11institutetext: Thüringer Landessternwarte, 07778 Tautenburg, Germany

# A comparison of compact, presumably young with extended, evolved radio
active galactic nuclei

Helmut Meusinger and Mukul Mhaskey 1111

(Received XXXXXX; accepted XXXXXX)

###### Abstract

Context.  The triggering and evolution of active galactic nuclei (AGNs) and
the interaction of the AGN with its host galaxy is an important topic in
extragalactic astrophysics. Radio sources with peaked spectra (peaked spectrum
sources, PSS) and compact symmetric objects (CSO) are powerful, compact, and
presumably young AGNs and therefore particularly suitable to study aspects of
the AGN-host connection.

Aims.  We use a statistical approach to investigate properties of a PSS-CSO
sample that are related to host galaxies and could potentially shed light on
the link between host galaxies and AGNs. The main goal is to compare the PSS-
CSO sample with a matching comparison sample of extended sources (ECS) to see
if the two have significant differences.

Methods.  We analysed composite spectra, diagnostic line diagrams, multi-band
spectral energy distributions (MBSEDs), star formation (SF) indicators,
morphologies, and cluster environments for a sample of 121 PSSs and CSOs for
which spectra are available from the Sloan Digital Sky Survey (SDSS). The
statistical results were compared with those of the ECS sample, where we
generally considered the two subsamples of quasi-stellar objects (QSOs) and
radio galaxies separately. The analysis is based on a large set of archival
data in the spectral range from the ultraviolet to mid-infrared.

Results.  We find significant differences between the PSS-CSO and the ECS
sample. In particular, we find that the ECS sample has a higher proportion of
passive galaxies with a lower star formation activity. This applies to both
sub-samples (QSOs or radio galaxies) as well as to the entire sample. The star
formation rates of the PSS-CSO host galaxies with available data are typically
in the range $\sim 0$ to 5 $\mathcal{M}_{\odot}$ yr-1, and the stellar masses
are in the range $3\cdot 10^{11}$ to $10^{12}\mathcal{M}_{\odot}$. Secondly,
in agreement with previous results, we find a remarkably high proportion of
PSS-CSO host galaxies with merger signatures. The merger fraction of the PSS-
CSO sample is $0.61\pm 0.07$, which is significantly higher than that of the
comparison sample ($0.15\pm 0.06$). We suggest that this difference can be
explained by assuming that the majority of the PSSs and CSOs cannot evolve to
extended radio sources and are therefore not represented in our comparison
sample.

###### Key Words.:

Galaxies: active - Galaxies: quasars: general - Galaxies: evolution -
Galaxies: interactions - Radio continuum: galaxies

##  1 Introduction

Radio-loud active galactic nuclei (AGNs) are typically associated with
powerful bipolar radio jets (Tadhunter, 2016; Hardcastle & Croston, 2020;
Saikia, 2022). The jets transport energy and momentum from the accreting
supermassive black hole (SMBH) into the surrounding interstellar medium of the
host galaxy and the intra-cluster medium of a galaxy cluster. This so-called
jet (or kinematic) feedback is thought to have an impact on the fate of the
ambient gas and thus on the star formation (SF) history of galaxies (McNamara
& Nulsen, 2007; Fabian, 2012; Morganti, 2017; Venturi et al., 2021).

The projected size of the radio jets is usually larger than that of typical
host galaxies and can reach a total extent of up to several megaparsecs. On
the other side, there are also compact radio galaxies that are just as
energetic as the most luminous extended ones, but much smaller and confined
within their host galaxies. The gigahertz peaked spectrum (GPS) sources,
compact steep spectrum (CSS) sources, and the high-frequency peakers (HFPs),
collectively referred to as peaked spectrum sources (PSSs), belong to this
class of radio AGNs. In the milliarcsecond resolution radio images taken with
the very long baseline interferometry technique, these sources show a similar
morphology to typical radio AGNs, albeit with a linear size extending only to
a few kiloparsecs (Wilkinson et al., 1994; Stanghellini et al., 1997; Orienti
et al., 2006). The flat-spectrum radio core is surrounded by symmetric steep-
spectrum lobes on both sides, similar to the large-scale morphology of
extended radio galaxies. The radio spectra of PSSs deviate from the canonical
straight power law and are characterised by a concave (inverted) shape with
steep slopes on either side of the peak. The peak frequencies (in the rest
frame) are around 1 GHz for GPS, below 500 MHz for CSS, and above 5 GHz for
HFP sources. Synchrotron self-absorption by relativistic electrons inside the
radio-emitting source (Slish, 1963) or free-free absorption by ionised plasma
either inside or outside is responsible for the inverted shape of the radio
spectrum of the PSSs (Kellermann, 1966). A comprehensive review on the PSSs
was given by O’Dea (1998) and O’Dea & Saikia (2021).

The origin and evolution of the PSSs are still a matter of debate. Kinematic
ages derived from the motion of the hot spots indicate a young age (¡ 5000
years) for the radio activity (Owsianik & Conway, 1998; Polatidis & Conway,
2003; Gugliucci et al., 2005). This is also supported by the spectral age
estimates from modelling the high-frequency spectral break (Murgia et al.,
1999; Orienti & Dallacasa, 2010). Statistical studies, however, indicate an
overabundance of PSSs over the extended radio galaxies (Readhead et al., 1996;
O’Dea & Baum, 1997; An & Baan, 2012). Slob et al. (2022) assumed that the PSSs
selected at low frequencies (¡ 144 MHz) represent young radio galaxies. Their
conclusion is based on the analysis of the luminosity function and radio
source counts of PSSs, both of which are found to be scaled down by a
significant factor. In an alternative evolution scenario, the PSSs are
considered as ‘old and frustrated’ sources that are confined to the host
galaxies due to an over-dense interstellar medium (van Breugel et al., 1984;
Bicknell et al., 1997; Dicken et al., 2012). The high detection rate of H i
absorption amongst PSSs over extended radio galaxies may indicate that a high
proportion of PSSs are embedded in a rich interstellar medium (Morganti &
Oosterloo, 2018). Furthermore, for some PSSs, the intermittent nature of AGN
activity may lead to their truncated evolution (Reynolds & Begelman, 1997;
Kunert-Bajraszewska et al., 2006).

Concerning the present work, we briefly summarise the following findings on
the host galaxies of compact radio sources from O’Dea & Saikia (2021): The
typical hosts appear to be large elliptical galaxies with magnitudes around
the Schechter luminosity, but there are also exceptions with significant disk
components. The optical spectrum is dominated by an old stellar population,
but often current or ongoing SF is also indicated, where the star formation
rates (SFRs) are typically a few to tens $\mathcal{M}_{\odot}$ yr-1. The
optical emission line spectra are very similar to those of the extended radio
sources, both types can be divided into high-excitation radio galaxies (HERGs)
and low-excitation radio galaxies (LERGs). Some well-studied hosts of compact
radio sources are classified as galaxy mergers and a few systematic studies
indicate that mergers and interactions are common. Their environment appears
similar to that of powerful large radio sources and they are not predominantly
in rich galaxy clusters.

