11institutetext: National Observatory of Athens, Institute for Astronomy,
Astrophysics, Space Applications and Remote Sensing, Ioannou Metaxa and
Vasileos Pavlou, 15236 Athens, Greece
email<EMAIL_ADDRESS>22institutetext: Department of Astrophysics, Astronomy &
Mechanics, School of Physics, Aristotle University of Thessaloniki, 54124
Thessaloniki, Greece 33institutetext: Sterrenkundig Observatorium,
Universiteit Gent, Krijgslaan 281 S9, 9000 Gent, Belgium 44institutetext: INAF
- Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125, Florence,
Italy 55institutetext: Instituto de Fisica de Cantabria (CSIC-Universidad de
Cantabria), Avenida de los Castros, 39005 Santander, Spain 66institutetext:
Cosmic Dawn Center (DAWN), Jagtvej 128, DK2200 Copenhagen N, Denmark
77institutetext: DTU-Space, Technical University of Denmark, Elektrovej 327,
DK2800 Kgs. Lyngby, Denmark 88institutetext: Niels Bohr Institute, University
of Copenhagen, Blegdamsvej 17, DK2100 Copenhagen Ø, Denmark

# Star-forming early- and quiescent late-type galaxies in the local Universe

E.-D. Paspaliaris 1122 E. M. Xilouris 11 A. Nersesian 33 S. Bianchi 44 I.
Georgantopoulos 11 V. A. Masoura 5511
G. E. Magdis 667788 M. Plionis 1122

(Received Aug 23, 2022 / Accepted Sep 27, 2022)

###### Abstract

Aims. The general consensus is that late-type galaxies undergo intense star-
formation activity while early-type galaxies are mostly inactive. We question
this general rule and investigate the existence of star-forming early-type and
quenched late-type galaxies in the local universe. By computing the physical
properties of these galaxies and by using information on their structural
properties as well as the density of their local environment we seek for
understanding the differences from their ‘typical’ counterparts.

Methods. We make use of the multi-wavelength photometric data (from the ultra-
violet to the sub-millimetre), for 2,209 morphologically classified galaxies
in the Galaxy And Mass Assembly survey. Furthermore, we separate the galaxies
into subsets of star-forming and quiescent based on their dominant ionising
process, making use of established criteria based on the $W_{\text{H}\alpha}$
width and the [N${}_{\text{II}}$/H$\alpha$] ratio. Taking advantage of the
spectral energy distribution fitting code CIGALE we derive galaxy properties,
such as the stellar mass, the dust mass, and the star formation rate and also
estimate the unattenuated and the dust-absorbed stellar emission, for both the
young ($\leq$ 200 Myr) and old (¿ 200 Myr) stellar populations.

Results. We find that about 47% of E/S0 galaxies in our sample show ongoing
star-formation activity and 8% of late-type galaxies are quiescent. The star-
forming elliptical galaxies, together with the little blue spheroids,
constitute a population that follows very well the star-forming main-sequence
of spiral galaxies. The fraction of the luminosity originating from young
stars in the star-forming early-type galaxies is quite substantial ($\sim
25\%$) and similar to that of the star-forming late-type galaxies. The stellar
luminosity absorbed by the dust (and used to heat the dust grains) is highest
in star-forming E/S0 galaxies (an average of 35%) followed by star-forming Sa-
Scd galaxies (27%) with this fraction becoming significantly smaller for their
quiescent analogues (6% and 16%, for E/S0 and Sa-Scd, respectively). Star-
forming and quiescent E/S0 galaxies donate quite different fractions of their
young stellar luminosities to heat up the dust grains (74% and 36%,
respectively) while this fractions are very similar for star-forming and
quiescent Sa-Scd galaxies (59% and 60%, respectively). Investigating possible
differences between star-forming and quiescent galaxies we find that the
intrinsic (unattenuated) shape of the SED of the star-forming galaxies is, on
average, very similar for all morphological types. Concerning their structural
parameters, quiescent galaxies tend to show larger values of the r-band Sérsic
index and larger effective radii (compared to star-forming galaxies). Finally,
we find that star-forming galaxies preferably reside in lower-density
environments compared to the quiescent ones, which exhibit a higher percentage
of sources being members of groups.

###### Key Words.:

galaxies: evolution - galaxies: ISM - galaxies: interactions - dust,
extinction - galaxies: star-formation - galaxies: stellar content

##  1 Introduction

There is a general perception that the star-forming activity in galaxies is
strongly correlated to their morphological type. Gas-poor elliptical (E),
lenticular (S0) and dwarf galaxies form stars in rates significantly lower
than $\sim$1 M⊙ yr-1, while gas-rich spirals and irregulars (Irr) have star-
formation rates (SFRs) that can reach up to $\sim$20 M⊙ yr-1 (Kennicutt 1983;
Gao & Solomon 2004; Calvi et al. 2018; Nersesian et al. 2019). Much higher
SFRs, exceeding several hundreds of M⊙ yr-1, can be found in local starburst
galaxies and ultra/luminous infrared galaxies (U/LIRGs) (da Cunha et al. 2010;
Combes et al. 2013; Kennicutt & De Los Reyes 2021; Paspaliaris et al. 2021).

E galaxies are considered to be amongst the most massive, old and red systems
(Bernardi et al. 2003; Kelvin et al. 2014b; González Delgado et al. 2015;
Nersesian et al. 2019). It is believed that they formed either by the collapse
of protogalaxies, having a prominent early burst of star formation and then
evolve passively (monolithic view; Partridge & Peebles 1967; Larson 1975) or
by merging galaxies with an unclear evolutionary path following (hierarchical
view; Toomre & Toomre 1972), leading to the quiescent (Q) systems we observe
today. In most cases, their stellar content is highly concentrated in the
centre with its density decreasing towards the outskirts of the galaxy. The
interstellar medium (ISM; if any) is mostly concentrated in the galaxy centre.
Conversely, late-type spiral galaxies are mostly bluer, actively star forming
systems, with a central bulge consisting mainly of old stars, with ongoing
star-formation activity occurring in the dusty spiral arms. Furthermore, the
existence of a bar can lead to the funnelling of gas towards the galactic
centre playing a significant role in the evolution of the properties of the
host galaxy (e.g. Sorensen et al. 1976; Athanassoula et al. 2013, and
references therein). A hybrid-like case also exists consisting of a blue, low-
mass, compact spheroidal population often referred to as ”Little Blue
Spheroids” (LBSs). This population of galaxies are structurally similar to
their higher-mass elliptical galaxy analogues, but with the scaling relations
of their physical properties, such as SFR, stellar mass ($M_{\text{star}}$),
and bolometric luminosity resembling star-forming (SF) spiral galaxies
(Mahajan et al. 2015; Mahajan et al. 2018). Moreover, they lie outside the
standard Hubble parametrisation range ($T$; Makarov et al., 2014) which is
defined to include galaxies with Hubble stage from $T$=-5 (pure Ellipticals)
to $T$=10 (Irregulars).

Although the trend in the average SFRs with the morphology is strong, SFRs of
galaxies of the same type may exhibit a dispersion of 1 dex (Kennicutt, 1998).
Additionally, a bimodal distribution has been found for the SFR of galaxies
(e.g. Wetzel et al., 2012; Trussler et al., 2020; Kalinova et al., 2021;
Sampaio et al., 2022). Combining two fundamental galaxy properties, the SFR
and $M_{\text{star}}$, we can quarry information about their current rate of
conversion of the gas into stars. In the case of SF galaxies, the two
parameters are found to be tightly correlated, occupying a distinct region in
the SFR-$M_{\text{star}}$ diagram, often referred to as the ‘star-forming main
sequence’ (SFMS) of galaxies (e.g. Noeske et al. 2007; Elbaz et al. 2007;
Wuyts et al. 2011; Whitaker et al. 2012, and references therein) or ‘the blue
cloud’. On the contrary, Q galaxies exhibit a weaker relation between SFR and
$M_{\text{star}}$, occupying the area below the SFMS, forming the ‘red cloud’.
The ‘blue cloud’ consists mainly of late-type galaxies (Sa-Irr; hereafter
LTGs), while the ‘red cloud’ is mostly occupied by early-type galaxies (E and
S0; hereafter ETGs111Due to their spheroidal morphology LBS galaxies are also
considered as ETGs in the current study.). This bimodality has been thoroughly
investigated in previous studies, such as Strateva et al. (2001), Blanton et
al. (2003), Baldry et al. (2004) and Taylor et al. (2015). However, several
studies have reported the existence of ETGs with on-going star formation and
also of LTGs with ceased star-forming activity (e.g. Rowlands et al. 2012;
Bitsakis et al. 2019; Cano-Díaz et al. 2019).

It has been shown that the rate that galaxies form stars can be strongly
influenced by the environment they reside in (e.g Barsanti et al. 2018; Davies
et al. 2019; Sampaio et al. 2022). When spiral galaxies traverse the dense
inter-cluster medium, their interstellar gas is removed through ram-pressure
stripping and they lose their ability to form new stars (e.g. Gunn & Gott
1972; Dressler 1980). In this manner, groups and clusters mainly consist of
elliptical and gas-poor galaxies which have different properties from their
counterparts settled in less dense environments (e.g. Baldry et al. 2006;
Skibba & Sheth 2009). Other proposed mechanisms through which galaxies may
lose their ability to form new stars or even perturb their morphology are
strangulation (Larson et al. 1980; Kauffmann et al. 1993; Diaferio et al.
2001), harassment (Moore et al., 1996) and minor-mergers of tidal interactions
(Park et al., 2008). McIntosh et al. (2014) and Haines et al. (2015) suggested
that SF Es have suffered a recent morphological transition without having
enough time to exhaust their gas reservoir, being in a post-starburst phase.
Alternatively, Es in low density environments may be rejuvenated by eventually
accreting cold gas as suggested by Thomas et al. (2010).

Spectral energy distribution (SED) modelling techniques allow us to decompose
the SED of galaxies and to derive useful information about their different
emitting components. These techniques usually combine the stellar emission
(originating from both old and young stars) in ultra-violet (UV), optical and
near-infrared (NIR) wavelengths with the dust infrared (IR) emission so that
the energy budget is fully conserved. In addition, a star-formation activity
through cosmic time (i.e. the star-formation history; SFH) is assumed allowing
for the determination of the current SFR but also the buildup of the current
stellar mass of the galaxy. Having set a grid of input parameters the SED-
fitting codes can then retrieve the best set of templates that better
reproduce the observations and, thus, provide useful information about
physical properties of the galaxies, such as, the current SFR and the stellar
and the dust masses as well as luminosities of the different components of the
stellar populations and the dust.

Figure 1: Typical GAMA galaxies with different morphologies (LBS, E, S0, Sa,
Sab-Scd, Sd-Irr; from left to right) and different star-forming activity
according to their dominant ionisation process (Q, SF; top and bottom
respectively). A composite g-, i-, H-band image is shown for each galaxy in a
frame of 30 kpc $\times$ 30 kpc. The GAMA catalogue identification number and
the redshift of each galaxy is indicated at the top-right of each panel.

In this paper we perform SED modelling of 2,209 local (z $<0.06$) galaxies in
the Galaxy And Mass Assembly (GAMA) survey222http://www.gama-survey.org using
the CIGALE333https://cigale.lam.fr SED fitting code. For all the galaxies
there is available morphological classification as well as classification
according to their star-forming activity as traced by optical spectral lines
(available in the GAMA survey). We examine all morphological types separately,
but, mainly, focusing on SF ETGs and Q LTGs. These subsets constitute obvious
exceptions of the general rule having ETGs being, mostly, quenched and LTGs,
mostly, actively forming new stars. The scope of this study is to explore the
physical and structural parameters that make them differentiate from their
‘typical’ counterparts. In Sect. 2 we present the sample properties and
classifications, as well as a description of the SED-fitting method used. In
Sect. 3 we present the typical SEDs as a function of morphology and star-
forming activity as well as the physical properties of the SF ETGs and Q LTGs.
The different stellar populations in different types of galaxies as well as
their role in the heating of the dust is discussed in Sect. 4. A discussion
follows in Sect. 5 describing the basic differences that we find between SF
and Q galaxies, while our findings are summarised in Sect. 6. A comparison
with the results provided in the GAMA survey obtained by the SED-fitting code
MAGPHYS is presented in Appendix A. Throughout the paper, we adopt $H_{0}$ =
70 km s-1 Mpc-1, $\Omega$${}_{\text{m}}$ = 0.3, $\Omega_{\Lambda}$ = 0.7, and
a Salpeter (1955) initial mass function (IMF).

##  2 Data and analysis

###  2.1 Sample selection

The multi-wavelength data used in this work are provided by the Panchromatic
Data Release (from FUV to FIR; Driver et al., 2016) of the GAMA survey (Driver
et al., 2009, 2011). For each source, there are available observations for up
to 21 different broad-band filters from five observatories. All the filters
provided by the GAMA survey, and used in the current analysis, are listed in
Table 1 (see also Table 1 in Wright et al. 2016). The fluxes of the galaxies
in the different bands were calculated using the Lambda Adaptive Multi-Band
Deblending Algorithm in R (LAMBDAR444https://github.com/AngusWright/LAMBDAR;
Wright et al. 2016). LAMBDAR performs deblended aperture-matched photometry
without requiring PSF or pixel matched images. Four major processes are
employed, aperture convolution, aperture deblending, sky subtraction and
aperture correction accounting for the different PSFs. Hence, the algorithm
avoids the necessity of the seeing and imaging being matched and in contrast
to other algorithms the images do not need to be smoothed to the resolution of
the lowest quality band. Fluxes, in the FUV-K wavelength range, were also
corrected for Galactic extinction.

Table 1: List of the 21 broad-band filters provided by the GAMA database and used in the current SED-fitting analysis. Observatory | Band (central wavelength)
---|---
GALEX | FUV (1550Å), NUV (2275Å)
SDSS | u (3540Å), g (4770Å), r (6230Å), i (7630Å), z (9134Å)
VISTA |  Z (8770Å), Y (1.0$\mu$m), J (1.3$\mu$m), H (1.7$\mu$m), K (2.2$\mu$m)
WISE |  W1 (3.4$\mu$m), W2 (4.6$\mu$m), W3 (12$\mu$m), W4 (22$\mu$m)
HSO |  PACS (100$\mu$m, 160$\mu$m) , SPIRE (250$\mu$m, 350$\mu$m, 500$\mu$m)

From the full sample of the GAMA survey we have selected a subsample of
galaxies in the GAMA II equatorial survey regions. All galaxies in this
subsample have been visually classified according to their Hubble type using
UKIDSS H and SDSS i and g band images as described in detail in Kelvin et al.
(2014a) and Moffett et al. (2016). The classification was held in two phases.
In the first phase logarithmically scaled (in brightness) 20” $\times$ 20”
images were used, while in the second phase 40” $\times$ 40” images scaled
with the arctan function (in brightness) were visually inspected. The arctan
function was chosen as a more effective method against manually defined upper
or lower brightness levels, which, potentially, lead to misclassification of
galaxies. A decision tree with three levels of classification options was
followed by three independent pairs of observers. In the first level the
observers had to divide the sample by type, into Spheroid Dominated, Disk
Dominated, LBS or Stars (in the case that a foreground star is in front of the
primary object or a supernova exists within a galaxy, the primary object is
classified as a star and is subsequently rejected). The options at the other
two classification levels were Single/Multi Component and Barred/Unbarred. The
final classification was determined by the majority opinion. This
classification has been used in various studies, so far, such as Kelvin et al.
(2014b, 2018), Agius et al. (2013), Moffett et al. (2019), Mahajan et al.
(2020) and Bellstedt et al. (2020).

