# The [OIII] profiles of far-infrared active and non-active optically-selected
green valley galaxies

Antoine Mahoro South African Astronomical Observatory (SAAO), P.O. Box 9,
Observatory 7935, Cape Town, South Africa  Department of Astronomy, University
of Cape Town (UCT), Private Bag X3, Rondebosch 7701, South Africa  Petri
Väisänen South African Astronomical Observatory (SAAO), P.O. Box 9,
Observatory 7935, Cape Town, South Africa  Southern African Larger Telescope
(SALT), P.O. Box 9, Observatory 7935, Cape Town, South Africa  Mirjana Pović
Astronomy and Astrophysics Research and Development Department, Entoto
Observatory and Research Center (EORC), Space Science and Geospatial Institute
(SSGI), P.O. Box 33679, Addis Ababa, Ethiopia  Instituto de Astrofísica de
Andalucía (IAA-CSIC), Glorieta de la Astronomía s/n, 18008 Granada, Spain
Physics Department, Faculty of Science, Mbarara University of Science and
Technology (MUST), P.O. Box 1410, Mbarara, Uganda  Pheneas Nkundabakura  MSPE
Department, School of Education, College of Education, University of Rwanda
(UR), P.O. Box 5039, Kigali, Rwanda  Kurt van der Heyden  Department of
Astronomy, University of Cape Town (UCT), Private Bag X3, Rondebosch 7701,
South Africa  National Research Foundation (NRF), P.O. Box 2600, Pretoria,
0001, South Africa  Sara Cazzoli Instituto de Astrofísica de Andalucía (IAA-
CSIC), Glorieta de la Astronomía s/n, 18008 Granada, Spain  Samuel B. Worku
Astronomy and Astrophysics Research and Development Department, Entoto
Observatory and Research Center (EORC), Space Science and Geospatial Institute
(SSGI), P.O. Box 33679, Addis Ababa, Ethiopia  Isabel Márquez Instituto de
Astrofísica de Andalucía (IAA-CSIC), Glorieta de la Astronomía s/n, 18008
Granada, Spain  Josefa Masegosa Instituto de Astrofísica de Andalucía (IAA-
CSIC), Glorieta de la Astronomía s/n, 18008 Granada, Spain  Solohery M.
Randriamampandry South African Astronomical Observatory (SAAO), P.O. Box 9,
Observatory 7935, Cape Town, South Africa  Southern African Larger Telescope
(SALT), P.O. Box 9, Observatory 7935, Cape Town, South Africa  A&A, Department
of Physics, Faculty of Sciences, University of Antananarivo, B.P. 906,
Antananarivo 101, Madagascar  Moses Mogotsi  South African Astronomical
Observatory (SAAO), P.O. Box 9, Observatory 7935, Cape Town, South Africa
Southern African Larger Telescope (SALT), P.O. Box 9, Observatory 7935, Cape
Town, South Africa

###### Abstract

We present a study of the $\rm{[OIII]\lambda\,5007\,\AA}$ line profile in a
sub-sample of 8 active galactic nuclei (AGN) and 6 non-AGN in the optically-
selected green valley at $\rm{z\,<\,0.5}$ using long-slit spectroscopic
observations with the 11 m Southern African Large Telescope. Gaussian
decomposition of the line profile was performed to study its different
components. We observe that the AGN profile is more complex than the non-AGN
one. In particular, in most AGN (5/8) we detect a blue wing of the line. We
derive the FWHM velocities of the wing and systemic component, and find that
AGN show higher FWHM velocity than non-AGN in their core component. We also
find that the AGN show blue wings with a median velocity width of
approximately 600 $\rm{km\,s^{-1}}$, and a velocity offset from the core
component in the range -90 to -350 $\rm{km\,s^{-1}}$, in contrast to the non-
AGN galaxies, where we do not detect blue wings in any of their
$\rm{[OIII]\lambda\,5007\,\AA}$ line profiles. Using spatial information in
our spectra, we show that at least three of the outflow candidate galaxies
have centrally driven gas outflows extending across the whole galaxy.
Moreover, these are also the galaxies which are located on the main sequence
of star formation, raising the possibility that the AGN in our sample are
influencing SF of their host galaxies (such as positive feedback). This is in
agreement with our previous work where we studied SF, morphology, and stellar
population properties of a sample of green valley AGN and non-AGN galaxies.

galaxies: active — galaxies: evolution – line:profiles

##  1 Introduction

It is often assumed that the essential properties of supermassive black holes
(SMBH) in active galactic nuclei (AGN) and the galaxies that host them are
connected (e.g., Kormendy & Ho, 2013, and references therein). These
properties include the relationship between black hole mass and stellar
velocity dispersion, bulge mass, light concentration, bulge luminosity, etc.
(e.g., Pović et al., 2009a, b; Beifiori et al., 2012; Davis et al., 2019; de
Nicola et al., 2019; Gaspari et al., 2019; Shankar et al., 2019; Caglar et
al., 2020; Ding et al., 2020; Marsden et al., 2020; Bennert et al., 2021; Luo
et al., 2021; Kovačević-Dojčinović et al., 2022; Matzko et al., 2022). These
relationships might be caused by an AGN feedback mechanism, where energy is
transmitted from the nucleus to the host galaxy, which then slows the galaxy
growth and quench star formation. However, AGN feedback may not be entirely
responsible for these links (e.g., Zubovas et al., 2013; Barai et al., 2018;
DiPompeo et al., 2018; Wang et al., 2018).

The AGN feedback operates in two general ways: by injecting energy into the
surrounding gas of the interstellar or intergalactic matter, and by halting
the in-fall of gas onto galaxies. These can then lead to matter compression,
gas heating, enrichment or depletion by clearing gas within the galaxy – all
processes which in turn can either enhance star formation (positive feedback;
e.g., Silk, 2013; Cresci et al., 2015; Cresci & Maiolino, 2018; Shin et al.,
2019; Perna et al., 2020), or quench star formation (negative feedback; e.g.,
Carniani et al., 2016; Karouzos et al., 2016; Kalfountzou et al., 2017; Bae et
al., 2017; Cresci & Maiolino, 2018; Shin et al., 2019; Perna et al., 2020).

In recent years, significant effort has been dedicated to searching for
observational signatures of AGN feedback using tracers of neutral atomic,
molecular, and ionised gas, leading to the detection of outflows in galaxies
(e.g., Hainline et al., 2013; Rupke & Veilleux, 2013; Harrison et al., 2014;
Carniani et al., 2016; Fiore et al., 2017; Rupke et al., 2017; Liu et al.,
2020; Lutz et al., 2020; Jarvis et al., 2020; Spilker et al., 2020a, b; Perna
et al., 2020; Veilleux et al., 2017, 2020; Al Yazeedi et al., 2021; Bewketu
Belete et al., 2021; Scholtz et al., 2021; Paillalef et al., 2021).

One of the common methods to trace outflows uses forbidden emission lines to
diagnose the dynamic state of the ionised gas in the AGN host galaxy (e.g.,
Bae et al., 2017; Woo et al., 2020; Schmidt et al., 2021; Zhang, 2021). In
particular, AGN-driven outflows have been studied using the velocity
measurements of the high-ionisation line $\rm{[OIII]\,\lambda 5007\,\AA}$. The
kinematics of $\rm{[OIII]}$ have typically been constrained by two
measurements: a principal ‘core’ component, with a velocity close to the
systemic redshift of the host galaxy, and an additional broad ‘wing’
component, which is asymmetric and usually stretches blue-ward (e.g., Cresci
et al., 2015; Kakkad et al., 2016, 2020; Sexton et al., 2021; Kovačević-
Dojčinović et al., 2022). The velocity-width of the core component is affected
by the gravitational potential of the host galaxy and the central SMBH. But
when the velocity-width of the blue wing is observed to be too broad, the gas
cannot be in dynamical equilibrium with the host galaxy (e.g., Liu et al.,
2013; Harrison et al., 2014; McElroy et al., 2015; Sun et al., 2017), and
commonly the blueshifted $\rm{[OIII]\,\lambda 5007\,\AA}$ emission indicates
gas outflows. Outflows of gas on the ”other” side of the galaxy, which would
be detected as redshifted, may be obscured due to dust, leaving only the
blueshifted, near side of the outflow, observed. Hence, the line profile, i.e.
the line’s width and (a)symmetry contains information about the dynamical
state of the gas in the galaxy, and potentially its connection to the
properties of the SMBH and the host galaxy $\rm{[OIII]\,\lambda 5007\,\AA}$
emission profile (e.g., Shen & Ho, 2014; Hryniewicz et al., 2022).