A critical source of information for a systematic investigation of the host
galaxies are optical spectra. Liao & Gu (2020) compiled a sample of 126
compact radio galaxies with spectroscopy available from the Sloan Digital Sky
Survey (SDSS; York et al., 2000). Besides GPS, CSS, and HFP sources, this
sample also includes Compact Symmetric Objects (CSOs), which have similar
properties as GPS radio galaxies. Liao & Gu (2020) derived the SMBH masses and
the Eddington ratios and found that they cover broad ranges with
$\log{\mathcal{M}}_{\rm SMBH}/\mathcal{M}_{\odot}\sim 7$ to 10 and $\log
R_{\rm Edd}\sim-4.9$ to 0.4. Nascimento et al. (2022) investigated the optical
and mid-infrared (MIR) properties of a sample of compact radio sources with
SDSS spectra consisting of 58 CSS or GPS sources and 14 Megahertz-Peaked-
Spectrum (MPS) sources at $z\leq 1$ from various radio-selection catalogues
publicly available in the literature. Based on stellar population synthesis,
they concluded that their sample is dominated by intermediate to old stellar
populations in a wide range of morphological galaxy types. The SFRs cover the
range from zero to $\sim 20\mathcal{M}_{\odot}$ yr-1, for most sources the SFR
is $\la 5\mathcal{M}_{\odot}$ yr-1. No strong correlation between the optical
and radio properties of these sources was found. Recently, Gordon et al.
(2023) investigated the relationship between SF and radio source size in a
sample of CSS sources and concluded that the bulk of SF ceased several hundred
Myr before the jet was triggered, where the broad range of SFRs is possibly a
consequence of episodic SF that can result from galaxy-galaxy interactions.
Using complete samples of CSOs, Kiehlmann et al. (2023) argued that their
relative numbers and the distributions of redshifts and sizes show
conclusively that most CSOs belong to a distinct population of jetted AGNs
that do not evolve into larger radio sources and should be described as
‘short-lived’, rather than ‘young’. Duggal et al. (2023) carried out a
systematic search for UV signatures from AGN feedback in CSS radio galaxies
based on new HST images. They claim that their observations are in line with
recent SF activity ‘likely triggered by current or an earlier episode of radio
emission, or by a confined radio source that has frustrated growth due to a
dense environment’.

The present work compares compact and presumably young radio galaxies from
Liao & Gu (2020) with old, extended radio galaxies. Special attention is paid
to the properties of the host galaxies. Because the Liao & Gu (2020) sample is
heterogeneous, O’Dea & Saikia (2021) warned that it may not match well with
other samples in the literature. For this reason, we have created a matched
‘comparison sample’ of extended radio galaxies and QSOs which we assume
represent advanced stages of radio source evolution. We look for significant
differences between the Liao & Gu (2020) sample and the comparison sample,
where we focus on three main aspects: (i) spectral energy distributions
(SEDs), in particular, composite spectra and multi-band SEDs (MBSEDs) from the
far ultraviolet (UV) to MIR and their implications for the stellar population
of the host galaxies, (ii) SF activity, and (iii) morphological indicators of
gravitational interactions and merging. Our study is based entirely on
existing archival data. It complements the study of Nascimento et al. (2022)
in which they compare the different types of compact sources and look for
correlations between optical/MIR and radio properties, while our work focuses
on the comparison of the sample of compact radio sources with the comparison
sample of extended sources. Of course, their sample of 72 compact radio
sources at $z\leq 1$ with spectra in SDSS Data Release (DR) 12 (Alam et al.,
2015) strongly overlaps with the sample of our study. The low-redshift ($z\leq
1$) subsample of the Liao & Gu (2020) sample contains 94 sources, the total
sample covers a larger redshift range up 3.6. We used the optical spectra from
the SDSS DR16 (Ahumada et al., 2020).

This paper is structured as follows. In Sect. 2, we describe our sample of
compact radio sources and explain in detail how the comparison sample of
extended radio sources was constructed. In Sect. 3, we discuss three different
diagnostic diagrams and compare the distributions of the spectral types from
the two samples. In Sect. 4, we use the SDSS composite spectra, the MBSEDs,
and the MIR data to discuss the spectral energy distributions. The SFRs and
stellar masses are discussed in Sect. 5. In Sect. 6, we determine the merger
fractions in the two samples and discuss possible reasons for the differences.
The summary is presented in Sect. 7. Additional data are provided in the
Appendices.

We assume Lambda Cold Dark Matter ($\Lambda$CDM) cosmology with $H_{0}=73$ km
s-1 Mpc-1, $\Omega_{\rm\Lambda}=0.73$, and $\Omega_{\rm M}=0.27$.

##  2 Samples

###  2.1 PSS-CSO sample

Liao & Gu (2020) collected all the radio samples of PSSs and CSOs available in
the literature and created a large sample of 250 CSS, 148 GPS, 116 HFP
sources, and 80 CSOs. By cross-identification with the SDSS, a sample of 147
sources with spectroscopic counterparts was selected from this, which they
called the ‘optical sample’. After removing blazar-type sources, which
contaminate this sample, their ‘final optical sample’ consists of 126 objects.
Liao & Gu (2020) and Liao & Gu (2021) have described the sample in detail and
listed several properties of the individual objects. The largest angular size
(LAS) and the largest linear size (LLS) of the radio structure are given there
for 109 sources (87%), all sources larger than 0.6 kpc are classified as CSS
sources. With one exception, LLS = 56 kpc for J090933.49+425346.5, all listed
sizes are smaller than 40 kpc.

In the present work, this PSS-CSO sample was slightly reduced for two reasons.
Firstly, as will be explained in Sect. 2.2 below, the comparison sample is
selected among extended radio AGNs with LLS $>50$ kpc in the Very Large Array
Sky Survey (VLASS; Lacy et al., 2020). For consistency, we therefore also
checked the extents of the PSSs and CSOs in the VLASS images. For eleven
objects that were found to be extended or possibly double we measured the FR
type and size (Table 2). All remaining objects do not appear to have been
resolved in our visual inspection of the VLASS images. Depending on the FR
type (Fanaroff & Riley, 1974), the LAS was identified with the distance
between the hot spots for FR II and with the extent of the 5$\sigma$ contour
for FR I and uncertain classifications, where $\sigma$ is the r.m.s. of the
local background. The three sources with LLS ¿ 50 kpc are shown in Fig. 1.
Traditionally the size limit has been put at 20 kpc for PSSs and CSOs (O’Dea &
Saikia, 2021). The PSS-CSO sample contains 4 sources with sizes larger than 50
kpc and seven sources with LLS between 20 and 50 kpc. For the present study,
we have thus decided to use a strict size limit of 50 kpc to distinguish
compact from extended sources. Therefore, we removed SDSS J090933.49+425346.5
and the three sources shown in Fig. 1. The case of J134536.94+382312.5 is not
entirely clear. We cannot be sure that the two radio sources seen in the image
are physically connected, but we cannot rule it out either. We decided to
remove this object from the sample as a precaution.

Secondly, we removed the CSS source 4C +39.29, which was assigned to SDSS
J101714.23+390121.1 at $z=0.211$ by Liao & Gu (2020). We argue that the host
galaxy of 4C +39.29 is SDSS J101714.10+390123.8 at $z=0.536$, for which no
SDSS spectrum is available. This interesting source is discussed in more
detail in Appendix A.

We refer to the remaining sample of 121 sources as the ‘PSS-CSO sample’. The
visual inspection of the SDSS spectra of all sources revealed incorrect
redshifts from the SDSS spectroscopic pipeline for SDSS J014109.16+135328.3
($z=0.621$ instead of 0.236 given by SDSS) and SDSS J080133.55+141442.8
($z=1.196$ instead of 0.246). We note that, in both cases, the correct
redshift is given by Liao & Gu (2020). For the source SDSS J085408.44+021316.1
we prefer the value $z=0.400$ from SDSS to the value 0.459 given by Liao & Gu
(2020). The PSS-CSO table containing the properties derived here is published
at CDS, an excerpt from the table (the first six lines) is given in Table 22.
With the exception of four sources outside the FIRST footprint area, the radio
luminosity $L_{\rm 1.4GHz}$ was calculated from the integrated 1.4 GHz flux
density provided by FIRST (Helfand et al., 2015). For the remaining four
sources, we took the flux densities from the
NED111https://ned.ipac.caltech.edu/. For all other data we refer to Liao & Gu
(2020).

Table 1: Sizes of sources from the Liao & Gu (2020) sample found to be
extended in the VLASS.