This volume- and luminosity-limited sample is referred to as GAMAnear and
consists of 6,433 galaxies in the redshift range 0.002 ¡ $z$ ¡ 0.06 and an
extinction corrected r-band SDSS Petrosian magnitude of $r$ ¡ 19.8 mag. For
the z¡0.02 galaxies, the flow-corrected redshifts (using the Tonry et al. 2000
flow model) were used (see Baldry et al. 2012). After applying two additional
selection criteria that all galaxies are observed by the Herschel Space
Observatory (HSO; Pilbratt et al., 2010), and, also, have available optical
spectroscopic information (with the available optical lines detected at
S/N¿3), we ended up with 2,597 galaxies. The selection of HSO observed sources
allows us to accurately compute the dust mass, while the availability of
optical spectroscopic indices provides information on the star formation and
nuclear activity for each galaxy. Further structural measurements, such as the
effective radius ($R_{\text{e}}$) and Sérsic index for all the galaxies in our
sample were provided by Kelvin et al. (2012), while information of the density
of the local environment for 1,221 galaxies is given in Robotham et al.
(2011).

Examples of different Hubble-type galaxies with different star-forming
activity (according to optical emission lines; see Sect. 2.2) are shown in
Fig. 1. It is worth noticing that even a visual inspection reveals a strong
difference in the appearance of these galaxies with Q galaxies (top panels)
having redder colours compared to their bluer SF counterparts (bottom panels).
After applying the aforementioned criteria, but also excluding galaxies with
possible contamination from an active galactic nucleus (AGN) activity (see
Sect. 2.2), the final sample consists of 269 E, 251 S0-Sa, 659 Sab-Scd, 704
Sd-Irr galaxies and 326 LBS. For the purposes of the current study we
performed a more detailed classification of the S0-Sa sources into two
different groups (S0 and Sa). This classification was conducted by three of
the authors of this study by inspecting images using the GAMA Single Object
Viewer and the SDSS SkyServer. Sources exhibiting spiral arms were classified
as Sa while the rest were considered S0 galaxies. The observers independently
classified the 251 sources and the final classification was assigned by
majority agreement, resulting in 224 S0 and 27 Sa galaxies (see Table 2).

As a reference sample for the very local galaxies, we use the dataset of the
DustPedia project555http://dustpedia.astro.noa.gr. DustPedia includes FUV to
submm data of 875 nearby galaxies ($D_{\text{L}}$ ¡ 40 Mpc), all observed by
HSO. The Hubble stage $T$ is available as a morphology indicator for the full
sample. For more information about the DustPedia project, we refer the reader
to Davies et al. (2019) and Clark et al. (2018). For 814 galaxies Nersesian et
al. (2019) performed SED-fitting analysis, in exactly the same way described
in this work (see Sect. 2.3), delivering their physical properties.

###  2.2 Classification by ionisation processes and star-forming activity

Figure 2: WHAN diagram for the classification of the galaxies due to their
dominant ionisation processes. Demarcation lines are from Cid Fernandes et al.
(2011), for star-forming and quiescent galaxies (blue and red circles,
respectively) and strong and weak AGN (light and dark green circles,
respectively).

Since GAMA is not only a photometric but also a spectroscopic survey (Driver
et al., 2009; Hopkins et al., 2013a; Gordon et al., 2017) we are able to
classify the galaxies in our sample, not only by their morphological
characteristics, but also by their dominant ionisation processes. Emission
line diagnostic diagrams (e.g. Baldwin et al. 1981; Kauffmann et al. 2003;
Kewley et al. 2006) are widely used to probe the ionization sources in
galaxies (e.g., star-formation, AGN, emission by evolved stellar populations).
For such a categorisation in this study we make use of the available
measurements of the $\text{H}\alpha$ line width ($W_{\text{H}\alpha}$) and the
flux ratio of [N${}_{\text{II}}$]/H$\alpha$. These quantities are physically
independent with $W_{\text{H}\alpha}$ being a measure of the ionising photons
absorbed by the gas, relative to the stellar mass, while
[N${}_{\text{II}}$]/H$\alpha$ a measure of the dependency of the ionisation
state and the gas temperature on the nitrogen gas abundance. The topology of
these quantities define areas of low- and high-ionisation as well as of star-
forming and quiescence stages and it is widely known as the WHAN diagram (Cid
Fernandes et al., 2010, 2011). In contrast to other similar classification
schemes, WHAN diagram requires only two emission lines (H$\alpha$ and
[N${}_{\text{II}}$]$\lambda$6584) which, in fact, are two of the most
prominent and easy to measure lines in the optical spectra of galaxies. This
permits us to maximise the number of sources in the original sample with
sufficient spectroscopic data and, thus, to infer the relevant information.
The emission lines in the optical spectra of the GAMA sources were fitted
using GANDALF (Sarzi et al., 2006) and a diffuse (stellar continuum)
obscuration correction, caused by diffuse dust in the galaxy, was applied
using a Calzetti (2001) obscuration curve (see Hopkins et al. 2013a for a
detailed description of the GAMA spectroscopic analysis).

We present our physically motivated classification scheme in a WHAN diagram in
Fig. 2. Within the WHAN diagram we define four classes of galaxies [Q, SF,
strong AGN (sAGN), and weak AGN (wAGN)]. log$W_{\text{H}\alpha}$ = 0.48 splits
our sample in line-less, Q galaxies (with log$W_{\text{H}\alpha}$ ¡ 0.48) from
emission line galaxies (ELGs; with log$W_{\text{H}\alpha}\geq 0.48$). In the
area of ELGs the vertical line at log[N${}_{\text{II}}$]/H$\alpha$ = -0.4
distinguishes the sources where star formation is responsible for the ionising
photon output from those that their spectrum can only be explained by a harder
ionising field originating from an AGN. Sources with log$W_{\text{H}\alpha}$
$\geq$ 0.48 and log[N${}_{\text{II}}$]/H$\alpha$ ¡ -0.4 are classified as pure
SF galaxies. The AGN locus consists of two areas; sAGN occupy the area where
log$W_{\text{H}\alpha}$ $\geq$ 0.78 and log[N${}_{\text{II}}$]/H$\alpha$ ¿
-0.4, while wAGN are the ones with 0.48 $\leq$ log$W_{\text{H}\alpha}$ $\leq$
0.78 and log[N${}_{\text{II}}$]/H$\alpha$ $\geq$ -0.4. For the purposes of
this work, we do not consider AGNs, so, sources in the sAGN and wAGN regions
of this diagram are excluded from the sample of 2,597 ending up with 2,209
galaxies. We caution the reader that out of these galaxies, two sub-classes, Q
LBS and Q Sd-Irr are underrepresented (3 and 12 galaxies respectively) so they
are not fully considered in the subsequent statistical analysis.

Table 2: Numbers of galaxies in different morphology bins and star-formation activity. AGN sources have been excluded (see Sect.2.2). morph. bin | $N_{\text{obj}}$ | perc.% |  Q${}_{\text{WHAN}}$ |  SF${}_{\text{WHAN}}$ |  Q${}_{\text{sSFR}}$ |  SF${}_{\text{sSFR}}$
---|---|---|---|---|---|---
LBS | 326 | 14.7 | 3 | 323 | 1 | 325
E | 269 | 12.2 | 135 | 134 | 141 | 128
S0 | 224 | 10.1 | 127 | 97 | 123 | 101
Sa | 27 | 1.2 | 15 | 12 | 9 | 18
Sab-Scd | 659 | 29.9 | 37 | 622 | 23 | 636
Sd-Irr | 704 | 31.9 | 12 | 692 | 14 | 690

Another way to separate SF and Q galaxies in the local universe, often adopted
in the literature (e.g. Brinchmann et al. 2004; Fontanot et al. 2009; Donnari
et al. 2019; Florez et al. 2020), includes setting a threshold of the specific
star-formation rate (sSFR) of $10^{-11}$ yr-1. Galaxies with sSFR above this
value are defined as SF, while the ones below are Q. Although this method is
more empirical and it may depend on the manner that stellar mass and SFR are
calculated, it agrees well with the first method. The number of Q and SF
galaxies predicted by the two methods are presented in Table 2 where a direct
comparison can be made.

###  2.3 SED modelling

In this study we use CIGALE SED fitting code (see Boquien et al. 2019, and
references therein) to model the SEDs of the galaxies in our sample. Having
defined a grid of values for the parameters of the various modules for the
stellar, gas, dust emission and taking into account the dust attenuation, we
use multi-wavelength observations, of each galaxy, to compare with the library
of model SEDs created by CIGALE. CIGALE includes all the different components
in such a way so that the amount of energy absorbed and re-emitted by the dust
grains is fully conserved (Noll et al., 2009; Roehlly et al., 2014). In the
final phase of this process, the real observations and the SED libraries
created by CIGALE are compared using a Bayesian approach and global properties
such as the $M_{\text{star}}$, the dust mass ($M_{\text{dust}}$), the SFR, the
minimum intensity ($U_{\text{min}}$) of the Inter-Stellar Radiation Field
(ISRF), as well as the different emitting components [old/young stellar
population, diffuse/photodissociation regions (PDR) dust emission] are
derived.

Since we aim to compare the properties of the galaxies in this sample with the
very local galaxies, and for consistency purposes, we adopt the parameter
space used in the reference DustPedia sample, introduced by Nersesian et al.
(2019). In this study, a flexible SFH is used, allowing for a late
instantaneous burst or quenching (i.e. module ‘sfhdelayedbq’; Ciesla et al.
2015) while the Bruzual & Charlot (2003) stellar population model, of solar
metallicity, along with the Salpeter (1955) IMF build the stellar components.
Two stellar populations are considered, an old ($>$ 200 Myr) and a young
($\leq$ 200 Myr). The emission from the stellar components as well as from the
ionised gas surrounding massive stars (i.e. nebular emission) are attenuated
using a power-law-modified starburst attenuation curve (i.e. module
‘dustatt_calzleit’; Calzetti et al. 2000), while the THEMIS dust model (Jones
et al., 2017) accounts for the dust emission parameters. The number of free
parameters used in the current analysis is ten while a total of 320,166,000
models were produced. The parameter grid used in the current work can be found
in Table 1 in Nersesian et al. (2019).

For validation purposes, the CIGALE-derived physical properties are compared
with the corresponding estimations using the MAGPHYS fitting code in Appendix
A.

Figure 3: Median template SEDs for different morphological classes shifted
arbitrarily for clarity. Solid curves represent the median SEDs of SF
galaxies, while dashed SEDs stand for their Q counterparts. The two vertical
lines, solid and dashed, connect, approximately, the dust emission peaks in
the SEDs of the SF and Q galaxies, respectively.

##  3 SEDs and physical properties

###  3.1 Template SEDs

Galaxies of different morphologies have different stellar and dust content
usually distributed, within the galaxies, in a very different way. These
differences are expected to be detectable in their energy output, making their
SEDs a unique signature of their morphologies (see Ciesla et al. 2014, Bianchi
et al. 2018, Nersesian et al. 2019, and references therein). Taking advantage
of the numerous galaxies in the GAMA sample and the relatively wide redshift
range of local galaxies sample (up to redshift 0.06) we present, in Fig. 3,
the median SEDs for the four morphological bins (Sd-Irr, Sa-Scd, E/S0, LBS;
top to bottom), separated in subsets according to their major ionising process
(SF and Q).

Figure 4: SFR vs $M_{\text{star}}$ diagram for the subsamples separated by
morphological classification. Early-type galaxies are plotted in the top
panels, while late-type galaxies are plotted in the bottom panels. The
colouring is same as in Fig. 2. In the top-right panel Es are represented by
circles, while stars stand for the S0 sources. The sSFR=10-11 yr-1 line is
also indicated, separating the galaxies in SF and Q in quite good agreement
with the classification by the WHAN diagram.

After normalising the best fitted, by CIGALE, SED of each galaxy to its
bolometric luminosity, we calculated a median SED per morphological bin and
per star-forming activity by computing the median specific flux density per
wavelength bin. A visual inspection of the shape of the SEDs in Fig. 3 already
provides qualitative information about the stellar populations and dust
content as well as the star-forming activity for each galaxy type. As stated
earlier, since Sd-Irr and LBS lack Q-type galaxies, we do not present the
respective SEDs.

The first thing that is obvious is that, the SF galaxies show enhanced dust
emission compared to their Q counterparts of the same bolometric luminosity.
The measured flux difference at 100 $\mu$m between SF and Q is high in E/S0s
(1.1 dex) with lower difference observed in Sa-Scd types (0.21 dex). This
indicates that the dust emission may be a clear signature of star-forming
activity in some classes of galaxies (E/S0s) but not an obvious one for others
(Sa-Scds) where both, SF and Q, types show similar dust SEDs. The stellar
emission of the SF galaxies, on the other hand, is always less in NIR and most
of the optical ($>0.4\mu$m) wavelengths compared to Q galaxies of the same
bolometric luminosity, but it gets higher, again, below $\sim 0.4\mu$m. The
peak of the stellar emission also shows clear differences, between SF and Q
galaxies of the same bolometric luminosities, with SF galaxies being fainter
at 1 $\mu$m by 0.21 and 0.30 dex for Sa-Scd, and E/S0s respectively. This
effect is to be expected given the higher dust content observed in SF
galaxies.

Another thing that is also obvious is that SF galaxies contain warmer dust
compared to the Q galaxies of the same morphological type. This is shown with
the peak of the dust emission in the SF galaxies always being to the left of
the corresponding peak of the Q galaxies (see the two vertical lines).

###  3.2 Galaxy morphology in the SFR-M${}_{\text{star}}$ diagram

Despite the slightly different slopes and the scatter around the SFMS found in
different studies, mainly depending on the methods used for estimating the SFR
as well as the differences in the samples used (Katsianis et al., 2020, and
references therein), quiescent systems always lie below the SFMS, forming a
separate distribution.

In Fig. 4 we present the SFR-$M_{\text{star}}$ diagram for the galaxies in our
sample of different morphological types and star-forming activity.
Independently of the two methods used to classify the galaxies in SF and Q
(see Sect. 2.2), it is obvious that different morphological types have
different fractions of SF and Q galaxies with the separation between the two
populations being more prominent in the earlier-type galaxies (E/S0; top-right
panel). As mentioned earlier (Table 2) Q LBS and Q Sd-Irr galaxies, are only a
very small fraction of sources, and thus are not represented in this analysis.
On the other hand, more than 92% of Sa-Scd are SF. The fact that the fraction
of Q sources in later-type galaxies is low has also been reported in previous
studies (e.g. Goto et al. 2003; Moran et al. 2006; Wolf et al. 2009; Masters
et al. 2010; Fraser-McKelvie et al. 2016, and references therein). Numerical
simulations have shown that, in some cases, spiral galaxies, even many Gyr
after a dramatic decrease of their star formation, are able to maintain their
spiral arm structure (Bekki et al., 2002). In a volume limited sample of 5,433
spiral galaxies from the Galaxy Zoo (GZ1) clean catalogue (Lintott et al.,
2008), selected from the SDSS Data Release 6, with redshift 0.03 ¡ z ¡ 0.085,
Masters et al. (2010) characterised 4-8% of them as “red” spirals. These
galaxies were found to be dominated by old stellar populations, while their
SFR is lower compared to the main population of the same morphology bin. In a
more recent study by Shimakawa et al. (2022), investigating a sample of 1,100
spiral galaxies with 0.01 ¡ z ¡ 0.3, from the Hyper Suprime-Cam Subaru
Strategic Program (HSC SSP; Aihara et al., 2018), found that 5% are passive,
having identical characteristics (in stellar populations) to the typical Q
galaxies, despite the different morphologies. These results are in quite good
agreement with the corresponding Q late-type population (8%) of the LTGs we
find in the GAMA sample (see bottom-left panel of Fig. 4). The SFR of these
galaxies is found to be, in most cases, systematically lower than their SF
counterparts of the same stellar mass bin. After visual inspection, as can be
seen in Fig. 1, these galaxies are spirals with a redder view and smoother
spiral arms, in agreement with the findings by Shimakawa et al. (2022). Goto
et al. (2003) refers to the red spirals as a population in transition between
red E/S0 galaxies in low-redshift clusters and blue spirals frequent in
higher-redshift clusters.

