) of our AO-assisted SINFONI observations, as full width
at half maximum (FWHM) of a 2D-Gaussian fitting to the total flux distribution
of the PSF star ($\theta_{\rm PSF}$ values in Table 2).

Below we summarise the main steps of the reduction procedure applied to
SINFONI type-2 AGN datacubes, whereas we refer the reader to Kakkad et al.
(2020) for a detailed description of the SUPER observational strategy. We
reduced all observations using the ESOREX pipeline (3.1.1), which returns a
distortion-corrected and wavelength-calibrated datacube of the science target,
as well as of the PSF and telluric stars. We then removed background sky
emission via the IDL routine ‘skysub.pro’ (Davies 2007), and used our own
custom-made python routines to perform flux calibration (based on Piqueras
López et al. 2012) and to reconstruct the final datacube for each observed
target. As a final check, we compared the synthetic photometry measured from
SINFONI integrated spectra with both archival photometric measurements
(Circosta et al. 2018), and the synthetic photometry from KMOS integrated
spectra, when available (Scholtz et al., in prep.). From this comparison, we
estimated a typical relative uncertainty on the flux calibration of our
SINFONI data of about 20% for all employed bands.

In Table 2, we show the main parameters of SINFONI observations of the 12
SUPER type-2 AGN. For each target, we specify the observing mode (AO/noAO),
and list the following parameters for the corresponding adopted grating (H, K,
HK): absolute (AB) magnitude measured from SINFONI integrated spectra, total
exposure time $t_{\rm exp}$, spatial resolution $\theta_{\rm PSF}$ (i.e. PSF
FWHM), and detected emission lines for the seven objects with [O iii] detected
with a total, spatially integrated S/N$>$2, that is the crucial tracer of
ionised outflows employed in our study. Five of these systems have also
H$\alpha$+[N ii] detected with a S/N$>$2, whereas H$\alpha$ is totally
undetected or marginally detected (S/N$<$2) in cid_1057 and cid_1143,
respectively. Although not suitable for our analysis, we report for
completeness that we also marginally detect [O iii] and/or H$\alpha$ in XID427
and cid_1253. Finally, the remaining three targets (i.e. XID522, XID57 and
cid_971) are totally undetected in SINFONI observations. We point out that
these non-detections or poor detections are a consequence of the lower
integration time, due to the observing time lost because of bad weather
conditions, which particularly penalised SUPER type-2 objects, being these
also the faintest sources of the SUPER sample ($L_{\rm bol}<10^{46}$ erg s-1).

In the following, this paper will focus on the seven type-2 AGN with [O iii]
detected at S/N$>$2, which allows us to perform a spatially-resolved analysis
of [O iii] line emission and thus search for ionised outflows signatures.
These sources have all a ‘yes’ associated in the last column of Table 1. In
particular, three of them are CDF-S sources (XID36, XID419, XID614), whereas
four are from COSMOS (cid_1057, cid_1143, cid_2682, cid_451).

##  3 Data analysis

In this section, we analyse SINFONI H-band observations of the seven type-2
AGN well detected (S/N$>$2) in [O iii], to spatially map [O iii] line emission
and its kinematics, looking for evidence of high-velocity outflowing gas
(Sect. 3.1). Unfortunately, the quality of K-band data is worse (i.e. lower
S/N) for most objects, thus preventing us from an accurate mapping of K-band
line emission across the SINFONI FoV. In Sect. 3.2 we use combined integrated
measurements of H$\alpha$ and H$\beta$ fluxes from K\- and H-band data,
respectively, to estimate dust extinction. Being these targets obscured
(type-2) AGN, correcting for dust extinction is a crucial step to get reliable
estimates of outflow energetics (see Sect. 4.2).

###  3.1 Spectral fitting

Figure 1: Integrated H\- and K-band spectra extracted from SINFONI datacubes
of XID36 (top), XID419 (middle) and XID614 (bottom), with a 0.25′′-radius
aperture, after subtracting continuum emission modelled with a 1st-degree
polynomial. Data are shown in black, with total emission-line model drawn on
the top in red. For multi-Gaussian modellings, we plot separately narrow,
systemic components (light blue) and broad high-velocity ones (green),
associated with outflows. In the top right panel, green solid and dashed lines
represent the outflow component of H$\alpha$ and [N ii] doublet, respectively.
Mean velocity $v$ and velocity dispersion $\sigma$ values of each Gaussian
component are displayed. Shaded grey regions indicate masked channels
contaminated by sky line residuals, while vertical dotted lines mark rest-
frame emission line wavelengths at the redshift of each source, as computed in
Sect. 3.1. Below each main panel, a second one shows corresponding residuals
(i.e. data–model). Figure 2: Integrated H\- and K-band spectra extracted from
SINFONI datacubes of cid_1143 (top), cid_2682 (middle) and cid_451 (bottom),
with a 0.25′′-radius aperture, after subtracting continuum emission modelled
with a 1st-degree polynomial. Same as in Fig. 2. In K-band spectrum of
cid_1143, we fit only H$\alpha$ line emission due to the low S/N (S/N$<$2) of
this dataset. Figure 3: Integrated H-band spectrum of cid_1057, extracted from
SINFONI datacube with a 0.25′′-radius aperture, after subtracting continuum
emission modelled with a 1st-degree polynomial. Same as in Fig. 2. Three
Gaussian components are used to properly model [O iii] line profile: one for
systemic, narrow line emission (light blue), two for the blue and red wings
detected in [O iii] (different green colours).

For the spectral fitting of SINFONI H-band datacubes, we adopt the fitting
code presented in Marasco et al. (2020) (see also Tozzi et al. 2021 for more
details), and fit all cubes in the rest-frame optical wavelength range of
4800–5200Å. Being distant type-2 AGN, their SINFONI spectra show neither BLR
emission nor strong (AGN and/or stellar) continuum, but consist of only
‘narrow’ (FWHM$\lesssim$2000 km s-1) emission lines, either originating from
AGN Narrow Line Region (NLR) or due to a different kinematics (i.e. outflow,
disk rotation). In addition to [O iii]$\lambda\lambda$5007,4959 emission line
doublet (detected in all seven examined sources), we also detect faint
H$\beta$ line emission in four H-band datacubes (i.e. XID36, XID914, cid_1143,
cid_1057; see Figs. 1-3).

As a first step, we apply a spatial Gaussian smoothing of the data channel by
channel to enhance the visibility of real gas structures. To do this without
deteriorating the instrumental PSF, we smooth each datacube with a Gaussian
kernel of width $\sigma_{\rm smooth}\sim 0.1-0.2^{\prime\prime}$, that is as
large as possible, yet still smaller than the instrumental PSF FWHM (i.e.
$\sigma_{\rm smooth}<\theta_{\rm PSF}/2.35$). After this, we model spaxel-by-
spaxel the observed line emission via multiple Gaussian components, following
the prescriptions summarised below:

  * •

We fit the [O iii]$\lambda\lambda$5007,4959 line doublet with two Gaussian
components sharing the same kinematics (mean velocity $v$ and velocity
dispersion $\sigma$), imposing a fixed flux ratio of 3. Hereafter, we refer to
the brighter doublet component (i.e. [O iii]$\lambda$5007) simply as [O iii].

  * •

When detected, faint H$\beta$ line emission is modelled with $v$ and $\sigma$
fixed to the best-fit values obtained for [O iii], hence with flux as the only
free parameter.

  * •

In each spaxel, we reiterate the line modelling using an increasing number of
Gaussian components for each emission line, from one to the maximum number
which best reproduces complex line profiles in high S/N spaxels. We then
select the optimal (minimum) number of Gaussian components required in each
spaxel via a Kolmogorov-Smirnov test on the residuals. Five H-band datasets
require up to two Gaussian components; cid_1057 up to three components, given
the presence of both blue and red [O iii] wings detected; instead, one
component is sufficient to model single-spaxel line emission in XID419.

  * •

We also add a 1st-degree polynomial to reproduce any faint continuum emission
over the entire fitted wavelength range.

Several spectra require multiple Gaussian components to reproduce
simultaneously both narrow, low-velocity line emission and broad, high-
velocity wings in [O iii] line profile. Whereas the latter are considered the
typical signature of fast outflowing gas (discussed in Sect. 4.1), narrow,
low-velocity line emission is typically due to nearly systemic gas motions
(e.g. NLR, disk rotation). In fact, we use the narrowest [O iii] line
component to measure the redshift of each source: we extract a H-band
integrated spectrum from a small aperture (0.1′′-radius) centred on the
overall H-band emission peak (corresponding to [O iii] emission peak), and set
to 0 km s-1 the peak of the narrowest [O, iii] line component. We estimate an
uncertainty of $\Delta\leavevmode\nobreak\ z=0.001$ on our [O iii]-based
measurements of $z$, due to SINFONI H-band spectral resolution ($R\sim$3000)
and the typical S/N of the line.

###  3.2 Dust extinction from integrated spectra

Since the targets examined in this work are all type-2 AGN highly obscured in
the X-rays ($N_{\rm H}\gtrsim 10^{23}$cm-2; Circosta et al. 2018), rest-frame
optical emission is expected to be significantly affected by extinction
effects. In particular, in Sect. 4.2 we correct our measurements of [O iii]
outflow luminosity for dust extinction to estimate intrinsic energetics of
ionised outflows. Below, we derive rest-frame optical dust extinction from
spatially integrated H$\alpha$/H$\beta$ ratios for the seven examined type-2
AGN.

To accurately correct for dust extinction, in principle we should: (i) account
for spatial variations of H$\alpha$/H$\beta$ ratios across the FoV using
spaxel-by-spaxel measurements; (ii) consider extinction effects exclusively on
the outflow component, which may differ from those affecting the bulk of the
ionised gas or any other kinematic component (e.g. disk rotation, AGN NLR).
Yet, the S/N on H$\alpha$ and H$\beta$ in our SINFONI data is not high enough
for such an accurate, spatial mapping of dust extinction. Indeed, their
detection is mostly limited to central brighter spaxels, with H$\beta$
undetected in three sources (even in integrated spectra), whereas H$\alpha$ is
totally undetected in cid_1057 and marginally (S/N$<$2) detected in cid_1143.
Therefore, we extract integrated K\- and H-band spectra of each object to
increase the S/N on H$\alpha$ and H$\beta$, so as to estimate (or put
constraints on) dust extinction. The only target of the 7-AGN sample for which
this is not possible is cid_1057, being totally undetected in H$\alpha$.
Therefore, in the following we describe first how we estimate (or constrain)
dust extinction from SINFONI integrated spectra in the six objects with
H$\alpha$ detected, and then assumptions adopted for cid_1057.

We extract integrated K\- and H-band spectra of each target using an aperture
of $\sim$0.25′′-radius centred on the source (Figs. 1, 2, 3), and fit them
following the same prescriptions described in Sect. 3.1. In particular,
Gaussian profiles fitted to H$\alpha$ and [N ii]$\lambda\lambda$6549,83 are
constrained to have the same velocity and velocity dispersion, with the
additional constraint of a fixed flux ratio of 3 on the two [N ii] doublet
components. In XID36, we model also faint [S ii]$\lambda\lambda$6716,31 line
emission, following the same prescriptions adopted for H$\beta$ in H-band
datasets. In most cases, one Gaussian component is enough to reproduce K-band
emission lines; XID36 and cid_451 instead require an additional broader
component ($\sigma\sim 700$ km s-1 in XID36, $\sigma\sim 400$ km s-1 in
cid_451).

In Figs. 1 and 2, we show H\- and K-band spectra extracted with a
0.25′′-radius aperture from SINFONI datacubes, after subtracting the 1st-
degree polynomial used to reproduce any residual faint continuum emission.
Data are shown in black, with total emission-line model drawn on the top in
red. For multi-Gaussian fittings, we separately plot components used to
reproduce narrow, systemic line emission (light blue), and broad high-velocity
line emission (green), likely associated with ionised outflows. For all
spectra, we also display velocity $v$ and velocity dispersion $\sigma$ values
of each Gaussian component, using same colours as for the corresponding
Gaussian component. In Fig. 3, we also show an integrated H-band spectrum of
cid_1057, extracted with the same 0.25′′-radius aperture. In this source, the
[O iii] emission line exhibits an asymmetric profile which is well reproduced
by three Gaussian components: one for systemic, narrow line emission (light
blue), two for the blue and red [O iii] wings (in different green colours).
Excluding XID419, the only target requiring a single-Gaussian modelling, we
find mean values of $\langle|v|\rangle\sim 380$ km s-1 and
$\langle\sigma\rangle\sim 440$ km s-1 for the broad outflow (green) component,
whereas $\langle|v|\rangle\sim 20$ km s-1 and $\langle\sigma\rangle\sim 160$
km s-1 for the narrow, nearly systemic one.