Several studies have shown a correlation of outflow velocities detected in
$\rm{[OIII]\,\lambda 5007\,\AA}$ with AGN luminosity in the optical, infrared
(IR), and X-rays (e.g., Zhang et al., 2011; Zakamska & Greene, 2014; Zakamska
et al., 2016; Fischer et al., 2017; Perna et al., 2017; DiPompeo et al., 2018;
Singha et al., 2022), suggesting that radiation pressure is driving the winds.
It has also been reported that outflow velocities have a relationship with
other AGN properties such as black hole mass (e.g., Rupke et al., 2017;
Behroozi et al., 2019; Terrazas et al., 2020; Schmidt et al., 2021), accretion
rate (e.g., Greene & Ho, 2005; Bian et al., 2006; Woo et al., 2016), star
formation (e.g., Harrison, 2017; Fluetsch et al., 2019; Scholtz et al., 2020;
Woo et al., 2020; Luo et al., 2021; Kim et al., 2022; Mulcahey et al., 2022),
although, in some cases, the correlation is weak at best (e.g., Zhang et al.,
2011; Peng et al., 2014).

In Mahoro et al. (2017) we found, in a sample of 103 far-infrared (FIR)
detected galaxies, that on average, star formation rates were higher in the
(X-ray detected) AGN in the sample, than in the mass-matched non-AGN galaxies.
We also found that green valley X-ray detected AGN with FIR emission still
have very active star formation rates, being located either on or above the
main sequence (MS) of star-forming galaxies. We also found that they did not
show signs of star formation quenching, but rather signs of its enhancement.
This has been observed independently when examining their morphology (Mahoro
et al., 2019). In addition, we did not find older stellar populations in AGN
hosts, when studying stellar ages and populations of a subsample of AGN and
non-AGN (Mahoro et al., 2022), nor signs of star formation quenching, as
suggested previously in X-ray and optical studies (e.g., Nandra et al., 2007;
Pović et al., 2012; Ellison et al., 2016; Leslie et al., 2016). Therefore, our
results may suggest that if AGN feedback influences the star formation in
green valley galaxies with X-ray detected AGN and FIR emission, it is positive
AGN feedback rather than negative feedback.

In this work, we go a step further in studying in detail the possibility of
detecting signs of positive AGN feedback in our sample, by analysing the
$\rm{[OIII]\lambda 5007\,\AA}$ emission line profiles in a sub-sample of FIR
AGN and non-AGN green valley galaxies, using our own observations from the 11m
Southern African Large Telescope (SALT).

The paper is organised as follows: a description of the sample and
observations is given in Section 2. Section 3 presents the spectroscopic
fitting procedure, while the main results are presented in Section 4.
Discussion and summary are given in Sections 5 and 6. Throughout, we assume a
flat universe with $\Omega_{m}=0.3,\,\Omega_{\Lambda}=0.7$ and $H_{0}=70$ km
s-1 Mpc-1 cosmology. In addition, we assume Salpeter (1955) initial mass
function (IMF). All magnitudes are in the AB system, and the stellar masses
are given in units of solar masses (M⊙).

##  2 Data and the Sample

The sample studied in this work was taken from a much larger sample of
optically-selected green valley galaxies using $\rm{U-B}$ rest-frame colour
criteria of $\rm{0.8\leq U-B\leq 1.2}$. Furthermore, the obtained optically-
selected green valley sample was then cross-matched with Herschel/PACS FIR
data (for a more detailed description see Mahoro et al., 2017). Thus, the
sample consists of optically-selected green valley galaxies with FIR
detections.

The data to study our sample were obtained using the 11-m class SALT (Buckley
et al., 2006), and its Robert Stobie Spectrograph (RSS) (Burgh et al., 2003;
Kobulnicky et al., 2003). The RSS is a versatile instrument providing several
observing modes and is designed to work in a wavelength range of
$\rm{3200-9000\,\AA}$.

For our study of the $\rm{[OIII]\lambda 5007\,\AA}$ line profile, we first
selected the AGN and non-AGN green valley galaxies from Mahoro et al. (2017)
observable with SALT, and within the magnitude limit of Vmag $\rm{\leq}$ 21.5,
in order to have reasonable exposure times. This resulted in 11 AGN and 669
non-AGN galaxies. Beyond approximately 7500 $\rm{\AA}$, there are strong sky
emission lines which affect the spectral frames and therefore the accuracy of
data reduction. Since our galaxies are fairly faint, we further selected only
those with redshifts $\rm{<}$ 0.5, to avoid the observed emission line falling
in this region of the spectral frames. We thus finally obtained nine AGN
galaxies. From the larger number of non-AGN that satisfied those criteria, we
selected a smaller matching sample by selecting the one that was brightest,
and in particular closest to each selected AGN galaxy in terms of its location
in a stellar mass versus star formation rate diagram. We thus obtained a final
sample of nine non-AGN galaxies also. Figure 1 shows both the AGN and non-AGN
sub-samples on the star formation rate vs. stellar mass relation diagram. We
used the results by Elbaz et al. (2011), which were obtained from galaxies
detected by Herschel, to represent the MS of star-forming galaxies. This MS is
based on a Salpeter IMF (Salpeter, 1955).

Note also, that using low-resolution Sloan Digital Sky Surveys fourth
phase111https://www.sdss.org(SDSS-IV, Blanton et al., 2017) spectra and
zCOSMOS survey spectra (Lilly et al., 2009), we checked that all our AGN
galaxies are type-2 AGN.

Figure 1: Star formation rate vs. stellar mass. AGN are represented by blue
squares, while the red circles represent seven non-AGN. The cyan triangles are
those non-AGN that finally were observed, while all the AGN were observed. The
solid black line shows the Elbaz et al. (2011) fit for MS, while the dashed
lines represent the MS width of $\rm{\pm}$ 0.3 dex. Open blue circles show the
cases with detected blue wings in their emission line profiles (see section
4), where the three sources with unambiguous gas flow interpretations are
marked with a thick blue circle (see section 5).

T

###  2.1 Observations and data reduction

In this section, we describe all observations done and the data reduction
process. The observations were planned and defined using the RSS simulator
tool222https://astronomers.salt.ac.za/software/#RSS. In total, 16 sources were
observed with the SALT/RSS, as part of the programs 2017-2-MLT-003 (PI: Pović
M.), 2020-1-DDT-001 and 2020-2-SCI-021 (PI: Mahoro A). Table 1 summarises the
observational information, including average seeing and exposure times for all
16 observed AGN and non-AGN. The [OIII]$\rm{\lambda 5007\,\AA}$ emission line
was detected in 14 of the target spectra (8 AGN and 6 non-AGN).

All RSS observations were taken in the long-slit mode using a 1.25 arcsec wide
slit with PG1800 or PG2300 gratings (column 8 in Table 1). In most cases, the
slit was placed along the galaxy minor axis. Figures with the slit position
are shown in Appendix A, while the position angle is given in Table 1 (column
9). RSS uses an array of three CCDs of $\rm{2048\,\times\,4096}$ pixels, and
the grating angle in the instrument was set in a way to avoid the
[OIII]$\rm{\lambda 5007\,\AA}$ emission line falling in the resulting two 15
pixels size gaps. Interpolated pixel values were used to fill in the two CCD
gaps. The RSS spatial pixel scale is 0.1267 arcsec, and the effective field of
view is 8 arcmin along the slit. We used a binning factor of 2 for a final
spatial sampling of 0.253 arcsec pixel-1. It is known that on RSS, using
spectral flats, the improvement of rms noise in data below 7000Å is less than
$\rm{<}$ 5 %, i.e. quite insignificant, and we thus chose to use the time for
extra exposure time
instead333https://pysalt.salt.ac.za/proposal_calls/current/ProposalCall.html#h.sc2wkzpxrdkc.
However, above 7500Å there is significant fringing, which can only be
corrected using the spectral flats, and hence for all setups extending to that
wavelength range we took them. As our AGN are at a slightly higher redshift
than non-AGN (see Table 1, column 3), and the observed wavelength can reach
7000Å, these spectra were taken with spectral flat-fields, while the non-AGN
were not.

Table 1: Summary of the properties of our sources and the instrumental set-up
for the observations.