Name | FR type | LAS | LLS
---|---|---|---
|  | (”) | (kpc)
J080442.23+301237.0 | II | 4.4 | 37
J080447.96+101523.7 | II | 4.0 | 33
J085601.22+285835.4 | II | 7.6 | 60
J090105.25+290146.9 | I | 37.4 | 116
J101251.77+403903.4 | II | 5.9 | 23
J115919.97+464545.1 | II | 6.0 | 20
J131718.64+392528.1 | II | 8.3 | 33
J134536.94+382312.5 | II: | 13.0 | 107
J144712.76+404744.9 | II | 3.1 | 27
J154754.12+351842.2 | : | 6.6 | 40
J161823.57+363201.7 | II: | 4.7 | 33
222 The angular sizes are as measured from the VLASS images. A colon in the
second column indicates uncertain classification.

###  2.2 Selection of the comparison sample

Figure 1:  VLASS contours of the three sources from the Liao & Gu (2020)
sample with VLASS sizes larger than 50 kpc. The ten red contour lines are
logarithmically spaced between $\sim 5\sigma$ of the local noise and the
maximum flux of the source. The green dashed lines are negative contours at
$-5\sigma$ to $-3\sigma$. The background images are from the SDSS i band. The
vertical bar at the bottom indicates a length of 50 kpc at the redshift of the
source.

Figure 2:  Comparison of the PSSs and CSOs with extended radio galaxies and
quasars in the $z-L_{\rm 1.4GHz}-M_{\rm i}$ parameter space. Left and middle
column: Projections of the distributions of the PSS-CSO sample (blue filled
squares) and the extended comparison galaxies and quasars. The panels on the
left-hand side show the different contributions to the basic sample from which
the comparison sample is drawn (small symbols: red plus signs - Shen et al.
(2011), black plus signs - Garon et al. (2019), cyan filled squares - Rafter
et al. (2011), green plus signs - Kuźmicz & Jamrozy (2021), magenta filled
squares - Kozieł-Wierzbowska & Stasińska (2011)). In the middle column, the
final ECS sample (green) is compared with the PSS-CSO sample (blue). Right
column: histogram distributions of the PSSs and CSOs (blue) and the ECSs
(green).

As mentioned above, the Liao & Gu (2020) sample was inhomogeneously selected
and cannot be considered statistically complete and representative of the
entire population. In particular, their optical sample is biased towards low
redshifts and, as a consequence, towards low luminosities. For the present
study, it is thus of key importance to select a control sample in a consistent
way. The construction of such a ‘matched’ comparison sample consists of two
steps. First, a basic sample must be defined from which the control sample is
to be selected. Especially in the frame of the evolutionary scenario, it is
meaningful to compare the PSS-CSO sample with their evolved counterparts.
Because the physical size of a radio AGN is expected to grow with time,
extended radio galaxies can be considered later stages of evolution than
compact sources, if a similar environment is assumed. Therefore, a possible
way is to select the comparison galaxies from a basic sample of extended radio
galaxies and QSOs with sizes larger than the upper limit for the PSS-CSO
sources. For the present study we use a size limit of 50 kpc (Sect 2.1).

The main difficulty in creating such a basic sample of extended radio galaxies
is the fact that only a small percentage of radio sources identified in SDSS
show extended radio emission (Ivezić et al., 2002) and not all of them have
SDSS spectra. As a result of an extensive literature search, we found several
suitable data collections based on radio data from the Faint Images of the
Radio Sky at Twenty-Centimeters (FIRST; Becker et al., 1995).

Garon et al. (2019) extensively discussed a sample of 4304 extended radio
galaxies from Radio Galaxy Zoo (RGZ). Their selection was restricted ‘to
sources with at least a 0.65 consensus in the radio component classification
from RGZ to remove sources with ambiguous structure, as well as non-physical
associations between radio lobes and coincident galaxies’. The host galaxies
were identified via cross-matching with the images from the Wide-Field
Infrared Survey Explorer (WISE; Wright et al., 2010), redshift data were taken
from SDSS. Here, we extracted the subsample of the 1743 sources with
spectroscopic redshifts. Kuźmicz & Jamrozy (2021) presented a sample of 174
newly discovered giant radio quasars (GRQs; their Table 1), which are defined
as having a projected linear size greater than 700 kpc, and 367 smaller, lobe-
dominated radio quasars found during their search for GRQs (their Table 2).
This search was based on the cross-match of spectroscopically identified
quasars and quasar candidates from SDSS with the NRAO Very Large Array Sky
Survey (NVSS; Condon et al., 1998) and the FIRST survey. Another list of
extended radio galaxies with SDSS spectroscopy was provided by Kozieł-
Wierzbowska & Stasińska (2011), who created a sample of 401 FR II radio
galaxies (no quasars) that have optical counterparts with SDSS spectra.
Furthermore, we included the sample of 63 broad-line AGNs from the SDSS with
extended emission in FIRST from Rafter et al. (2011).

All these data cover the redshift range $z>1$ only insufficiently. Therefore,
we added a subsample of quasars from the ‘Catalog of Quasar Properties from
SDSS DR7’ (Shen et al., 2011). To include radio properties, Shen et al. (2011)
matched the SDSS DR7 quasar catalogue with the FIRST catalogue with a matching
radius of $30\arcsec$ (corresponding to $\sim 250$ kpc at $z=2$) and flagged
those quasars which have multiple FIRST sources within $30\arcsec$ as lobe-
dominated (FIRST = 2). We extracted a list of 2105 lobe-dominated quasars with
$z<4$ from that catalogue. Unlike the other data sources described above, the
Shen et al. (2011) catalogue does not contain information of the LAS of the
FIRST source. In the first step, we identified the LAS with the largest
distance between two radio components from the FIRST catalogue within an
aperture of 500 kpc diameter centred on the respective target, where only
sources with a probability of being a side lobe less than 0.1 were considered.
The final sizes were measured more precisely on the VLASS images when
selecting the comparison sample (see below).

The combination of the above-mentioned five compilations results in a total of
3930 sources covering a wide range of radio power and size, with the vast
majority having LLS $>50$ kpc in the literature. As discussed above, we use a
size limit of 50 kpc and define the sample of 3875 FIRST-SDSS sources with LLS
$>50$ kpc as the basic sample for selecting our comparison sample. The left
column of Fig. 2 illustrates the distributions of $z$, radio luminosity
$L_{\rm 1.4GHz}$ at 1.4 GHz (rest frame), and absolute i band magnitude
$M_{\rm i}$. The latter two were $k$-corrected following, for example,
Kennefick & Bursick (2008), where we assumed $F_{\nu}\propto\nu^{\,\alpha}$
with a mean spectral index $\alpha_{\rm o}=-0.5$ in the optical and
$\alpha_{\rm r}=-0.7$ in the radio. This sample of extended radio galaxies
with SDSS spectroscopy populates a region of parameter space that is similar
but not identical to that of the PSS-CSO sample. In particular, it extends
towards fainter 1.4 GHz luminosities, illustrating the well-known fact that
PSSs and CSOs are among the most luminous radio AGNs. We note that all radio
sources from this sample are more luminous than the threshold $L_{\rm
1.4GHz}=10^{23}$ W Hz-1 above which the radio luminosity distribution of radio
galaxies is dominated by AGNs (Sadler et al., 2002).