The Q galaxy population is more numerous in earlier-type galaxies. However,
although SF galaxies are, traditionally, considered to be mainly spirals and
irregulars, we also observe a non-negligible population of early-spirals and
ellipticals being SF too; a typical E with ongoing star formation is presented
in Fig. 1. About 43% (45% according to their sSFR) of the S0 galaxies are SF,
while this fraction is 50% (48% according to sSFR) for Es. If we take into
account the LBS galaxies as well, for which the vast majority are SF, we find
a large population (68%) of galaxies with elliptical or spheroidal morphology
in the area of active star formation. Previous studies, such as Fukugita et
al. (2004), Schawinski et al. (2009), Rowlands et al. (2012) have found
EW(H$\alpha$) of ETGs that correspond to star-forming galaxies. Bitsakis et
al. (2019) and Cano-Díaz et al. (2019) find 18.5% and 28% of the ETGs being
actively star-forming, respectively. Although these fractions are lower than
the one found in our work, Bitsakis et al. (2019) although using a slightly
more stringent criterion to define their quiescent sources (log sSFR ¡ -11.5
yr-1), their target selection criteria lead to a lack of lower mass galaxies
($10^{8}-10^{10}$ M⊙). Similarly, Cano-Díaz et al. (2019) uses a different
criterion, a mix of the BPT (Baldwin et al., 1981) and the WHAN diagram. In
morphologically selected samples of E galaxies, Yi et al. (2005), Kaviraj et
al. (2007) and Schawinski et al. (2007) conclude that, at least, 15%-30% of
the sources show evidence of recent star formation activity.

The high fraction of SF ETGs found in our work, might be affected by the
exclusion, by the WHAN classification, of Q sources with weak emission lines,
below the S/N=3 threshold. What is noteworthy though, is the fact that the SF
galaxies of any morphological bin show very similar SFMS. The corresponding
scaling relations are discussed in the following paragraph.

###  3.3 The place of SF early- and Q late-type galaxies in the local Universe

Previous studies (e.g. Davies et al. 2019; Paspaliaris et al. 2021; and
references therein) have shown that the SFMS is, mainly, occupied by spiral
galaxies, with the area below consisting, mainly, of E galaxies. Galaxies of
intermediate Hubble-stages (i.e. S0, Sa) show a wider dispersion occupying
both regimes as well as the area between them (often referred to as the green
valley). The numerous SF ETGs found in the GAMA sample let us introduce a
different perception of this conception.

Figure 5: SFR vs $M_{\text{star}}$ (top panel) and $f_{\text{abs}}$ vs
$L_{\text{bol}}$ (bottom panel) for the late-type (Sa-Scd and Sd-Irr) galaxies
in the GAMA and DustPedia samples. SF Sa-Scd and Sd-Irr galaxies are
represented with blue and yellow symbols, respectively, while Q Sa-Scd
galaxies (given that Q Sd-Irr are under-represented and are not shown here;
see Sect. 2.2) are shown with red symbols. Blue and red stars stand for the
GAMA SF and Q Sa galaxies, while Sab-Scd sources are represented by circles.
Open symbols show the DustPedia Sa-Irr galaxies. All values are plotted along
with their corresponding uncertainties. Solid lines are the linear fits for
the GAMA SF and Q galaxies (blue and red, respectively), while the dashed
lines are the linear fits for the DustPedia SF and Q galaxies (blue and red,
respectively).

In Fig. 5 we plot the SFR as a function of the stellar mass (top panel), as
well as the dust-to-bolometric luminosity ratio ($f_{\text{abs}}$ hereafter;
discussed in detail in Bianchi et al. 2018) as a function of the bolometric
luminosity (bottom panel) for LTGs. $f_{\text{abs}}$ is a quantity that
provides an estimate of the amount of radiation that is reprocessed by dust.
For comparison, representing the very local universe, the DustPedia galaxies
are overplotted. We note that the linear fits between the corresponding
quantities of each subset presented in this paper are estimated using the
python UltraNest666https://johannesbuchner.github.io/UltraNest/ package
(Buchner, 2021). UltraNest derives the posterior probability distributions and
the Bayesian evidence with the nested sampling Monte Carlo algorithm MLFriends
(Buchner, 2016, 2019). This method is a robust way to estimate the scaling
relations of two properties taking into account the uncertainty of the data in
both axes and being able to obtain the intrinsic scatter of the data and its
uncertainties.

As expected, the vast majority of the Sa-Irr galaxies are distributed along
the SFMS (top panel). The SFMS, as determined by the subset of GAMA SF Sa-
Irrs, is described by the linear regression:

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=0.79^{+0.01}_{-0.01}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-7.83^{+0.13}_{-0.13}$ (1)

(blue solid line) and shows a very strong correlation (Pearson correlation
coefficient $\rho$ = 0.8) and with an intrinsic scatter of
0.29${}^{+0.01}_{-0.01}$ dex. This is very similar to what is found for the
local universe with the DustPedia relation being:

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=0.84^{+0.02}_{-0.03}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-8.41^{+0.25}_{-0.21}$

(dashed blue line; $\rho$ = 0.86) with the corresponding intrinsic scatter in
this subsample being 0.38${}^{+0.01}_{-0.01}$ dex. This also comes in
agreement with other studies in the local universe. For instance, Renzini &
Peng (2015) define the SFMS as a straight-line fit with a slope of
0.76$\pm$0.01 and an offset of -7.64$\pm$0.02 for $\sim$240,000 SDSS DR7
galaxies (Abazajian et al., 2009), lying at 0.02 ¡ z ¡ 0.085. Similarly, Cano-
Díaz et al. (2019) found a slope of 0.79$\pm$0.01 for the SF Sbc-Irr subset of
their sample and 0.74$\pm$0.01 for their full sample of SF z$\sim$0 MaNGa
galaxies. In a more recent study, Fraser-McKelvie et al. (2021), for their 9.0
¡ log($M_{\text{star}}$[M⊙]) ¡ 10.0, z $\sim$ 0 galaxies of the SAMI DR3 and
MaNGA DR15 GALEX-Sloan-WISE legacy catalogue 2, found a slope of 0.67.

Figure 6: Comparison of the distributions of the CIGALE-derived dust-to-
stellar-mass ratio (top panel) and sSFR (bottom panel) for the different
subclasses of late-type galaxies. The normalised distributions for the SF Sa-
Scd, the Q Sa-Scd galaxies, and the SF Sd-Irr are shown in blue, red, and
yellow, respectively. The median value of each parameter, for each population
is given in the plot with the corresponding colour. The vertical dash-dotted
line indicates the sSFR = 10-11 yr-1 threshold.

Concerning the Q LTGs of the GAMA sample we find a weaker correlation ($\rho$
= 0.57) of:

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=0.95^{+0.22}_{-0.20}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-10.59^{+2.30}_{-2.25}$ (2)

(red solid line), with a scatter of 0.82${}^{+0.10}_{-0.08}$ dex. Considering
the LTGs in the DustPedia sample with sSFR lower than 10-11 yr-1 as being Q
systems (red open circles) we calculate a relation of:

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=1.03^{+0.07}_{-0.06}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-11.88^{+0.73}_{-0.61}$

(red dashed line) with a Pearson correlation coefficient of 0.88 and a scatter
of 0.29${}^{+0.01}_{-0.01}$ dex. Given that the scatter is quite large, the
correlations are quite similar, though the GAMA sample shows a shift to higher
SFR values. This is probably due to the fact that, using the WHAN diagram as
the criterion to distinguish between SF and Q in the GAMA sample, we find some
Q galaxies being above sSFR=10-11 yr-1, resulting in dragging the relation
into higher SFR values.

In the $f_{\text{abs}}$-$L_{\text{bol}}$ space (bottom panel) the distribution
of the SF LTGs, in the GAMA sample, is described by the relation

$\text{log}(f_{\text{abs}}[\%])=0.28^{+0.01}_{-0.01}\leavevmode\nobreak\
\text{log}(L_{\text{bol}}[L_{\odot}])-1.35^{+0.13}_{-0.13}$ (3)

(blue solid line) with the intrinsic scatter being 0.14${}^{+0.01}_{-0.01}$
dex and a correlation coefficient $\rho$ = 0.60. The corresponding relation
for the DustPedia sample is:

$\text{log}(f_{\text{abs}}[\%])=0.29^{+0.01}_{-0.01}\leavevmode\nobreak\
\text{log}(L_{\text{bol}}[L_{\odot}])-1.45^{+0.14}_{-0.13},$

(blue dashed line; $\rho$ = 0.74) which, given the scatter of the data
(0.17${}^{+0.01}_{-0.01}$ dex), is in excellent agreement with GAMA. The
correlation coefficient ($\rho$ = 0.4) suggests that there is no correlation
between $f_{\text{abs}}$ and $L_{\text{bol}}$ for the Q LTGs.

In order to further investigate the similarities and differences between Q and
SF LTGs we examine their dust mass content (normalised to their stellar mass)
as well as their sSFR. We do so in Fig. 6 with the dust-to-stellar mass ratio
distributions presented in the top panel and the sSFR distributions in the
bottom panel. From the top panel it is notable that SF Sa-Scd and SF Sd-Irr
show similar distributions of dust-to-stellar mass ratio with the Q Sa-Scd
showing significantly lower ratio. The median values of the distributions are
$1.6\times 10^{-3}$, $1.9\times 10^{-3}$, and $5\times 10^{-4}$ for the SF Sa-
Scd, the SF Sd-Irr, and the Q Sa-Scd, respectively, indicating that the Q Sa-
Scd are the most dust-poor types of all LTGs. A similar picture is seen in the
bottom panel of Fig. 6 with SF Sa-Scd and SF Sd-Irr showing similar
distributions in sSFR with that of Q Sa-Scd significantly deviating to lower
values. Their median values are 1.58-10 yr-1 for SF Sa-Scd and SF Sd-Irr and
10-11 yr-1 for Q Sa-Scd. All the above lead to the conclusion that the Q Sa-
Scd have less dust content, compared to their SF counterparts, but also they
are, currently, forming new stars, at lower rates than the other types. Their
stellar content will be revisited later in this paper (Sect. 4) where the
median SEDs will be presented.

The existence of a bar in galaxies has been suggested as a possible mechanism
for galaxy quenching (Kormendy & Kennicutt, 2004; Masters et al., 2011; Kruk
et al., 2018; Fraser-McKelvie et al., 2020). Additionally, several studies
(e.g. Masters et al. 2010; Fraser-McKelvie et al. 2018; Pak et al. 2019) find
a high fraction of passive spiral galaxies (up to $\sim$78%) hosting a bar
component, proposing that perturbation from the bar could cause star-formation
quenching in these galaxies. In our sample we find that 12% of the SF and 25%
of the Q Sa-Scd galaxies are barred. These results are in quite good agreement
with the corresponding results found in Géron et al. (2021) showing that 13%
of their SF and 22% of their Q galaxies possess a strong bar. Moreover, as in
Géron et al. (2021), we also find that barred spiral galaxies (i.e. SBa-SBcds)
have enhanced SFR compared to their unbarred analogues (0.88
M${}_{\odot}\text{yr}^{-1}$ and 0.63 M${}_{\odot}\text{yr}^{-1}$,
respectively), indicating probable rapid evolution of barred galaxies with the
bar expediting the ceasing procedure. George et al. (2019) found that 54.3% of
barred galaxies in the local Universe are quenched, with this fraction
becoming even larger (66.5%) for galaxies with $M_{\text{star}}$ ¿ 1010.2 M⊙.
In our sample 13.2% of barred galaxies are quiescent with this fraction
becoming 74.3% for galaxies with $M_{\text{star}}$ ¿ 1010.2 M⊙. The enhanced
bar fraction in Q spirals could be a possible indication that bars (or their
creation mechanisms) are, at some level, responsible for the process of the
ceasing of star formation. Combes & Sanders (1981) suggested that bars are
able to redistribute the material of disc galaxies by funnelling gas towards
the galaxy centre. Such a process could have occurred in Q spirals in our
sample, removing the available cold gas reservoir from their disc and making
it available in the central regions for the induction of starburst/AGN
activity followed by quenching (e.g. Knapen et al. 2002; Jogee et al. 2005).

As already presented in the top panels of Fig. 4, local ETGs (LBS; left panel,
and E/S0; right panel) occupy different loci in the SFR-$M_{\text{star}}$
plane, depending on their stellar mass and their star-forming activity. To
further investigate similarities and differences between these ETG populations
(SF E/S0, Q E/S0 and SF LBS), in Fig. 7 we plot, as for the case of LTGs, the
SFMS (top panel), as well as the $f_{\text{abs}}$ as a function of the
bolometric luminosity (bottom panel). The fact that SF LBS galaxies follow the
same distribution with the SF Es confirms the argument of Kormendy et al.
(2009) that the population of high surface brightness concentrated galaxies
(such as LBSs) is similar to the giant elliptical population. The best linear
fits for the two different populations (SF and Q) are plotted in the top panel
of Fig. 7, with the majority of the SF E/S0s along with the SF LBS galaxies
constituting a separate distribution, following, very closely, a relation
similar to the SFMS of the local SF spiral galaxies [Eq. (1); blue solid
line]. The SFMS of the GAMA SF ETGs ($\rho$ = 0.73; blue dashed line) is
described by the following linear relation,

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=0.78^{+0.03}_{-0.02}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-7.79^{+0.25}_{-0.23}$

and their intrinsic scatter is found to be 0.36${}^{+0.01}_{-0.01}$ dex. This
is in agreement with the findings by Wu & Zhang (2021) who have found a slope
of 0.74 $\pm$ 0.01 and an offset of -7.22 $\pm$ 0.08 for their sample of SDSS
DR7 SF ETGs.

Figure 7: SFR vs $M_{\text{star}}$ (top panel) and $f_{\text{abs}}$ vs
$L_{\text{bol}}$ (bottom panel) for the early-type (E/S0 and LBS) galaxies in
the GAMA and DustPedia samples. SF E/S0 and SF LBS galaxies are represented
with blue and cyan symbols, respectively, while Q E/S0 galaxies (given that Q
LBS are under-represented and are not shown here; see Sect. 2.2) are shown
with red symbols. Blue and red stars stand for the GAMA SF and Q S0 galaxies,
while E sources are represented by circles. Open symbols show the DustPedia
E/S0 galaxies. All values are plotted along with their corresponding
uncertainties. Solid lines are the linear fits for the GAMA SF and Q galaxies
(blue and red, respectively), while the dashed lines are the linear fits for
the DustPedia SF and Q galaxies (blue and red, respectively).  Figure 8:
Comparison of the distributions of the CIGALE-derived dust-to-stellar-mass
ratio (top panel) and sSFR (bottom panel) for the different subclasses of
early-type galaxies. The normalised distributions for the SF E/S0, the Q E/S0
galaxies, and the SF LBS are shown in blue, red, and cyan, respectively. The
median value of each parameter, for each population is given in the plot with
the corresponding colour. The vertical dash-dotted line indicates the sSFR =
10-11 yr-1 threshold. Figure 9: Median SEDs of the GAMA ETGs (top panels) and
the LTGs (bottom panels). The median total luminosity of each sub-class is
indicated in black, while the old and young stellar components are shown in
red and blue, respectively. The orange curve stands for the diffuse dust,
while dust in PDRs is shown in green. Shaded areas correspond to the 16th-84th
percentile range.

A second correlation is present in this plot for the Q E/S0s, with a larger
scatter though (0.64${}^{+0.04}_{-0.03}$ dex) and lower Pearson correlation
coefficient $\rho$ = 0.58. GAMA Q E/S0s occupy the same locus with the
DustPedia Q E/S0s in the SFR-$M_{\text{star}}$ plane. The corresponding linear
regression to the GAMA Q E/S0s gives:

$\text{log}(\textit{SFR}[M_{\odot}\text{yr}^{-1}])=0.59^{+0.08}_{-0.08}\leavevmode\nobreak\
\text{log}(M_{\text{star}}[M_{\odot}])-7.65^{+0.84}_{-0.89},$

(red dashed line). Compared to the Q LTGs [Eq. (2); red solid line] we see
that the Q ETGs occupy a slightly different space in the SFR-$M_{\text{star}}$
plane with lower values of SFR per stellar mass bin.