Since we do not detect broad, high-velocity wings in H$\beta$ line profile,
possibly associated with outflows, in any object (in H$\alpha$ only in XID36
and cid_451), we take integrated total H$\alpha$/H$\beta$ ratios to get an
estimate of total dust extinction (Calzetti et al. 2000), and assume it to
also affect outflow emission. For XID36, XID614 and cid_1143, with both
hydrogen lines detected, we estimate $A_{V}$ and relative uncertainty via
error propagation. For cid_1143, due to the low S/N on H$\alpha$ and H$\beta$,
we obtain a value of $A_{V}$ consistent with 0 (i.e. no extinction) within the
uncertainty ($A_{V}=1.2\pm 1.8$). Since $A_{V}$ must be non-negative, we adopt
$A_{V}=1.2^{+1.8}_{-1.2}$. In XID419, cid_2682 and cid_451 instead, where we
do not detect H$\beta$, we estimate a lower limit to $A_{V}$, considering a
2$\sigma_{\rm noise}$ upper limit to H$\beta$ flux. Finally, for cid_1057, the
only source totally undetected in H$\alpha$, we consider an $A_{V}$ value
computed as the average of the three $A_{V}$ measurements obtained for XID36,
XID614 and cid_1143, taking the maximum distance of these estimates from the
mean as uncertainty (i.e. $A_{V}=1.8\pm 1.0$). We will use these $A_{V}$
values later in Sect. 4.2 to get the dust-corrected [O iii] outflow flux in
each source, hence the corresponding outflow mass.

##  4 Results

###  4.1 Evidence of spatially resolved outflows

Figure 4: Morphology and kinematics of ionised gas in XID36 and XID419, as
traced by total [O iii] line emission. For each target, maps show: the
intensity (moment-0) field; $v_{\rm 50}$, $v_{\rm 10}$, $v_{\rm 90}$
percentile velocities and $w_{\rm 80}$ line width; and the ratio $f_{300}$ of
the flux contained in [O iii] wings (i.e. $|v|>300$ km s-1) to the moment-0
flux. Dashed and dotted circles correspond, respectively, to the 0.25′′-radius
aperture used to extract integrated spectra (Sect. 3.2), and to the mean
H-band PSF of radius $\langle\theta_{\rm PSF}\rangle/2$ (see Table 2). In
XID419 the two circles have the same 0.25′′ radius. In all maps, we apply a
S/N$>$3 cut and mark the position of [O iii] peak emission with a cross.
Figure 5: Morphology and kinematics of ionised gas in XID614 and cid_1143, as
traced by total [O iii] line emission. Same as in Fig. 4. Figure 6: Morphology
and kinematics of ionised gas in cid_2682 and cid_451, as traced by total [O
iii] line emission. Same as in Fig. 4. Figure 7: Morphology and kinematics of
ionised gas in cid_1057, as traced by total [O iii] line emission. Same as in
Fig. 4.

Our spectral modelling of [O iii] line emission has clearly revealed prominent
wings in the [O iii] line profile of four galaxies (i.e. XID36, cid_1143,
cid_451, cid_1057; see Figs. 1-3), pointing to the presence of gas moving at
high speed with respect to the galaxy systemic velocity. Also in XID614 and
cid_2682, the [O iii] line profile shows a faint blue wing (see Figs. 1, 2),
whereas it is hard to assess at first sight the presence of any possible blue
[O iii] wing in XID419 (see Fig. 1), due to bright residual sky emission. In
this section we inspect kinematic maps of [O iii] line emission (Figs. 4-7) to
confirm the presence of ionised outflows in higher-S/N detections, and to
search for evidence in more ambiguous cases.

In Figs. 4–7, we show maps of [O iii] line emission with a S/N$>$3 cut,
displaying the spatial distribution and kinematics of ionised gas in all seven
examined type-2 AGN. First-column maps show the [O iii] intensity field
computed as moment-0 of the total line profile (upper panel); and ratio values
($f_{300}$; lower panel) of the flux in high-velocity channels ($|v|>300$ km
s-1) to moment-0 flux, a simplified but reasonable approximation of [O iii]
flux carried by outflows (Kakkad et al. 2020). For the seven examined type-2
AGN, we find that the total (i.e. spatially integrated) flux at $|v|>300$ km
s-1 represents a significant fraction of the total moment-0 flux, ranging from
21% (in cid_1057) to 66% (in XID36).

In addition, maps in the second and third columns of Figs. 4–7 display ionised
gas kinematics described in terms of non-parametric percentile
velocities333Percentile velocities $v_{\rm xx}$ used in the following are
defined as the velocity containing xx% of the total [O iii] line flux..
Compared to parametric values, percentile velocities have the advantage of
being independent on the adopted fitting function (e.g. the number of Gaussian
components), which in turn may strongly depend on the S/N of the examined data
(e.g. Harrison et al. 2014; Zakamska & Greene 2014). In particular, we compute
$v_{\rm 50}$, $v_{\rm 10}$, $v_{\rm 90}$ velocities, and the $w_{\rm 80}$ line
width (i.e. $w_{\rm 80}=v_{\rm 90}-v_{\rm 10}$), approximately equal to the
FWHM of a Gaussian profile. Extreme $v_{\rm 10}$ and $v_{\rm 90}$ values are
widely adopted as a reliable approximation of outflow velocity, for its
approaching and receding components (e.g. Harrison et al. 2014; Carniani et
al. 2015; Cresci et al. 2015), respectively. Compared to moment-1 and moment-2
values (i.e. mean velocity and velocity dispersion, respectively), which may
be affected by significant geometrical projection effects, $v_{\rm 10}$ and
$v_{\rm 90}$ help avoid possible underestimates of the real outflow velocity,
due to our ignorance about intrinsic outflow geometry and inclination to the
line of sight. Maps of $v_{\rm 10}$ and $v_{\rm 90}$ overall feature high
velocities, with absolute values up to 800–1100 km s-1 in four targets (XID36,
XID419, cid_1143, cid_451), and to 600–700 km s-1 in the remaining three
(XID614, cid_1057, cid_2682). These large velocities are accompanied by
$w_{\rm 80}$ line widths larger than 800 km s-1 in most of the FoV of all
targets, with maximum values of at least 1000 km s-1 (about 1700 km s-1 in
XID36 and 1200 km s-1 in cid_451).

Ionised outflows are the most likely explanation to the high-velocity
kinematics observed in all targets, since alternative phenomena can hardly
lead to such high values of velocity and line width. In fact, SF-driven
outflows are typically characterised by lower velocities (e.g. Arribas et al.
2014; Förster Schreiber et al. 2019); whereas galactic inflows are mostly
observed in absorption with low bulk velocities and $\sigma$ values (e.g.
Bouché et al. 2013). Large line widths might also be due to turbulence in the
interstellar medium induced by the interaction of nuclear activity with the
host environment, as observed in low-redshift type-2 active galaxies (e.g. Woo
et al. 2016; Fischer et al. 2018; Venturi et al. 2021; Girdhar et al. 2022).
Yet, such turbulence-induced effects are often associated with relatively low
centroid velocities (i.e. $|v|<200-300$ km s-1), typical of gas at systemic or
following rotation (Woo et al. 2016; Fischer et al. 2018). This does not seem
to be the case of the broad components reproducing [O iii] line wings in SUPER
type-2 systems, which exhibit centroid velocities $|v|\gtrsim 300$ km s-1 (see
green $v$ values shown in Figs. 1–3), unlikely associated with galaxy
rotation. Moreover, we point out that turbulence-induced effects have been
traced so far in observations of local Seyfert galaxies at high spatial
resolution and sensitivity (see e.g. Fischer et al. 2018; Venturi et al. 2021;
Girdhar et al. 2022). As a consequence, it might be hard to detect them in
lower-quality observations at $z\sim 2$ like SUPER, with a spatial resolution
of 2–4 kpc.

Similarly, the observed large [O iii] line widths and prominent asymmetries
(mostly blueshifted wings) can be hardly accounted for by merging scenarios.
This is also supported by the lack of clear [O iii] double peaks and by the
relatively unperturbed morphology of stellar continuum emission, as traced by
rest-frame 260 $\mu$m FIR continuum emission (when available for SUPER
targets, see Lamperti et al. 2021). Although harder to detect than line
emission due to gas, stellar continuum can better unveil the presence of a
second merging galaxy. The only SUPER galaxy showing some degree of perturbed
morphology is cid_1143, which exhibits an offset of about 2 kpc between the
centroids of rest-frame optical line emission (i.e. H$\alpha$ and [O iii]) and
rest-frame 260 $\mu$m FIR continuum, respectively (Lamperti et al. 2021). This
may indicate the presence of a companion for this galaxy but, even if it did,
given the offset of the possible companion from FIR continuum, this is not
expected to contribute to the flux in the galaxy center, where we observe
$v_{10}$ velocities of about $1000$ km s-1. For the other sources we can
instead likely rule out early mergers based on the observational evidence
available so far, although the S/N of our SINFONI observations might not be
sufficient to detect faint merger signatures and to identify especially final-
state mergers.

On the other side, $v_{\rm 50}$ velocities are useful to describe the velocity
field of the component dominating the overall kinematics of ionised gas. In
all galaxies, we find moderate $v_{\rm 50}$ values ($|v_{\rm 50}|<400$ km s-1;
even $|v_{\rm 50}|<100$ km s-1 in XID614 and cid_2682), with a predominance of
blueshifted $v_{\rm 50}$ values. This means that a significant fraction of [O
iii] line emission is found in the blue line wing, further supporting the
presence of fast ionised outflows in these galaxies. In XID36 instead, $v_{\rm
50}$ velocities appear redshifted almost everywhere (reaching 400 km s-1), as
a consequence of the extended [O iii] red wing, which dominates the overall [O
iii] kinematics in this object (see Fig. 1). In a few galaxies, we also detect
a smooth velocity gradient of $v_{\rm 50}$: in cid_1143 it is outflow-
dominated, with all blueshifted $v_{\rm 50}$ velocities of increasing absolute
value from NW to SE (N is up, E to left); in contrast, in XID419 and XID614
outflow emission does not seem to be the dominant component, being the $v_{\rm
50}$ field consistent with disk rotation, with velocities ranging from
negative to positive values passing through the galaxy centre. The presence of
outflows in these two galaxies is better highlighted by the high values of
$v_{\rm 10}$ and $v_{\rm 90}$, as previously discussed.

In all maps of Figs. 4-7, we draw two circles corresponding: to the aperture
used in Sect. 3.2 to extract integrated SINFONI spectra (dashed); and to the
mean H-band PSF (dotted), namely, with a radius equal to $\langle\theta_{\rm
PSF}\rangle/2$ (see Table 2). In XID419 (Fig. 4) the two circles have the same
radius. Compared to the corresponding H-band PSF, we see that the extent of
S/N$>$3 [O iii] line emission at $|v|>300$ km s-1 is clearly larger than the
instrumental PSF radius in all galaxies except for XID614. This ensures that
the high-velocity [O iii] emission associated with the outflows is spatially
resolved in at least six objects of our sample. In XID614, S/N$>$3 [O iii]
maps and H-band PSF have approximately the same extent in all directions (see
Fig. 5). However, the fact that we see structures and variations in gas
kinematics across the FoV of XID614 suggests that high-velocity [O iii] line
emission at large distance is at least marginally spatially resolved in our
data.

###  4.2 Ionised outflow properties and energetics

Since H$\beta$ is detected at low S/N in our H-band observations, due to
extinction effects, we derive ionised outflow properties from higher-S/N [O
iii] line emission444Using [O iii] line emission requires more assumptions
than H$\alpha$ or H$\beta$, but the latter are overall detected at a lower S/N
compared to [O iii], thus motivating our choice.. Properties such as outflow
velocity $v_{\rm out}$, radial extent $R_{\rm out}$, and [O iii] luminosity
$L^{\rm[OIII]}_{\rm out}$ can be directly measured from SINFONI data, while
other physical quantities (i.e. electron density $n_{\rm e}$, oxygen abundance
[O/H]) must be assumed in the calculation of outflow mass rate $\dot{M}_{\rm
out}$.