In the second step, the comparison sample was selected. Liao & Gu (2020)
explicitly excluded relativistically beamed sources from their sample. To
obtain a comparison sample that is also free from such sources, we have
reduced the basic sample only to objects with steep radio spectra. In
addition, we also checked the radio morphology and excluded compact, core-
dominated sources in a later step (see below). We used the SPECFIND V3.0
catalogue (Stein et al., 2021) and found that 978 sources from our basic
sample have steep radio spectra with $\alpha_{\rm r}<-0.5$ measured at two or
more frequencies $\nu>1$ GHz. This is the parent sample from which the
comparison sample was finally drawn. The comparison sample was selected so
that the distributions of the main physical parameters are as similar as
possible to those of the PSS-CSO sample. Here, we consider the redshift $z$,
the radio luminosity $L_{\rm 1.4GHz}$, the i band absolute magnitude $M_{\rm
i}$, and the SDSS spectral class as the most relevant properties. In
particular, this means that for a PSS-CSO with a host-dominated spectrum, the
host galaxy of the corresponding source in the comparison sample has a similar
mass. In practice, the apparent SDSS i band magnitude and the radio loudness
$R_{i}$ are used after $z$ is fixed.333$R_{i}$ is the decadic logarithm of the
ratio of the 1.4 GHz flux to the flux in the SDSS i band without including the
$k$-corrections (Ivezić et al., 2002). That is, we selected for each PSS-CSO
$k$ with $(z_{k},i_{k},R_{i,k})$ a counterpart from the basic sample of
extended sources with $(z,i,R_{i})$ within $(z_{k}\pm\Delta z,i_{k}\pm\Delta
i,R_{i,k}\pm\Delta R_{i})$, where the interval widths $\Delta z,\Delta i$, and
$\Delta R_{i}$ were chosen as small as possible. The resulting comparison
sample thus has the same size as the PSS-CSO sample, that is, it consists of
121 sources. We henceforth refer to these sources as ‘extended comparison
sources’ (ECSs).

Finally, we used the VLASS-SDSS overlays to determine the FR type and the LAS
of the selected ECSs to ensure that they are indeed larger than 50 kpc. The
LAS was measured in the same way as for the PSSs and CSOs (Sect. 2.1).
Compact, core-dominated and unclear or suspicious radio sources were removed
from the selection and replaced by other sources from the parent sample in an
iterative process. In the final sample, 112 ECSs were classified as FR II and
9 as FR I, where for both FR types the classification is uncertain due to more
complex structures for $\sim$10% of the sources. A mosaic of the VLASS-SDSS
overlays is shown in Figure 25. Figure 3 shows the histogram distributions of
the radio slope $\alpha_{\rm r}$ (left) and the LLS (right). The full table of
the ECS sample is published in electronic form at CDS, an excerpt is shown in
Table 23.

Figure 3:  Frequency distribution of the spectral slope $\alpha_{\rm r}$
(left) and the LLS (right) for the ECS sample.  Figure 4:  Power - linear size
diagram for the PSS-CSO (blue) and the ECS (green) samples. Left arrows
indicate upper limits for the LLS. For the PSS-CSO sample, the sizes are taken
from Liao & Gu (2020). The dashed lines depict evolutionary tracks from An &
Baan (2012) for sources of high radio-power (red) and low radio-power (blue).

In the middle column of Fig. 2, the ECS positions in the parameter space
$z-M_{\rm i}-\log\,L_{\rm 1.4\,GHz}$ are compared with those of the PSSs and
CSOs. The histogram distributions of $\log\,L_{\rm 1.4GHz},M_{\rm i}$, and $z$
are shown in the right column. It is obvious that both samples populate almost
the same area of the parameter space. A small but notable difference is the
lack of ECSs with $\log\,L_{\rm 1.4GHz}>28.4$. On the other hand, adopting a
threshold $R_{i}=1$ to distinguish radio-quiet from radio-loud sources, 100%
of the ECS and 98% of the PSS-CSO sample are radio-loud. The two sources
J103719.33+433515.3 ($R_{i}=0.61$) and J105731.17+405646.1 ($R_{i}=0.01$),
which have the lowest redshifts ($z=0.025$) and the lowest 1.4 GHz luminosity,
are not radio-loud according to our criterion. As can be seen in Fig. 2, the
parameter space around these two sources is almost empty. This means that the
selection intervals $\Delta z,\Delta i$, and $\Delta R_{i}$ had to be extended
considerably. Both selected ECS counterparts are radio-loud. We performed the
two-sided two-sample Kolmogorov-Smirnov (KS) test with the null hypothesis
${\rm H}^{0}$ that the histogram distributions in the panels of the right
column of Fig. 2 are the same for the two samples against the alternative
hypothesis ${\rm H}^{\rm A}$ that they are different. ${\rm H}^{0}$ is
discarded in favour of ${\rm H}^{\rm A}$ if the test statistic exceeds a
critical value that depends on the significance level $\alpha$. The test
result says that there is no reason to discard ${\rm H}^{0}$ for all three
quantities at a significance level $\alpha=0.05$ (i.e. the probability of
mistakenly not rejecting ${\rm H}^{0}$ is $<0.05$). We also note here that the
proportion of the SDSS spectral class GALAXY is very similar for the two
samples: $43\pm 5$ % for the ECSs and $45\pm 5$ % for the PSS-CSO sample.

Fig. 4 shows the PSS-CSO and the ECS samples in the power-size diagram. The
LLSs of the PSSs and CSOs are from Liao & Gu (2020). The size distributions of
the two samples do not overlap. We over-plotted two evolutionary pathways from
An & Baan (2012) based on parametrised models. It is obvious that the
positions of the selected ECSs are not inconsistent with the assumption that
they represent later evolutionary stages of sources similar to those from the
PSS-CSO sample.

##  3 Diagnostic emission line diagrams

Figure 5:  Diagnostic diagrams for the galaxies (SDSS spectral class = GALAXY)
from the PSS-CSO (blue) and the ECS (green) samples with $\mbox{S/N}\geq 3$
for used lines. Top: [N ii] based BPT diagram for $z<0.35$ with the upper
limit for SF galaxies from Kewley et al. (2006) (dashed), the lower limit for
AGNs from Kauffmann et al. (2003a) (dotted), and the separation between AGNs
and LINERs from Schawinski et al. (2007) (dashed). Middle: KEx diagram with
demarcation lines from Zhang & Hao (2018). Bottom: WHAN diagram with
demarcation lines from Cid Fernandes et al. (2011).

Figure 6:  Distributions of the types from the diagnostic diagrams in Fig. 5
for the galaxies from the PSS-CSO (blue) and the ECS (green) samples: [N
ii]-based BPT diagram (top), KEx diagram (middle), and WHAN diagram (bottom).
The vertical bars indicate the standard errors of the proportions.  Table 2:
Properties (mean values with standard errors of the mean) of galaxies (SDSS
spectral class = GALAXY) from the PSS-CSO and the ECS samples.

| PSS-CSO sample | ECS sample
---|---|---
$\log\,[{O\textsc{iii}}]$/H$\beta$\ a\ afootnotemark: $\ a$ |  $0.32\pm 0.06$ (29) |  $0.41\pm 0.08$ (19)
$\log\,[{N\textsc{ii}}]$/H$\alpha$\ a\ afootnotemark: $\ a$ |  $0.10\pm 0.03$ (29) |  $-0.05\pm 0.04$ (32)
$N_{\rm HERG}/N_{\rm LERG}$\ a\ afootnotemark: $\ a$ |  $0.17\pm 0.12$ (27) |  $0.50\pm 0.35$ (19)
EW($[{O\textsc{ii}}]$) ($\AA$)\ b\ bfootnotemark: $\ b$ |  $-27.4\pm 2.7$ (51) |  $-12.1\pm 2.1$ (29)
EW(H$\beta$) ($\AA$)\ b\ bfootnotemark: $\ b$ |  $-2.9\pm 0.4$ (35) |  $-2.6\pm 0.7$ (23)
EW($[{O\textsc{iii}}]$) ($\AA$) \ b\ bfootnotemark: $\ b$ |  $-8.9\pm 1.3$ (49) |  $-11.1\pm 0.4$ (29)
$\log\,[{O\textsc{iii}}]$/H$\beta$\ b\ bfootnotemark: $\ b$ |  $0.41\pm 0.06$ (38) |  $0.5\pm 0.1$ (23)
$\sigma_{\rm[OIII]}\,(\mbox{km\,s}^{-1})$\ b\ bfootnotemark: $\ b$ |  $259\pm 16$ (50) |  $208\pm 15$ (38)
D4000n\ b\ bfootnotemark: $\ b$ |  $1.70\pm 0.04$ (41) |  $1.88\pm 0.02$ (41)
log ${\mathcal{M}_{\ast}/\mathcal{M}_{\odot}}$\ c\ cfootnotemark: $\ c$ |  $11.28\pm 0.06$ (19) |  $11.23\pm 0.06$ (27)
log SFR ($\mathcal{M}_{\odot}$/yr)\ c\ cfootnotemark: $\ c$ |  $-1.64\pm 0.45$ (19) |  $-2.08\pm 0.36$ (27)
log sSFR (Gyr-1)\ c\ cfootnotemark: $\ c$ |  $-3.92\pm 0.49$ (19) |  $-4.32\pm 0.38$ (27)
merger fraction $f_{\rm m}$\ d\ dfootnotemark: $\ d$ |  $0.61\pm 0.07$ (43) |  $0.15\pm 0.06$ (41)
444 The upper index in the first column refers to the selection criteria for
the corresponding subsamples: $a$$a$footnotetext: $z<0.35$, S/N $>3$;
$b$$b$footnotetext: all galaxies with S/N $>3$; $c$$c$footnotetext: data from
Chang et al. (2015) with flag = 1; $d$$d$footnotetext: low-$z$ galaxy sample
($z<0.3$). The number of objects used for the statistics is given in brackets.