Nersesian et al. (2019) have shown that $f_{\text{abs}}$ is larger in
intermediate spiral galaxies (Sb-Sc), where $\sim 35\%$ of their intrinsic
luminosity is affected by the dust. On the other hand, it is also shown in
that study that, pure elliptical galaxies exhibit very low $f_{\text{abs}}$
values ($\sim 2\%$). Bianchi et al. (2018) explored the dependence of this
quantity on the bolometric luminosity, finding a positive correlation for the
late spirals. The Es though were found to have no correlation. Similarly, in
the bottom-panel of Fig. 7 we plot $f_{\text{abs}}$ as a function of the
bolometric luminosity with the nomenclature being the same as in the top panel
of the same figure. As was also observed in the SFR-$M_{\text{star}}$ plane,
again we find the SF E/S0s, along with the LBS galaxies, constituting a
separate distribution in the $f_{\text{abs}}$-$L_{\text{bol}}$ plane.
Regardless their elliptical morphology they follow a positive trend (blue
dashed line; $\rho$ = 0.62), described by the relation:

$\text{log}(f_{\text{abs}}[\%])=0.27^{+0.02}_{-0.02}\leavevmode\nobreak\
\text{log}(L_{\text{bol}}[L_{\odot}])-1.12^{+0.17}_{-0.18}$

with intrinsic scatter 0.12${}^{+0.01}_{-0.01}$. This correlation is in great
agreement with what is found for the SF LTGs (Eq. (3); solid blue line). No
clear correlation seems to be present for the $f_{\text{abs}}$ with the
$L_{\text{bol}}$ for the Q E/S0 galaxies ($\rho$ = -0.12).

To further investigate the differences in the physical properties between the
SF and Q ETGs, in Fig. 8 we present the histograms of the dust-to-stellar-mass
ratio (top panel) and the sSFR (bottom panel). The histogram of the dust-to-
stellar-mass ratio suggests that among the three populations (SF LBS, Q E/S0,
and SF E/S0) it is the SF LBS that show the highest relative dust content,
compared to their stellar mass, with a median value of the dust-to-stellar-
mass ratio of 0.0016. The corresponding ratio of the SF E/S0s is $8\times
10^{-4}$, whilst the Q E/S0 galaxies are the most dust deficient for their
stellar mass (a ratio of $8\times 10^{-5}$). The median ratio we find for the
SF ETGs (0.0016) is in excellent agreement with the findings by Rowlands et
al. (2012) who computed the ratio for a Herschel-Astrophysical Terahertz Large
Area Survey (H-ATLAS; Eales et al. 2010)/GAMA matched sample, highly
contaminated by UV/optical blue ETGs. In another volume-limited sample of 62
ETGs of the Herschel Reference Survey (HRS), with 24% of them having detected
dust emission, Smith et al. (2012) found a mean ratio of $5\times 10^{-5}$.
This result is in agreement (within 0.2 dex difference) with the ratio we find
for the Q E/S0s.

By examining the distributions of the sSFR for the different earlier-type
galaxy populations, we find the SF E/S0s having a median sSFR of $10^{-10.0}$
yr-1 compared to the Q ones ($6\times 10^{-13}$ yr-1). As was indicated by
their high dust-to-stellar-mass ratio SF LBSs is found to be the most actively
star-forming early-type population with a median value of $2\times 10^{-10}$
yr-1. The mean sSFR averaged over the last 100 Myr for blue ETGs reported by
Rowlands et al. (2012) is $1.2\times 10^{-11}$ yr-1 (1 dex lower from the mean
sSFR of SF E/S0s). Respectively, the median sSFR for the DustPedia E/S0s
(which in vast majority are Q) is found to be $6.3\times 10^{-13}$ yr-1, the
same for GAMA Q E/S0s.

As was also reported in previous studies for blue ETGs (e.g. Ferreras & Silk
2000; Kaviraj et al. 2007; Sampaio et al. 2022) SF ETGs are mainly found
having lower masses compared to their Q counterparts. In GAMAnear we found 90%
of SF ETGs lying in the $1.6\times 10^{8}$ M⊙ to $2.0\times 10^{10}$ M⊙ range
of $M_{\text{star}}$, while Q ETGs exceed to stellar masses up to $4.0\times
10^{11}$ M⊙. A similar behaviour is observed for spirals, where Q Sa-Scds are
mainly found at the high-mass end. Furthermore, as we move from LTGs to ETGs
we find the dust-to-stellar-mass ratio to decrease for SF galaxies (0.3 dex)
and more significantly for Q galaxies (0.8 dex). This decreasing trend is in
agreement with the findings by Cortese et al. (2012) studying the dust scaling
relations of a HRS volume- and magnitude-limited sample of $\sim$300 galaxies.

It is worth noticing that the number of Q Sa-Scds with $f_{\text{abs}}$ larger
than 10% ($\sim$70%) is significantly larger than the corresponding fraction
for the Q E/S0s ($\sim$21%). This suggests that the dust effects in Q Sa-Scds
are more important than in Q E/S0s. This is to be expected since Q Sa-Scds are
found to be more dusty for their stellar masses, with their corresponding
ratio being 0.8 dex higher than the one for Q E/S0s (see Figs 6 and 8).

##  4 Stellar populations in SF early- and Q late-type galaxies and their role
in dust heating

Investigating the stellar populations in galaxies and the way their radiation
interacts with the dust grains is essential to understanding the energy
balance that is taking place inside galaxies. The approach that we utilise, by
modelling multi-wavelength SEDs of galaxies with CIGALE, allows us to
parametrise the stellar content of galaxies into two broad categories, namely,
an old and a young stellar population. The old stars are modelled with an
exponentially declining SFR with the e-folding time (0.5-20 Gyr) and the age
(2-12 Gyr) as free parameters. The young stars are formed after a bursting or
quenching event of star formation, started 200 Myrs ago, with the ratio of the
SFR before and after the event being a free parameter. The choice of a
constant moment for the latter event was indicated by Ciesla et al. (2016) who
found that the shape of the SED is not sensitive to variations of this
parameter (see also Nersesian et al. 2019).

Figure 10: The fraction of the old (red) and young (blue) stellar populations
to the mean unattenuated ($f_{\text{old,young}}^{\text{unatt}}$ =
$L_{\text{old,young}}^{\text{unatt}}$/$L_{\text{bol}}$; left panel) and to the
mean attenuated luminosity ($f_{\text{old,young}}^{\text{att}}$ =
$L_{\text{old,young}}^{\text{att}}$/$L_{\text{bol}}$; right panel), to the
bolometric luminosity, per galaxy subset. In the right panel the ratio of the
mean dust luminosity over the bolometric luminosity is also shown in orange
colour ($f_{\text{abs}}$ = $L_{\text{dust}}$/$L_{\text{bol}}$).

Nersesian et al. (2019) studied the fraction of the luminosities of the two
stellar components to the total luminosity, as well as their effect in the
dust heating (the fractions of the stellar luminosities absorbed by dust) for
the galaxies in the DustPedia sample, as a function of their morphology. This
analysis suggested that the old stars in local galaxies, of all morphological
types, always constitute the dominant population in terms of luminosity, with
the contribution of young stars being less significant following an
evolutionary sequence, decreasing from later- to earlier-types of galaxies. On
average, it was found that, the ratio of the luminosity of the young stars to
the bolometric luminosity of the galaxy is 25% for Sb-Irr morphologies, while
it drops to less than 10% for E/S0 morphologies. Despite this trend where
ETGs, in the local universe, are mostly composed of older stars and with LTGs
hosting larger fractions of young stars, other studies have reported the
existence of galaxies where populations, significantly, depart from this
general picture. Schawinski et al. (2009), investigating a sample of visually
identified blue ETGs, selected from Galaxy Zoo and SDSS DR6, found that a
young stellar component is present in this galaxy population. Measuring the
Balmer absorption-line index H$\delta_{\text{A}}$ (tracer of the recent star
formation) and the break at $\sim$4000 ${\AA}$ (tracer of mean stellar age)
Masters et al. (2010) found the range of stellar ages in passive spirals being
similar to typical ETGs and older than the one of star-forming spirals.
Similarly, Rowlands et al. (2012), estimating the r-band light-weighted age of
passive spiral galaxies found that they are mostly dominated by old stars with
current star-formation activity well below the normal spirals.

Taking advantage of the fact that the galaxies in our sample are categorised
in SF and Q in an unbiased and independent way (optical spectra; see Fig. 2)
we are able to examine how the relative contributions of the two stellar
populations (old and young) change, not only with morphology, but also, with
star-formation activity. In Fig. 9 we present median SEDs of ETGs (Q E/S0, SF
E/S0, SF LBS; top panels, left to right) and LTGs (Q Sa-Scd, SF Sa-Scd, SF Sd-
Irr; bottom panels, left to right). In the panels describing the different
types, apart from the total median SED, the various components comprising the
SED (old and young stellar population, diffuse and PDR dust emission) are also
shown. A visual inspection of the SEDs reveals a dependence of their shape
with, both, the morphology and star-formation activity.

Amongst the ETGs (top row in Fig. 9) the median SEDs of SF E/S0s and SF LBSs
are very similar, while that of Q E/S0s deviates a lot. The similarity of the
first two types of SEDs is more evident when comparing the flux at 100 $\mu$m
(a difference of 0.14 dex) and at 1.0 $\mu$m (a difference of 0.06 dex), an
indication of similar dust and stellar emission, respectively. One needs to
notice here that LBSs are more compact and less massive systems, compared to
SF E/S0s, so, even though the shape of their SED (normalised by their
bolometric luminosity) is similar, they are expected, on average, to be less
luminous. The similarities in the SEDs suggest that SF LBSs and SF E/S0s
(especially Es which have similar overall morphology with LBSs) belong to the
same population of galaxies with SF LBSs occupying the low-mass end of this
galaxy population. Q E/S0s, on the other hand (top-left panel), show a very
different SED with the FIR emission being weaker, compared to the other two
types, and with the stellar SED dominated by emission from old stars. The
contribution from young stars in Q E/S0s is almost negligible. Q ETGs, for
instance, compared to SF ETGs, have 0.3 dex higher flux at 1.0 $\mu$m and 1.1
dex lower flux at 100 $\mu$m. The lack of young stars in Q E/S0s is also
reflected by the absence of PDR emission (linked to emission originating from
star-forming sites) in the IR part of the SED, compared to the other two types
where this type of emission has a significant contribution, especially in the
case of SF E/S0s.

Although spiral galaxies (Q/SF Sa-Scd) show a very similar FIR emission (left
and middle bottom panels) with a difference of only 0.21 dex at 100 $\mu$m,
they differ a lot in their optical and FUV emission (e.g. 0.75 dex difference
at $\sim$0.1 $\mu$m). Similar to the case of SF E/S0s and SF LBSs which are
found to have very similar median SEDs, in the LTGs case, SF Sd-Irrs have
almost identical median SED with the SF Sa-Scds (0.03 dex and 0.06 dex
difference at 1.0 and 100 $\mu$m, respectively). This suggests that (as
opposed to ETGs where one can see differences between Q and SF in both the
stellar and the dust emission) LTGs galaxies differ mostly in their stellar
content with SF types hosting significantly more young stars.

All the aforementioned properties are better quantified in Fig. 10, where we
present the relative contributions of each stellar component (old and young;
red and blue colours, respectively) to the total bolometric luminosity. In
this figure, the leftmost panels show the relative contributions, to the
bolometric luminosity, of the pure, unattenuated, stellar components (i.e. if
there was no dust present in the galaxies) while in the rightmost panels the
effects of dust are taken into account. In both panels, the relevant
information for both ETGs (Q E/S0, SF E/S0, SF LBS) and LTGs (Q Sa-Scd, SF Sa-
Scd, SF Sd-Irr) are presented. All the luminosity fractions are presented in
Table 3.

The first thing to notice from this plot is that, regardless of the
morphological type or the star-forming activity, the old, more evolved,
stellar population dominates the bolometric luminosity. This was also evident
in Nersesian et al. (2019) with more than $\sim$75% of the bolometric
luminosity, in all types of galaxies, originating from old stars, and with Es
being the most extreme cases with this fraction being as high as 98%. On the
other hand, in Sbc to Irr galaxies the fraction of the luminosity coming from
the young stars is more significant, ranging between 20% and 30% depending on
the galaxy type. With the current analysis we can update these results, not
only as a function of morphology, but also, as a function of the star-forming
activity in galaxies. Concerning the ETGs we find that Q E/S0s have a
negligible fraction of young stars (1%), while SF E/S0s and SF LBSs show a
quite substantial contribution of young stars (23% and 25%, respectively)
comparable to what is seen for the most actively star-forming spiral
galaxies). Nevertheless as was reported by Huang & Gu (2009), although the
young stellar population is enhanced in SF Es (and SF S0s in our sample), they
should not be considered as young objects, since their main stellar population
is as old as the ones in the Q Es. LTGs, on the other hand, show a small
fraction (4%) of young stars when being quiescent (Q Sa-Scds) which gets
higher (25%) in their star-forming counterparts (SF Sa-Scds/Sd-Irrs). Huang &
Gu (2009) found that due to their star-formation activity, SF Es have higher
levels of dust attenuation. On the other hand, as was also discussed in
paragraph 3.3, Q E/S0s are the most dust poor for their stellar mass, among
the ETGs. A comparison of the dust content for the different types of LTGs is
also provided in the same paragraph, finding that Q Sa-Scds are the most dust
deficient.

Table 3: Average values of the ratios of various combinations of the stellar
and dust luminosity components extracted using the CIGALE SED fitting tool.
The different ratios (also presented in Nersesian et al. 2019 and Paspaliaris
et al. 2021) are defined as
$f^{\text{unatt}}_{\text{old}}=L^{\text{unatt}}_{\text{old}}/L_{\text{bol}}$,
$f^{\text{unatt}}_{\text{young}}=L^{\text{unatt}}_{\text{young}}/L_{\text{bol}}$,
$f^{\text{att}}_{\text{old}}=L^{\text{att}}_{\text{old}}/L_{\text{bol}}$,
$f^{\text{att}}_{\text{young}}=L^{\text{att}}_{\text{young}}/L_{\text{bol}}$,
$f_{\text{abs}}=L_{\text{dust}}/L_{\text{bol}}$,
$F^{\text{att}}_{\text{old}}=L^{\text{att}}_{\text{old}}/L^{\text{unatt}}_{\text{old}}$,
$F^{\text{abs}}_{\text{old}}=L^{\text{abs}}_{\text{old}}/L^{\text{unatt}}_{\text{old}}$,
$F^{\text{att}}_{\text{young}}=L^{\text{att}}_{\text{young}}/L^{\text{unatt}}_{\text{young}}$,
$F^{\text{abs}}_{\text{young}}=L^{\text{abs}}_{\text{young}}/L^{\text{unatt}}_{\text{young}}$,
$S^{\text{abs}}_{\text{old}}=L^{\text{att}}_{\text{old}}/L_{\text{dust}}$, and
$S^{\text{abs}}_{\text{young}}=L^{\text{att}}_{\text{young}}/L_{\text{dust}}$,
where, $L^{\text{unatt}}_{\text{old}}$ and $L^{\text{unatt}}_{\text{young}}$
are the unattenuated luminosities of the old and the young stars,
$L_{\text{bol}}$ is the bolometric luminosity of each system
($L_{\text{bol}}=L^{\text{unatt}}_{\text{old}}+L^{\text{unatt}}_{\text{young}}$),
$L_{\mathrm{dust}}$ is the dust luminosity, $L_{\mathrm{old}}^{\mathrm{att}}$
and $L_{\mathrm{young}}^{\mathrm{att}}$ are the attenuated luminosity of the
old and young stars, and $L_{\mathrm{old}}^{\mathrm{abs}}$ and
$L_{\mathrm{young}}^{\mathrm{abs}}$ are the luminosity of the old and young
stars absorbed by dust. These ratios are shown for each galaxy subset.