ID | RA(J2000) | Dec(J2000) | Redshift | V-band | Seeing | Total Exposure | Grating | Pos. ang. | Galaxy Type
---|---|---|---|---|---|---|---|---|---
| hours | degrees |  | AB(mag) | (arcsec) | time (s) |  | degrees |
COSMOS_17759 | 09h 59m 07.66s | +02∘ 08${}^{{}^{\prime}}$ 20.80${}^{{}^{\prime\prime}}$ | 0.354 | 20.50 | 1.35 | 3130 | PG2300 | 50.0 | AGN
COSMOS_29436 | 09h 58m 38.84s | +02∘ 23${}^{{}^{\prime}}$ 48.40${}^{{}^{\prime\prime}}$ | 0.355 | 20.10 | 1.25 | 1862 | PG2300 | 83.0 | AGN
COSMOS_20881 | 10h 01m 40.41s | +02∘ 05${}^{{}^{\prime}}$ 06.70${}^{{}^{\prime\prime}}$ | 0.424 | 21.19 | 1.5 | 6405 | PG1800 | 330.0 | AGN
COSMOS_8450 | 09h 59m 26.89s | +01∘ 53${}^{{}^{\prime}}$ 41.20${}^{{}^{\prime\prime}}$ | 0.443 | 20.93 | 1.8 | 1500 | PG1800 | 15.5 | AGN
COSMOS_11702 | 09h 59m 47.50s | +01∘ 38${}^{{}^{\prime}}$ 52.60${}^{{}^{\prime\prime}}$ | 0.283 | 21.29 | 1.2 | 6405 | PG2300 | 83.0 | AGN
COSMOS_28917 | 09h 58m 40.65s | +02∘ 04${}^{{}^{\prime}}$ 26.60${}^{{}^{\prime\prime}}$ | 0.339 | 20.40 | 1.25 | 1365 | PG2300 | 4.0 | AGN
COSMOS_9239 | 10h 02m 36.52s | +02∘ 02${}^{{}^{\prime}}$ 17.60${}^{{}^{\prime\prime}}$ | 0.368 | 20.66 | 1.7 | 4270 | PG2300 | 293.0 | AGN
COSMOS_30402 | 09h 58m 40.32s | +02∘ 08${}^{{}^{\prime}}$ 07.00${}^{{}^{\prime\prime}}$ | 0.338 | 20.39 | 1.6 | 4270 | PG2300 | 44.0 | AGN
COSMOS_12899∗ | 09h 59m 09.57s | +02∘ 19${}^{{}^{\prime}}$ 16.30${}^{{}^{\prime\prime}}$ | 0.383 | 21.29 | 1.5 | 5847 | PG2300 | 343.0 | AGN
COSMOS_6026 | 10h 02m 41.69s | +02∘ 13${}^{{}^{\prime}}$ 34.70${}^{{}^{\prime\prime}}$ | 0.214 | 19.93 | 1.5 | 2970 | PG2300 | 320.0 | Non-AGN
COSMOS_7952 | 09h 58m 02.19s | +02∘ 48${}^{{}^{\prime}}$ 12.70${}^{{}^{\prime\prime}}$ | 0.249 | 19.96 | 1.1 | 2970 | PG2300 | 3.5 | Non-AGN
COSMOS_19854 | 10h 00m 47.40s | +02∘ 15${}^{{}^{\prime}}$ 33.60${}^{{}^{\prime\prime}}$ | 0.214 | 19.91 | 1.7 | 2235 | PG2300 | 90.0 | Non-AGN
COSMOS_21658 | 09h 58m 21.48s | +02∘ 25${}^{{}^{\prime}}$ 18.60${}^{{}^{\prime\prime}}$ | 0.187 | 19.94 | 1.3 | 3148 | PG2300 | 29.0 | Non-AGN
COSMOS_21854 | 09h 59m 04.99s | +02∘ 35${}^{{}^{\prime}}$ 06.60${}^{{}^{\prime\prime}}$ | 0.219 | 19.95 | 1.5 | 2670 | PG2300 | 289.0 | Non-AGN
COSMOS_22743 | 10h 00m 05.49s | +02∘ 40${}^{{}^{\prime}}$ 02.50${}^{{}^{\prime\prime}}$ | 0.218 | 19.96 | 1.5 | 2970 | PG2300 | 347.0 | Non-AGN
COSMOS_14973∗ | 10h 02m 22.51s | +01∘ 52${}^{{}^{\prime}}$ 28.30${}^{{}^{\prime\prime}}$ | 0.252 | 19.95 | 1.7 | 2870 | PG2300 | 66.0 | Non-AGN

Note. — Sources observed with no detection of $\rm{[OIII]\lambda\,5007\,\AA}$
emission line are marked with ’*’.

We made use of the SALT primary reduced product data sets, generated by the
in-house pipeline called PySALT444http://pysalt.salt.ac.za (see Crawford et
al., 2010), which mosaics the individual CCD data to a single FITS file,
corrects for cross-talk effects, and performs bias and gain corrections.
Further steps, including flat-field correction (for AGN only) were done by us
with the help of the IRAF package555IRAF is distributed by the National
Optical Astronomy Observatories, which are operated by the Association of
Universities for Research in Astronomy, Inc., under cooperative agreement with
the NSF.. Two consecutive exposures were combined to increase the signal-to-
noise ratio, and to remove the cosmic ray effects using the L.A.Cosmic task in
IRAF (van Dokkum, 2001). Using Arc lamp spectra (Argon, Neon-Argon, and
Xenon), we determined the dispersion solution using the noao.twodspec package.
After wavelength calibration, the RMS error in the wavelength solution was
$\rm{\sim\,0.02\,\AA}$. Flux calibration was performed using
spectrophotometric standard stars (LTT4364, HILT600 and LTT3218). The standard
stars were observed with the same settings as our respective observation
blocks. Note that in this work the flux calibration was done for the purpose
of determining the relative spectral shape, as an absolute flux calibration is
not required.

##  3 Spectral fitting

The main aim of this paper is to analyse the [OIII]$\rm{\lambda 5007\,\AA}$
emission line profile. Before [OIII]$\rm{\lambda 5007\,\AA}$ line profile
fitting, we did a cross-check with all spectra and applied a three-pixel and
five-pixel boxcar smoothing separately to all our spectra. After visual
checking, we do not see a significant change in the spectral feature to be
analysed between the two cases, and in addition we made sure that the latter
smoothing kernel corresponds to the spectral resolution of the instrumental
setup. Thus, in order to maximise the signal-to-noise ratio in the data, we
decided to use the five-pixel boxcar smoothed spectra for our analysis.

We show the spectral fitting for all our spectra in Figure 2, where a solid
black line represents the data. For the spectral fitting process, we first fit
the continuum, where each spectrum was visually inspected to select regions
free of emission features around the $\rm{[OIII]\lambda\,5007\,\AA}$ line; a
continuum region is shown in green in Figure 2, though these regions extend
beyond the depicted panel ranges. After this, a linear pseudo-continuum was
fitted using a polynomial with a degree between four and six using all the
spectral windows with those emission line free regions, and this was then
subtracted from the spectrum. After the subtraction of the continuum, the
[OIII]$\rm{\lambda 5007\,\AA}$ line profile in the spectrum was modelled with
a single or multiple Gaussian-profiles with
PySpeckit666https://pyspeckit.readthedocs.io/en/latest/, an extensive
spectroscopic analysis toolkit for astronomy, which uses a Levenberg-Marquardt
minimisation algorithm (Ginsburg & Mirocha, 2011). For the Gaussian-profile
fit process, we set all parameters to be free.

To prevent over-fitting a Gaussian component, and to allow for the appropriate
number of Gaussians, we perform two tests. First, following Cazzoli et al.
(2018), we calculated the standard deviation of a segment of the continuum
free of both emission and absorption lines ($\varepsilon_{\rm c}$). We then
compared this value with the standard deviation estimated from the residuals
under the [OIII]$\rm{\lambda 5007\,\AA}$ emission line ($\varepsilon_{\rm
line}$). We have considered a fit as reliable when
$\mathrm{\varepsilon_{c}\,\approx\,\varepsilon_{line}}$, being aware that the
standard deviation under the fitted line becomes smaller than the true
(featureless) continuum if the emission line is over-fitted. Secondly, we use
the Bayesian Information Criterion (BIC) statistic,
$\mathrm{\Delta\,BIC\,=\,\left(\chi^{2}_{\rm 1}+k_{\rm
1}ln\left(n\right)\right)-\left(\chi^{2}_{\rm 2}+k_{\rm
2}ln\left(n\right)\right)}$, where $\chi^{2}_{\rm 1,2}$ are the total
$\chi^{2}$ of the line fit from the single and double Gaussian, $k_{\rm 1,2}$
are the number of degrees of free parameters in the fit, and $n$ is the number
of data points. Typically $\rm{\Delta}$ BIC$\rm{\,>}$10 (see $\rm{\Delta}$ BIC
value in Figure 2) is used as strong evidence that a double component is
required (e.g., Swinbank et al., 2019; Avery et al., 2021; Vietri et al.,
2022).

Figure 2: All spectra of our fitting. For each object, we show the spectral
data (in black), the components fitted for emission lines (in blue), the
overall total best-fitting model (in red), and the residuals (in magenta). The
dashed vertical lines mark the location of the rest-frame [OIII]$\rm{\lambda
5007\,\AA}$ line. The first two rows are AGN, while the last two are non-AGN.

As shown in Figure 2 (blue lines), our process gives different fitted
components for the [OIII]$\rm{\lambda 5007\,\AA}$ emission line. The overall
total best-fit model (red line) and the residuals (magenta line below) are
also shown.