The BPT diagrams of diagnostic line ratios (Baldwin et al., 1981), in
particular the [O iii] 5007Å/H$\beta$ versus [N ii] 6585Å/H$\alpha$ diagram,
are commonly used to classify emission line galaxies. (Hereafter, we refer to
these lines as [O iii] and [N ii].) For line data from SDSS spectra, the
diagram applies to sources at $z\la 0.35$. The top panel of Fig. 5 shows the
[N ii]-based BPT diagram for the galaxies (SDSS spectral class GALAXY) with
$z<0.35$ and a signal-to-noise ratio S/N $>3$ for all four lines. The line
ratios were computed from the line fluxes, we did not try to correct for
intrinsic reddening. The fluxes and the equivalent width (EWs) are available
from the SDSS table galSpecLine for the majority of the sources and were
measured by us from the SDSS spectra for the rest. The mean ratios are listed
in Table 4. The line ratios plotted in the BPT diagram can be used to
distinguish between high-excitation radio galaxies (HERGs) and low-excitation
radio galaxies (LERGs). With the criterion of Buttiglione et al. (2010) we
find 23 LERGs and 4 HERGs among the PSS-CSO galaxies with $\mbox{S/N}>3$, to
be compared with 12 LERGs and 6 HERGs for the ECS sample. The ECS sample thus
appears to contain a higher proportion of HERGs (Table 4). However, the ECS
sample also contains a higher proportion of galaxies with low-S/N spectra that
are not included in the diagram and are most likely LERGs (see below). All
HERGs are classified as Seyferts in the BPT diagram.

Zhang & Hao (2018) proposed the so-called kinematics-excitation (KEx) diagram
that uses the [O iii] emission line dispersion,
$\sigma_{\rm[{O\textsc{iii}}]}$, in combination with the [O iii]/H$\beta$ line
ratio and can be applied up to $z\sim 0.9$. The basic physics behind it is the
difference between AGNs and SF galaxies in the kinematics of the ionised gas
in combination with the correlation between gas kinematics, stellar mass, and
metallicity. (The [N ii]/H$\alpha$ ratio is known to correlate with the
metallicity in SF galaxies.) The KEx diagram for the galaxies from the PSS-CSO
and the ECS samples is shown in the middle panel of Fig. 5. Our data confirm
the finding of previous studies (O’Dea & Saikia, 2021, and references therein)
that compact sources have larger [O iii] line widths than extended ones.
According to the two-sided KS test with $\alpha=0.05$, our PSS-CSO sample and
the ECS sample have significantly different distributions of the line
dispersion $\sigma_{\rm[OIII]}$. The mean value is 25% higher for the former
than for the latter (Table 4). We applied the one-sided T test for two
independent means to test ${\rm
H}^{0}:\overline{\sigma}_{\rm[OIII]}^{\rm\,PSS}=\overline{\sigma}_{\rm[OIII]}^{\rm\,ECS}$
against ${\rm H}^{\rm
A}:\overline{\sigma}_{\rm[OIII]}^{\rm\,PSS}>\overline{\sigma}_{\rm[OIII]}^{\rm\,ECS}$
at a significance level of $\alpha=0.05$. The result is that ${\rm H}^{0}$
must be clearly rejected in favour of ${\rm H}^{\rm A}$ ($p=0.01$).

In both the BPT diagram and the KEx diagram, the two samples show similar
distributions of the line ratios. The KS test shows that there is no reason to
reject the null hypothesis that the PSS-CSO and the ECS samples come from the
same distribution at a significance level $\alpha=0.05$. However, the
selection constraint S/N $>3$ excludes a significant proportion of galaxies in
both samples. In the BPT diagram, 17% of the PSSs and CSOs are missing because
they have only weak emission lines, especially weak H$\beta$ lines. For the
ECS sample, however, this proportion is about twice as high (39%). This
difference is significant according to the two-sided Z test ($p=0.005$).

The bottom panel of Fig. 5 shows the WHAN diagram (Cid Fernandes et al.,
2011), which uses only the EW of the H$\alpha$ line in combination with the
line ratio [N ii]/H$\alpha$. It was developed as a robust and economic
classification scheme to cope with the large population of weak-line galaxies
in SDSS. Following Cid Fernandes et al. (2011), five different regions can be
distinguished: pure star-forming galaxies (SF), strong AGNs (sAGN; e.g.
Seyferts), weak AGNs (wAGN; e.g. LINERS), retired galaxies (RG; i.e. galaxies
that have stopped SF and are ionised by evolved low-mass stars), and passive,
basically lineless galaxies. The majority of the ECS galaxies, 67%, are
classified as retired or passive, compared to 14% in the PSS-CSO sample. This
difference is highly significant and the KS test yields a significant
difference between the EW(H$\alpha$) distributions of the two samples
($p<10^{-5}$).

Next, we calculated the number of objects in the regions indicated by the
demarcation lines of the three diagnostic diagrams. The resulting histogram
distributions are shown in Fig. 6. To find out whether these differences are
significant or just by chance, we used Pearson’s chi-square test for two
independent samples. There is no reason to reject the null hypothesis of no
difference between the PSS-CSO and the ECS samples at $\alpha=0.05$ for the [N
ii]-based BPT diagram and the KEx diagram. However, the WHAN diagram reveals a
highly significant difference ($p=4\cdot 10^{-5}$) with a significantly larger
proportion of weak-line galaxies in the ECS sample.

##  4 Spectral energy distribution

Figure 7:  Composite spectra for the PSS-CSO (top) and the ECS (bottom)
sample. The QSO subsamples are shown in blue, the galaxy samples in red. The
former were corrected for slight internal reddening using the SMC dust model
to match the composite spectrum of the FBQS radio-loud quasars from Brotherton
et al. (2001) (magenta). The spectra for the galaxy subsamples were shifted
upwards by 2.5 flux units for clarity. The green spectra are best-fit models
for the continuum (see text). The positions of the strongest emission lines
are indicated by dotted vertical lines and labelled by ion in the lower panel.