####  4.2.1 Directly measured outflow properties

The outflow properties we can measure directly are $v_{\rm out}$, $R_{\rm
out}$ and $L^{\rm[OIII]}_{\rm out}$. As outflow velocity $v_{\rm out}$, we
consider for each target the maximum absolute value of all observed $v_{10}$
or $v_{90}$ velocities (i.e. $v_{\rm out}={\rm max}[|v_{10}|,|v_{90}|]$). This
definition is widely adopted in the literature (e.g. Cresci et al. 2015;
Carniani et al. 2015; Tozzi et al. 2021; Vayner et al. 2021a, b) and relies on
the assumption that all observed lower velocities are consequence of
projection effects. Other works instead use $w_{80}$ as outflow velocity (e.g.
Harrison et al. 2012; Kakkad et al. 2016, 2020), being $w_{80}$ less affected
by projection effects compared to $v_{10}$ and $v_{90}$. Yet, we point out
that $w_{80}$ values depend more strongly on line shapes, systematically
leading to higher velocities for symmetric line profiles than asymmetric ones,
where only one wing is detected as a possible consequence of dust extinction.
Dust effects indeed seem to be the most likely explanation for the
(blueshifted) asymmetric [O iii] profiles found in most of our type-2 AGN,
which could be hardly accounted for by standard unified models (e.g. Antonucci
1993; Urry & Padovani 1995; further discussions are presented in Sect. 6).

For this reason, we here adopt $v_{\rm out}={\rm max}[|v_{10}|,|v_{90}|]$ to
define the outflow velocity, unlike the previous $w_{80}$-based approach
adopted in Kakkad et al. (2020) for the SUPER type-1 sample. However, as
pointed out in Sect. 4.1, all type-2 AGN show $w_{80}>800$ km s-1 in most of
the FoV, thus meeting the $w_{80}>600$ km s-1 criterion used in Kakkad et al.
(2020) to identify [O iii] outflow emission (e.g. see also Harrison et al.
2016).

Overall, we find wind velocities within the range $v_{\rm out}\sim 600-1100$
km s-1, with a mean outflow velocity $\langle v_{\rm out}\rangle\sim 830$ km
s-1. For comparison, we also compute for each object the outflow velocity
defined as $v_{\rm max}=v_{\rm bro}+2\sigma_{\rm bro}$ (Rupke & Veilleux
2013), where $v_{\rm bro}$ and $\sigma_{\rm bro}$ are the velocity and
velocity dispersion, respectively, of the broad Gaussian components associated
with outflows. This parametric definition of outflow velocity typically
delivers values slightly larger compared to the $v_{\rm out}$ definition based
on $v_{10,90}$ but, being adopted in several works (e.g. Brusa et al. 2015;
Fiore et al. 2017; Leung et al. 2019; Perrotta et al. 2019; Kakkad et al.
2020), it is a useful quantity to compute for comparing our results with other
from the literature (see Sect. 6). For XID419, we estimate $v_{\rm max}$ as
twice the $\sigma$ value of the single Gaussian employed, as done in Kakkad et
al. (2020). The $v_{\rm out}$ values inferred for each galaxy are listed in
Table LABEL:tab:super_wind, along with maximum observed values of $v_{\rm
max}$ and $w_{80}$ (as well as other outflow properties inferred in the
following).

To estimate $R_{\rm out}$ instead, we first infer the observed (maximum)
outflow radius $R_{\rm obs}$, measured as the maximum extent from the centre
of [O iii] line emission at $|v|>300$ km s-1. We then correct $R_{\rm obs}$
for the H-band PSF (i.e. $R_{\rm out}=\sqrt{R^{2}_{\rm
obs}-(\langle\theta_{\rm PSF}\rangle/2)^{2}}$, with $\langle\theta_{\rm
PSF}\rangle$ being the average PSF FWHM, see Table 2), thus obtaining
intrinsic radii of a few kpc for spatially resolved outflows ($R_{\rm
out}\sim$2–4 kpc). For XID614, where the ionised outflow is marginally
resolved, we consider the resulting value as an upper limit to $R_{\rm out}$
(i.e. $R_{\rm out}<1.9$ kpc). All $R_{\rm out}$ estimates and upper limits are
listed in Table LABEL:tab:super_wind.

For a measurement of the [O iii] flux associated with ionised winds, we follow
the same prescriptions adopted by Kakkad et al. (2020) for the SUPER type-1
AGN. We hence consider the total [O iii] flux contained in the high-velocity
line channels (i.e. $|v|>300$ km s-1), by summing up the flux contributions of
all spaxels with S/N$>$3 on [O iii]. Considering the quality of our data, a
non-parametric approach is more suitable than a parametric one based on the
results from the multi-Gaussian modelling, where the detection of a broad
‘outflow’ component depends on the S/N of the data. In XID419 indeed, due to
the low S/N, the [O iii] line profile is sufficiently well reproduced by a
single Gaussian component across the FoV, with no need for an additional
component. Yet, the $f_{300}$ ratio map shown in Fig. 4 unveils a non-
negligible fraction (varying within 0.3–0.5) of [O iii] flux in $|v|>300$ km
s-1 line channels, overall suggesting that high-velocity gas is outflowing in
this galaxy as well (as discussed in Sect. 4.1). However, we point out that by
summing spaxel-by-spaxel the flux of broad Gaussian components (for the six
objects requiring a multi-Gaussian modelling of [O iii]) we obtain [O iii]
outflow fluxes close to our non-parametric estimates, differing on average by
a factor of about 2 (same found in Kakkad et al. 2020 for type-1 AGN).

Finally, we correct all inferred (non-parametric) [O iii] outflow fluxes for
dust extinction using the $A_{V}$ values inferred in Sect. 3.2 (see Table
LABEL:tab:super_wind), and convert them to intrinsic [O iii] outflow
luminosities $L^{\rm[OIII]}_{\rm out}$. All resulting dust-corrected
$L^{\rm[OIII]}_{\rm out}$ values are also reported in Table
LABEL:tab:super_wind.

Table 3: Main properties of ionised outflows in the seven SUPER type-2 AGN examined in this work. From left, columns list for each galaxy: (1) $V$-band extinction $A_{V}$ (inferred in Sect. 3.2); (2) outflow radius $R_{\rm out}$; (3) outflow velocity $v_{\rm out}={\rm max}[|v_{10}|,|v_{90}|]$; (4) dust-corrected [O iii] outflow luminosity $L^{\rm[OIII]}_{\rm out}$, inferred from $|v|>300$ km s-1 line channels; (5) outflow mass rate $\dot{M}_{\rm out}$; (6) escape velocity $v_{\rm esc}$ of the total galaxy system, computed at $R_{\rm out}$ (see Sect. 5.2); maximum observed values of (7) $w_{80}$ and (8) $v_{\rm max}=v_{\rm bro}+2\sigma_{\rm bro}$ (Rupke & Veilleux 2013). Target ID | $A_{V}$ | $R_{\rm out}$ | $v_{\rm out}$ | $L^{\rm[OIII]}_{\rm out}$ | $\dot{M}_{\rm out}$ | $v_{\rm esc}$ | $w_{80}$ | $v_{\rm max}$
---|---|---|---|---|---|---|---|---
|  | kpc |  km s-1 |  1042 erg s-1 |  M⊙ yr-1 |  km s-1 |  km s-1 |  km s-1
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
XID36 | $1.3\pm 0.8$ | 3.5 | $990\pm 50$ | $14\pm 5$ | $10\pm 6$ | $860\pm 220$ | $1760\pm 120$ | $1450\pm 80$
XID419 | $>0$ | $3.6$ | $790\pm 120$ | $>0.3$ | $>0.2$ | $980\pm 210$ | $1020\pm 110$ | $800\pm 40$
XID614 | $2.8\pm 1.6$ | $<1.9$ | $590\pm 60$ | $2.2\pm 1.6$ | $>1.7$ | $>980$ | $960\pm 140$ | $910\pm 80$
cid_1057 | $1.8\pm 1.0$ | 3.1 | $720\pm 70$ | $2.2\pm 1.0$ | $1.3\pm 0.9$ | $960\pm 240$ | $1130\pm 130$ | $1420\pm 130$
cid_1143 | $1.2^{+1.8}_{-1.2}$ | 2.9 | $1070\pm 120$ | $3^{+14}_{-2}$ | $3^{+29}_{-2}$ | $760\pm 290$ | $1320\pm 70$ | $1370\pm 120$
cid_2682 | $>0.6$ | 2.0 | $670\pm 80$ | $>0.9$ | $>0.8$ | $1190\pm 270$ | $1130\pm 90$ | $1240\pm 130$
cid_451 | $>2.7$ | 2.6 | $950\pm 40$ | $>30$ | $>27$ | $1400\pm 330$ | $1270\pm 70$ | $1290\pm 40$

####  4.2.2 Ionised outflow mass rate

The three quantities directly measured in the previous subsection - namely,
$v_{\rm out}$, $R_{\rm out}$ and $L^{\rm[OIII]}_{\rm out}$ \- are fundamental
ingredients to compute the mass rate of ionised outflows. Following the
prescriptions adopted by Kakkad et al. (2020) for the SUPER type-1 AGN sample,
we calculate ionised outflow masses $M_{\rm out}$ as follows (for an electron
temperature of $T\sim 10^{4}$ K; e.g. Carniani et al. 2015; Kakkad et al.
2016):

$M_{\rm out}=0.8\times 10^{8}\leavevmode\nobreak\
\bigg{(}\frac{L^{\rm[OIII]}_{\rm out}}{10^{44}\leavevmode\nobreak\ \text{erg
s}^{-1}}\bigg{)}\leavevmode\nobreak\ \bigg{(}\frac{n_{\rm
e}}{500\leavevmode\nobreak\ \text{cm}^{-3}}\bigg{)}^{-1}\leavevmode\nobreak\
\frac{\text{$\textup{M}_{\odot}$}}{10^{\rm[O/H]}},$ (1)

where all oxygen is assumed to be ionised to O2+, $n_{\rm e}$ and [O/H] are
electron density and oxygen abundance in solar units, respectively. For a
uniformly filled (i.e. constant $n_{\rm e}$) outflow of bi-conical geometry,
the mass rate $\dot{M}_{\rm out}$ at a certain radius $R_{\rm out}$ can be
then calculated as (Fiore et al. 2017):

$\dot{M}_{\rm out}\ =\ 3\ \frac{M_{\rm out}v_{\rm out}}{R_{\rm out}},$ (2)

which gives the instantaneous mass rate of ionised gas crossing a spherical
sector at a distance $R_{\rm out}$ from the central AGN.

Equation 1 requires values of [O/H] and $n_{\rm e}$, but none of them can be
measured from our data. In principle, $n_{\rm e}$ can be inferred from [S
ii]$\lambda$6716/[S ii]$\lambda$6731 flux ratio (e.g. Osterbrock & Ferland
2006), but [S ii] is undetected in our SINFONI K-band data, except for some
faint [S ii] emission in XID36 (see Fig. 1). Therefore, we assume a solar
[O/H] abundance and $n_{\rm e}=500\pm 250$ cm-3 (i.e. 50% uncertainty) in
agreement with previous studies (e.g. Storchi-Bergmann et al. 2010; Carniani
et al. 2015; Riffel et al. 2015; Davies et al. 2020; Cresci et al. 2023) and
consistently with prescriptions adopted in Kakkad et al. (2020) for SUPER
type-1 AGN.

In Table LABEL:tab:super_wind, we summarise the properties of the ionised
outflows in the seven SUPER type-2 AGN, with corresponding uncertainties. We
also list the escape velocity $v_{\rm esc}$ from the total galaxy
gravitational potential, computed at $R_{\rm out}$ (see Sect. 5.2) and the
$A_{V}$ estimates (or lower limits, derived in Sect. 3.2) used to obtain the
corresponding dust-corrected $L^{\rm[OIII]}_{\rm out}$ measurements (or lower
limits). Errors $v_{\rm out}$, $w_{80}$ and $v_{\rm max}$ are the
uncertainties resulting from the kinematic analysis, while those on $A_{V}$,
$L^{\rm[OIII]}_{\rm out}$ and $\dot{M}_{\rm out}$ are computed via error
propagation. Due to the asymmetric uncertainty on $A_{V}$ for cid_1143, errors
on the $L^{\rm[OIII]}_{\rm out}$ and $\dot{M}_{\rm out}$ for this object
correspond to the minimum-maximum range of possible values, determined by the
minimum/maximum values of the physical quantities they depend on. Despite
being not obvious how to estimate an uncertainty on $R_{\rm out}$, we do not
expect errors smaller than the SINFONI spatial pixel (i.e. 0.05′′), which
corresponds to about 0.4 kpc at $z\sim 2$. Nevertheless, the uncertainty on
$\dot{M}_{\rm out}$ is dominated by the large errors on $A_{V}$ (larger than
50%) and $n_{\rm e}$ (assumed equal to 50%). Finally, we notice that the
inferred upper limit to $R_{\rm out}$ in XID614 leads to a corresponding lower
limit to $\dot{M}_{\rm out}$ (i.e. $\dot{M}_{\rm out}>1.7$ M⊙ yr-1).