Generally, we consider as best fit a case when both of the two conditions
mentioned above are fulfilled. In four of the AGN (COSMOS_17759, COSMOS_29436,
COSMOS_20881, and COSMOS_11702), two Gaussians per line profile are required
for a satisfactory fit. Only in one AGN (COSMOS_8450) is an additional
Gaussian component required to reproduce the observed line profile (i.e., a
total of three kinematic components). The remaining three AGN (COSMOS_28917,
COSMOS_9239 and COSMOS_30402) are well fitted with a single Gaussian
component. All non-AGN are well reproduced with a single Gaussian component.
Hence, we found that AGN have more complex [OIII]$\rm{\lambda 5007\,\AA}$ line
profiles.

###  3.1 Error estimation

In order to estimate uncertainties of all measured parameters, synthetic
spectra were created from the original spectra. We randomly generated 100
spectra for each galaxy, by adding a value (positive or negative) to each data
point in the original spectrum, drawn from a Gaussian normal distribution
based on the standard errors of each data point at that location in the
original spectra. Using the newly created random spectra, we repeated the
entire fitting procedure with pseudo-continuum subtraction and line emission
component fitting. We then estimated the uncertainty of the fitted parameters
by computing the standard deviation of the relevant parameter from the fits to
the 100 realisations of the random galaxy spectra. To ensure the consistency
of the procedure, we in addition visually verified each emission line fitting.
The estimated errors are given in Table 2,and are of order of 2% $-$ 4% for
the wing component and 1$-$7% for the core component.

##  4 Results

As explained in section 3, five out of eight spectra show a broad/blue wing,
hence their modelling requires two kinematic components. We decomposed the
[OIII]$\rm{\lambda 5007\,\AA}$ line into a core component $\rm{([OIII]_{cc})}$
and a wing component referred to as a blue wing $\rm{([OIII]_{bc})}$. We
measured the velocity using full width at half maximum (FWHM) to analyse both
components.

The FWHM of the Gaussian components are corrected for instrumental SALT FWHM
following the formula:
$\rm{FWHM_{cor}\,=\,(FWHM^{2}_{obs}\,-\,FWHM^{2}_{inst})^{1/2}}$, where
$\rm{FWHM_{obs}}$ is the Gaussian FWHM measured from the spectrum,
$\rm{FWHM_{inst}}$ is the instrumental FWHM, and $\rm{FWHM_{cor}}$ is the
corrected value used in the analysis.

In Figure 3, we show the FWHM distribution of measured $\rm{[OIII]_{cc}}$ and
$\rm{[OIII]_{bc}}$ for AGN and non-AGN. Table 2 presents the line widths of
the FWHM [OIII]$\rm{\lambda 5007\,\AA}$ components and their errors (columns 2
- 4). All tabulated FWHM were corrected for the instrumental broadening, as
explained above.

Table 2: [OIII]$\rm{\lambda 5007\,\AA}$ emission line properties. The mid-horizontal line separates AGN (located above) and non-AGN galaxies (located below). The ”bc” subscript denotes the blue-shifted component, ”cc” core component and ”rc” the redshifted component. ID | $\rm{FWHM_{bc}}$ | $\rm{FWHM_{cc}}$ | $\rm{FWHM_{rc}}$ | $\rm{\Delta V}$ | $\rm{V_{max}}$
---|---|---|---|---|---
| ($\rm{km\,s^{-1}}$) | ($\rm{km\,s^{-1}}$) | ($\rm{km\,s^{-1}}$) | ($\rm{km\,s^{-1}}$) | ($\rm{km\,s^{-1}}$)
COSMOS_17759 | 353 $\pm$15 | 264 $\pm$5 | – |  $-$169 $\pm$18 | 468 $\pm$22
COSMOS_29436 | 832 $\pm$34 | 383 $\pm$15 | – |  $-$94 $\pm$25 | 801 $\pm$32
COSMOS_20881 | 597 $\pm$17 | 334 $\pm$4 | – |  $-$262 $\pm$16 | 769 $\pm$19
COSMOS_8450 | 812 $\pm$31 | 450 $\pm$12 | 640 $\pm$43 |  $-$351 $\pm$28 | 1041 $\pm$12
COSMOS_11702 | 372 $\pm$16 | 234 $\pm$11 | – |  $-$157 $\pm$10 | 474 $\pm$13
COSMOS_28917 | – | 333 $\pm$2 | – | – | –
COSMOS_9239 | – | 398 $\pm$9 | – | – | –
COSMOS_30402 | – | 368 $\pm$4 | – | – | –
COSMOS_6026 | – | 235 $\pm$13 | – | – | –
COSMOS_7952 | – | 219 $\pm$3 | – | – | –
COSMOS_19854 | – | 189 $\pm$2 | – | – | –
COSMOS_21658 | – | 139 $\pm$10 | – | – | –
COSMOS_21854 | – | 269 $\pm$4 | – | – | –
COSMOS_22743 | – | 192 $\pm$2 | – | – | –

It can be seen from Table 2 that our AGN core components $\rm{[OIII]_{cc}}$
have higher velocities than in non-AGN (see also Figure 3 left panel). The AGN
sources cover a range of $\rm{FWHM_{cc}}$ from 234 to 450 $\rm{km\,s^{-1}}$,
with a median velocity of 350 $\rm{km\,s^{-1}}$, while the respective range
for the non-AGN covers velocities of 139$-$269 $\rm{km\,s^{-1}}$, with a
median velocity of 205 $\rm{km\,s^{-1}}$.

In Figure 3 (right panel), for the AGN sample, we compared our
$\rm{[OIII]_{cc}}$ FWHM measurements (where available) with those of Schmidt
et al. (2018, 2021). The study of Schmidt et al. (2018), with 28 narrow-line
Seyfert 1 galaxies at $\rm{z\,<\,0.15}$, and of Schmidt et al. (2021), with 45
broad-line Seyfert 1 galaxies at $\rm{z\,<\,0.06}$, is concentrated on
[OIII]$\rm{\lambda 5007\,\AA}$ line profiles related to outflows. The authors
confirmed the correlation between the blueshift and FWHM of the line core,
between the outflow velocity and the black hole mass using a multi-Gaussian
component fitting process. It can be seen that our AGN $\rm{[OIII]_{cc}}$
measurements cover the same range as Schmidt et al. (2018, 2021). Our
measurements are consistent with also other works focused on NLR emission line
studies (e.g., Osterbrock1989; RodrguezArdila2000; Boroson, 2005;
Schmidt2016).

Figure 3: Left: Normalised distributions of $\rm{FWHM_{[OIII]cc}}$ velocity of
our AGN (dashed blue lines) and non-AGN (solid red lines). The vertical dashed
lines show the median values for each histogram, using the same colour style.
Right: Normalised distributions of $\rm{FWHM_{[OIII]cc}}$ velocity of our AGN
(dashed blue lines), and the comparison samples of Schmidt et al. (2018, 2021)
(solid green and red lines, respectively).

The comparison of the widths of the blue wings, i.e. our FWHM
$\rm{[OIII]_{bc}}$ velocities compared with the FWHM $\rm{[OIII]_{bc}}$
velocities from Schmidt et al. (2018, 2021), is shown in Figure 4. We found
that our AGN FWHM $\rm{[OIII]_{bc}}$ velocities range from is 353 to 832
$\rm{km\,s^{-1}}$, with a FWHM median velocity of 597 $\rm{km\,s^{-1}}$. These
values are lower than those obtained by Schmidt et al. (2018) and Schmidt et
al. (2021), where the 50% ranges of FWHM $\rm{[OIII]_{bc}}$ are
$\rm{668-1007}$ $\rm{km\,s^{-1}}$ and $\rm 561-1069$ $\rm km\,s^{-1}$,
respectively. This could be due to the fact that our sample consists of type-2
AGN, compared to type-1 AGN analysed by Schmidt et al. (2018, 2021).

Figure 4: Comparison of normalised distributions of the widths of the blue
components, FWHM [OIII]bc velocities, in our sample, and in the samples of
Schmidt et al. (2018, 2021).

###  4.1 Kinematics from the $\rm{[OIII]\lambda\,5007\,\AA}$ emission line

The spectral fitting of $\rm{[OIII]\lambda\,5007\,\AA}$ explained in section 3
shows that double-Gaussian decomposition (core and blue wing components) was
required for five out of the eight AGN. To quantify the velocity shift of the
wing relative to the core component in these sources, we computed the
difference between core $\rm{[OIII]_{cc}}$ and wing $\rm{[OIII]_{bc}}$ line
centroids, defined as $\rm{\Delta
V\,=\,\big{(}\big{(}\lambda[OIII]_{cc}-\lambda[OIII]_{bc}\big{)}/\lambda[OIII]_{cc}\big{)}\times
c}$.

We found that the five AGN show blue wings in the $\rm{\Delta V}$ range from
-90 to -350 $\rm{km\,s^{-1}}$ (see Table 2, column 5). These values are well
within the typical ranges reported in the literature (e.g., Véron-Cetty et
al., 2001; Cracco et al., 2016; Boroson, 2005; Schmidt et al., 2018; Cooke et
al., 2020; Schmidt et al., 2021), although in some cases they are narrower
that what was commonly reported, which is however expected taking into account
a small number of sources in our sample. Typical uncertainty in our velocity
determination is approximately $\rm{10\,-\,30\,km\,s^{-1}}$.