###  4.1 SDSS composite spectra

Table 3: Diagnostic line ratios measured from the composite spectra. sample | $\log\,[{O\textsc{ii}}]/[{Ne\textsc{v}}]$ | $\log\,[{O\textsc{ii}}]/[{O\textsc{iii}}]$ | $\log\,[{O\textsc{iii}}]/\mbox{H}\beta$ | $\log\,[{N\textsc{ii}}]/\mbox{H}\alpha$ | FWHM [OIII] (Å)
---|---|---|---|---|---
PSS-CSO-Q | $0.88\pm 0.04$ | $-0.33\pm 0.03$ |  $1.01\pm 0.10$ |  $0.26\pm 0.07$ | $13.1\pm 0.3$
ECS-Q | $0.52\pm 0.01$ | $-0.65\pm 0.02$ |  $1.03\pm 0.04$ | $-0.16\pm 0.05$ | $8.1\pm 0.2$
PSS-CSO-G | $1.09\pm 0.09$ |  $0.09\pm 0.03$ |  $1.29\pm 0.24$ |  $0.13\pm 0.05$ | $7.6\pm 0.2$
ECS-G | $0.75\pm 0.08$ | $-0.11\pm 0.06$ |  $1.63\pm 0.20$ |  $0.08\pm 0.05$ | $7.3\pm 0.1$

Composite spectra provide insight into average spectral properties of AGN
samples (e.g. Francis et al., 1991; Brotherton et al., 2001; Vanden Berk et
al., 2001; Reichard et al., 2003; Pol & Wadadekar, 2017; Ren et al., 2022). We
followed the approach described in detail by Vanden Berk et al. (2001). The
spectra were downloaded from SDSS DR16 and inspected individually to check the
redshifts and spectral classification (see also Sect. 2.1). Next, the spectra
were corrected for foreground extinction using $E(B-V)$ from Schlafly &
Finkbeiner (2011) provided by IRSA’s Galactic Dust Reddening and Extinction
Service555https://irsa.ipac.caltech.edu/applications/DUST/docs/background.html.
The corrected spectra were rebinned to a common wavelength scale in their
restframe. Afterwards, the corrected spectra were normalised, i.e. the spectra
were ordered by redshift, the flux density of the lowest-$z$ spectrum was
arbitrarily scaled and the other spectra were scaled in order of redshift to
the average of the flux density in the common wavelength region of the mean
spectrum of all the lower-$z$ spectra. Finally, the median flux density and
the standard deviation of all scaled spectra were determined in each
wavelength bin.

It is useful to consider the composite spectra of quasars and galaxies
separately. Therefore, we subdivided the PSS-CSO sample into two sub-samples
differentiated by spectral class (G = GALAXY and Q = QSO), i.e. PSS-CSO-Q and
PSS-CSO-G. In the same way, the ECS sample is subdivided into the two
subsamples ECS-Q and ECS-G. The final arithmetic median composite rest-frame
spectra are shown in Fig. 7 with the two PSS-CSO composites at the top and the
ECS composites at the bottom. For comparison, the composite spectrum of radio-
loud quasars from the FIRST Bright QSO Survey (FBQS; Brotherton et al., 2001)
is overplotted (magenta). All composite spectra have been scaled such that the
mean flux density has the value of 1 in the QSO continuum window $3030-3090$
Å, but the spectra for the galaxy subsamples were shifted upwards by 2.5 flux
units for clarity. The continua of the four composites were fitted by model
spectra (see Sect. 4.1.2), the best-fitting model spectra are also plotted
(green). At first glance, the composites for the same spectral class are quite
similar. The more subtle differences are discussed below.

####  4.1.1 Emission lines and line ratios

The arithmetic median preserves the relative fluxes along the spectra, the
composites can thus be used to measure diagnostic line ratios. We measured the
(arbitrarily scaled) line fluxes from our composite spectra after subtraction
of a modelled continuum (Sect. 4.1.2). The [N ii]-H$\alpha$ complex was de-
blended using a four-component Gaussian fit with a broad H$\alpha$ component
and three narrow components for H$\alpha$, [N ii] $\lambda$ 6549Å, and [N ii]
$\lambda$ 6585Å. For the measurement of the H$\beta$ line, a two-component
Gaussian fit was used for the narrow and the broad line components. Only the
narrow Balmer components were used for the line ratios [N ii]/H$\alpha$ and [O
iii]/H$\beta$. The results (Table 3) can be compared with the diagnostic
diagrams in Sect. 3. For all four subsamples, the line ratios from the
composite spectra are clearly in the region of strong AGNs in the BPT and the
KEx diagram. In accordance with Table 4, the ECS-G sample has a higher line
ratio $[{O\textsc{iii}}]/\mbox{H}\beta$ than the PSS-CSO-G sample.666We note
that the line ratios $[{O\textsc{III}}]/\mbox{H}\beta$ and
$[{N\textsc{II}}]/\mbox{H}\alpha$ from the composite spectra are larger than
those in Table 4. This is only partly due to the different ways of averaging
(logarithmic mean in Table 4, arithmetic median in Table 3). The main
difference lies in the inclusion of $S/N<3$ spectra, and thus of objects with
weak Balmer lines in the composite spectra. The difference is marginal for the
QSO subsamples. In the WHAN diagram, three subsamples are also in the region
of strong AGNs, while the ECS-G subsample is close to the border between weak
AGNs and retired galaxies, which reflects the weak H$\alpha$ line in the
composite spectrum.

All four composites in Fig. 7 show relatively strong forbidden lines [O iii]
$\lambda$ 5007Å and [O ii] $\lambda$ 3726Å 777This is actually a line doublet
at 3725 Å and 3727 Å. (hereafter [O iii] and [O ii]). Liao & Gu (2020) found a
significant correlation between the [O III] line luminosity and the 5 GHz
luminosity in their PSS-CSO sample. A correlation of the relative strength of
[O iii], radio loudness and radio luminosity is known also from other studies
(Andika et al., 2020; Wang et al., 2022). With this in mind, strong lines are
to be expected because the radio sources in our samples are radio-loud and
belong to the upper end of the radio luminosity distribution at their redshift
(Sect. 2.2). Radio jets may play an important role in ionising the narrow line
region (NLR) gas and the [O iii] and [O ii] lines could be significantly
amplified by shocks caused by strong jet interaction with the line emitting
gas clouds (e.g. Best et al., 2000; Labiano, 2008; Kalfountzou et al., 2012;
Mullaney et al., 2013; Andika et al., 2020). The higher
$[{O\textsc{iii}}]/\mbox{H}\beta$ ratio of the ECS-G sample compared with the
PSS-CSO-G sample is consistent with the idea that the [O iii] line strength is
enhanced significantly by the jet expansion through the host galaxy (Labiano,
2008). In this context, we also note that the blends of Fe ii lines and Balmer
continuum between $\sim 2000$ and 4000 Å (the ‘3000 Å bump’) are relatively
weak in both QSO composites of the present study, in agreement with the known
anti-correlation between the [O iii] line and the strength of the Fe ii
emission (Boroson & Green, 1992).

In Sect. 3, we found that the mean [O iii] line dispersion for the PSS-CSO
galaxies is 25% larger than for the ECS galaxies, but these galaxy samples are
biased against spectra with weak H$\beta$ lines (and thus high [O
iii]/H$\beta$ ratios). In the PSS-CSO-G composite spectrum, the FWHM of the [O
iii] line is also larger than that of the ECS-G composite, but the difference
is now only $\sim 5$% (Table 3).

The host galaxies of radio-loud QSOs are known to have elevated [O ii] with
respect to hosts from radio-quiet QSOs. The [O ii] line is stronger in the
PSS-CSO composites, in agreement with the fact that its emission-line strength
correlates inversely with the radio source size (Best et al., 2000). The line
ratio [O ii]/[O iii] is sensitive to the ionisation parameter (Penston et al.,
1990; Kewley & Dopita, 2002; Shirazi et al., 2014). The ratio [O ii]/[O iii]
for the ECS-Q subsample is close to that for the SDSS quasar composite
spectrum (Vanden Berk et al., 2001). The value for the PSS-CSO-Q subsample is
twice as high and thus indicates a lower ionisation. This is again in
agreement with the interpretation of the strength of the [O iii] line. The
same trend is indicated by the comparison of the PSS-CSO-G and ECS-G
composites. For the PSS-CSO-G subsample the line ratio [O ii]/[O iii] is
slightly larger than 1, as typical of LINERs.