Galaxy Type | $f_{\mathrm{old}}^{\mathrm{unatt}}$ | $f_{\mathrm{young}}^{\mathrm{unatt}}$ | $f_{\mathrm{old}}^{\mathrm{att}}$ | $f_{\mathrm{young}}^{\mathrm{att}}$ | $f_{\mathrm{abs}}$ | $F_{\mathrm{old}}^{\mathrm{att}}$ | $F_{\mathrm{old}}^{\mathrm{abs}}$ | $F_{\mathrm{young}}^{\mathrm{att}}$ | $F_{\mathrm{young}}^{\mathrm{abs}}$ | $S_{\mathrm{old}}^{\mathrm{abs}}$ | $S_{\mathrm{young}}^{\mathrm{abs}}$
---|---|---|---|---|---|---|---|---|---|---|---
Q E/S0 | 0.99 | 0.01 | 0.93 | 0.01 | 0.06 | 0.94 | 0.06 | 0.64 | 0.36 | 0.91 | 0.09
Q Sa-Scd | 0.96 | 0.04 | 0.82 | 0.02 | 0.16 | 0.88 | 0.12 | 0.40 | 0.60 | 0.76 | 0.24
SF LBS | 0.75 | 0.25 | 0.66 | 0.20 | 0.19 | 0.89 | 0.11 | 0.56 | 0.44 | 0.39 | 0.61
SF E/S0 | 0.77 | 0.23 | 0.50 | 0.05 | 0.35 | 0.76 | 0.24 | 0.26 | 0.74 | 0.53 | 0.47
SF Sa-Scd | 0.75 | 0.25 | 0.63 | 0.10 | 0.27 | 0.84 | 0.16 | 0.41 | 0.59 | 0.43 | 0.57
SF Sd-Irr | 0.75 | 0.25 | 0.68 | 0.14 | 0.18 | 0.90 | 0.10 | 0.58 | 0.42 | 0.41 | 0.59

The interplay between the two stellar populations and dust (in the different
galaxy types) is shown in the right panel of Fig. 10. A decrease in the
luminosity of both stellar populations is observed and this energy is absorbed
and then re-emitted by dust (yellow bars). What is worth noticing from this
plot is that although, Q E/S0s have low contribution to the total luminosity
by dust, as expected, the contribution is very significant in SF E/S0s and SF
LBSs, 35% and 19%, respectively.

When dust is considered the observed stellar luminosities are now suppressed
as a result of the absorption of the stellar radiation by dust grains. The
quantitative picture of this effect is described in the rightmost panels of
Fig. 10 with blue and red bars indicating the mean ratios of the observed to
the bolometric luminosities of the young and old stellar populations,
respectively. Additionally, yellow bars indicate the fraction of the stellar
radiation that is absorbed and gone into the dust heating ($f_{\text{abs}}$ =
$L_{\text{dust}}$/$L_{\text{bol}}$). As also discussed in Bianchi et al.
(2018) and Nersesian et al. (2019) this quantity indicates the significance of
the dust in galaxies and the effectiveness of the dust grains in absorbing the
stellar radiation, a combination of the total amount of dust, the geometry,
and the strength of the ISRF (see also Paspaliaris et al. 2021). It is
interesting to notice here that SF E/S0s show the highest fraction (35%) of
the stellar radiation absorbed by dust, followed by SF spirals (27%), while SF
LBSs and SF Sd-Irr have a similar fraction (19% and 18%, respectively). Q
E/S0s have the lowest fraction $f_{\text{abs}}$ (6%).

Figure 11: Mean values of the fraction of the luminosity of each stellar
population (red for old and blue for young stars) used for the heating of the
dust. The ratio of the dust-absorbed luminosity to the unattenuated luminosity
of the corresponding stellar component is indicated by the dashed bars
($F_{\text{old,young}}^{\text{abs}}$ =
$L_{\text{old,young}}^{\text{abs}}$/$L_{\text{old,young}}^{\text{unatt}}$).
Solid bars represent the ratios of the attenuated luminosity to the
unattenuated luminosity ($F_{\text{old,young}}^{\text{att}}$ =
$L_{\text{old,young}}^{\text{att}}$/$L_{\text{old,young}}^{\text{unatt}}$).

The ratios of the stellar luminosity absorbed by the dust
($L_{\text{old,young}}^{\text{abs}}$) to the total, unattenuated, stellar
luminosity ($L_{\text{old,young}}^{\text{unatt}}$) for each stellar component
(old and young; $F_{\text{old,young}}^{\text{abs}}$) can provide an estimation
of how effectively each stellar population can heat up the dust. These
luminosity ratios are presented in Fig. 11,with the left panel showing the
relative contribution of the old stars and the right panel the relative
contribution of the young stars to the dust heating, respectively, for the
various types of ETGs and LTGs. The dashed part of each bar corresponds to the
part of the intrinsic luminosity, of each stellar component, that is absorbed
by the dust, while the solid part of each bar indicates the remaining, emitted
directly by the stars, luminosity. At a first glance, it is obvious that, in
all cases, young stars are the ones that donate a higher fraction of their
energy to the heating of the dust (already noted in Nersesian et al. 2019 and
Paspaliaris et al. 2021). Amongst the ETGs (Q E/S0, SF E/S0, and SF LBS) it is
the SF E/S0 in which the stars are more efficiently heating the dust with old
stars donating up to 24% (and young stars up to 74%) of their luminosity to
heat up the dust. Between the two classes of LTGs (Q and SF) there is not much
variation with the old stars donating $\sim$ 10%-16% while, surprisingly
enough, the Q spirals give similar portion of their young-stellar luminosity
to heat up the dust (60% compared to 59% for the SF spirals). The relative
distribution of stars and dust inside galaxies is expected to affect the
fraction of the radiation of each stellar population offered for the dust
heating. The fact that the offered (absorbed) fraction of each stellar
population is comparable in Q and SF spiral galaxies (which geometry is
obviously very similar), while the corresponding percentages in Q and SF E/S0
galaxies are significantly different, may indicate differences in the internal
structure of these sources (especially in SF Es), despite their similar
overall structural characteristics.

Figure 12: Mean values of the ratios of the old (red) and the young (blue)
stellar luminosity absorbed by dust to the dust luminosity
($S_{\text{old,young}}^{\text{abs}}$ =
$L_{\text{old,young}}^{\text{abs}}$/$L_{\text{dust}}$).

The relative contribution of each stellar population to the heating of the
dust ($S_{\text{old,young}}^{\text{abs}}$) is shown in Fig. 12, with the red
and blue bars representing the mean ratio of the absorbed, by the dust,
luminosity originating from the old and the young stars
($L_{\text{old,young}}^{\text{abs}}$), respectively, to the total luminosity
emitted by the dust ($L_{\text{dust}}$). It is interesting to notice that it
is only the SF Sa-Scd, SF Sd-Irr galaxies and the SF LBSs in which the
contribution of the young stars is, on average, the dominant source of dust
heating with values of $S_{\text{young}}^{\text{abs}}$ of 57%, 59% and 61%,
respectively. In Q spirals and SF E/S0s there is a significant contribution in
the dust heating from the young stars (24% and 47%, respectively), while the
dust in Q E/S0s is heated, almost exclusively, by old stars
($S_{\text{old}}^{\text{abs}}$ of 91%). All the relevant average fractions are
presented in Table 3.

Figure 13: Median SEDs for the different morphological classes (different
colours are for different classes, as indicated in the upper-right panel). The
SEDs are grouped in SF galaxies (top panels) and Q galaxies (bottom panels).
The unattenuated SEDs are plotted in the left panels, while the attenuated
ones are plotted in the right panels.

##  5 Discussion

The coexistence of both types (Q and SF) in the various morphological types of
galaxies is a very interesting finding and deserves a more thorough
investigation in order to reveal the actual causes of this bimodal behaviour.
In what follows we investigate possible differences between Q and SF galaxies
regarding their energetic output (SEDs), their morphologies (as paramatrised
by their $R_{\text{e}}$ and Sèrsic index), and the effects that the local
environment may have on the galaxies.

###  5.1 Differences between SEDs of Q and SF galaxies

In Fig. 13 we compare the median attenuated and unattenuated SEDs (each
individual SED normalised to its bolometric luminosity) of all morphological
classes, but, grouped according to their star-forming activity. What is
immediately evident is that the unattenuated SEDs of SF galaxies of all types
of morphologies are very similar (almost identical; see top-left panel). There
are only minor differences observed with the SEDs of SF LBS, SF Sa-Scd, and SF
Sd-Irr galaxies, being slightly brighter, in the FUV-optical wavelength range
($\sim$0.1 dex at 0.2 $\mu$m), compared to those of SF E/S0 of the same
bolometric luminosity. On the other hand, the infrared luminosity
($L_{\text{IR}}$) is also, on average, very similar between all types of SF
galaxies with only SF E/S0s showing a higher peak value at $\sim 100\mu$m (by
$\sim$0.18 dex) compared to the rest morphologies. This suggests that all SF
galaxies of the same bolometric luminosity, independently of their morphology,
exhibit the same (within the statistical uncertainties imposed by our
analysis) unattenuated stellar SEDs. When the effects of dust are taken into
account, the differences become clearer in the attenuated stellar SEDs (top-
right panel in Fig. 13) with SF LBSs, SF Sa-Scds, and SF Sd-Irrs showing
similar SEDs which are, on average, brighter in the FUV/optical wavelengths,
compared to those of SF E/S0 types (by $\sim$0.4 dex at 0.2 $\mu$m).

The Q galaxies (Sa-Scd, E/S0), on the other hand, show a diversity in the
shape of their SEDs concerning both their attenuated and unattenuated stellar
emission but also their dust emission (see the bottom panels in Fig. 13). Here
we see that the unattenuated SEDs of the Q Sa-Scd galaxies, of the same
bolometric luminosity, (bottom-left panel) show an enhanced stellar emission
compared to those of Q E/S0s. This means that, on average, Q galaxies of the
same bolometric luminosity in the Sa-Scd morphology bin are brighter in the
FUV-optical wavelengths, compared to E/S0 types (meaning that they are more
rich in young stars) but, at the same time, have more dust content (higher
emission at FIR) leading to higher extinction values.

Figure 14: Kernel density estimates for the distribution of different kinds of galaxies in the $n_{\text{r}}$-$R_{\text{e}}$ plane. ETGs and LTGs are plotted in the left, and right panels, respectively. Different colours indicate different morphologies and star forming activities (as described in the insets in each panel) The median values of each subset are indicated by ‘+’ symbols. The dashed-grey line and the dashed-dotted line are from D’Onofrio (2001) and from Graham (2013), respectively (see the text for details).  Table 4: Median r-band Sérsic indices ($n_{\text{r}}$) and effective radii ($R_{\text{e}}$) for the different galaxy populations. Galaxy type | $N_{\text{obj}}$ | $n_{\text{r}}$ |  $R_{\text{e}}$ [kpc]
---|---|---|---
Q E/S0 | 262 | 3.5 | 4.3
Q Sa-Scd | 52 | 2.2 | 5.8
SF LBS | 323 | 1.8 | 1.0
SF E/S0 | 231 | 1.5 | 2.2
SF Sa-Scd | 634 | 1.2 | 4.0
SF Sd-Irr | 692 | 1.0 | 2.7

###  5.2 Structural characteristics

The Sérsic index ($n_{\lambda}$) is considered a parameter to distinguish
between different morphologies of galaxies (see, e.g. Ravindranath et al.
2004; Vika et al. 2015; Mosenkov et al. 2019, and references therein). In
Mosenkov et al. 2019, it was shown that, although there is a lot of overlap, a
borderline between LTGs and ETGs is at $n_{\text{3.4}}$=2 (with ETGs occupying
the larger values). On the other hand, the effective radius ($R_{\text{e}}$)
is a direct measure of the size of a galaxy, often relate to $n_{\lambda}$
(see, e.g., D’Onofrio 2001).

Aiming at better understanding the morphological discrepancies between the
star-forming and quiescent galaxies in our sample, we investigate how the
structural parameters (in particular the r-band Sérsic index, $n_{\text{r}}$
and the effective radius, $R_{\text{e}}$) vary within galaxies of different
morphologies and star-forming activity. As described in detail in Kelvin et
al. (2012), such structural parameters have been retrieved through single-
Sérsic modelling using the SIGMA (Structural Investigation of Galaxies via
Model Analysis) code. The galaxies in the GAMA sample are modelled with GALFIT
(Peng et al., 2010a) (implemented in SIGMA) allowing for multiple parametric
functions (e.g. Sérsic, exponential, Gaussian, etc.). The model of each galaxy
is then produced with a downhill algorithm used by GALFIT to minimise the
global $\chi^{2}$. The final products are finally analysed by SIGMA for any
obvious errors.

In Fig. 14 we plot Kernel density estimates of the $n_{\text{r}}$ as a
function of the $R_{\text{e}}$. ETGs and LTGs are plotted in the left and
right panels, respectively. The median values of $n_{\text{r}}$ and
$R_{\text{e}}$, for all galaxy types are presented in Table 4. For the ETGs
(left panel in Fig. 14) we see that there is an increase in n${}_{\text{r}}$,
on average, with increasing $R_{\text{e}}$, with SF LBS being least extended
(median value of $R_{\text{e}}$=1 kpc), SF E/S0 being intermediate cases
($R_{\text{e}}$=2.2 kpc), and Q E/S0 being the most extended galaxies
($R_{\text{e}}$=4.3 kpc). The best-fit calculated for the current sample of
ETGs is expressed by the equation

$\text{log}(n_{\text{r}})=0.05^{+0.04}_{-0.04}\leavevmode\nobreak\
\text{log}(R_{\text{e}}[\text{kpc}])+0.25^{+0.02}_{-0.01},$

with a correlation coefficient $\rho$ = 0.41, which is to be expected given
the large dispersion of the distributions. Concerning the r-band Sérsic index
the SF ETGs (LBS, E/S0) show, on average, lower indices (1.8, and 1.5,
respectively) with the Q E/S0 showing a much steeper profile with
$n_{\text{r}}=3.5$. Although it is clear that in all cases the median value of
$n_{\text{r}}$ indicate non-exponential profiles ($n_{\text{r}}>1$), it is the
Q E/S0 types that show clear cases of steep radial profiles. LTGs (right panel
in Fig. 14) show, on average, larger values of $R_{\text{e}}$, compared to
ETGs, with SF Sd-Irr being the most compact LTGs (median value of
$R_{\text{e}}$=2.7 kpc), followed by SF Sa-Scd ($R_{\text{e}}$=4 kpc) and Q
Sa-Scd ($R_{\text{e}}$=5.8 kpc). The larger values, compared to ETGs, can be
explained due to the more extended, disk-like, geometry of the spiral galaxies
as well as the flattened and more irregular distribution of Sd-Irr types (as
opposed to the generally more elliptical shape of ETGs). The increase of the
$R_{\text{e}}$ as a function of the $n_{\text{r}}$ for the LTGs is described
by the relation

$\text{log}(n_{\text{r}})=0.21^{+0.01}_{-0.01}\leavevmode\nobreak\
\text{log}(R_{\text{e}}[\text{kpc}])-0.07^{+0.01}_{-0.01},$

with a correlation coefficient $\rho$ = 0.28, which is again expected given
the large dispersion of the distributions.

Concerning the Sérsic index LTGs show, on average lower values, compared to
ETGs, consistent with exponential and more flattened profiles for SF Sa-Scd
and SF Sd-Irr ($n_{\text{r}}=1.2$ and 1, respectively) with the Q Sa-Scd
showing a steeper profile ($n_{\text{r}}=2.2$). The larger value of
$n_{\text{r}}$ in the Q Sa-Scd galaxies may be an indication of a more
prominent bulge component.

The fact that the median values of $n_{\text{r}}$ of SF LSB and E/S0 are
closer to unity (see Table 4) may indicate that, despite the overall
elliptical shape of the galaxy, an embedded disk may be present. Mahajan et
al. (2018), studying the Blue Spheroid (BSph) galaxies in the GAMA sample
(here called the LBS), showed that although these galaxies have very similar
structure to ellipticals, they resemble star-forming spirals in terms of age,
metallicity and star formation. In their analysis they also revealed the
underlying structures of the galaxies by fitting a Sèrsic profile. The
residuals indicated the existence of a disk or a nuclear component in
$\sim$38% of BSph and in $\sim$43% of ellipticals in the sample. In a
different sample, consisting of 55 blue ETGs from the SDSS DR6, George (2017)
found that $\sim$58% show similar structures, attributed to recent
interactions.