Before SUPER, the galaxy cid_415 was observed with SINFONI in AO-mode, but
with the largest 0.250′′ pixel-scale and the lower spectral-resolution HK
filter ($R\sim$1500). We notice that our inferred values of $R_{\rm out}$ and
$v_{\rm out}$ ($R_{\rm out}\sim 2.6$ kpc and $v_{\rm out}\sim 950$ km s-1) are
both smaller than those previously found ($R_{\rm out}\sim 5$ kpc and $v_{\rm
out}\sim 1600$ km s-1; Perna et al. 2015b), based on these first SINFONI
dataset. The smaller value of $R_{\rm out}$ is likely consequence of a surface
brightness loss in our SINFONI data, due to the smaller pixel (0.100′′)
adopted in SUPER. Instead, the discrepancy in $v_{\rm out}$ is due to a more
extreme definition of outflow velocity adopted in Perna et al. (2015b) (i.e.
$v_{05}$), combined also with slightly different inferred redshifts ($z=2.444$
in this work against $z=2.45$ in Perna et al. (2015b)), which leads to a
systematic difference of about $\sim$520 km s-1 in the adopted systemic galaxy
velocity (with respect to which all velocities are computed). After accounting
for these discrepancies, the two $v_{\rm out}$ values are consistent.

##  5 Comparison with SUPER type-1 AGN

In addition to presenting results on ionised outflows in SUPER type-2 AGN,
this paper investigates ionised wind properties as a function of AGN
properties (i.e. AGN bolometric luminosity $L_{\rm bol}$, hydrogen column
density $N_{\rm H}$) and host galaxy parameters (i.e. stellar mass $M_{*}$,
star formation rate, escape velocity $v_{\rm esc}$) for the full SUPER sample,
aiming at unveiling any difference between type-1 and type-2 systems which
possibly sheds light on the intrinsic nature of these two AGN classes. Except
for $v_{\rm esc}$ (see Sect. 5.2) and $N_{\rm H}$ (measured from archival
X-ray spectra, Circosta et al. 2018), all other AGN/host galaxy properties
were derived by Circosta et al. (2018) using the SED fitting code CIGALE (Code
Investigating GALaxy Emission; Noll et al. 2009), and are overall confirmed by
the latest updates presented in Bertola et al. (2024). Values of $M_{*}$, star
formation rate (SFR), $L_{\rm bol}$ and $N_{\rm H}$ for the SUPER type-2 AGN
are found listed in Table 1 (see Kakkad et al. 2020 for the type-1 AGN).

###  5.1 Faster outflows in type-2 AGN at the low-luminosity end

Figure 8: Outflow velocities $v_{\rm out}$ and $v_{\rm max}$ (left and middle
panels) as a function of $L_{\rm bol}$, and $v_{\rm out}/L_{\rm bol}$ ratios
versus X-ray $N_{\rm H}$ measurements (right panel). Coloured crosses and grey
squares represent SUPER type-2 and type-1 AGN; triangles in the right panel
indicate upper/lower limits to $N_{\rm H}$. Blue dashed-dotted lines represent
our best-fit linear relation to the type-1 measurements, from which type-2
points depart increasingly at decreasing $L_{\rm bol}$ (mean deviation of
0.4dex). In the middle panel, we draw for comparison the empirical relation by
Fiore et al. (2017) (dotted line), along with a more recent version from
Musiimenta et al. (2023) (dashed). The orange shading in the left and middle
panels marks the low-$L_{\rm bol}$ range (i.e. $L_{\rm bol}=10^{44.8-46.5}$
erg s-1), where we compare type-1 and type-2 AGN measurements. In the right-
hand panel we consistently use light-orange markers to identify type-1 AGN
lying in the low-$L_{\rm bol}$ regime. All panels overall point to faster
winds in type-2 obscured AGN than in type-1 systems at lower $L_{\rm bol}$, as
a consequence of a more efficient acceleration of outflows in dusty, obscured
environments.

The key result of this work is shown by the three panels of Fig. 8, which
overall display outflow velocity as a function of AGN bolometric luminosity
$L_{\rm bol}$ for the full SUPER sample, divided into type-2 (coloured thick
crosses) and type-1 (grey squares) AGN. In particular, in the left and middle
panels $v_{\rm out}=\textrm{max}[|v_{10}|,|v_{90}|]$ and $v_{\rm max}=v_{\rm
bro}+2\sigma_{\rm bro}$ are displayed along the y-axis, respectively, with the
type-1 AGN measurements taken consistently from Kakkad et al. (2020). In
addition to showing the well-known trend of larger outflow velocity at
increasing AGN luminosity (e.g. Bae & Woo 2014; Woo et al. 2016; Perna et al.
2017; Rakshit & Woo 2018; discussed in Sect. 6), both panels unveil
consistently the same following result in the lower $L_{\rm bol}$ regime (i.e.
$L_{\rm bol}\sim 10^{44.8-46.5}$ erg s-1; orange shading), where all SUPER
type-2 AGN are found. Interestingly, the SUPER type-2 AGN host overall faster
ionised outflows than their type-1 counterparts within the same $L_{\rm bol}$
range, and appear to depart from the global $v_{\rm out,max}-L_{\rm bol}$
increasing trend followed by the SUPER type-1 AGN. Not surprisingly, the
$v_{\rm max}$ value of XID419 is the lowest of the type-2 sample, since
$v_{\rm max}$ for this target is computed as $2\sigma$ (see Sect. 4.2.1), with
$\sigma$ referring to the single Gaussian component employed.

To better appreciate the deviation of the type-2 velocity measurements from
the type-1 ones, we fit a linear relation to the type-1 distribution in both
$v_{\rm out}-L_{\rm bol}$ and $v_{\rm max}-L_{\rm bol}$ planes (blue dashed-
dotted line), from which type-2 velocities deviate increasingly at lower
luminosities by up to about 0.8dex in both panels, and with a mean deviation
of 0.4 dex. In the middle panel, we also draw for comparison the empirical
$v_{\rm max}-L_{\rm bol}$ relation inferred in Fiore et al. (2017) (black
dotted line), and an updated version by Musiimenta et al. (2023) (black dashed
line), including recent results and considering $z>0.5$ AGN only. The type-1
population appears to overall follow the empirical relation by Musiimenta et
al. (2023), as opposed to the type-2 measurements. We point out that the small
number (i.e. seven) of type-2 measurements prevents us from an accurate linear
fit to the type-2 distribution of measurements.

In Appendix A we show additional plots where we compare other outflow
properties and AGN/host galaxy parameters, none of which pointing to either a
clear trend or interesting discrepancy between the type-1 and type-2
populations in SUPER. In particular, the left and right panels of Fig. 12
display $v_{\rm out}$ plotted against $M_{*}$ and SFR, respectively; whereas
in Fig. 13 we searched for trends of outflow mass rate with $L_{\rm bol}$.
Although the $v_{\rm out}-L_{\rm bol}$ plane reveals a clear type-1/type-2
dichotomy, it is hard to establish whether a separation between the two AGN
populations is also present in terms of outflows mass rate, due to the large
uncertainty on this quantity. In fact, mass rate estimates strongly depend on
the accuracy of other measured parameters (e.g. $A_{V}$, $R_{\rm out}$), and
also require several quantities to be assumed (e.g. $n_{\rm e}$, [O/H],
ionisation). All this uncertainty inevitably increases the scatter in the
resulting mass rate values, thus possibly hiding the type-1/type-2 dichotomy
instead visible in terms of the more directly inferred outflow velocities.

We investigate the potential effect of different nuclear obscuring conditions
on driving outflows in the right panel of Fig. 8, aiming at shedding light on
the nature of our unveiled type-1/type-2 dichotomy. To remove any dependence
of $v_{\rm out}$ on AGN luminosity (discussed in Sect. 6), we normalise
$v_{\rm out}$ to $L_{\rm bol}$ (in units of 1045 erg s-1), and plot the
resulting $v_{\rm out}/L_{\rm bol}$ ratios against the hydrogen column density
$N_{\rm H}$, measured from X-ray data in Circosta et al. 2018 for the full
SUPER sample. Light-orange markers identify type-1 AGN lying in the
low-$L_{\rm bol}$ regime (i.e. $L_{\rm bol}\sim 10^{44.8-46.5}$ erg s-1,
orange shading in the left and middle panels), whereas high-$L_{\rm bol}$
type-1 objects are plotted as grey markers. Upper/lower limits to $N_{\rm H}$
are finally shown as triangles. In the right panel, we can clearly see that
SUPER type-1 and type-2 AGN occupy two distinct regions of the $(v_{\rm
out}/L_{\rm bol})-N_{\rm H}$ plane. The distribution of SUPER measurements
along the x-axis shows their X-ray obscured/unobscured classification (see
Circosta et al. 2018). All SUPER type-2 AGN lie in the X-ray highly-obscured
regime ($N_{\rm H}\gtrsim 10^{23}$ cm-2), whereas most of the type-1
population exhibits unobscured conditions ($N_{\rm H}\lesssim 10^{22}$ cm-2),
with some system lying in the intermediate-obscuration region ($N_{\rm H}\sim
10^{22}-10^{23}$ cm-2). The combined resulting separation along the y-axis is
more interesting and unexpected: the SUPER type-2 sample indeed features
$v_{\rm out}/L_{\rm bol}$ values varying within $\sim$30–1500 (with only
cid_451 smaller than 80), with a mean value of $\sim$400, while almost all
type-1 AGN have ratios smaller than 50, with an overall mean value of
$\sim$40\. This points to the picture where AGN-driven outflows are
accelerated more efficiently in more obscured environments (further discussion
in Sect. 6).

To further check and quantify the observed discrepancy in outflow velocity
between SUPER type-1 and type-2 AGN, we directly compare the ‘mean’ [O iii]
line profiles of SUPER type-1 and type-2 AGN, by separately stacking [O iii]
integrated spectra of the type-1/type-2 objects with $L_{\rm bol}\sim
10^{44.8-46.5}$ erg s-1 (Fig. 9): these are all seven type-2 AGN and eleven
type-1 systems (i.e. those lying within the orange shading of Fig. 8).
Selecting objects over this broad (about two orders of magnitude) range of
luminosity ensures us to account for possible over/underestimates of $L_{\rm
bol}$ from SED fitting in type-2 AGN (Circosta et al. 2018), due to fainter
AGN emission hard to constrain. However, we check the reliability of the SED-
based measurements for the type-2 AGN by estimating $L_{\rm bol}$ also from
dust-corrected [O iii] luminosity (Lamastra et al. 2009) and from X-ray 2-10
keV luminosity (Duras et al. 2020). We thus find $L_{\rm bol}$ values derived
from SED fitting to agree with the [O iii]-based estimates within a factor of
2 on average (computed for the four objects with $A_{V}$ estimates, see Table
LABEL:tab:super_wind), and from X-ray-based values by a mean factor of 4,
without obvious biases towards higher or lower $L_{\rm bol}$ with respect to
the SED fitting derivation.

For each selected AGN, we extract an integrated [O iii]$\lambda$5007 spectrum
using an aperture of 0.25′′-radius, after removing continuum emission in SUPER
type-2 datacubes (see Sect. 3.1), and continuum+BLR emission in type-1
datacubes (Kakkad et al. 2020). We exclude from the stack spectra with a
S/N$<$5 on [O iii], to allow for a more accurate comparison of line profiles.
We thus finally remain with six S/N$>$5 type-2 spectra (all but XID419) and
six S/N$>$5 type-1 spectra (X_N_66_23, X_N_115_23, cid_467, S82X1905,
S82X1940, S82X2058). Each type-1/type-2 spectrum is weighed by the inverse of
its squared noise before being added to the stack, and then the final stacked
type-2 and type-1 spectra (red and blue spectra in Fig. 9, respectively) are
normalised to the resulting respective [O iii] peak. We point out that results
similar to those described below are obtained even by stacking unweighted
spectra. In Fig. 9, we show the resulting stacked [O iii] line profiles of the
selected type-2 (red) and type-1 (blue) SUPER AGN, using a binning twice
larger than SINFONI spectral channel to remove noisier channels. The type-2 [O
iii] line profile exhibits more prominent blue and red wings than type-1
spectrum. Via multiple stacks of type-2 integrated spectra, each time
excluding one object, we check that all type-2 objects contribute to the [O
iii] blue wing, whereas the red wing is dominated by XID36. Because of the
brighter and more prominent blue wing, the type-2 [O iii] line profile also
appears more asymmetric than the type-1 one.