The [O ii] line is often taken as a SF indicator, but it can also be emitted
by low-density gas ionised by the AGN. On the other hand, the [Ne v] $\lambda$
3425Å line is unambiguously related to AGN activity, either via direct photo-
ionisation from the AGN or ionisation from AGN-driven shocks. The [O II]/[Ne
v] line ratio can thus be taken as an indicator of whether there is an excess
of [O II] emission that can be attributed to radio jets acting within the host
galaxies or to SF in the host galaxies (Maddox, 2018). This excess is higher
in the PSS-CSO than in the ECS samples (Table 3).

Figure 8:  Residual spectra (composite minus model) for the PSS-CSO-G (a) and
the ECS-G (b) subsample. The model spectra were arbitrarily scaled in such a
way that the mean flux in the interval 4200-4800 Å is the same as in the
corresponding observed composite spectrum.

####  4.1.2 Continuum and absorption lines

The composite spectra of the galaxy subsamples are dominated by the light from
an old stellar population. There is, however, a discernible difference between
the PSS-CSO-G and the ECS-G composite: the former has a flatter slope between
$\sim 3000$ and 4500 Å, indicating an additional blue component. We fitted the
corresponding composites by model spectra consisting of two stellar components
represented by the template spectra of a 13 Gyr old E galaxy (Ell 13) and an
Sc galaxy from Polletta et al. (2007), where their relative proportion is a
free parameter. Another free parameter is the internal reddening $E(B-V)$ of
the starlight assuming the reddening law for Milky Way dust from Pei (1992).
In reality, the stellar populations of the host galaxies may show a much
broader variety and we cannot assume that such a simplified model perfectly
reflects the observed spectrum in all its details (e.g. Schmitt et al., 1999).
Nevertheless, we find that the model provides a reasonable fit to the
continuum in the wavelengths range $\lambda\approx 3000$ to 7000 Å. In the
best-fit model for the PSS-CSO-G sample, the Sc component contributes $\sim
40\pm 3\,\%$ of the starlight at $3000-3200$ Å, compared to only $\sim 7\pm
3\,\%$ for the ECS-G sample. The internal reddening is slightly stronger in
the PSS-CSO-G model, $E(B-V)=0.07$ compared to 0.03 for ECS-G. The differences
between the observed composites and the model spectra are shown in Fig. 8. We
note again the stronger [O ii] line in the PSS-CSO-G sample, in accordance
with Sect. 4.1.1 and Table 4.

In an alternative model, we described the blue component by a power-law
$F_{\lambda}\propto\lambda^{\alpha_{\lambda}}$ for the AGN continuum with
$\alpha_{\lambda}=-0.5$ for radio-loud AGNs (Brotherton et al., 2001). Here,
the fraction of the AGN continuum is a free parameter. This model provides a
similarly good match when the AGNs contribute $\sim 20\%$ to the flux at
$3000-3200$ Å in the PSS-CSO-G sample, but negligibly little in the ECS-G
sample. A moderate internal reddening of $E(B-V)\la 0.05$ cannot be excluded
but does not significantly improve the fits.

Adding the blue component has the effect of reducing the discontinuity in the
model spectrum at $\sim 4000$ Å. For a single stellar population, the strength
of this discontinuity increases with age, but it depends also on metallicity
(e.g. Bruzual, 1983; Vazdekis et al., 2015) and, due to the mass-metallicity
relation (Tremonti et al., 2004), on stellar mass (Haines et al., 2017).
Because of the large stellar masses of the host galaxies in our samples (Table
4 and Sect. 5), it may be that the 4000 Å discontinuity is stronger than in
the Ell13 template spectrum, which would increase the necessary contribution
from the blue component. However, this effect should be similar for both
subsamples.

To summarise, the mean stellar populations of the hosts in the PSS-CSO-G and
ECS-G subsamples appear to be slightly different. The flatter slope of the
PSS-CSO-G composite at $\sim 3000-4500$ Å suggests a stronger contribution
from a blue component, which could be either a younger stellar population or
AGN continuum. If PSSs and CSOs are young, the former would suggest a link
between triggering radio activity and star formation. The latter could also be
understood as an evolutionary effect: Let us assume that a percentage of $\sim
20$% of the AGNs in the PSS-CSO-G sample are in an accretion mode with a
standard accretion disk. If the lifetime of such an AGN phase (i.e. the
accretion disk) is smaller than the growth time of the extended radio
structure, we can assume that the percentage of such AGNs in the ECS sample is
much smaller.

Because the QSOs in our samples are radio loud, the QSO composites can be
compared with the composite spectrum of the about 400 radio-loud quasars from
the FBQS. Compared to the latter, our QSO composites have a slightly redder
(i.e. flatter in Fig. 7) UV continuum. The continuum slope can be influenced
by several physical properties, such as intrinsic reddening, inclination of
the accretion disk, accretion rate, SMBH mass and spin (Francis et al., 1992;
Richards et al., 2002; Reichard et al., 2003; Shankar et al., 2016), or by
observational effects (e.g. Harris et al., 2016; Milaković et al., 2021).
Here, we assume that reddening by dust in the host galaxy is the most
plausible cause, although we cannot exclude that spectrum-to-spectrum
variations in our small samples play a role. We de-reddened our composite
spectra using the SMC reddening curve (Pei, 1992) and found a good agreement
of the UV continuum of both spectra with that of the FBQS composite if we
assume an additional intrinsic reddening of $\Delta E(B-V)=$ 0.04. We note
that Fig. 7 shows the de-reddened spectra.

The change of the slope of the QSO composites at $\lambda\ga 4000$ Å can be
attributed to the stellar light from the host galaxies, as indicated by the
easily recognisable stellar absorption lines Ca ii H and K, Mg i at 5190 Å,
and Na ii at 5893 Å. Evidently, the composite spectra can be described as a
combination of an emission-line spectrum, a featureless continuum, and an
absorption-line galaxy spectrum. We modelled the continuum plus stellar light
in a similar way as described above for the galaxy composites and found an
acceptable fit up to $\sim 6000$ Å if we assume that the AGN continuum
accounts for $87\pm 3\,\%$ in the PSS-CSO-Q and $93\pm 3\,\%$ in the ECS-Q
composite at 3000 to 3200 Å. The uncertainty is a few percent, so the
difference is probably not significant. For both QSO subsamples, the stellar
continuum is well described by the Ell 13 template, the contribution from the
Sc template is nearly negligible (a few per cent). At wavelengths $\ga 6000$
Å, the models underestimate the observed continuum flux. Such a trend of a
greater contribution from starlight with increasing wavelength, which is also
seen in other QSO composites, could be caused by emission from hot dust
(Vanden Berk et al., 2001, and references therein) and by the high mass and
correspondingly high metallicity of the host galaxies (Zhu et al., 2010).