We also examined the relationship between $\rm{\Delta V}$ and the FWHM of the
$\rm{[OIII]_{cc}}$ for those galaxies showing wings. We observed a modest
Pearson correlation value of $\rm{r_{p}=-0.531}$ between these two parameters.
As a result, galaxies with a higher core component FWHM tend to have more
strongly blueshifted wings, which is consistent with previous findings (e.g.,
Ludwig et al., 2012; Berton et al., 2016).

###  4.2 [OIII] luminosity versus X-ray luminosity

We used the [OIII] emission-line luminosity ($\rm{L_{[OIII]}}$) and 2 - 10 keV
X-ray luminosity ($\rm{L_{X}}$) to test the $\rm{L_{X}}$ vs. $\rm{L_{[OIII]}}$
correlation (e.g., Caccianiga et al., 2007; Yan et al., 2011; Harrison et al.,
2016; Kakkad et al., 2020). These two quantities can be used to study the
total AGN power (e.g., Maiolino et al., 2003; Schmitt et al., 2003; Masegosa
et al., 2011). To measure the X-ray luminosities we used the public XMM-Newton
(Brusa et al., 2007) and Chandra (Civano et al., 2016; Marchesi et al., 2016)
catalogues available in the COSMOS
survey777https://cosmos.astro.caltech.edu/page/xray. We extracted the X-ray
fluxes in the 2 -10 keV band and made sure that all X-ray fluxes have reliable
values with errors $<$ 20%. X-ray luminosities were measured using the
k-corrected fluxes, where the k-correction was measured as in Ptak et al.
(2007) using the photon index of 1.9. Figure 5 shows the [OIII] luminosity
versus the X-ray luminosity for our AGN green valley sample. To compare our
sample with previous works, we make use of the K-band Multi-Object
Spectrograph (KMOS) AGN Survey at High redshift (KASHz, z
$\rm{\approx\,0.6-1.7}$) taken form Harrison et al. (2016), where a sample of
$\sim$ 40 X-ray selected AGN were analysed, and from which a $\sim$ 50%
fraction with outflows was reported. For a low-redshift AGN comparison sample,
we make use of Mullaney et al. (2013), this sample contains optically selected
AGN at z $<$ 0.4 from the SDSS spectroscopic database. This sample is similar
to our AGN in terms of redshift (see Table 1, column 3).

In general, although we have a small number of sources, they are closely
following the trend in relation between those of both low- and high-redshift
AGN (see Figure 5). We find a median luminosity ratio of
$\rm{\log(L_{[OIII]}/L_{X})\,=\,-1.86}$ for our eight AGN, which is fully
consistent with the value obtained at lower redshift.

Figure 5: X-ray luminosity (2–10 keV) versus total [OIII] emission-line
luminosity for our eight AGN green valley galaxies (blue dots). The blue
dashed line indicates the best fit of our sample. Grey cross symbols represent
the AGN at $\rm{z\,<\,0.4}$ from Mullaney et al. (2013). The red dots
represent KASHz data, with a median value $\rm{-2.1_{-0.5}^{+0.3}}$ of
$\rm{\log(L_{{\rm[O~{}III]}}/L_{{\rm X}})}$, over-plotted by the black dashed
line. The transparent green region between the two solid black lines indicates
the $\approx$1$\sigma$ scatter on this ratio (more details are given in
Harrison et al. (2016)). The green dot-dashed line shows the relationship
between the local Seyferts and luminous type 1 Seyfert galaxies presented in
Panessa et al. (2006).

###  4.3 Total asymmetry

The shift of the whole $\rm{[OIII]\lambda\,5007\,\AA}$ emission line, or the
”blue emission”, which quantifies the full blue extension of the wing
component in all our AGN with $\rm{[OIII]\lambda\,5007\,\AA}$ line profile
with double-Gaussian decomposition, is one of the properties in which we are
most interested. To measure the blue emission, we employed the Schmidt et al.
(2018, 2021) definition:

$\rm{blue\,emission\,=\,\Delta V\,-FWHM[OIII]_{bc}}$ (1)

The obtained blue emission values for our sample range from -1117 to -521
$\rm{km\,s^{-1}}$, with a mean value of -790 $\rm{km\,s^{-1}}$. This range is
similar to that in Schmidt et al. (2018, 2021), who find a range from -1674 to
-469 $\rm{km\,s^{-1}}$.

###  4.4 Ionised outflow detection

Previous works suggested that ionised winds have a non-Gaussian profile in the
$\rm{[OIII]\lambda\,5007\,\AA}$ emission line, with somewhat greater
wings/tails than a Gaussian (e.g., Zakamska et al., 2016; Xu et al., 2020; Li
et al., 2022). The profiles often include a blue (or in rare cases, red) wing,
indicating that the line profile is recreated with two components, a narrow
one linked to the narrow-line region (NLR) emission, and the other wide
component, that may be shifted. This second component is regarded as an
outflow candidate in this work.

Furthermore, we adopted the Rupke & Veilleux (2013) and Bischetti et al.
(2017) methodology to determine a maximum outflow velocity, considering that
the outflow expands at a constant velocity:

$\rm{V_{max}\,=\,|\Delta V|\,+\,2\sigma_{[OIII]_{bc}}}$ (2)

where $\rm{\sigma_{[OIII]_{bc}}}$ is the velocity dispersion of the Gaussian
representing the wing component of the $\rm{[OIII]\lambda\,5007\,\AA}$
emission line.

Using these definitions, we calculated the maximum outflow velocities
$\rm{V_{max}}$ for those five AGN galaxies exhibiting wings, out of the eight
total observed, to span the range of $\rm{470\,-\,1040\,kms^{-1}}$ (see Table
2, column 6).

##  5 Discussion

In this study we present detailed observations of the
$\rm{[OIII]\lambda\,5007\,\AA}$ emission line for a small sample of AGN and
non-AGN galaxies with FIR emission in the green valley at $z<$ 0.4, and argue
that their properties are consistent with previous works (e.g., Mullaney et
al., 2013; Harrison et al., 2016; Schmidt et al., 2018, 2021). We demonstrate
that the $\rm{[OIII]\lambda\,5007\,\AA}$ line profile of the green valley FIR
AGN are different from those of equivalent FIR non-AGN galaxies, with respect
to the complexity of their line profiles (see section 3), as well as the core
component velocity width $\rm{FWHM_{[OIII]cc}}$ (see section 4). Our results
suggest a possible presence of gas outflows in the AGN sample.

In order to study any characteristics of the outflowing gas, it is however
important to distinguish between gravitational and outflow processes. To do
this, we first compare to a conservative velocity threshold of
$\rm{V_{max}\,=\,650\,km\,s^{-1}}$, as used in other works such as in Perna et
al. (2017) and Rojas et al. (2020). The value was chosen, therein, following
the fact that 95% of BOSS galaxies at $\rm{z\,<\,0.8}$ had characteristics
matching this limit. Using this $\rm{V_{max}}$ criterion, only three of our
AGN would be classified as ”true” outflow signals (COSMOS_29436, COSMOS_20881,
and COSMOS_8450). However, we note that no matching of masses of the
respective samples, i.e. ours vs. the BOSS galaxies referred to, was done. We
also checked against an outflow criterion of Perna et al. (2015a, b), where
$\rm{FHWM_{bc}}$ threshold of 550 $\rm{kms^{-1}}$ was used. The same three
galaxies satisfy this limit, with $\rm{FHWM_{bc}}$ of 653 $\rm{kms^{-1}}$, 613
$\rm{kms^{-1}}$ and 790 $\rm{kms^{-1}}$, for COSMOS_29436, COSMOS_20881, and
COSMOS_8450, respectively.

We then use the spatial information available in our SALT spectra to gain more
information regarding the origin of the motion of gas, whether nuclear, or
disk rotation, or outflow. The broadening of the observed
$\rm{[OIII]\lambda\,5007\,\AA}$ line can be caused by the rotation of the
galaxy, if any significantly rotating parts of it enter the slit. Therefore,
it is important to extract the 1D spectra centred on the galaxy nucleus and
make the observations along the minor axes, as was indeed performed. Moreover,
we compared extracted 1D spectra of various size apertures to check if the
blue wings origin is spatially dependent. In addition to extracting nucleus-
centered aperture 1D spectra, we also extracted apertures separately from the
two sides of the galaxy nucleus. All this was done with all AGN showing wing
components (namely COSMOS_17759, COSMOS_29436, COSMOS_20881, COSMOS_8450,
COSMOS_11702). The nuclear apertures are shown in Appendix B, where we
illustrate the different profiles extracted from the centres (blue boxes) and
off-nuclear (magenta boxes) of galaxies.