The star forming galaxies of all morphological types, show, systematically,
lower median values of $n_{\text{r}}$ and $R_{\text{e}}$ in comparison to
their Q counterparts. In addition, quiescent galaxies seem to be, on average,
more extended than the star forming ones. This comes to an agreement with the
study of Xu et al. (2022) where they find that for a sample of S0s in the
SDSS-IV MaNGA survey, the SF S0s show lower, average, bulge Sérsic indices
compared to a control sample of S0 normal galaxies. The general trend, despite
the very large scatter, is that there is an increase of the value of
$n_{\text{r}}$ with $R_{\text{e}}$. This is evident in both morphologies (ETGs
and LTGs, left and right panels of Fig. 14, respectively). Such a trend has
previously been reported by D’Onofrio (2001), for a volume- and magnitude-
limited sample of 73 ETGs belonging to the Virgo and Fornax clusters (see the
dashed-gray line in the left panel of Fig. 14). This finding is also supported
by the $n_{\text{r}}$-$R_{\text{e}}$ relation by Graham (2013) (dash-dotted
curve in the left panel of Fig. 14) being approximately linear for high-$n$
($n$¿2) cases, but curved for the low-$n$ sources. Our data are in good
agreement with this relation with the curved part of the fit mainly being
shaped by the inclusion of LBS types.

###  5.3 The role of the environment

It has been found that the processes that control the star-forming activity in
galaxies depend, not only on their stellar masses (see, e.g., Fig. 4), but
also on the environment the galaxies lie in and evolve (Peng et al. 2010b;
Vulcani et al. 2010; Paccagnella et al. 2016; Darvish et al. 2017). The
dependence of SFR for a sample of GALEX NUV-detected SDSS ETGs with
environment was explored by Schawinski et al. (2009). In the latter study the
authors found that blue ETGs with high rates of star formation inhabit low-
density environments. In another study, Goto et al. (2003), the authors
concluded that there is a strong dependence of the star-formation activity
with environment with the passive spiral galaxies mainly being cluster
members. On the other hand, other studies downgrade the role of environment on
the shaping of the properties of galaxies, making internal processes more
efficient mechanisms. For instance, Rowlands et al. (2012) reported that the
environment is not the only physical quantity affecting whether galaxies will
evolve as passive, or not, with the processes that turn spirals to passive
systems may happen in either high or low density environments. Furthermore,
Davies et al. 2019 found no or mild differences between the field and cluster
galaxies in the DustPedia sample by studying their gas and dust properties.

Figure 15: The fractions of grouped (pink) and field (grey) galaxies for each
subclass. The actual fraction of the grouped galaxies for each subclass is
given in the top of each bar.

The GAMA Galaxy Group Catalogue (G3C; Robotham et al. 2011) obtained by
applying a friends-of-friends (FoF) algorithm provides information about the
grouping of galaxies in the GAMA II equatorial regions of G09, G12 and G15 as
well as the G02 survey region. By cross-matching the sample of the current
study with the G3Cv10 version of the catalogue, we find available information
for 930 galaxies (42% of the current sample). According to this
classification, galaxy pairs or groups of more than two galaxy members are
labelled as grouped, while field galaxies are totally isolated. The fractions
of grouped and field galaxies in the current sample, for each morphological
class, are presented in Fig. 15. Out of the 930 galaxies, 491 are grouped,
while 439 are isolated. Although the Q galaxies are under-represented (e.g.,
only 3 Q E/S0 out of 24 and 3 Q Sa-Scd out of 17) some general trends are
evident. The general picture is that dense environments harbour more Q
galaxies (85% grouped, 15% field Q galaxies) while SF galaxies are similarly
divided between dense and isolated environments (51% grouped, 49% field SF
galaxies). The only exception is for SF LBS galaxies with 69% of these star-
forming galaxies residing in isolated environments. The actual fractions of
the field galaxies for each subsample are shown in Fig. 15. Our findings are
in good agreement with Fraser-McKelvie et al. (2018) who find that high-mass
passive spiral galaxies reside mostly in groups. Similarly, Sampaio et al.
(2022) studying cluster and field galaxies from the SDSS-DR7 found that 62% of
the cluster galaxies are ’red cloud’ (Q) systems, while the majority (53%) of
the field galaxies are ’blue cloud’ (SF) systems.

A measure of the density of the local environment of a galaxy is the surface
density, based on the distance to the 5th nearest neighbour. These
measurements are provided for 1,049 sources in our sample in Brough et al.
(2013). In Fig. 16 the data are grouped in 9 bins from 0.008 Mpc-2 (minimum
density) to 3,034 Mpc-2 (maximum density) logarithmically spaced, with the
lines connecting the measurements to guide the eye. From this plot it is
evident that, as in Fig. 15, SF galaxies tend to reside in less dense
environments, as opposed to Q galaxies which tend to reside in rich
environments. In particular, the peak of the distributions of all SF types of
galaxies is around 0.3 Mpc-2 (a median value of 0.49 Mpc-2), while all Q types
peak at around 6 Mpc-2 (a median value of 2.71 Mpc-2). It is notable, though
that the range of the distributions is quite large ($\sim 0.1-10$ Mpc-2 for SF
types and $\sim 0.1-100$ Mpc-2 for Q types) with significant overlap. More
specifically, the median surface density of the environment of SF E/S0s and SF
Sa-Scds is 0.67 Mpc-2 and 0.60 Mpc-2, respectively, while these values are
0.37 Mpc-2 and 0.49 Mpc-2 for SF LBS and SF Sd-Irrs, respectively. The Q
galaxies, on the other hand, show higher median values of 2.79 Mpc-2 for the Q
E/S0s and 2.47 Mpc-2 for the Q Sa-Scds, respectively.

Rowlands et al. (2012) found that both blue ETGs and passive spiral galaxies
reside in environments with comparable densities to those of their ‘normal’
counterparts, but probably this could be due to the small sample used (10 blue
ETGs and 15 passive spirals). For a similar GAMA sample, such as the one used
in this work, Pearson et al. (2021) found that the fraction of quiescent
galaxies increases as the environment becomes more massive (supporting our
findings). Our results also agree with the findings by Agius et al. (2015) who
study 220 isolated ($\sim$0.1-10 Mpc-2) ETGs from the GAMA/H-ATLAS sample
(Agius et al., 2013) and 33 ETGs from the HeViCS sample (di Serego Alighieri
et al., 2013) belonging to the Virgo cluster ($\sim$25-500 Mpc-2). They find
that cluster galaxies have very little, or no, ongoing star-formation
activity, while isolated galaxies present some ongoing star-formation, which,
in some cases is comparable to that in star-forming spiral galaxies.

Figure 16: The morphology-density relation for the different types of
galaxies. Open circles and dashed lines stand for the Q galaxies, while full
circles and solid lines represent the SF galaxies. Since Q LBSs and Q Sd-Irrs
consist of a small number of sources they are not presented in this plot.

###  5.4 Are SF Es a post-U/LIRG phase?

In Paspaliaris et al. (2021) the authors investigated the evolution of the
physical properties of luminous (1011 L⊙ $\leq$ $L_{\text{IR}}$ ¡ 1012 L⊙;
LIRGs) and ultra-luminous IR galaxies ($L_{\text{IR}}$ $\geq$ 1012 L⊙; ULIRGs)
along the merging sequence. In an SED-fitting analysis, they studied 67 such
local systems grouped in subsets according to their merging stage. The
morphology of these systems was spanning from single (s) spiral galaxies or
slightly disturbed spiral galaxies due to minor mergers (m) to spirals in a
pre-merging stage (M1) and disturbed systems during a major merging event
(M2-M5). As was indicated by the morphology of the late-merger (M5) systems in
that study and also by earlier studies based on simulations (e.g. Springel et
al. 2005, Cox et al. 2006 Di Matteo et al. 2008, and Hopkins et al. 2013b),
the product of the coalescence is an elliptical galaxy. Moreover, simulations
have shown that, after major merging events, galaxies undergo a morphological
change, prior to the drop in their SFR (Dubois et al. 2016; Martin et al.
2018; Tacchella et al. 2019; Joshi et al. 2020). The same result was found by
de Sá-Freitas et al. (2022) for a sample of blue E galaxies from SDSS DR12
(Alam et al., 2015). In the latter study, the blue Es although having enhanced
SFR, they present a quenching behaviour attributed to a possible past merging
event or rejuvenation process. In Paspaliaris et al. (2021), however, no E
galaxies (presumably being the final ‘relaxed’ stage of a merging event) were
analysed. This is a selection effect, since E galaxies mostly come with low
luminosities (much less than the 1011 L⊙ LIRG limit). However, the diversity,
in terms of morphology and star-forming activity, of the GAMAnear sample,
analysed in this study, gives us the opportunity to make an investigation of
the possibility of SF E galaxies being a post-U/LIRG product.

Figure 17: Evolution of the young stellar population (YSP; top panel) and SFR
(bottom panel), along the U/LIRGs merging sequence and the post-U/LIRG phase.
U/LIRGs from Paspaliaris et al. (2021) are shown in orange, while SF Es and Q
Es are represented by blue and red squares, respectively. Error bars indicate
the range between the 16th and 84th percentiles from the median. A black line
connects the median values indicating the general trend.

Assuming that the IR luminosity of galaxies after the coalescence decreases
with time, we grouped the SF Es in $L_{\text{IR}}$ bins, namely L1
($L_{\text{IR}}$ ¡ 109 L⊙; 42 SF Es), L2 (109 L⊙ $\leq$ $L_{\text{IR}}$ ¡ 1010
L⊙; 69 SF Es), L3 ($L_{\text{IR}}$ $\geq$ 1010 L⊙; 23 SF Es) assuming that
time evolves in the direction from L3 to L1 luminosity bins. We note that no E
sources where found to have $L_{\text{IR}}$ $\geq$ 1011 L⊙. In the top panel
of Fig. 17 we plot the median fractions of the young stellar population (YSP)
for the different subsets of U/LIRGs and E galaxies. An increasing trend of
the YSP in U/LIRGs (orange symbols) is observed between M2 and M4 systems
(going from 50% to 83%), which then decreases back to 57% for M5 sources. For
the SF Es (blue symbols) there is a decreasing trend with decreasing IR
luminosity, however a jump of 0.4 dex exists between L2 and L1 sources. While
L3 and L2 sources have YSP median values of 31% and 24% respectively, the
corresponding value for L1 sources is 10%. This difference could also indicate
two different families of SF E galaxies, with the luminous ones (L2 and L3)
consisting, mainly, of post-U/LIRG systems and L1 sources, with lower
$L_{\text{IR}}$, might be galaxies rejuvenated by minor merging events or by
accretion of gas available in their environment (see Thom et al. 2012 for the
latter scenario). The L1 and L2 bins of Q Es consist of 111 and 23 galaxies,
respectively. We do not present the L3 bin for the Q Es because only one
source was found within this $L_{\text{IR}}$ range. The YSP of Q Es (red
symbols) also shows a decreasing trend, with decreasing IR luminosity. A
notable characteristic that also supports the scenario which differentiates
the L1 sources from the more luminous ones is that with increasing
$L_{\text{IR}}$, the difference in the YSP between SF E and Q E subsets is
larger (1.4 dex, 1.7 dex difference between L1, L2 Q/SF Es, respectively),
while L3 bin is underrepresented for Q Es.

The evolution of the SFR is plotted in the bottom panel of Fig. 17. In the
case of U/LIRGs, a similar trend is found with the maximum occurring in M4
systems with a median value of 99 M⊙ yr-1. In M5 systems the median value is
lower (71 M⊙ yr-1) and a dramatic decrease is observed in L3 and L2 SF Es
(2.29 M⊙ yr-1 and 0.45 M⊙ yr-1, respectively). The decreasing trend of the SFR
with decreasing $L_{\text{IR}}$ continues in L1 SF Es (0.07 M⊙ yr-1). The same
trend is also observed for Q Es with 0.05 M⊙ yr-1 for L2 Q Es and 0.01 M⊙ yr-1
for L1 Q Es, respectively. A steep decrease in the SFR after the major merger
has been also predicted by simulations. For instance, for realistic
simulations of Milky Way-like galaxies at z=0, Hopkins et al. (2013b) finds
the SFR falling to $\sim$2 M⊙ yr-1 in post-merging systems. In an earlier
numerical study Springel et al. (2005) predicted that in the post-merger era,
the SFR decreases to levels lower than 10 M⊙ yr-1, with similar results also
reported by Cox et al. (2006) and Di Matteo et al. (2008). Deeper observations
that could reveal ‘hidden’ filamentary structures in the SF Es could confirm
their possible merger-induced origin. As was also suggested by Rowlands et al.
(2012) dusty ETGs, such as the SF Es in our sample, may be examples of post-
merging systems with low inventories of hot gas, where a major star-formation
event occurred a few Gyr before, creating their dust. This idea is also
supported by the low on-average Sérsic indices found for the SF Es which could
witness an imprint of a prior major merging event in these galaxies. Since
x-ray emission traces the hot-gas content, such kind of observations are
required to corroborate this scenario.

##  6 Summary and conclusions

In this work we have examined a number of SED-derived global physical
properties and the interplay between stars and dust of 2,209 GAMAnear galaxies
observed by HSO. We classified the sources in subgroups according to their
morphology and their dominant ionisation process (SF and Q galaxies). We
excluded the sources that were flagged as AGN according to our classification
method. By exploring the SFR-$M_{\text{star}}$ plane, we find that it is their
star-formation activity (i.e., if they are classified as SF or Q) and not
their morphological type that determines their place in the diagram. For
instance, we find ETGs (E and S0) that are classified as SF and occupy the
SFMS locus but also LTGs (Sa-Scd types) that are classified as Q and occupy
the space below the SFMS. We also investigated if different local environments
favour the existence of SF or Q galaxies of different morphologies.
Furthermore, we explored if galaxies of different morphologies and star-
formation activity show differences in their structural characteristics. Our
main conclusions are as follows:

  * •

The median SEDs of galaxies of different star-formation activity indicate
that, on average, SF galaxies, compared to their Q analogues, show enhanced
dust emission with the dust being warmer.

  * •

The place of a galaxy in the SFR-$M_{\text{star}}$ plane does not depend only
on its morphology but mainly on its ability to convert gas into stars. In the
GAMAnear sample, examined in this study, we find a large fraction (47%) of the
E/S0 types showing ongoing star-formation activity and 8% of Sa-Scds being
quiescent.

  * •

SF E/S0s and SF LBS occupy the well-known ‘star-forming main-sequence’ (SFMS),
previously known to be, mainly, populated by LTGs. This finding as well as the
fact that SF E/S0s and SF LBS show similar physical properties (e.g., dust-to-
stellar-mass ratio and sSFR) and structural properties (Sérsic index) indicate
that LBSs may be smaller, in size, analogues of the SF ETGs.

  * •

The low, average, dust-to-stellar-mass ratio found in Q Sa-Scds, compared to
their SF counterparts, and their enhanced luminosity originating by the old
stars indicate that their redder optical colours can be explained, not only
due to dust deficiency but also due to enhanced old stellar population.

  * •

The fraction of young stars in SF ETGs is found to be quite substantial (23%
and 25% for SF E/S0 and SF LBS, respectively) with this fraction being only 1%
for the Q E/S0. SF LTGs, on the other hand, show similar fractions of young
stars to that of SF ETGs (25% for both types, SF Sa-Scds and SF Sd-Irrs) with
this fraction being only 4% for the Q Sa-Scds.

  * •

A significant contribution to the bolometric luminosity is found to be
originating by dust ($f_{\mathrm{abs}}$) in SF ETGs (35% in SF E/S0s and 19%
in LBSs) in contrast to that found for Q E/S0s (6%). On the other hand, SF
LTGs show high values of $f_{\mathrm{abs}}$ (27% in SF Sa-Scd and 18% in SF
Sd-Irr) with the corresponding contribution by dust in Q Sa-Scds being 16%.

  * •

SF E/S0s use 24% and 74% of the luminosities originating from the old and the
young stars to heat up the dust, while these fractions for the Q E/S0s are
only 6% and 36%, respectively. SF Sa-Scds, on the other hand, use 16% and 59%
of the luminosities originating from the old and the young stars to heat up
the dust, while these fractions for the Q Sa-Scds are, surprisingly enough,
very similar 12% and 60%, respectively. SF LBS and SF Sd-Irr galaxies show
very similar fractions (11% and 10% of the luminosity of the old stars, and
44% and 42% of the luminosity of the young stars heating up the dust,
respectively).