Figure 9: Stacked [O iii]$\lambda$5007 spectra of type-2 (red) and type-1
(blue) AGN from SUPER with $L_{\rm bol}\sim 10^{44.8-46.5}$ (orange shading in
Fig. 8), resulting from integrated (aperture of 0.25′′-radius), subtracted [O
iii] spectra with S/N$>$5\. Integrated spectra are averaged by the inverse of
squared noise, and then the resulting stacked spectrum is normalised to the
final [O iii] peak. Comparing the two stacked spectra, we find a statistically
significant ($2.7\sigma$) difference in [O iii] blue wing, which is more
prominent in type-2 spectrum of about 500 km s-1 compared to the type-1 stack.

We measure the $v_{10}$ velocity of each resulting stacked type-1/type-2
spectrum (vertical dashed lines in Fig. 9) and quantify the corresponding
uncertainty as the standard deviation of the $v_{10}$ distribution resulting
from a bootstrap of 400 stacks of six type-1/type-2 spectra, randomly selected
with equal probability and allowing for repetitions. We thus obtain $v^{\rm
ty2}_{10}=-1200\pm 80$ km s-1 for the type-2 line profile, and $v^{\rm
ty1}_{10}=-730\pm 90$ km s-1 for the type-1 spectrum (see Fig. 9),
corresponding to a $2.7\sigma$ difference in the [O iii] blue wing of the AGN
samples. We notice that the identification of extended [O iii] wings at high
velocity is generally more challenging in type-1 AGN than in type-2 systems,
due to the presence of bright BLR emission (i.e. H$\beta$ and Fe ii) in the [O
iii] wavelength range. Therefore, as a check of the goodness of the BLR and
continuum modelling of the SUPER type-1 AGN, we also stack type-1 spectra
without removing BLR emission (i.e. only continuum emission removed),
employing best-fit results from Kakkad et al. (2020). We find that no evident
Fe ii emission bumps are present in correspondence of the [O iii] line wings,
and that the bulk of BLR emission in this spectral region is due to extremely
large red wings of the H$\beta$ BLR line component (see Figs. A.1-A.4 in
Kakkad et al. 2020). This supports the reliability of our comparison between
type-1 and type-2 stacked spectra (see Fig. 9) and furthermore strengthens our
result of a type-1/type-2 dichotomy in ionised outflow velocity, as firstly
pointed out by our spatially-resolved measurements (shown in Fig. 8). The
origin of such an observed difference, revealing faster ionised outflows in
type-2 AGN, is discussed in Sect. 6.

###  5.2 Can ionised outflows escape galaxy gravitational potential?

Figure 10: Outflow velocity $v_{\rm out}$ as a function of escape velocity
computed at $R_{\rm out}$, as inferred from our mass modelling of SUPER type-2
(coloured symbols) and all those type-1 (grey) AGN galaxies with known
$M_{*}$, hence not $L_{\rm bol}$-matched to the type-2 AGN sample. We display
two sets of escape speed values: one inferred according to the classical
definition of escape velocity to infinity ($v_{\rm out}$, left panel), hence
that one needed to escape the DM halo ($R_{\rm vir}\sim 80-160$ kpc); the
other one corresponding to the escape velocity required to reach
$10\leavevmode\nobreak\ R_{\rm e}$ ($v^{\rm 10Re}_{\rm esc}$, right panel),
that is $\sim$30–50 kpc for the galaxies here considered. Triangles represent
lower limits to the escape speed (due to an inferred upper limit to $R_{\rm
out}$), whereas dashed line indicates $v_{\rm out}=v_{\rm esc}$ and $v_{\rm
out}=v^{\rm 10Re}_{\rm esc}$ values, respectively. The overall distribution of
measurements suggests that, although possibly unable to escape the DM halo,
the outflows however can impact the host galaxy reservoir up to galaxy scales
(i.e. tens of kpc).

One of the main channels via which outflows can quench (or at least reduce)
galactic SF is through the ejection of a substantial amount of gas out of the
host galaxy, otherwise available to form new stars. This requires outflows
energetic enough to efficiently sweep away the gas on large scales, eventually
escaping the gravitational potential of their galaxy. Kakkad et al. (2020)
presented a first investigation of the ‘ejective’ ability of the ionised
outflows detected in the SUPER type-1 sample and found that, for most
galaxies, only a modest fraction ($<10$%) of the outflowing gas can
effectively escape the host’s gravitational potential well. In this section,
we compare the outflow velocities with the escape velocity of each galaxy for
the full SUPER sample, homogeneously for all objects using a more accurate
mass modelling.

Following prescriptions from the literature (Marasco et al. 2023), we use the
python package galpy (Bovy 2015) to build a dynamical mass model consisting of
a dark matter (DM) halo, a stellar disk, and a gaseous disk, to derive the
total escape velocity profile for SUPER targets with known $M_{*}$, derived
from SED fitting (Circosta et al. 2018). All examined type-2 AGN galaxies have
$M_{*}$ estimates ranging within $M_{*}\sim 10^{10.7-11.2}$ M⊙ (see Table 1),
while for only few type-1 systems $M_{*}$ estimates are available ($M_{*}\sim
10^{10.1-11.2}$ M⊙). In fact, stellar emission is typically buried in the
bright AGN component of type-1 systems, thus preventing us from constraining
$M_{*}$ in most cases (only six SUPER type-1; see Table A.2 in Circosta et al.
2018).

For the DM halo, we assume a Navarro-Frenk-White (NFW, Navarro et al. 1997)
profile, with a virial mass $M_{200}$ derived from $M_{*}$ via the stellar-to-
halo mass relation of Girelli et al. (2020), and a concentration $c$
determined from the $M_{200}-c$ relation of Dutton & Macciò (2014); both
relations are computed at the redshift of each target. We model the stellar
disk with a double-exponential profile with scale-length $R_{\rm d}$ and
scale-height $R_{\rm d}$/5, assuming $R_{\rm d}$ equal to the half-light
radius $R_{50}$ divided by 1.68 (correct for a pure exponential disk, Sérsic
index $n=1$). We determine $R_{50}$ from the size-$M_{*}$ relation for a disk
galaxy ($n=1$) at z$\sim 2.2$ (Mowla et al. 2019). Since we have CO-based
measurements of molecular gas mass $M_{\rm gas}$ for a few SUPER AGN (Circosta
et al. 2021), we model a gaseous disk of $M_{\rm gas}$ equal to the mean value
measured for SUPER targets (i.e. $M_{\rm gas}\sim 6\times 10^{9}$ M⊙), and
same size as the stellar disk, since we expect the molecular gas to extend on
scales comparable to those of stars. In doing so, we derive the escape
velocity profile of the total modelled mass distribution (i.e. DM halo +
stellar disk + gas disk).

Figure 10 displays $v_{\rm out}$ as a function of the escape velocity at the
maximum inferred outflow radius $R_{\rm out}$ for SUPER type-2 (coloured
symbols) and all those type-1 (grey) AGN with known $M_{*}$ (Circosta et al.
2018), hence not $L_{\rm bol}$-matched to the type-2 AGN sample. The two
panels display data points corresponding to two distinct definitions of escape
speed: one derived according to the classical definition555The minimum
velocity required by an object to reach to infinity, with a final null
velocity. ($v_{\rm esc}$ in the left panel; also listed in Table
LABEL:tab:super_wind), which translates in more practical terms to the
velocity needed to reach the virial radius $R_{\rm vir}$ of the DM halo, as
derived from our mass modelling ($R_{\rm vir}\sim 80-160$ kpc); the other one
corresponding to a less rigid definition of escape speed ($v^{\rm 10Re}_{\rm
esc}$ in the right panel), namely the velocity required to reach
$10\leavevmode\nobreak\ R_{\rm e}$ (i.e. $\sim$30-50 kpc, depending on the
galaxy). This second set of values shows whether the outflows can travel up to
distances of tens of kpc from the centre of the galaxy, and escape (at least)
the gravitational potential of the galaxy baryonic component (i.e. stellar
disk + gas disk). Triangles indicate lower limits to the escape speed,
corresponding to upper limits to $R_{\rm out}$, since escape speed curves
increase at decreasing $R_{\rm out}$. A dashed line indicates the locus of
points where $v_{\rm out}$ are equal to escape speed values. Under the
simplifying assumption that our dynamical modelling introduces no further
uncertainty, we consider the error on our escape speed values to be due to the
total uncertainty on $M_{*}$, including both statistical (from SED fitting
Circosta et al. 2018) and systematic ($\sim$0.1dex; Pacifici et al. 2023)
uncertainties.

In the left panel of Fig. 10, the overall majority of galaxies have $v_{\rm
out}$ values consistent or nearly consistent with $v_{\rm esc}$ to infinity.
Unfortunately, the uncertainties and simplified assumptions of our dynamical
modelling prevent us from firmly testing whether these outflows can
effectively sweep up a significant amount of gas out of the DM halo, as
predicted by the ejective feedback scenario. Indeed, we derived the outflow
velocity (assumed to be constant in our model) from our observed velocity
measurements, which are subject to inclination effects and limited to few kpc
from the centre of each galaxy, beyond which line emission gets fainter and
the S/N drops. Moreover, our dynamical model includes only gravity and no
hydrodynamic effects, which may instead play a major role. Outflows could
hence get trapped in gaseous halos, eventually cool and be re-accreted, even
if they have high initial velocities. This would contribute to hampering the
wind ejective ability to expel the gas reservoir of their host, unlike model
predictions (e.g. King & Pounds 2015; Zinger et al. 2020). Yet, the
distribution of measurements in the right panel of Fig. 10 strongly suggests
that, even if these outflows do not successfully escape the total
gravitational potential well (i.e. the DM halo), they are likely to impact the
host gas reservoir up to galaxy scales (i.e. 30–50 kpc). They can hence sweep
gas out of the galaxy disk and/or provide feedback through a ‘preventive’ mode
(e.g. van de Voort et al. 2011), by injecting energy and driving turbulence in
the surrounding medium. All this will eventually prevent gas from cooling and
collapsing to form new stars. Even inferring escape speeds at a fixed radius
of 50 kpc for all galaxies, we obtain escape speeds similar to $v^{\rm
10Re}_{\rm esc}$. Finally, as opposed to Fig. 8, we notice that Fig. 10 shows
no clear separation between type-1 and type-2 AGN measurements.

##  6 Discussion

Our analysis has unveiled that SUPER type-2 AGN host faster ionised outflows
than their type-1 counterparts in the lower $L_{\rm bol}$ regime ($L_{\rm
bol}\sim 10^{44.8-46.5}$), as traced by higher [O iii] velocities. At low
redshift ($z<0.8$), several studies have searched for differences in ionised
outflow properties between type-2 and type-1 systems in large AGN samples
(e.g. Mullaney et al. 2013; Bae & Woo 2014; Rakshit & Woo 2018; Wang et al.
2018; Rojas et al. 2020). In general, they found narrower and more symmetric
[O iii] line profiles in type-2 AGN, indicating the detection of both
blueshifted and redshifted outflow components; whereas, almost exclusively
blueshifted outflows in type-1 AGN, with typically larger line widths and more
negative velocities. This can be explained in terms of biconical outflow
geometry in the context of standard unification models (e.g. Antonucci 1993;
Urry & Padovani 1995), as a consequence of orientation effects along the line
of sight, combined with dust obscuration. Indeed, assuming a nearly face-on
orientation of type-1 AGN, with well visible BLR emission along the line of
sight, the receding cone of the outflow remains hidden on the the opposite
side of the galaxy disk plane.

However, most of these low-redshift studies do not consider the well known
dependence of outflow kinematics on AGN luminosity and accretion rate (e.g.
Bae & Woo 2014; Woo et al. 2016; Rakshit & Woo 2018), and overall compare more
luminous type-1 AGN with fainter type-2 samples, possibly leading to an only
apparent discrepancy. Moreover, nowadays observational evidence increasingly
challenges the standard unified picture of AGN, such as the discovery of
changing-look AGN (see Ricci & Trakhtenbrot 2023 for a recent review), which
favours the interpretation of the type-1/type-2 AGN dichotomy in terms of
distinct phases within a common evolutionary sequence.