The results of these tests were as below. In three of the cases (COSMOS_29436,
COSMOS_20881, and COSMOS_11702) the blue wing in the line profile persists in
all tested apertures, regardless of the size and position with respect to the
nucleus. In COSMOS_11702 the smaller apertures in fact showed a more
pronounced blue wing than that seen in the larger apertures shown of Figure 2.
That the blue wing is present everywhere tells us that there is outflowing
material from the galaxy projected size over the whole face of it (several
kpc), i.e. most likely originating from the nucleus towards the observer in a
wide cone-like structure typical of galactic super-winds (see e.g.
Heckman1990). In particular, the observed shape of the line profile when
connected with the geometry, cannot be due to e.g. disk rotation. As it can be
seen from the spatial information (Appendix B), we are findings that outflow
extension along the minor axis are extend up to $\rm{\sim}$ 6 kpc for
COSMOS_17759, $\rm{\sim}$ 6.6 kpc for COSMOS_29436, $\rm{\sim}$ 7 kpc for
COSMOS_20881, $\rm{\sim}$ 6.5 kpc for COSMOS_8450, and $\rm{\sim}$ 5 kpc for
COSMOS_11702.

In the case of COSMOS_8450, on the other hand, the closer inspection of
profiles at different spatial locations showed the following: a _broad_ but
fairly symmetric component was detected at all locations, whether small or
larger aperture, and also on both sides of the nucleus. The best fit model to
these profiles sometimes comes out with three components, as shown in Figure
2, or with two components, a narrow and broad component one but without any
significant relative velocity offset. This could well be due to high velocity
gas in all apertures, including inflows and outflows, but it could also be due
to disk rotation signal entering the _width_ of the 1.25″slit (approximately
7kpc at this redshift). The implied rotational velocity is high, more than 350
$\rm{kms^{-1}}$. However, interestingly, this galaxy is the most massive of
our sample: see Figure 1, where this galaxy is located at approximately
$10^{11.1}$ $M_{\odot}$. The same behaviour of a broader component persisting
together with a narrow one was seen in COSMOS_17759, though the large aperture
as seen in Figure 2 did give us a blue wing detection (albeit the weakest and
narrowest of the five). And interestingly, this galaxy is the second most
massive of our AGNs, the one seen just below $10^{11.0}$ $M_{\odot}$ in Figure
1. Hence, while we see signal of high velocity gas in these two galaxies, and
possible outflows, we cannot rule out the possibility of
rotational/gravitational effects being the reason for that signal without
higher spatial resolution data.

We do also wish to highlight that in examining the morphological types of our
galaxies, we see an ongoing interaction of galaxies in (at least) the cases of
COSMOS_29436 and COSMOS_20881 (see Appendix A and Appendix B). This could
potentially affect the line profile due to tidal gravitational effects.
However, the fact that the blue wings were clearly seen in the small nuclear
apertures just as they were in the larger apertures, as well as on both sides
of the galaxy, strongly suggests that the outflow is driven from the nucleus,
and is not, in these cases, an artefact of e.g. tidal stream of the
neigbouring galaxy entering the slit aperture. Nevertheless, more data with
higher angular resolution would be beneficial to fully understand the
$\rm{[OIII]\lambda\,5007\,\AA}$ line profiles of these sources, and,
furthermore, to study the intriguing role of the interaction in feeding and/or
triggering the AGN activity.

Finally, we note that the three AGN, COSMOS_29436, COSMOS_20881, and
COSMOS_11702, that we consider the most unambiguous cases for gas outflow
signatures, and indeed nuclear-driven gas flow signatures as discussed above,
are also ones within or very close to the MS of star forming galaxies. They
are depicted with the thick open blue circles in Figure 1.
This particular result in this paper, based on the study of the
$\rm{[OIII]\lambda\,5007\,\AA}$ emission line profiles of a small sample of
optically-selected green valley AGN with FIR emission, is thus very much in
line with that obtained using photometric techniques of statistical samples in
Mahoro et al. (2017, 2019, 2022), where we suggested the possibility of on-
going positive AGN feedback in FIR green valley galaxies. It will be
interesting to further distinguish whether we indeed are witnessing positive
feedback by the AGN on star formation activity of the host galaxies, or, for
example, whether any SF quenching has not _yet_ started in these galaxies, and
if so, why, and what the exact physical role of gas outflows is. To do this,
we require larger AGN samples in the green valley observed with high spatial
resolution.

##  6 Summary

In order to study the $\rm{[OIII]\lambda\,5007\,\AA}$ emission line profile,
we collected optical spectroscopic data for 14 sample of FIR AGN and non-AGN
optically-selected green valley galaxies using 11 m SALT telescope. The
$\rm{[OIII]\lambda\,5007\,\AA}$ emission line profile is used to investigate
the presence of gas outflows. A multi-component Gaussian fitting approach was
adopted to account for the wings of the $\rm{[OIII]\lambda\,5007\,\AA}$
emission line. The outflow velocities were estimated using different
established method and criteria, and in particular by studying the line
profiles combined with the spatial information along the slits provided by the
observations.

In what follows, we summarise the main results of this work and highlight
future prospects:

  * •

Overall, we see that the optically-selected FIR AGN green valley galaxies
display a complex $\rm{[OIII]\lambda\,5007\,\AA}$ emission line profile (at
least two components are required to fit the $\rm{[OIII]\lambda\,5007\,\AA}$
line). The profile is more complex than that observed in non-AGN, where the
$\rm{[OIII]\lambda\,5007\,\AA}$ emission line is well-fitted by a single
component. We thus confirm a difference in the $\rm{[OIII]\lambda\,5007\,\AA}$
line profiles between FIR AGN and non-AGN green valley galaxies.

  * •

We see indications of outflows in five out of eight observed FIR AGN
optically-selected green valley galaxies as determined by blue wings to their
line profiles. Among these five AGN, two and three, respectively, also satisfy
certain literature outflow criteria, such as $\rm{V_{max}\,>\,650\,kms^{-1}}$
(Perna et al. (2017); Rojas et al. (2020)) and
$\rm{FWHM[OIII]_{bc}\,>\,550\,kms^{-1}}$ (Perna et al. (2015a, b)).

  * •

Moreover, using spatial and geometrical arguments, as studied from multiple
spectral apertures in our own data, three of those five blue-wing galaxies
unambiguously show centrally driven galaxy-wide gas outflows, while in the two
other cases we cannot rule out with present data a contribution to a potential
outflow signature from rotation (or velocity dispersion) of these two
relatively massive galaxies.

  * •

Finally, we note that the three unambiguous cases of outflows in the FIR AGN
green valley galaxies, are situated within, or just at the border, of the MS
of SF galaxies. This suggests the possibility that we are witnessing the
influence of AGN on their host galaxies in this sample (such as possible
positive feedback), in line with results we have reported previously in Mahoro
et al. (2017, 2019, 2022). Nevertheless, we consider this a preliminary
result, and plan further observations of larger samples, and with data using a
new generation of telescopes, including other wavelengths and with higher
spatial resolution (e.g., JWST/NIRSpec or ALMA).

## acknowledgments

We thank the anonymous referee for accepting to review this paper, and for
giving us constructive and useful comments that improved this paper. This work
was supported by the National Research Foundation of South Africa (Grant
Numbers 110816 and 132016). This paper makes use of observations taken with
SALT under programs 2017-2-MLT-003, 2020-1-DDT-001 and 2020-2-SCI-021. AM
gratefully acknowledges financial support from the Swedish International
Development Cooperation Agency (SIDA) through the International Science
Programme (ISP) - Uppsala University to the University of Rwanda through the
Rwanda Astrophysics, Space and Climate Science Research Group (RASCSRG). PV
acknowledges support from the National Research Foundation of South Africa. MP
acknowledges the support from the Space Science and Geospatial Institute under
the Ethiopian Ministry of Innovation and Technology (MInT), the Spanish
Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación through
project PID2019-106027GB-C41, and the State Agency for Research of the Spanish
MCIU through the Center of Excellence Severo Ochoa award to the Instituto de
Astrofísica de Andalucía (SEV-2017-0709). We are also grateful to the python,
Virtual Observatory, and TOPCAT teams for making their packages freely
available to the scientific community.

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##  Appendix A Slit positon

Hubble Space Telescope (HST) images taken with the Advanced Camera for Surveys
(ACS) in F814W band of our 14 sample galaxies. All images were generated using
COSMOS cutouts888https://irsa.ipac.caltech.edu/data/COSMOS/index_cutouts.html.
Each galaxy is scaled to 15″cutout size and the placement of the 1.25″wide
slit is shown in blue.

##  Appendix B Illustration of differences in AGN line profiles extracted from
the centre of a galaxy

A comparison between line profiles extracted at $\rm{\sim}$3 kpc to
$\rm{\sim}$7 kpc from the center COSMOS_17759, COSMOS_29436, COSMOS_20881,
COSMOS_8450 and COSMOS_11702 is shown. The blue boxes at the centres of AGN
images show the regions where nuclear-only apertures were extracted and the
magenta boxes represent the off-nuclear extraction regions.