  * •

For the SF Sa-Scd, SF Sd-Irr, and the SF LBS types, the contribution of the
young stars is, on average, the dominant source of dust heating (compared to
the old stars) with values of 57%, 59% and 61%, respectively. In Q Sa-Scd and
SF E/S0s there is a significant contribution in the dust heating from the
young stars (24% and 47%, respectively), while the dust in Q E/S0s is heated,
almost exclusively, by old stars (91%).

  * •

The SED analysis conducted using CIGALE indicates that SF galaxies of the same
bolometric luminosity, independently on their morphology, exhibit the same
(within the statistical uncertainties) unattenuated stellar SED. Q galaxies,
on the other hand, show different unattenuated SEDs.

  * •

Concerning the structural characteristics of the different types of galaxies
we find that, for ETGs, there is an increase, on average, of the effective
radius, with SF LBS being the less extended (median value of $R_{\text{e}}$=1
kpc), SF E/S0 being intermediate cases ($R_{\text{e}}$=2.2 kpc), and Q E/S0
being the most extended galaxies ($R_{\text{e}}$=4.3 kpc). LTGs, on the other
hand, show, on average, larger values of $R_{\text{e}}$, compared to ETGs,
with SF Sd-Irr being the most compact ones (median value of $R_{\text{e}}$=2.7
kpc), followed by SF Sa-Scd ($R_{\text{e}}$=4 kpc) and Q Sa-Scd
($R_{\text{e}}$=5.8 kpc). The r-band Sérsic index for SF ETGs (LBS, E/S0)
show, on average, lower indices (1.8, and 1.5, respectively) with the Q E/S0
showing a much steeper profile with $n_{\text{r}}=3.5$. The fact that SF ETGs
show relatively low values of the Sérsic index may indicate that, despite
their overall elliptical shape, an embedded disk component may present in the
centres of these galaxies. LTGs, on the other hand, show, on average, lower
values, compared to ETGs, consistent with exponential and more flattened
profiles for SF Sa-Scd and SF Sd-Irr ($n_{\text{r}}=1.2$ and 1, respectively)
with the Q Sa-Scd showing a steeper profile ($n_{\text{r}}=2.2$).

  * •

The local environment is found to affect star-forming activity in galaxies. We
find that dense environments tend to favour the existence of quiescent
galaxies in contrast to low-density environments which are mostly populated by
SF galaxies.

Our analysis indicates that a significant fraction of ETGs may exhibit current
star-formation activity with SFRs comparable to those of SF LTGs, while, on
the opposite side many LTGs show ceased star-formation activity similar to
quenched ETGs. A number of the SF ETGs in our sample show signs of
interactions or even host a disk-like or spiral-like structures at their
centres. Similar indications have been shown by others (e.g., Gomes et al.
2016, George 2017, Nyland et al. 2017). Eales et al. (2017) proposed that the
common hypothesis that ETGs are deficient in cold ISM is a consequence of the
low instrumental sensitivity. Deep imaging (e.g. JWST, ELT or ALMA
observations) might help reveal possible correlation between their enhanced
SFRs and the morphological disturbances. Furthermore, X-ray and radio
observations could shed light in the mechanisms that cease their star-forming
activity in the quiescent LTGs, such as the ram pressure stripping in
jellyfish galaxies. The results of the current work possibly indicate,
however, that the star-formation activity and galaxy morphology transitions
are not always linked needing different timescales to occur.

###### Acknowledgements.

We would like to thank the anonymous referee for the useful comments and
suggestions, which helped improving the quality of the manuscript. EDP
acknowledges the financial support from Greece and the European Union
(European Social Fund - ESF) through the Operational Programme ”Human
Resources Development, Education and Lifelong Learning” in the context of the
project ”Strengthening Human Resources Potential via Doctorate research -
Subaction 2: IKY grant program for doctoral candidates of Greek Universities”
(MIS-5113934), implemented by the State Scholarships Foundation (IKY). AN
acknowledges the support of the Research Foundation - Flanders (FWO
Vlaanderen). VAM acknowledges support by the Grant RTI2018-096686-B-C21 funded
by MCIN/AEI/10.13039/501100011033 and by ‘ERDF A way of making Europe’. We
would also like to thank Angel Ruiz (National Observatory of Athens) for
constructive discussions regarding the UltraNest algorithm. This research made
use of Astropy777http://www.astropy.org, a community-developed core Python
package for Astronomy (Astropy Collaboration et al., 2013, 2018) GAMA is a
joint European-Australasian project based around a spectroscopic campaign
using the Anglo-Australian Telescope. The GAMA input catalogue is based on
data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky
Survey. Complementary imaging of the GAMA regions is being obtained by a
number of independent survey programmes including GALEX MIS, VST KiDS, VISTA
VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage.
GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the
participating institutions. VISTA VIKING data are based on observations made
with ESO Telescopes at the La Silla Paranal Observatory under programme ID
179.A-2004.

## References

  * Abazajian et al. (2009) Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543
  * Agius et al. (2015) Agius, N. K., di Serego Alighieri, S., Viaene, S., et al. 2015, MNRAS, 451, 3815
  * Agius et al. (2013) Agius, N. K., Sansom, A. E., Popescu, C. C., et al. 2013, MNRAS, 431, 1929
  * Aihara et al. (2018) Aihara, H., Arimoto, N., Armstrong, R., et al. 2018, PASJ, 70, S4
  * Alam et al. (2015) Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12
  * Astropy Collaboration et al. (2018) Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123
  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33
  * Athanassoula et al. (2013) Athanassoula, E., Machado, R. E. G., & Rodionov, S. A. 2013, MNRAS, 429, 1949
  * Baldry et al. (2006) Baldry, I. K., Balogh, M. L., Bower, R. G., et al. 2006, MNRAS, 373, 469
  * Baldry et al. (2012) Baldry, I. K., Driver, S. P., Loveday, J., et al. 2012, MNRAS, 421, 621
  * Baldry et al. (2004) Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681
  * Baldwin et al. (1981) Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5
  * Barsanti et al. (2018) Barsanti, S., Owers, M. S., Brough, S., et al. 2018, ApJ, 857, 71
  * Bekki et al. (2002) Bekki, K., Couch, W. J., & Shioya, Y. 2002, ApJ, 577, 651
  * Bellstedt et al. (2020) Bellstedt, S., Robotham, A. S. G., Driver, S. P., et al. 2020, MNRAS, 498, 5581
  * Bernardi et al. (2003) Bernardi, M., Sheth, R. K., Annis, J., et al. 2003, AJ, 125, 1882
  * Bianchi et al. (2018) Bianchi, S., De Vis, P., Viaene, S., et al. 2018, A&A, 620, A112
  * Bitsakis et al. (2019) Bitsakis, T., Sánchez, S. F., Ciesla, L., et al. 2019, MNRAS, 483, 370
  * Blanton et al. (2003) Blanton, M. R., Hogg, D. W., Bahcall, N. A., et al. 2003, ApJ, 594, 186
  * Boquien et al. (2019) Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103
  * Brinchmann et al. (2004) Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151
  * Brough et al. (2013) Brough, S., Croom, S., Sharp, R., et al. 2013, MNRAS, 435, 2903
  * Bruzual & Charlot (2003) Bruzual, G. & Charlot, S. 2003, MNRAS, 344, 1000
  * Buchner (2016) Buchner, J. 2016, Statistics and Computing, 26, 383
  * Buchner (2019) Buchner, J. 2019, PASP, 131, 108005
  * Buchner (2021) Buchner, J. 2021, The Journal of Open Source Software, 6, 3001
  * Calvi et al. (2018) Calvi, R., Vulcani, B., Poggianti, B. M., et al. 2018, MNRAS, 481, 3456
  * Calzetti (2001) Calzetti, D. 2001, PASP, 113, 1449
  * Calzetti et al. (2000) Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682
  * Cano-Díaz et al. (2019) Cano-Díaz, M., Ávila-Reese, V., Sánchez, S. F., et al. 2019, MNRAS, 488, 3929
  * Chabrier (2003) Chabrier, G. 2003, PASP, 115, 763
  * Cid Fernandes et al. (2011) Cid Fernandes, R., Stasińska, G., Mateus, A., & Vale Asari, N. 2011, MNRAS, 413, 1687
  * Cid Fernandes et al. (2010) Cid Fernandes, R., Stasińska, G., Schlickmann, M. S., et al. 2010, MNRAS, 403, 1036
  * Ciesla et al. (2014) Ciesla, L., Boquien, M., Boselli, A., et al. 2014, A&A, 565, A128
  * Ciesla et al. (2016) Ciesla, L., Boselli, A., Elbaz, D., et al. 2016, A&A, 585, A43
  * Ciesla et al. (2015) Ciesla, L., Charmandaris, V., Georgakakis, A., et al. 2015, A&A, 576, A10
  * Clark et al. (2018) Clark, C. J. R., Verstocken, S., Bianchi, S., et al. 2018, A&A, 609, A37
  * Combes et al. (2013) Combes, F., García-Burillo, S., Braine, J., et al. 2013, A&A, 550, A41
  * Combes & Sanders (1981) Combes, F. & Sanders, R. H. 1981, A&A, 96, 164
  * Cortese et al. (2012) Cortese, L., Ciesla, L., Boselli, A., et al. 2012, A&A, 540, A52
  * Cox et al. (2006) Cox, T. J., Jonsson, P., Primack, J. R., & Somerville, R. S. 2006, MNRAS, 373, 1013
  * da Cunha et al. (2008) da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595
  * da Cunha et al. (2010) da Cunha, E., Charmandaris, V., Díaz-Santos, T., et al. 2010, A&A, 523, A78
  * Darvish et al. (2017) Darvish, B., Mobasher, B., Martin, D. C., et al. 2017, ApJ, 837, 16
  * Davies et al. (2019) Davies, J. I., Nersesian, A., Baes, M., et al. 2019, A&A, 626, A63
  * de Sá-Freitas et al. (2022) de Sá-Freitas, C., Gonçalves, T. S., de Carvalho, R. R., et al. 2022, MNRAS, 509, 3889
  * Di Matteo et al. (2008) Di Matteo, P., Bournaud, F., Martig, M., et al. 2008, A&A, 492, 31
  * di Serego Alighieri et al. (2013) di Serego Alighieri, S., Bianchi, S., Pappalardo, C., et al. 2013, A&A, 552, A8
  * Diaferio et al. (2001) Diaferio, A., Kauffmann, G., Balogh, M. L., et al. 2001, MNRAS, 323, 999
  * Donnari et al. (2019) Donnari, M., Pillepich, A., Nelson, D., et al. 2019, MNRAS, 485, 4817
  * D’Onofrio (2001) D’Onofrio, M. 2001, MNRAS, 326, 1517
  * Dressler (1980) Dressler, A. 1980, ApJ, 236, 351
  * Driver et al. (2011) Driver, S. P., Hill, D. T., Kelvin, L. S., et al. 2011, MNRAS, 413, 971
  * Driver et al. (2009) Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12
  * Driver et al. (2016) Driver, S. P., Wright, A. H., Andrews, S. K., et al. 2016, MNRAS, 455, 3911
  * Dubois et al. (2016) Dubois, Y., Peirani, S., Pichon, C., et al. 2016, MNRAS, 463, 3948
  * Dunne et al. (2000) Dunne, L., Eales, S., Edmunds, M., et al. 2000, MNRAS, 315, 115
  * Eales et al. (2017) Eales, S., de Vis, P., Smith, M. W. L., et al. 2017, MNRAS, 465, 3125
  * Eales et al. (2010) Eales, S., Dunne, L., Clements, D., et al. 2010, PASP, 122, 499
  * Elbaz et al. (2007) Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33
  * Ferreras & Silk (2000) Ferreras, I. & Silk, J. 2000, ApJ, 532, 193
  * Florez et al. (2020) Florez, J., Jogee, S., Sherman, S., et al. 2020, MNRAS, 497, 3273
  * Fontanot et al. (2009) Fontanot, F., De Lucia, G., Monaco, P., Somerville, R. S., & Santini, P. 2009, MNRAS, 397, 1776
  * Fraser-McKelvie et al. (2018) Fraser-McKelvie, A., Brown, M. J. I., Pimbblet, K., Dolley, T., & Bonne, N. J. 2018, MNRAS, 474, 1909
  * Fraser-McKelvie et al. (2016) Fraser-McKelvie, A., Brown, M. J. I., Pimbblet, K. A., et al. 2016, MNRAS, 462, L11
  * Fraser-McKelvie et al. (2021) Fraser-McKelvie, A., Cortese, L., van de Sande, J., et al. 2021, MNRAS, 503, 4992
  * Fraser-McKelvie et al. (2020) Fraser-McKelvie, A., Merrifield, M., Aragón-Salamanca, A., et al. 2020, MNRAS, 499, 1116
  * Fukugita et al. (2004) Fukugita, M., Nakamura, O., Turner, E. L., Helmboldt, J., & Nichol, R. C. 2004, ApJ, 601, L127
  * Gao & Solomon (2004) Gao, Y. & Solomon, P. M. 2004, ApJ, 606, 271
  * George (2017) George, K. 2017, A&A, 598, A45
  * George et al. (2019) George, K., Subramanian, S., & Paul, K. T. 2019, A&A, 628, A24
  * Géron et al. (2021) Géron, T., Smethurst, R. J., Lintott, C., et al. 2021, MNRAS, 507, 4389
  * Gomes et al. (2016) Gomes, J. M., Papaderos, P., Vílchez, J. M., et al. 2016, A&A, 585, A92
  * González Delgado et al. (2015) González Delgado, R. M., García-Benito, R., Pérez, E., et al. 2015, A&A, 581, A103
  * Gordon et al. (2017) Gordon, Y. A., Owers, M. S., Pimbblet, K. A., et al. 2017, MNRAS, 465, 2671
  * Goto et al. (2003) Goto, T., Okamura, S., Sekiguchi, M., et al. 2003, PASJ, 55, 757
  * Graham (2013) Graham, A. W. 2013, Elliptical and Disk Galaxy Structure and Modern Scaling Laws, ed. T. D. Oswalt & W. C. Keel, Vol. 6, 91–140
  * Gunn & Gott (1972) Gunn, J. E. & Gott, J. Richard, I. 1972, ApJ, 176, 1
  * Haines et al. (2015) Haines, T., McIntosh, D. H., Sánchez, S. F., Tremonti, C., & Rudnick, G. 2015, MNRAS, 451, 433
  * Hopkins et al. (2013a) Hopkins, A. M., Driver, S. P., Brough, S., et al. 2013a, MNRAS, 430, 2047
  * Hopkins et al. (2013b) Hopkins, P. F., Cox, T. J., Hernquist, L., et al. 2013b, MNRAS, 430, 1901
  * Huang & Gu (2009) Huang, S. & Gu, Q. S. 2009, MNRAS, 398, 1651
  * Hunt et al. (2019) Hunt, L. K., De Looze, I., Boquien, M., et al. 2019, A&A, 621, A51
  * James et al. (2002) James, A., Dunne, L., Eales, S., & Edmunds, M. G. 2002, MNRAS, 335, 753
  * Jogee et al. (2005) Jogee, S., Scoville, N., & Kenney, J. D. P. 2005, ApJ, 630, 837
  * Jones et al. (2017) Jones, A. P., Köhler, M., Ysard, N., Bocchio, M., & Verstraete, L. 2017, A&A, 602, A46
  * Joshi et al. (2020) Joshi, G. D., Pillepich, A., Nelson, D., et al. 2020, MNRAS, 496, 2673
  * Kalinova et al. (2021) Kalinova, V., Colombo, D., Sánchez, S. F., et al. 2021, A&A, 648, A64
  * Katsianis et al. (2020) Katsianis, A., Gonzalez, V., Barrientos, D., et al. 2020, MNRAS, 492, 5592
  * Kauffmann et al. (2003) Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055
  * Kauffmann et al. (1993) Kauffmann, G., White, S. D. M., & Guiderdoni, B. 1993, MNRAS, 264, 201
  * Kaviraj et al. (2007) Kaviraj, S., Schawinski, K., Devriendt, J. E. G., et al. 2007, ApJS, 173, 619
  * Kelvin et al. (2018) Kelvin, L. S., Bremer, M. N., Phillipps, S., et al. 2018, MNRAS, 477, 4116
  * Kelvin et al. (2014a) Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2014a, MNRAS, 439, 1245
  * Kelvin et al. (2012) Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2012, MNRAS, 421, 1007
  * Kelvin et al. (2014b) Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2014b, MNRAS, 444, 1647
  * Kennicutt (1983) Kennicutt, R. C., J. 1983, ApJ, 272, 54
  * Kennicutt (1998) Kennicutt, Robert C., J. 1998, ARA&A, 36, 189
  * Kennicutt & De Los Reyes (2021) Kennicutt, Robert C., J. & De Los Reyes, M. A. C. 2021, ApJ, 908, 61
  * Kewley et al. (2006) Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961
  * Knapen et al. (2002) Knapen, J. H., Pérez-Ramírez, D., & Laine, S. 2002, MNRAS, 337, 808
  * Kormendy et al. (2009) Kormendy, J., Fisher, D. B., Cornell, M. E., & Bender, R. 2009, ApJS, 182, 216
  * Kormendy & Kennicutt (2004) Kormendy, J. & Kennicutt, Robert C., J. 2004, ARA&A, 42, 603
  * Kruk et al. (2018) Kruk, S. J., Lintott, C. J., Bamford, S. P., et al. 2018, MNRAS, 473, 4731
  * Larson (1975) Larson, R. B. 1975, MNRAS, 173, 671
  * Larson et al. (1980) Larson, R. B., Tinsley, B. M., & Caldwell, C. N. 1980, ApJ, 237, 692
  * Lintott et al. (2008) Lintott, C. J., Schawinski, K., Slosar, A., et al. 2008, MNRAS, 389, 1179
  * Madau & Dickinson (2014) Madau, P. & Dickinson, M. 2014, ARA&A, 52, 415
  * Mahajan et al. (2018) Mahajan, S., Drinkwater, M. J., Driver, S., et al. 2018, MNRAS, 475, 788
  * Mahajan et al. (2015) Mahajan, S., Drinkwater, M. J., Driver, S., et al. 2015, MNRAS, 446, 2967
  * Mahajan et al. (2020) Mahajan, S., Gupta, K. K., Rana, R., et al. 2020, MNRAS, 491, 398
  * Makarov et al. (2014) Makarov, D., Prugniel, P., Terekhova, N., Courtois, H., & Vauglin, I. 2014, A&A, 570, A13
  * Martin et al. (2018) Martin, G., Kaviraj, S., Devriendt, J. E. G., Dubois, Y., & Pichon, C. 2018, MNRAS, 480, 2266
  * Masters et al. (2010) Masters, K. L., Mosleh, M., Romer, A. K., et al. 2010, MNRAS, 405, 783
  * Masters et al. (2011) Masters, K. L., Nichol, R. C., Hoyle, B., et al. 2011, MNRAS, 411, 2026
  * McIntosh et al. (2014) McIntosh, D. H., Wagner, C., Cooper, A., et al. 2014, MNRAS, 442, 533
  * Moffett et al. (2016) Moffett, A. J., Ingarfield, S. A., Driver, S. P., et al. 2016, MNRAS, 457, 1308
  * Moffett et al. (2019) Moffett, A. J., Phillipps, S., Robotham, A. S. G., et al. 2019, MNRAS, 489, 2830
  * Moore et al. (1996) Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Nature, 379, 613
  * Moran et al. (2006) Moran, S. M., Ellis, R. S., Treu, T., et al. 2006, ApJ, 641, L97
  * Mosenkov et al. (2019) Mosenkov, A. V., Baes, M., Bianchi, S., et al. 2019, A&A, 622, A132
  * Nersesian et al. (2019) Nersesian, A., Xilouris, E. M., Bianchi, S., et al. 2019, A&A, 624, A80
  * Noeske et al. (2007) Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43
  * Noll et al. (2009) Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793
  * Nyland et al. (2017) Nyland, K., Young, L. M., Wrobel, J. M., et al. 2017, MNRAS, 464, 1029
  * Paccagnella et al. (2016) Paccagnella, A., Vulcani, B., Poggianti, B. M., et al. 2016, ApJ, 816, L25
  * Pak et al. (2019) Pak, M., Lee, J. H., Jeong, H., et al. 2019, ApJ, 880, 149
  * Pappalardo et al. (2016) Pappalardo, C., Bizzocchi, L., Fritz, J., et al. 2016, A&A, 589, A11
  * Park et al. (2008) Park, C., Gott, J. Richard, I., & Choi, Y.-Y. 2008, ApJ, 674, 784
  * Partridge & Peebles (1967) Partridge, R. B. & Peebles, P. J. E. 1967, ApJ, 147, 868
  * Paspaliaris et al. (2021) Paspaliaris, E.-D., Xilouris, E. M., Nersesian, A., et al. 2021, A&A, 649, A137
  * Pearson et al. (2021) Pearson, W. J., Wang, L., Brough, S., et al. 2021, A&A, 646, A151
  * Peng et al. (2010a) Peng, C. Y., Ho, L. C., Impey, C. D., & Rix, H.-W. 2010a, AJ, 139, 2097
  * Peng et al. (2010b) Peng, Y.-j., Lilly, S. J., Kovač, K., et al. 2010b, ApJ, 721, 193
  * Pilbratt et al. (2010) Pilbratt, G. L., Riedinger, J. R., Passvogel, T., et al. 2010, A&A, 518, L1
  * Ravindranath et al. (2004) Ravindranath, S., Ferguson, H. C., Conselice, C., et al. 2004, ApJ, 604, L9
  * Renzini & Peng (2015) Renzini, A. & Peng, Y.-j. 2015, ApJ, 801, L29
  * Robotham et al. (2011) Robotham, A. S. G., Norberg, P., Driver, S. P., et al. 2011, MNRAS, 416, 2640
  * Roehlly et al. (2014) Roehlly, Y., Burgarella, D., Buat, V., et al. 2014, in Astronomical Society of the Pacific Conference Series, Vol. 485, Astronomical Data Analysis Software and Systems XXIII, ed. N. Manset & P. Forshay, 347
  * Rowlands et al. (2012) Rowlands, K., Dunne, L., Maddox, S., et al. 2012, MNRAS, 419, 2545
  * Salpeter (1955) Salpeter, E. E. 1955, ApJ, 121, 161
  * Sampaio et al. (2022) Sampaio, V. M., de Carvalho, R. R., Ferreras, I., Aragón-Salamanca, A., & Parker, L. C. 2022, MNRAS, 509, 567
  * Sarzi et al. (2006) Sarzi, M., Falcón-Barroso, J., Davies, R. L., et al. 2006, MNRAS, 366, 1151
  * Schawinski et al. (2007) Schawinski, K., Kaviraj, S., Khochfar, S., et al. 2007, ApJS, 173, 512
  * Schawinski et al. (2009) Schawinski, K., Lintott, C., Thomas, D., et al. 2009, MNRAS, 396, 818
  * Shimakawa et al. (2022) Shimakawa, R., Tanaka, M., Bottrell, C., et al. 2022, arXiv e-prints, arXiv:2203.00969
  * Skibba & Sheth (2009) Skibba, R. A. & Sheth, R. K. 2009, MNRAS, 392, 1080
  * Smith et al. (2012) Smith, M. W. L., Gomez, H. L., Eales, S. A., et al. 2012, ApJ, 748, 123
  * Sorensen et al. (1976) Sorensen, S. A., Matsuda, T., & Fujimoto, M. 1976, Ap&SS, 43, 491
  * Springel et al. (2005) Springel, V., Di Matteo, T., & Hernquist, L. 2005, MNRAS, 361, 776
  * Strateva et al. (2001) Strateva, I., Ivezić, Ž., Knapp, G. R., et al. 2001, AJ, 122, 1861
  * Tacchella et al. (2019) Tacchella, S., Diemer, B., Hernquist, L., et al. 2019, MNRAS, 487, 5416
  * Taylor et al. (2015) Taylor, E. N., Hopkins, A. M., Baldry, I. K., et al. 2015, MNRAS, 446, 2144
  * Thom et al. (2012) Thom, C., Tumlinson, J., Werk, J. K., et al. 2012, ApJ, 758, L41
  * Thomas et al. (2010) Thomas, D., Maraston, C., Schawinski, K., Sarzi, M., & Silk, J. 2010, MNRAS, 404, 1775
  * Tonry et al. (2000) Tonry, J. L., Blakeslee, J. P., Ajhar, E. A., & Dressler, A. 2000, ApJ, 530, 625
  * Toomre & Toomre (1972) Toomre, A. & Toomre, J. 1972, ApJ, 178, 623
  * Trussler et al. (2020) Trussler, J., Maiolino, R., Maraston, C., et al. 2020, MNRAS, 491, 5406
  * Vika et al. (2015) Vika, M., Vulcani, B., Bamford, S. P., Häußler, B., & Rojas, A. L. 2015, A&A, 577, A97
  * Vulcani et al. (2010) Vulcani, B., Poggianti, B. M., Finn, R. A., et al. 2010, ApJ, 710, L1
  * Wetzel et al. (2012) Wetzel, A. R., Tinker, J. L., & Conroy, C. 2012, MNRAS, 424, 232
  * Whitaker et al. (2012) Whitaker, K. E., van Dokkum, P. G., Brammer, G., & Franx, M. 2012, ApJ, 754, L29
  * Wolf et al. (2009) Wolf, C., Aragón-Salamanca, A., Balogh, M., et al. 2009, MNRAS, 393, 1302
  * Wright et al. (2016) Wright, A. H., Robotham, A. S. G., Bourne, N., et al. 2016, MNRAS, 460, 765
  * Wu & Zhang (2021) Wu, Y.-Z. & Zhang, W. 2021, MNRAS, 503, 2340
  * Wuyts et al. (2011) Wuyts, S., Förster Schreiber, N. M., van der Wel, A., et al. 2011, ApJ, 742, 96
  * Xu et al. (2022) Xu, K., Gu, Q., Lu, S., et al. 2022, MNRAS, 509, 1237
  * Yi et al. (2005) Yi, S. K., Yoon, S. J., Kaviraj, S., et al. 2005, ApJ, 619, L111