Figure 11: Same panels as in Fig. 8, showing SUPER type-2 (red filled circles)
and type-1 (blue filled circles) measurements along with IFS-based results
from the literature of other type-2 (red empty star; XID5395, Brusa et al.
2016) and type-1 AGN (blue empty symbols; Cano-Díaz et al. 2012; Carniani et
al. 2015; Vayner et al. 2021a) at $z\sim 1-3$. Orange symbols represent red
obscured systems which are nevertheless optically classified as type-1 AGN
(Perrotta et al. 2019; Vayner et al. 2021b; Cresci et al. 2023; Veilleux et
al. 2023). Outflow velocities from Brusa et al. (2016) and Perrotta et al.
(2019) have been corrected to account for discrepancies with $v_{\rm out}$ and
$v_{\rm max}$ definitions here adopted (see Sect. 6). While ‘standard’ blue
type-1 and red type-2 from the literature agree with values found in SUPER,
the orange points, almost all at $L_{\rm bol}\gtrsim 10^{47}$ erg s-1, clearly
show that higher outflow velocities are recovered if we select optical type-1
but dusty obscured objects.

At high redshift ($z>1$), where more complex processes are expected to take
place - especially at crucial cosmic epochs of galaxy evolution such as $z\sim
2$ \- orientation effects might play a minor role compared to low redshift.
Previous works have indeed provided evidence that at high redshift the bulk of
the dust obscuration comes from the host galaxy rather than being due to the
classical dusty torus (e.g. Gilli et al. 2014, 2022; Perna et al. 2018;
Circosta et al. 2019). This is consistent with results from numerical
simulations according to which different AGN classes correspond to distinct
evolutionary phases of galaxies’ life cycle (Hopkins et al. 2006; Menci et al.
2008). In particular, X-ray bright but obscured ($L_{\rm
2-10\leavevmode\nobreak\ keV}>10^{44}$ erg s-1, $N_{\rm H}\gtrsim 10^{22}$
cm-2) AGN at $z\sim 2$ have been proposed to be in the active ‘blow-out’ phase
of AGN galaxies’ evolution (e.g. Brusa et al. 2010; Urrutia et al. 2012) and,
in fact, powerful outflows have been detected in such systems (e.g. Brusa et
al. 2015, 2016; Zakamska et al. 2016; Perna et al. 2015a, b; Vayner et al.
2023; Glikman et al. 2024). In these obscured sources, dusty material favours
the acceleration of outflows (e.g. Ishibashi & Fabian 2015; Costa et al.
2018), which efficiently clear out obscuring dust and finally unveil the AGN.
Radiation-pressure on dust has been indeed identified as the main mechanism
responsible for the larger [O iii] velocities and line widths observed in
extremely red and luminous quasars (ERQs; $L_{\rm bol}>10^{47}$ erg s-1),
compared to blue quasars at $z=2-3$ (Perrotta et al. 2019).

However, Villar Martín et al. (2020) provided a revised comparison of the [O
iii] kinematics in 20 ERQs from Perrotta et al. (2019) with other AGN samples
matched in luminosity, and found the [O iii] velocities of ERQs to be overall
consistent with those of luminous blue quasars. A similar result was also
found by Temple et al. (2019), who compared the [O iii] kinematics in a sample
of luminous ($L_{\rm bol}\approx 10^{47}$ erg s-1), heavily reddened quasars
at $z>2$ to unobscured quasars in the same luminosity and redshft range. On
the contrary, larger [O iii] velocities have been observed by DiPompeo et al.
(2018) in obscured AGN at $z<0.4$, resembling the obscured high-redshift
population based on their optical/IR colours, and matched in luminosity with
unobscured objects. All these controversial results show how complex carrying
out such a comparison is, and leaves the debate open.

Figure 11 displays the same panels as in Fig. 8 and compares the results
obtained for SUPER targets with measurements from the literature. In
particular, we collect from the literature only results based on IFS
observations and with outflow velocities defined consistently with ours in
SUPER, to allow for a proper comparison as homogeneous as possible. In all
three panels of Fig. 11, SUPER results of type-2 and type-1 AGN are shown as
red and blue filled markers, respectively, along with other IFS-based
measurements of type-2 (red empty star; XID5395, Brusa et al. 2016) and type-1
(blue empty symbols; Cano-Díaz et al. 2012; Carniani et al. 2015; Vayner et
al. 2021a) AGN at $z=1-3$ from the literature. In orange, we highlight the
measurements relative to red obscured AGN showing BLR emission (hence, optical
type-1 systems), which should be considered as an intermediate class of
objects between standard (blue) type-1 and (red) type-2 AGN. These are: the
obscured quasar XID2028 at $z\sim 1.6$ (orange empty pentagon; Cresci et al.
2023; Veilleux et al. 2023), the powerful obscured ERQs from Perrotta et al.
(2019) (orange empty circles); and the sample of obscured quasars from Vayner
et al. (2021b) (orange diamonds), among which all but one (the lowest-velocity
point) are selected as ERQs.

The first panel compares $v_{\rm out}$ measurements from SUPER and the
literature based on $v_{10}$/$v_{90}$, as obtained by Vayner et al. (2021a, b)
(blue/orange diamonds) and by Cresci et al. (2023) and Veilleux et al. (2023)
consistently for XID2028; whereas higher percentile outflow velocities were
used in Brusa et al. (2016) and Perrotta et al. (2019) (i.e. $v_{05}$ and
$v_{02}$, respectively). Therefore, we correct outflow velocities inferred in
Brusa et al. (2016) and Perrotta et al. (2019) by a factor of 0.86 and 0.77,
respectively, corresponding to the mean ratio of $v_{10,90}$-to-$v_{05,95}$
and $v_{10,90}$-to-$v_{02,98}$ measured for SUPER type-2 AGN. We also add
measurements from Cano-Díaz et al. (2012) and Carniani et al. (2015).
Similarly, we re-scale $v_{02}$ outflow velocities from Perrotta et al. (2019)
by a factor of 1.2, as obtained from the mean $v_{{\rm max}}$-to-$v_{02,98}$
ratio for SUPER type-2 AGN, and plot them in the $v_{{\rm max}}-L_{\rm bol}$
plane (middle panel), along with $v_{{\rm max}}$ measurements for SUPER type-2
(derived in Sect. 4.2.1) and type-1 AGN (Kakkad et al. 2020).

As shown in Fig. 11, standard blue (unobscured) type-1 and red (obscured)
type-2 AGN from the literature lie close to SUPER type-1 and type-2 points,
respectively. For instance, the obscured type-2 AGN XID5395 (empty red star;
Brusa et al. 2016) exhibits outflow velocity and $v_{\rm out}/L_{\rm bol}$
ratio (i.e. $\sim$130) comparable with those of SUPER type-2 systems, and a
column density ($N_{H}\sim 10^{23}$ cm-2) that places this source in the
nuclear highly-obscured regime. Within the optical type-1 AGN population
instead, we notice that ‘hybrid’ sources (i.e optical type-1 but X-ray
obscured AGN; orange markers) show higher outflow velocities, consistently
with the picture proposed in this work. In fact, the hybrid type-1 AGN XID2028
(empty pentagon; Perna et al. 2015a; Cresci et al. 2023; Veilleux et al. 2023)
behaves like the SUPER type-2 objects in terms of $v_{\rm out}-L_{\rm bol}$,
while it is more similar to SUPER type-1 counterparts in the $v_{\rm
out}/L_{\rm bol}-N_{\rm H}$ parameter space, falling at the boundary of the
obscured vs. unobscured regimes ($N_{H}\sim 10^{22}$ cm-2).

Therefore, radiation pressure of dust might be the dominant mechanism
explaining the presence of faster-ionised outflows in type-2 AGN, also at
lower bolometric luminosity with respect to that typical of ERQs ($L_{\rm
bol}>10^{47}$ erg s-1) from Perrotta et al. (2019). Our analysis has
furthermore revealed a clear type-1/type-2 dichotomy which fully reflects the
X-ray unobscured/obscured classification based on obscuring material on
nuclear scales. Interestingly, SUPER type-2 AGN are all X-ray highly obscured
with $N_{\rm H}\gtrsim 10^{23}$ cm-2, which may explain the higher observed
velocities of their ionised outflows, compared to those observed in SUPER
type-1 AGN within the same luminosity range. Rather than being a consequence
of projection effects, this result supports the scenario in which obscured
optical type-2 AGN at $z\sim 2$ might indeed evolve towards the unobscured
type-1 phase, as radiation-pressure driven outflows blow out obscuring
material and unveil the central AGN and its BLR emission.

Despite previous studies already pointed to individual sources as likely
‘blow-out’ candidates (e.g. Brusa et al. 2016), SUPER has provided
observational evidence of such an evolutionary scenario in a larger AGN
sample, and also pointed to an optical type-1/type-2 AGN dichotomy in outflow
velocity, for the first time, in a sample of moderate-luminous AGN at $z\sim
2$ (i.e. $L_{\rm bol}\sim 10^{44.8-46.5}$ erg s-1). As a comparison, the ERQ
sample at $z=2-3$ studied by Perrotta et al. (2019) consists of highly
luminous AGN ($L_{\rm bol}>10^{47}$ erg s-1), all showing BLR emission (hence,
optical type-1 systems). On another side, the published results from the KASHz
survey (Harrison et al. 2016) relative to X-ray-selected AGN ($L_{\rm
2-10\leavevmode\nobreak\ keV}>10^{42}$ erg s-1) at $z=0.6-1.7$, shows no
evidence for more extreme gas kinematics in obscured (type-2) objects than in
unobscured (type-1) counterparts (see left panel of Fig. 8). We point out that
this is likely consequence of the lower S/N of KASHz data compared to SUPER,
which hampers the detection of any extended [O iii] line wing. This is indeed
the case for cid_1143 and cid_451, among the targets in common to both surveys
(Scholtz et al., in prep.), where the shallower KMOS data completely miss the
broad [O iii] wing due to the outflow, thus underestimating its velocity.

##  7 Conclusions

In this paper we presented SINFONI H\- and K-band observations of the SUPER
type-2 AGN sample, thus complementing the results previously on the type-1
systems (Kakkad et al. 2020, 2023). The spectral analysis of SINFONI datacubes
(Sect. 3.1) allowed us to study the spatial distribution and kinematics of
ionised gas via [O iii] line emission (Sect. 4.1), and found evidence for
ionised outflows in all seven examined type-2 AGN, moving at $v_{\rm out}>600$
km s-1. The detected outflows are spatially resolved in six objects and extend
up to 2–4 kpc from the centre, while marginally resolved in the remaining
source (XID614, $R_{\rm out}<1.9$ kpc). We also compute outflow mass rates
from dust-corrected [O iii] outflow luminosity (Sect. 4.2.2).

In Sect. 5, we investigated outflows velocity as a function of AGN bolometric
luminosity, aiming at unveiling any difference between the type-1 and type-2
AGN populations in SUPER. The key result of this paper is presented in Sect.
5.1 and clearly shown in Fig. 8, where the outflow velocity is plotted against
$L_{\rm bol}$: SUPER type-2 AGN host faster winds than the type-1 systems
within the same luminosity range (i.e. $L_{\rm bol}=10^{44.8-46.5}$ erg s-1).
Interestingly, these type-2 AGN (namely, all type-2 systems with high-S/N [O
iii] detection in our SINFONI observations) are all highly obscured systems
($N_{\rm H}\gtrsim 10^{23}$ cm-2). By separately stacking integrated type-1
and type-2 spectra, we found a 2.7$\sigma$ difference in the blue wing of the
[O iii] line profile, which appears to be more prominent in type-2 AGN than in
type-1 counterparts (Fig. 9). We interpret this as a consequence of the larger
amount of obscuring material ($N_{\rm H}\gtrsim 10^{24}$ cm-2 in all examined
type-2 AGN), which favours the acceleration of winds via radiation-pressure on
dust (e.g. Ishibashi & Fabian 2015; Costa et al. 2018).