Figure 6: For each object, we show the spectral data (in black), the
components fitted for emission lines (in blue), the overall total best-fitting
model (in red), and the residuals (in magenta). The dashed vertical lines mark
the location of the rest-frame [OIII]$\rm{\lambda 5007\,\AA}$ line.


# Formatting the Landscape: Spatial conditional GAN for varying population in
satellite imagery

Tomas Langer
Intuition Machines Inc
Natalia Fedorova
Max Planck Institute for Evolutionary Anthropology
Ron Hagensieker
osir.io
contact author<EMAIL_ADDRESS>
###### Abstract

Climate change is expected to reshuffle the settlement landscape: forcing
people in affected areas to migrate, to change their lifeways, and continuing
to affect demographic change throughout the world. Changes to the geographic
distribution of population will have dramatic impacts on land use and land
cover and thus constitute one of the major challenges of planning for climate
change scenarios. In this paper, we explore a generative model framework for
generating satellite imagery conditional on gridded population distributions.
We make additions to the existing ALAE [31] architecture, creating a spatially
conditional version: SCALAE. This method allows us to explicitly disentangle
population from the model’s latent space and thus input custom population
forecasts into the generated imagery. We postulate that such imagery could
then be directly used for land cover and land use change estimation using
existing frameworks, as well as for realistic visualisation of expected local
change. We evaluate the model by comparing pixel and semantic reconstructions,
as well as calculate the standard FID metric. The results suggest the model
captures population distributions accurately and delivers a controllable
method to generate realistic satellite imagery.

##  1 Introduction

Human beings are not actionless pawns in the face of climate change, they
adapt to both direct and indirect pressures brought about by an increasingly
unstable climate. People can either choose to leave problematic areas,
migrating both locally and internationally to adapt to their circumstances, or
they can stay and change their lifeways. In either case, and on top of
expected demographic change, human adaptation to climate change thus
reshuffles the settlement landscape [9]. As local populations ebb and flow,
land use and land cover change in response. Due to this high mobility of
populations, particularly given recent work on climate induced migration [32],
one of the major challenges for planning for climate change scenarios is
thinking about where people will be, and how this will change the landscape.
State-of-the-art work on gridded population forecasts shows us the value of a
greater geographic resolution and border oblivious approach [32, 20]. However,
such forecasts still require analytic processing to evaluate their
consequences for local landscapes.

In this paper, we explore the potential for generating satellite imagery
conditional on population change as an in-between step for analysis, and as a
means of directly visualizing forecasts in a realistic way. To do so, we
employ the latest generative models from the field of unsupervised machine
learning, namely generative adversarial networks [13]. GANs are a state-of-
the-art technique for high resolution image generation, as they can generate
images from random noise, also called the latent space. We refer the reader to
a review article [39] for additional details. Generative models have been
successfully applied to various high resolution datasets, mainly faces (e.g.
CelebAHQ [21], FFHQ [22]), objects (e.g. Imagenet [8]), and scenes (e.g. LSUN
[40], Cityscapes [7]). However, generative models in the high resolution earth
observation domain are relatively under-explored [33].

On top of image generation, one might wish to edit a real image via a trained
generative model. For this, a mapping from image space to latent space is
required. This is a natural feature of autoencoders [25] or flow based
generative models [24]. However, one can learn the mapping for a trained GAN
model as well, which is called projection [1, 23]. In order to facilitate fast
and accurate mapping, we decided to train a hybrid model based on Adversarial
Latent AutoEncoder (ALAE) [31], which combines a GAN and an AE by training a
generator and an encoder end-to-end.

Furthermore, to explicitly control the generated population, we add a
conditional input to the generator (more information in appendix 5.1), where
the input label is a pixel-level population map.

Our main contributions are:

  * •

Adding spatial conditioning module to the ALAE architecture

  * •

Training the model on satellite + population data

  * •

Visualizing population in generated images

  * •

Evaluating the generative model’s performance in terms of quality and the
population change effect

##  2 Methods

###  2.1 Data

Two data sets are utilized over a study site of continental Central America
including Mexico. We focus on Central America here as it has been identified
as a region already experiencing high levels of internal and international
migration due to climate change, and has been the focus of prior modelling
efforts [20]. However, this approach could of course be applied to any
geographic region.

Image data is derived from surface reflectance data from ESA’s Sentinel-2
mission [26]. Sentinel-2 is a constellation of two satellites, which collect
images at a 5 day revisit. The second data set is the Global Human Settlement
population data (GHS-POP) for the years $2000$ and $2015$ from the European
Commission’s Joint Research Centre [34, 11]. Both datasets are publicly
available, and the details of how we sample the data are available in appendix
5.3.

###  2.2 Model architecture

Figure 1: SCALAE model training where white circles represent original ALAE
inputs, yellow circles are our additional inputs, green boxes are original
ALAE modules, blue boxes are our modified modules, and red boxes are the
losses.

We use ALAE [31] for the basis of our model, which is in turn based on the
StyleGAN [22] architecture. We refer the reader to these papers for in depth
description of the architecture, and focus here instead on our modifications.
We adapt the ALAE codebase [30] for our training and evaluation, and our
additional code and trained models can be accessed here
111https://github.com/LendelTheGreat/SCALAE.

Our training model differs from ALAE in 2 ways: the added _Spatial Conditional
Style_ (SCS) module, and the modified encoder input. Hence, the equation 2
from the ALAE paper can be modified to equation 1. All training modifications
are also shown in figure 1.

${\textrm{G = }G\circ SCS\circ F\textrm{, and D = }D\circ E}$ (1)

The SCS module is used to feed conditional information (population map) to the
generator. The style input _w_ and appropriately resized population map _pop_
are combined together by a learned function that maps both to the same number
of channels, then summed together, and finally fed into the adaptive instance
normalization layers of the generator. This process can be seen in figure 5 in
the appendix 5.5.

The discriminator gets the conditional information (population map) as an
input so that it provides a learning signal based on how the population map
and the generated image fit together. This is done via a simple concatenation
of the RGB and population channels. Because the ALAE discriminator is partly
an encoder, the population map is concatenated with the RGB channels of either
real or fake image and fed into the encoder to predict the style _w_ , which
is then fed into the discriminator head.

###  2.3 Reconstruction

After model training, we reconstruct a real satellite image by mapping it to
the latent space of the model. This is trivial thanks to the autoencoder
architecture, visualized in figure 6 in appendix 5.6.

Since the population map serves as an input to the generator, we can feed in a
custom population map and control the population in the reconstructed output.
This process can be used to visualize how real world places would look like
with an alternative population distribution, for example, a climate change
induced population change.

##  3 Results

###  3.1 Generation model quality

To evaluate quality of generations we calculate the Fréchet Inception Distance
(FID) [16], more information in appendix 5.8. The overall model FID is $45.13$
(lower is better) for random generations. Our work would benefit from general
benchmarking in the field, as at the moment, FID scores are only useful as a
comparison across different models evaluated on the same data set. We thus
report the FID score with the hopes of stimulating further comparison in the
future.

In the absence of a direct comparison, we visually check the images, and as
portrayed by figure 8 in appendix 5.10.1 we confirm that generations strongly
resemble realistic satellite imagery with sufficient diversity.

###  3.2 Reconstruction quality

To further evaluate model quality we focus on reconstructing real satellite
imagery. In this case, we give the model a real reference image as an
additional input, which, as expected, improves the FID to $35.97$. Examples of
reconstructions can be seen in figure 2 and more in appendix 9.

Figure 2: _left_ : original input image, _center_ : input population map,
_right_ : generated image

By generating reconstructions of real images, we create matching pairs that
can be used for evaluation. We calculate the difference between the pairs of
each real vs reconstructed image. The difference measure can be done on the
pixel level, and on the semantic level using pretrained Inception features
(same features as used in the FID score calculation). We use a standard l2
distance to compute the difference measures.

For the pixel level, the mean pixel l2 distance between pairs is $277.40$, but
a frequency plot of the distribution of pixel distances for $9436$ image
comparisons has a long tail, so much worse images in terms of pixel
reconstruction are also possible, shown in figure 7(a) in appendix 5.9. The
mean value of the semantic distance is $15.15$ and here a frequency plot of
the distances is much more normally distributed, meaning better and worse
images are equally likely (see figure 7(b) in appendix 5.9). The extreme tails
of both of the above measure are visualized in figure 12 in appendix 5.10.5.

###  3.3 Population effect

To evaluate the effect of population on reconstructed images, and thus show
the efficacy of our model (i.e. how well the population conditioning works),
we produce generations for varying population inputs and visualize the pixel
difference between them. In figure 3, we calculate the pixel difference
averaged over 20 samples of style vectors _w_ , showing the high consistency
of the model reconstruction. Thus highlighting that the population
conditioning is spatially consistent. An example of this process is visualized
in figure 10 in appendix 5.10.3 and in figure 11 in appendix 5.10.4.