##  Appendix A Validation of the results

In order to investigate how accurately the derived parameters can be
constrained from the multi-wavelength SED fitting that CIGALE performs, we
examine the distribution of the reduced $\chi^{2}$ (estimated as the
$\chi^{2}$ divided by the number of observations minus the population of the
free parameters). The reduced $\chi^{2}$ distributions for different subsets
of our sample are presented in Fig. 18. The median reduced $\chi^{2}$ for all
the 2,209 sources modelled is 0.38, while it is 0.36 for the ETGs and 0.40 for
LTGs. Out of the sources modelled, 36 (1.6%) have $\chi^{2}_{\text{red}}$ ¿ 2
and only 5 (0.2%) have $\chi^{2}_{\text{red}}$ ¿ 4, with the highest
$\chi^{2}_{\text{red}}$ being equal to 15.3.

Figure 18: Distributions of the reduced $\chi^{2}$ for the 2,209 galaxies
modelled with CIGALE (black line). The distributions of ETGs and LTGs are
plotted in red and blue, respectively.

###  A.1 Comparison between CIGALE and MAGPHYS

Figure 19: Comparison between the CIGALE-derived (this work) and the MAGPHYS-
derived (Driver et al. 2016) properties of the galaxies in our sample.
$M_{\text{star}}$, $M_{\text{dust}}$, SFR, and $L_{\text{dust}}$ are shown in
the top-left, top-right, bottom-left, and bottom-right panels, respectively.

The GAMA sample investigated in the current work has already been studied by
Driver et al. (2016) and the basic parameters discussed in this paper have
been computed using a similar approach with the SED-fitting code MAGPHYS (da
Cunha et al. 2008). In Fig. 19 we compare what is computed in the current work
with what is provided in the GAMA survey DR3 file-server.

In the top-left panel of Fig. 19 we compare the stellar masses as derived by
the two codes. Different colors indicate different morphological types as
indicated in the top-right panel. For the comparison of the stellar masses one
needs to take into account that in da Cunha et al. (2008) the Chabrier (2003)
IMF is considered, while the Salpeter (1955) IMF is used in our analysis
leading in a systematic underestimation of $M_{\text{star}}$ (see also da
Cunha et al. 2010; Paspaliaris et al. 2021). To rescale stellar masses from
Chabrier to Salpeter IMF, we divide by a constant factor of 0.61 as indicated
in Madau & Dickinson (2014). The stellar masses seem to be in a good agreement
with roughly a $\pm$0.25 dex scatter and no obvious systematic offset (a
median value of the differences of 0.0006 dex).

In the top-right panel of Fig. 19 the comparison of the dust masses as derived
by MAGPHYS (da Cunha et al. 2008) and by CIGALE (current study) is presented.
Compared to what is found for the stellar masses, the data show a larger
dispersion, roughly $\pm$1 dex, with a median offset of 0.2 dex. This large
dispersion and offset are to be expected since a different SED model and a
different dust grain model are used in each fitting code. In CIGALE we use the
THEMIS dust model. THEMIS is based on the optical properties of amorphous
silicate and amorphous hydrocarbon materials measured in the laboratory (see
Jones et al. 2017, and references therein), with a dust absorption coefficient
$\kappa_{250{\mu}\text{m}}$ = 6.4 cm2g-1. On the other hand, as described in
da Cunha et al. (2008) MAGPHYS models the IR emission from stellar birth-
clouds as the sum of a component of polycyclic aromatic hydrocarbons (PAHs), a
mid-IR continuum component originating from hot grains (130-250 K) and a
component of grains in thermal equilibrium (adjustable temperature 30-60 K).
In addition, for the emission from the ISM it uses the latter three components
to reproduce the spectral shape of the Milky Way diffuse cirrus emission along
with a cold grain component in thermal equilibrium (adjustable temperature
15-25 K). The warm and cold components are described by modified blackbody
spectra and absorption cross section with spectral indices $\beta$ equal to
1.5 and 2.0, respectively, normalised to $\kappa_{850{\mu}\text{m}}$ = 0.77
cm2g-1 (Dunne et al. 2000; James et al. 2002).

The comparison of the SFR, as estimated by the two codes, is presented in the
bottom-left panel of Fig. 19. In order to account for the different IMFs used
between the two codes, we use a constant conversion factor of 0.63 to convert
from the Chabrier SFRs to Salpeter SFRs (see Madau & Dickinson 2014). A
systematic CIGALE underestimation of SFR is observed for sources with SFR less
than about one M${}_{\odot}\text{yr}^{-1}$. This might be explained by the
different SFH modules used in the two codes. In particular, the SFH used in
the current study is a late burst or quench superimposed to a more passively
evolving component, while the one used in MAGPHYS is a continuous
exponentially declining SFH, with additional random bursts of star formation.

In the case of dust luminosity (bottom-right panel) there is clear
underestimation of $L_{\text{dust}}$, as computed by CIGALE, especially
evident at low luminosities. A tendency of the MAGPHYS code to maximise the
dust content within the bounds defined by the errors of the FIR observations
has already been reported by Driver et al. (2011). Several other studies (e.g.
Pappalardo et al. 2016; Bianchi et al. 2018; Hunt et al. 2019) have also
reported a broader bump at the FIR emission, when MAGPHYS is used, especially
in the case where insufficient wavelength coverage exists in the range of
24-100 $\mu$m. This effect is clearly observed in SEDs of Fig. 20, where the
median templates fitted in this work using CIGALE (solid curves) are compared
with the ones produced by Driver et al. (2016) with MAGPHYS (dashed lines).
While the SEDs of Sab-Scd (blue) and Sd-Irr galaxies (yellow) are almost
identical up to the $\sim$10 $\mu$m regime, the FIR bump is broader in the
MAGPHYS SEDs and peaks at shorter wavelengths. Thus the overestimation of the
dust luminosity by MAGPHYS is expected, since in both cases it is calculated
by integrating the SEDs in the area of 8-1000 $\mu$m.

Figure 20: Comparison of the median SEDs fitted by CIGALE in the current work
(solid curves) and by MAGPHYS (dashed curves) presented in Driver et al.
(2016).