SUPER survey has hence provided observational evidence of an evolutionary
origin of the type-1/type-2 dichotomy at $z\sim 2$, consistently with
theoretical predictions and with previous observational evidence, whereas
orientation effects might play a minor role. Within this picture, type-2 AGN -
even at lower luminosity - represent the blow-out phase (e.g. Hopkins et al.
2008), whose obscured environment favours the acceleration of fast winds via
radiation pressure on dust. Such an interpretation was already proposed for
obscured AGN at $z\sim 2$ (e.g. Brusa et al. 2015, 2016; Perna et al. 2015b;
Zakamska et al. 2016), but not clearly associated with the type-2 optical
class, with some of these examined X-ray obscured AGN being optical type-1
systems (e.g. Perna et al. 2015a; Perrotta et al. 2019). Moreover, the ionised
outflows overall detected in SUPER AGN galaxies have velocities compatible
with the escape speed of their DM halo (Fig. 10, left panel), pointing to no
type-1/type-2 discrepancy though. Unfortunately, the uncertainties and the
simplified assumptions of our dynamical model prevent us from testing
definitely the ejective feedback scenario. However, we found that, although
possibly unable to successfully escape the DM halo of their galaxy, these
ionised outflows are likely to reach 30–50 kpc scales (Fig. 10, right panel),
where they can still provide effective feedback by sweeping away gas and/or
injecting energy in the galaxy medium (‘preventive’ mode), thus halting the
formation of new stars.

To confirm this novel result found in SUPER, in future we will need larger
samples of high-redshift type-1 and type-2 AGN. The new generation of IFS
facilities, both from space (e.g NIRSpec on JWST) and on the ground with AO
(e.g. ERIS at VLT), will allow us to exploit as much as possible the power of
spatially resolved observations, to finally take the next step to fully
understand feedback mechanisms and AGN evolution at the Cosmic Noon.

###### Acknowledgements.

We thank Giacomo Venturi for the insightful discussion and his advise on
dynamical mass modelling. We acknowledge financial support from INAF under the
Large Grant 2022 “The metal circle: a new sharp view of the baryon cycle up to
Cosmic Dawn with the latest generation IFU facilities”. We also acknowledge
support from the Italian Ministry for University and Research (MIUR) for the
BLACKOUT project funded through grant PRIN 2017PH3WAT.003, and for the project
“Black Hole winds and the Baryon Life Cycle of Galaxies: the stone-guest at
the galaxy evolution supper”, funded through grant PRIN #2017PH3WAT. MP
acknowledges support from Grant PID2021-127718NB-I00 funded by the Spanish
Ministry of Science and Innovation/State Agency of Research (MICIN/AEI/
10.13039/501100011033). CMH acknowledges funding from a United Kingdom
Research and Innovation grant (code: MR/V022830/1). IL acknowledges support
from PID2022-140483NB-C22 funded by AEI 10.13039/501100011033 and BDC 20221289
funded by MCIU by the Recovery, Transformation and Resilience Plan from the
Spanish State, and by NextGenerationEU from the European Union through the
Recovery and Resilience Facility. AP acknowledges support by an Anniversary
Fellowship at the University of Southampton.

## References

  * Antonucci (1993) Antonucci, R. 1993, ARA&A, 31, 473
  * Arribas et al. (2014) Arribas, S., Colina, L., Bellocchi, E., Maiolino, R., & Villar-Martín, M. 2014, A&A, 568, A14
  * Bae & Woo (2014) Bae, H.-J. & Woo, J.-H. 2014, ApJ, 795, 30
  * Bertola et al. (2024) Bertola, E., Circosta, C., Ginolfi, M., et al. 2024, A&A (submitted)
  * Bischetti et al. (2017) Bischetti, M., Piconcelli, E., Vietri, G., et al. 2017, A&A, 598, A122
  * Bouché et al. (2013) Bouché, N., Murphy, M. T., Kacprzak, G. G., et al. 2013, Science, 341, 50
  * Bovy (2015) Bovy, J. 2015, ApJS, 216, 29
  * Brusa et al. (2015) Brusa, M., Bongiorno, A., Cresci, G., et al. 2015, MNRAS, 446, 2394
  * Brusa et al. (2010) Brusa, M., Civano, F., Comastri, A., et al. 2010, ApJ, 716, 348
  * Brusa et al. (2016) Brusa, M., Perna, M., Cresci, G., et al. 2016, A&A, 588, A58
  * Calzetti et al. (2000) Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682
  * Cano-Díaz et al. (2012) Cano-Díaz, M., Maiolino, R., Marconi, A., et al. 2012, A&A, 537, L8
  * Carniani et al. (2015) Carniani, S., Marconi, A., Maiolino, R., et al. 2015, A&A, 580, A102
  * Carniani et al. (2023) Carniani, S., Venturi, G., Parlanti, E., et al. 2023, arXiv e-prints, arXiv:2306.11801
  * Chartas et al. (2020) Chartas, G., Davidson, E., Brusa, M., et al. 2020, MNRAS, 496, 598
  * Cicone et al. (2018) Cicone, C., Brusa, M., Ramos Almeida, C., et al. 2018, Nature Astronomy, 2, 176
  * Cicone et al. (2014) Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21
  * Circosta et al. (2021) Circosta, C., Mainieri, V., Lamperti, I., et al. 2021, A&A, 646, A96
  * Circosta et al. (2018) Circosta, C., Mainieri, V., Padovani, P., et al. 2018, A&A, 620, A82
  * Circosta et al. (2019) Circosta, C., Vignali, C., Gilli, R., et al. 2019, A&A, 623, A172
  * Civano et al. (2016) Civano, F., Marchesi, S., Comastri, A., et al. 2016, ApJ, 819, 62
  * Costa et al. (2018) Costa, T., Rosdahl, J., Sijacki, D., & Haehnelt, M. G. 2018, MNRAS, 479, 2079
  * Costa et al. (2014) Costa, T., Sijacki, D., & Haehnelt, M. G. 2014, MNRAS, 444, 2355
  * Cresci et al. (2015) Cresci, G., Mainieri, V., Brusa, M., et al. 2015, ApJ, 799, 82
  * Cresci et al. (2023) Cresci, G., Tozzi, G., Perna, M., et al. 2023, A&A, 672, A128
  * Davies et al. (2020) Davies, R., Baron, D., Shimizu, T., et al. 2020, MNRAS, 498, 4150
  * Davies et al. (2008) Davies, R., Rabien, S., Lidman, C., et al. 2008, The Messenger, 131, 7
  * Davies (2007) Davies, R. I. 2007, MNRAS, 375, 1099
  * DiPompeo et al. (2018) DiPompeo, M. A., Hickox, R. C., Carroll, C. M., et al. 2018, ApJ, 856, 76
  * Duras et al. (2020) Duras, F., Bongiorno, A., Ricci, F., et al. 2020, A&A, 636, A73
  * Dutton & Macciò (2014) Dutton, A. A. & Macciò, A. V. 2014, MNRAS, 441, 3359
  * Eisenhauer et al. (2003) Eisenhauer, F., Abuter, R., Bickert, K., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood, 1548–1561
  * Fabian (2012) Fabian, A. C. 2012, ARA&A, 50, 455
  * Fawcett et al. (2023) Fawcett, V. A., Alexander, D. M., Brodzeller, A., et al. 2023, MNRAS[arXiv:2308.14790]
  * Ferrarese & Merritt (2000) Ferrarese, L. & Merritt, D. 2000, ApJ, 539, L9
  * Feruglio et al. (2010) Feruglio, C., Maiolino, R., Piconcelli, E., et al. 2010, A&A, 518, L155
  * Fiore et al. (2017) Fiore, F., Feruglio, C., Shankar, F., et al. 2017, A&A, 601, A143
  * Fischer et al. (2018) Fischer, T. C., Kraemer, S. B., Schmitt, H. R., et al. 2018, ApJ, 856, 102
  * Förster Schreiber et al. (2014) Förster Schreiber, N. M., Genzel, R., Newman, S. F., et al. 2014, ApJ, 787, 38
  * Förster Schreiber et al. (2019) Förster Schreiber, N. M., Übler, H., Davies, R. L., et al. 2019, ApJ, 875, 21
  * Gilli et al. (2022) Gilli, R., Norman, C., Calura, F., et al. 2022, A&A, 666, A17
  * Gilli et al. (2014) Gilli, R., Norman, C., Vignali, C., et al. 2014, A&A, 562, A67
  * Girdhar et al. (2022) Girdhar, A., Harrison, C. M., Mainieri, V., et al. 2022, MNRAS, 512, 1608
  * Girelli et al. (2020) Girelli, G., Pozzetti, L., Bolzonella, M., et al. 2020, A&A, 634, A135
  * Glikman et al. (2024) Glikman, E., LaMassa, S., Piconcelli, E., Zappacosta, L., & Lacy, M. 2024, MNRAS, 528, 711
  * Harrison et al. (2016) Harrison, C. M., Alexander, D. M., Mullaney, J. R., et al. 2016, MNRAS, 456, 1195
  * Harrison et al. (2014) Harrison, C. M., Alexander, D. M., Mullaney, J. R., & Swinbank, A. M. 2014, MNRAS, 441, 3306
  * Harrison et al. (2012) Harrison, C. M., Alexander, D. M., Swinbank, A. M., et al. 2012, MNRAS, 426, 1073
  * Hopkins et al. (2006) Hopkins, P. F., Hernquist, L., Cox, T. J., et al. 2006, ApJS, 163, 1
  * Hopkins et al. (2008) Hopkins, P. F., Hernquist, L., Cox, T. J., & Kereš, D. 2008, ApJS, 175, 356
  * Ishibashi & Fabian (2015) Ishibashi, W. & Fabian, A. C. 2015, MNRAS, 451, 93
  * Kakkad et al. (2016) Kakkad, D., Mainieri, V., Padovani, P., et al. 2016, A&A, 592, A148
  * Kakkad et al. (2020) Kakkad, D., Mainieri, V., Vietri, G., et al. 2020, A&A, 642, A147
  * Kakkad et al. (2023) Kakkad, D., Mainieri, V., Vietri, G., et al. 2023, MNRAS, 520, 5783
  * King (2005) King, A. 2005, ApJ, 635, L121
  * King & Pounds (2015) King, A. & Pounds, K. 2015, ARA&A, 53, 115
  * Klindt et al. (2019) Klindt, L., Alexander, D. M., Rosario, D. J., Lusso, E., & Fotopoulou, S. 2019, MNRAS, 488, 3109
  * Kormendy & Ho (2013) Kormendy, J. & Ho, L. C. 2013, ARA&A, 51, 511
  * Lamastra et al. (2009) Lamastra, A., Bianchi, S., Matt, G., et al. 2009, A&A, 504, 73
  * Lamperti et al. (2021) Lamperti, I., Harrison, C. M., Mainieri, V., et al. 2021, A&A, 654, A90
  * Leung et al. (2019) Leung, G. C. K., Coil, A. L., Aird, J., et al. 2019, ApJ, 886, 11
  * Luo et al. (2017) Luo, B., Brandt, W. N., Xue, Y. Q., et al. 2017, ApJS, 228, 2
  * Madau & Dickinson (2014) Madau, P. & Dickinson, M. 2014, ARA&A, 52, 415
  * Maiolino et al. (2012) Maiolino, R., Gallerani, S., Neri, R., et al. 2012, MNRAS, 425, L66
  * Marasco et al. (2023) Marasco, A., Belfiore, F., Cresci, G., et al. 2023, A&A, 670, A92
  * Marasco et al. (2020) Marasco, A., Cresci, G., Nardini, E., et al. 2020, A&A, 644, A15
  * Marasco et al. (2021) Marasco, A., Cresci, G., Posti, L., et al. 2021, MNRAS, 507, 4274
  * Marconi & Hunt (2003) Marconi, A. & Hunt, L. K. 2003, ApJ, 589, L21
  * Marshall et al. (2023) Marshall, M. A., Perna, M., Willott, C. J., et al. 2023, A&A, 678, A191
  * Menci et al. (2008) Menci, N., Fiore, F., Puccetti, S., & Cavaliere, A. 2008, ApJ, 686, 219
  * Morganti et al. (2015) Morganti, R., Oosterloo, T., Oonk, J. B. R., Frieswijk, W., & Tadhunter, C. 2015, A&A, 580, A1
  * Mowla et al. (2019) Mowla, L., van der Wel, A., van Dokkum, P., & Miller, T. B. 2019, ApJ, 872, L13
  * Mullaney et al. (2013) Mullaney, J. R., Alexander, D. M., Fine, S., et al. 2013, MNRAS, 433, 622
  * Musiimenta et al. (2023) Musiimenta, B., Brusa, M., Liu, T., et al. 2023, arXiv e-prints, arXiv:2309.16528
  * Navarro et al. (1997) Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493
  * Noll et al. (2009) Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793
  * Osterbrock & Ferland (2006) Osterbrock, D. E. & Ferland, G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei
  * Pacifici et al. (2023) Pacifici, C., Iyer, K. G., Mobasher, B., et al. 2023, ApJ, 944, 141
  * Perna et al. (2023) Perna, M., Arribas, S., Marshall, M., et al. 2023, A&A, 679, A89
  * Perna et al. (2015a) Perna, M., Brusa, M., Cresci, G., et al. 2015a, A&A, 574, A82
  * Perna et al. (2015b) Perna, M., Brusa, M., Salvato, M., et al. 2015b, A&A, 583, A72
  * Perna et al. (2017) Perna, M., Lanzuisi, G., Brusa, M., Cresci, G., & Mignoli, M. 2017, A&A, 606, A96
  * Perna et al. (2018) Perna, M., Sargent, M. T., Brusa, M., et al. 2018, A&A, 619, A90
  * Perrotta et al. (2019) Perrotta, S., Hamann, F., Zakamska, N. L., et al. 2019, MNRAS, 488, 4126
  * Piqueras López et al. (2012) Piqueras López, J., Colina, L., Arribas, S., Alonso-Herrero, A., & Bedregal, A. G. 2012, A&A, 546, A64
  * Rakshit & Woo (2018) Rakshit, S. & Woo, J.-H. 2018, ApJ, 865, 5
  * Ricci & Trakhtenbrot (2023) Ricci, C. & Trakhtenbrot, B. 2023, Nature Astronomy, 7, 1282
  * Riffel et al. (2015) Riffel, R. A., Storchi-Bergmann, T., & Riffel, R. 2015, MNRAS, 451, 3587
  * Rojas et al. (2020) Rojas, A. F., Sani, E., Gavignaud, I., et al. 2020, MNRAS, 491, 5867
  * Rupke et al. (2005) Rupke, D. S., Veilleux, S., & Sanders, D. B. 2005, ApJS, 160, 87
  * Rupke & Veilleux (2013) Rupke, D. S. N. & Veilleux, S. 2013, ApJ, 768, 75
  * Storchi-Bergmann et al. (2010) Storchi-Bergmann, T., Lopes, R. D. S., McGregor, P. J., et al. 2010, MNRAS, 402, 819
  * Temple et al. (2019) Temple, M. J., Banerji, M., Hewett, P. C., et al. 2019, MNRAS, 487, 2594
  * Tozzi et al. (2021) Tozzi, G., Cresci, G., Marasco, A., et al. 2021, A&A, 648, A99
  * Urrutia et al. (2012) Urrutia, T., Lacy, M., Spoon, H., et al. 2012, ApJ, 757, 125
  * Urry & Padovani (1995) Urry, C. M. & Padovani, P. 1995, PASP, 107, 803
  * van de Voort et al. (2011) van de Voort, F., Schaye, J., Booth, C. M., & Dalla Vecchia, C. 2011, MNRAS, 415, 2782
  * Vayner et al. (2021a) Vayner, A., Wright, S. A., Murray, N., et al. 2021a, ApJ, 919, 122
  * Vayner et al. (2023) Vayner, A., Zakamska, N. L., Ishikawa, Y., et al. 2023, ApJ, 955, 92
  * Vayner et al. (2021b) Vayner, A., Zakamska, N. L., Riffel, R. A., et al. 2021b, MNRAS, 504, 4445
  * Veilleux et al. (2023) Veilleux, S., Liu, W., Vayner, A., et al. 2023, ApJ, 953, 56
  * Venturi et al. (2021) Venturi, G., Cresci, G., Marconi, A., et al. 2021, A&A, 648, A17
  * Venturi et al. (2018) Venturi, G., Nardini, E., Marconi, A., et al. 2018, A&A, 619, A74
  * Vietri et al. (2020) Vietri, G., Mainieri, V., Kakkad, D., et al. 2020, A&A, 644, A175
  * Villar Martín et al. (2020) Villar Martín, M., Perna, M., Humphrey, A., et al. 2020, A&A, 634, A116
  * Wang et al. (2018) Wang, J., Xu, D. W., & Wei, J. Y. 2018, ApJ, 852, 26
  * Woo et al. (2016) Woo, J.-H., Bae, H.-J., Son, D., & Karouzos, M. 2016, ApJ, 817, 108
  * Wylezalek et al. (2022) Wylezalek, D., Vayner, A., Rupke, D. S. N., et al. 2022, ApJ, 940, L7
  * Yang et al. (2020) Yang, G., Boquien, M., Buat, V., et al. 2020, MNRAS, 491, 740
  * Zakamska & Greene (2014) Zakamska, N. L. & Greene, J. E. 2014, MNRAS, 442, 784
  * Zakamska et al. (2016) Zakamska, N. L., Hamann, F., Pâris, I., et al. 2016, MNRAS, 459, 3144
  * Zinger et al. (2020) Zinger, E., Pillepich, A., Nelson, D., et al. 2020, MNRAS, 499, 768