Figure 3: _left_ : population input, _right_ : pixel difference averaged over
20 styles

##  4 Discussion and Concluding remarks

We have created a model architecture that makes it possible to spatially
condition style-based generative methods and thus to explicitly disentangle
the latent space from a spatial label. We show that the population in the
generated images can be manually controlled in a fine-grained manner, giving
the user the ability to change population in specific parts of an image.
Moreover, the encoder of our network can be used to map real images to the
latent space, making it possible to not only edit fake, but also real,
imagery. We believe this model could be useful for visualizing climate change
related population forecasts such as those modelled in [20], as it allows
practitioners and researchers to generate imagery flexibly, concretely, and
with a means to characterize uncertainty.

Furthermore, the ability to map real images to the latent space opens up
several image editing possibilities. We can continuously perform latent space
arithmetic to create meaningful changes in the generated images, following
previous GAN examples (e.g. [22, 23, 18]). Moreover, combining latent space
arithmetic with explicit population conditioning delivers more control over
exactly what is generated and where. Importantly, this can be done
continuously, not just to generate a static outcome, but also to interpolate
between or visualize a distribution of possible outcomes.

Likewise, it is difficult to evaluate the climate change effect on population
on real imagery without reference longitudinal data. This will become more
possible as longitudinal satellite data collections with matching population
grids become available for longer time spans. Finally, the imagery we generate
can be fed directly into existing frameworks for land use and land cover
analysis, without further retraining or adaptation.

## Acknowledgments and Disclosure of Funding

First of all, we would like to thank Lucas Kruitwagen, our mentor from the
NeurIPS 2020 “Tackling Climate Change with Machine Learning” workshop
mentorship program, for feedback on structuring the project and positioning
within wider literature. Next we are thankful to Intuition Machines Inc for
providing the necessary compute resources for this project, and Tom Bishop
from Intuition Machines for general feedback on the paper. Last but not least,
we would like to thank Björn Lütjens, Esther Wolf, and Aruna Sankaranarayanan
for fruitful discussion on the topic of generating satellite imagery and its
relation to climate change.

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## Broader Impact

We envision that our research here could be of benefit for both local and
international organizations who are committed to integrating AI methodologies
into their planning and policy workflows. However, given the complexity of the
models and the infrastructure and financing required for training, there is an
obvious gap in who is able to actually use these models leading to concerns of
centralization.

In terms of biases, we identified several sources of bias in our method, that
could lead to undesired outcomes, and should be explicitly taken into account
before any direct application of this method. First of all, there is the model
bias of GANs themselves. GANs are know to suffer from mode dropping, which
could results in uncommon features of the dataset being ignored by the
generative model, even if they are presents in the data. This bias can be
approximately quantified by measuring diversity of generations using recent
methods [15] and visualized, which means it can be evaluated in relation to
particular use cases. Moreover, recent methods have made substantial
improvements to mitigate this bias [36, 10, 41]. Note that our method SCALAE
is a hybrid that includes an autoencoder in the latent space, which partly
reduces the mode dropping problem, however, proper evaluation of this
phenomena is ongoing research and is left for future work.

Secondly, there are several sources of bias in the data itself. On one hand,
it is the data collection bias. For example, because of cloud cover, satellite
data collection usually focuses on the dry season only. On the other hand,
there is some concern for leveraging path dependency biases, coming from the
fact that the generated images can only reflect patterns that were observed
before. It thus cannot capture new developmental trajectories. Nonetheless,
there is interesting new research being done in the direction of novelty
generation [6].

Finally, we do not anticipate, or recommend, that a generative approach be
used in isolation for policy and planning - it is a tool to aid field
professionals and ideally should be linked directly with theoretically and
locally informed behavioral models, in fact, we postulate that the
conditioning approach we develop here makes this imminently possible.

##  5 Appendices

###  5.1 Additional background on conditional GANs

In addition to image generation, GANs and other methods have shown promising
results on controlling the generation process. This can be done when the
latent space is sufficiently disentangled, in which case, adjusting a section
of the latent space results in a meaningful semantic change in the generated
image. A well disentangled latent space can be learned in a completely
unsupervised fashion [5], however, providing an explicit signal to the model,
if available, results in more precise control over the generated image [4].
Therefore, our method focuses on so called conditional image generation.
Conditioning, even though it requires additional labels, helps disentanglement
and allows explicit content generation.

The difference between unconditional and conditional image generation is that
the former generates realistic images from random noise, whereas the latter
generates realistic images from a given input label. This label can come in
many forms, usually image-level [27, 3, 28] or pixel-level [19, 29, 43]
attributes, or even images from a different domain [38, 42]. In our case, the
input label is a pixel-level population map.

###  5.2 Related works

A popular section of generative models relevant to our problem are the domain
transfer models [19, 38, 42]. These methods produce a function that translate
images between 2 or more domains (e.g. horse to zebra, day to night, etc). In
comparison with our method, they do not allow for sampling random images.
Thus, our method has the advantage of being used outside of its current use
case without retraining, an important point when we consider the financial and
environmental costs. However, most domain transfer methods contain direct skip
connections between the reference real image and the generated output, which
is known to reproduce better pixel-wise reconstructions.

Another side of work focuses on discovering meaningful latent space directions
after the model was fully trained [18, 35, 2]. This is complimentary to our
model and the exploration of these is left for future work.

###  5.3 Data: Sampling information

Sampling sites for Sentinel-2 imagery were determined based on largest
population increase in this time period, and a set of $9436$ tiles with
extents of $1024$ by $1024$ pixels was extracted. The selected sites are shown
in appendix 5.4. Surface reflectance data of the dry season (Jan-March) was
cloud-masked and averaged utilizing Google Earth Engine [14]. Finally, GHS-POP
data was reprojected into the corresponding UTM-zones of the Sentinel-2 tiles.
Given the illustration purposes of this study we have not included the non-
visible bands of Sentinel-2, and focus on the RGB channels only, as those are
easily interpretable to the human eye. However, our method is invariant to the
number of channels used and could be trivially retrained with the full multi-
spectral depth of Sentinel-2.

###  5.4 Sites for image acquisition

Figure 4: Sites for image acquisition Image acquisition based on population
growth between 2000 and 2015; brighter color indicates higher population
growth.

###  5.5 SCS module

Figure 5: SCS module To the best of our knowledge, adding spatial conditioning
to a StyleGAN-like architecture has not been explored before. [28]
successfully add a non-spatial class conditioning into a StyleGAN by feeding a
class label _c_ together with noise _z_ into the F network and subsequently
into the G network. This approach however, does not take spatial dimensions
into account. We take inspiration from [29, 43] as they use the same AdaIN
[17] layers as StyleGAN, and introduce the SCS module.

###  5.6 SCALAE reconstruction method

Figure 6: SCALAE reconstruction method where white circles represent original
ALAE inputs, yellow circles are our additional inputs, green boxes are
original ALAE modules, and blue boxes are our modified modules.

###  5.7 Training details

We train the SCALAE model end-to-end on the paired Sentinel-2 and GHS-POP data
sets on 4xV100 GPUs, following the default training parameters from the ALAE
codebase [30]. The Sentinel-2 imagery RGB channels are scaled between -1 and
1. The population map is log transformed and likewise scaled between -1 and 1.
We train with progressive growing for 200 epochs, switching to higher
resolution every 16 epochs. Our base learning rate is 0.002 and batch size
512, both adjusted accordingly with the default progressive growing schedule.
The training losses remain unchanged.

Our code and all training parameters will be publicly released upon
publication.

###  5.8 FID details

The FID score uses features from an Inception network [37] trained on the
Imagenet [8] data set, which consists of natural photos of various objects.
However, it does not include any images from satellite or other earth
observation domain. We note that the domain shift in this case is not
problematic because Imagenet-trained networks mostly focus on textures [12],
which are the main features we are trying to quantify.

###  5.9 Reconstruction: distance histograms

(a) Pixel distance

(b) Semantic distance

Figure 7: Histograms of distances

###  5.10 Additional visualisations

####  5.10.1 Random generations

Figure 8: Random generated samples

####  5.10.2 Reconstructions

Figure 9: Targeted reconstructions Additional examples

####  5.10.3 Population pixel difference

Figure 10: Population pixel difference Panel D contains the pixel difference
between the generated images (panels A and C) and clearly reproduces the input
population (panel B). Still, speckles of small pixel differences appear
throughout the image. This shows that changing the population has some impact
globally, suggesting some level of entanglement between the population map and
the latent style _w_.

####  5.10.4 Population manipulation

Figure 11: Custom population input We fix the latent style input _w_ and
change population input. The population is accurately generated, even with
unrealistic population distribution in the last row.

####  5.10.5 Best and worst reconstructions

Figure 12: Best and worst generations From top to bottom: semantic worst,
pixel worst, pixel best, semantic best