##  Appendix A Further comparison of outflow properties with AGN/host
parameters

In this appendix we further search for any dependence of outflow properties on
AGN/host galaxy parameters, yet finding no result as interesting as that shown
in Fig. 8. In Fig. 12 we plot $v_{\rm out}$ against $M_{*}$ and SFR in the
left and right panel, respectively, for those SUPER AGN sources with available
$M_{*}$ and SFR estimates (or upper limits) from SED fitting (Circosta et al.
2018). As pointed in Sect. 5.2, constraining (or at least placing upper limits
to) stellar properties might be particularly challenging in bright type-1 AGN,
where the dominant AGN emission outshines the fainter stellar counterpart. For
this reason, whereas the seven SUPER type-2 AGN galaxies have all $M_{*}$ and
SFR estimates (or upper limits), only six and seven type-1 systems have
estimates (or upper limits) of $M_{*}$ and SFR available, respectively. Errors
on $M_{*}$ and SFR estimates are the total uncertainty, including both
statistical (from SED fitting) and typical systematic uncertainties
($\sim$0.1dex in $M_{*}$ and 0.3dex in SFR, Pacifici et al. 2023). Colours and
symbols are the same as in Figs. 8 and 10. We point out that these $M_{*}$ and
SFR values derived by Circosta et al. (2018) are consistent within the
uncertainty with the updated SED fitting results (Bertola et al. 2024)
obtained with CIGALE (Noll et al. 2009; Yang et al. 2020), recently
implemented to fit also X-ray data. Unlike Fig. 8, we find no clear trend of
$v_{\rm out}$ with either $M_{*}$ or SFR, with type-1 and type-2 measurements
scattered together across the two planes. All this overall suggests no
connection of $v_{\rm out}$ with host galaxy stellar properties, which is
instead well known to increase with AGN luminosity (e.g. Bae & Woo 2014; Woo
et al. 2016; Rakshit & Woo 2018). However, we argue that the modest number of
measurements and the difficulty in constraining stellar properties of AGN
galaxies (especially for type-1 AGN hosts) might conceal some link between
these quantities.

Considering the interesting type-1/type-2 dichotomy found by comparing outflow
velocity with bolometric luminosity (see Fig. 8), we check whether the same
discrepancy between the SUPER type-2 and type-1 samples is also present in
terms of outflow mass rates. To compare type-1 and type-2 measurements as
homogeneously as possible, we re-compute outflow mass rates for the type-1
sample consistently with the prescriptions adopted on $v_{\rm out}$ and
$R_{\rm out}$ in this work (see Sect. 4.2), still using values inferred in
Kakkad et al. (2020). As discussed in Sect. 4.2.1, $v_{10}$ and $v_{90}$
indeed provide a more suitable definition of $v_{\rm out}$ than $w_{80}$,
being less sensitive to the line shape which may be driven by dust extinction.
Regarding the value of $R_{\rm out}$ to use in Eq. 2, the type-1 outflow mass
rates were all computed at a fixed radius of 2 kpc (Kakkad et al. 2020), since
[O iii] line emission is unresolved in most of type-1 observations, as opposed
to type-2 datasets. Therefore, for the few type-1 AGN with clear detection of
spatially resolved outflows, we consistently take as outflow radius $R_{\rm
out}$ the maximum distance of [O iii] outflow emission (Kakkad et al. 2020).
For all other unresolved type-1 AGN, we use the maximum distance (or
corresponding upper limit) from the central AGN where is found the bulk of the
ionised gas, as resulted from spectroastrometry analysis (Kakkad et al. 2020).
With these values of $v_{\rm out}$ and $R_{\rm out}$, we obtain $\dot{M}_{\rm
out}$ type-1 mass rates, consistent with the type-2 estimates.

In Fig. 13, we plot outflow mass rates as a function of $L_{\rm bol}$ for the
full SUPER sample. The upper panel shows the $\dot{M}_{\rm out}$ values (same
coloured markers as in Fig. 8), derived according to our trust prescriptions
(see Sect. 4.2), most of which are lower limits (triangles) due to an upper
limit to $R_{\rm out}$ and/or a lower limit to $A_{V}$ (see Table
LABEL:tab:super_wind). Similarly, the lower panel shows the $\dot{M}_{\rm
max}$ values obtained by using $v_{\rm max}$ values (Rupke et al. 2005) in Eq.
2, which can be compared with the scaling relations by Fiore et al. (2017)
(dotted line) and Musiimenta et al. (2023) (dashed). To allow a proper
comparison, both scaling relations have been re-scaled to our adopted $n_{\rm
e}\sim 500$ cm -3, whereas all $\dot{M}_{\rm max}$ points have been corrected
for a factor 2, since the scaling relations have been obtained by considering
outflow mass rates computed from the flux of broad Gaussian components. The
factor 2 corresponds to the approximated mean ratio of [O iii] luminosity from
broad Gaussian components to that from line channels at $|v|>300$ km s-1,
obtained separately for both type-2 and type-1 AGN (Kakkad et al. 2020).

As opposed to Fig. 13, the large number of inferred lower limits to mass rates
hampers any accurate comparison within the low-luminosity range previously
selected ($L_{\rm bol}\sim 10^{44.8-46.5}$; orange shading). However,
excluding lower limits, we find mean values of $\dot{M}_{\rm out}\sim 5$ M⊙
yr-1 and $\dot{M}_{\rm max}\sim 7$ M⊙ yr-1 for type-2 AGN, and $\dot{M}_{\rm
out}\sim 5$ M⊙ yr-1 and $\dot{M}_{\rm max}\sim 15$ M⊙ yr-1 for low-$L_{\rm
bol}$ selected type-1 AGN. Compared to Fig. 8, here it is harder to establish
whether a separation between type-2 and type-1 AGN is actually present or not,
due to the large uncertainty on outflow mass rates. Indeed, as explained in
Sect. 4.2.2, mass rate estimates require several quantities to be assumed
(e.g. $n_{\rm e}$, [O/H], ionisation), and strongly depend on the accuracy of
the other measured parameters (e.g. $A_{V}$, $R_{\rm out}$). All this
uncertainty contributes to increasing the scatter in the resulting mass rate
values, which might hide any possible type-1/type-2 dichotomy, visible instead
in terms of outflow kinematics. Finally, we argue that the larger scatter in
$\dot{M}_{\rm out}$ measurements further complicates any search for trends
with $M_{*}$ and SFR estimates, which are more uncertain than $L_{\rm bol}$,
as discussed at the beginning of this appendix.

Figure 12: Outflow velocity $v_{\rm out}$ as a function of host galaxy stellar
stellar mass ($M_{*}$, left panel) and star formation rate (SFR, right panel),
for those SUPER AGN sources with available SED-based estimates or upper limits
(Circosta et al. 2018). Errorbars on $M_{*}$ and SFR values represent the
total uncertainty, including both statistical and typical systematic
uncertainties ($\sim$0.1dex in $M_{*}$ and 0.3dex in SFR, Pacifici et al.
2023). Colours and symbols are the same as in Figs. 8 and 10. The two panels
show neither clear trend of $v_{\rm out}$ with the host stellar properties nor
separation between type-1 and type-2 measurements, which appear scattered
together across the two planes. Figure 13: Outflow mass rates as a function of
$L_{\rm bol}$ for the full SUPER sample. Upper and lower panels show our trust
$\dot{M}_{\rm out}$ values and $\dot{M}_{\rm max}$ mass rates, derived using
$v_{\rm max}$ in Eq. 2. Triangles indicate lower limits to mass rates. In the
lower panel, scaling relations from Fiore et al. (2017) and Musiimenta et al.
(2023) are shown as dotted and dashed lines, after being re-scaled to $n_{\rm
e}=500$ adopted for our targets, while values of $\dot{M}_{\rm max}$ have been
multiplied by a factor of 2 to match definitions adopted in Fiore et al.
(2017) and Musiimenta et al. (2023). Compared to Fig. 8, the distribution of
measurements is more scattered, with no clear discrepancy between the type-1
and type-2 samples. The comparison is furthermore hampered by the large number
of lower limits to mass rates.