Some particular ‘features’ in QSO spectra have sometimes been associated with
early evolutionary phases of AGNs, such as weak broad emission lines (e.g.
Hryniewicz et al., 2010; Meusinger & Balafkan, 2014; Kumar et al., 2023) or
broad absorption lines (BALs; e.g. Priddey et al., 2007; Bruni et al., 2015;
Zeilig-Hess et al., 2020). For example, iron low-ionisation BALs (FeLoBALs)
were considered to represent an early-stage of merger-driven accretion and a
transition between an obscured AGN and an ordinary optical QSO (e.g. Voit et
al., 1993; Farrah et al., 2010; Wethers et al., 2021).888For contrary results
see e.g. Violino et al. (2016); Villforth et al. (2019). BAL properties (the
‘balnicity’) and radio emission may be linked to the same underlying process
(Morabito et al., 2019). Strong iron emission bands provide another type of
unusual spectral feature. FeLoBAL QSOs often exhibit impressive Fe ii and Fe
iii emission bands in the UV (Hall et al., 2002; Meusinger et al., 2012).
Strong optical Fe ii emission is one of the classification characteristics of
the rare type of Narrow Line Seyfert 1 (NLS1) galaxies (Goodrich, 1989; Véron-
Cetty et al., 2001). NLS1s harbour relatively low-mass SMBHs
($\mathcal{M}_{\bullet}\la 10^{8}\mathcal{M}_{\odot}$) that accrete at high
Eddington ratios and are interpreted as young or rejuvenated AGNs (Komossa et
al., 2006; Mathur et al., 2012). Radio-loud NLS1s show similarities with CSS
sources (e.g. Yuan et al., 2008; Caccianiga et al., 2014; Berton et al.,
2017). Liao & Gu (2020) identified the CSS source SDSS J133108.29+303032.9 as
a NLS1, Järvelä et al. (2022) found five new NLS1s that exhibit properties
consistent with the CSS classification. We see in our PSS-CSO composite
spectrum neither signs of conspicuously weak broad emission lines nor strong
Fe multiplets in emission or absorption. In addition, we note that there is no
indication of a substantial unobscured post-starburst stellar population of an
age of less $\sim 1$ Gyr, which would be indicated by the Balmer absorption
lines of A- and F-type stars, as has been observed in a few AGN galaxies (Liu
et al., 2007; De Propris & Melnick, 2014).

Figure 9:  MBSEDs (in the rest frame) for a selection of nine sources from the
PSS-CSO sample (black symbols with error bars). Overplotted are the SDSS
spectrum (green) and the best-fitting template (red). The number and name of
the best-fitting template are given in the top right corner of each panel.

###  4.2 Multi-band spectral energy distributions

We used photometric data from major sky surveys to derive the multi-band
spectral energy distributions (MBSEDs) from the far UV to the MIR. Apart from
the SDSS five-band photometry, we used data from the Galaxy Evolution Explorer
(Galex; Morrissey et al., 2007) survey in the UV, the Two Micron All-Sky
Survey (2MASS; Skrutskie et al., 2006), the UKIRT Infrared Deep Sky Survey
(UKIDSS; Lawrence et al., 2007; Hambly et al., 2008) in the near-infrared
(NIR), and the AllWISE Data Release (Cutri et al., 2021) from the Wide-Field
Infrared Survey Explorer (WISE; Wright et al., 2010) in the MIR. The
magnitudes were extracted from the catalogues using the VizieR Service at
CDS999http://vizier.u-strasbg.fr/viz-bin/VizieR and the NASA/IPAC Infrared
Science Archive
(IRSA)101010https://irsa.ipac.caltech.edu/applications/Gator/index.html. On
average, an object from our PSS-CSO sample has measurements in 11 different
bands, 95% are measured in at least 8 bands and 68% in at least 10 bands. The
magnitudes were corrected for Galactic foreground extinction using $E(B-V)$
from Schlafly & Finkbeiner (2011) and converted into fluxes in the rest frame.

The observed MBSEDs were compared with galaxy template spectra from the SWIRE
library (Lonsdale et al., 2003; Polletta et al., 2007), which consists of 25
SEDs covering the wavelength range from 0.1 to 1000 $\mu$m. It contains all
major galaxy types, including ellipticals of different ages, early-type and
late-type spirals, starburst (SB) galaxies, type 1 AGNs, type 2 AGNs, and
composite types SB+AGN (Table 4). The spectra for the ellipticals, the spirals
and the SB galaxies were generated with a spectral evolution code. The SB
templates correspond to the observed SEDs of well-studied SB galaxies.
Templates of Seyfert 1.8 and 2 galaxies were obtained by combining models,
broad-band photometric data and infrared spectra of a random sample of 28
Seyfert galaxies. Another six AGN templates represent optically selected QSOs
with different infrared-to-optical flux ratios (QSO1, TQSO1, and BQSO1) and
two versions of type 2 QSOs (QSO2 and Torus). The templates for composite
types are empirical SEDs of objects that contain a powerful SB component,
mainly responsible for their large infrared luminosities, and an AGN component
that contributes to the MIR luminosities.

Table 4: SED templates from Polletta et al. (2007) assigned to six types ($t_{\rm SED}$, see text). name | explanation | $t_{\rm SED}$ |
---|---|---|---
Ell2 | 2 Gyr old elliptical galaxy | 1 |
Ell5 | 5 Gyr old elliptical galaxy | 1 |
Ell13 | 13 Gyr old elliptical galaxy | 1 |
S0 | S0 galaxy | 1 |
Sa | Sa galaxy | 2 |
Sb | Sb galaxy | 2 |
Sc | Sc galaxy | 2 |
Sdm | Sdm galaxy | 2 |
Sd | Sd galaxy | 2 |
Spi4 | Sc galaxy | 2 |
I20551 | IRAS 20551-4250, SB | 3 |
I22491 | IRAS 22491-1808, SB | 3 |
M82 | M 82, SB | 3 |
N6090 | NGC 6090, SB | 3 |
N6240 | NGC 6240, SB | 3 |
Arp220 | SB, ULIRG | 4 |
I19254 | IRAS 19254-7245 S, Sy 2 + SB, ULIRG | 4 |
Mrk231 | BAL QSO + SB, Sy 1, ULIRG | 4 |
Sey18 | moderately luminous AGN, type Sy 1.8 | 5 |
Sey2 | moderately luminous AGN, type Sy 2 | 5 |
QSO2 | QSO of type 2 | 5 |
Torus | heavily obscured QSO | 5 |
BQSO1 | optically selected QSO type 1 (faint) | 6 |
QSO1 | optically selected QSO type 1 | 6 |
TQSO1 | optically selected QSO type 1 (bright) | 6 |

Figure 10:  Histogram distributions of type $t_{\rm SED}$ for the G (top) and
the Q subsample (bottom) of the PSS-CSO (blue) and the ECS (green) sample. The
vertical bars indicate the standard errors of the proportions.

We fitted all template spectra from Table 4 to the observed MBSEDs and
determined the best-fitting template using the least chi-square method. Figure
9 shows nine examples of resulting MBSEDs in the rest-frame. The fluxes from
the photometric data are shown as black squares with error bars. In most
cases, the error bars are smaller than the symbol size. The best-fitting
template is shown in red. Also plotted is the SDSS spectrum corrected for
Galactic foreground reddening, transformed into the rest image and scaled to
the MBSED in the optical domain (SDSS bands g, r, and i).

To facilitate the use of the classification scheme, we have grouped the
different SED templates into six groups (last column of Table 4). The meaning
of the group number code is $t_{\rm SED}=1$ for E and S0 galaxies (‘early
types’), 2 for spiral galaxies, 3 for SBs, 4 for SB-AGN composites (or
transition objects), 5 for AGNs of type 1.8 to 2, and 6 for type 1 QSOs. The
histograms in Fig. 10 show the distributions of these types in the PSS-CSO
(blue) and the ECS (green) sample, respectively, with both samples subdivided
again into the two subsamples G and Q. We also note that the histogram
distributions from the second-best fit do not differ much from those in Fig.
10. As expected, there is a clear and strong difference between the G and Q
subsamples, both for the PSS-CSO sample and the ECS sample. The vast majority
of objects assigned to spectral class QSOs based on SDSS spectra, i.e.
subsample Q, also have MBSEDs of type 1 QSOs. On the other hand, most of the
objects from the subsample G are found to show MBSEDs of normal galaxies
(early type or spiral). The proportion of early-type galaxies is higher in the
ECS-G sample than in the PSS-CSO-G sample, but overall Fig. 10 shows a similar
distribution of types in both samples. For a quantitative analysis, we
performed Pearson’s chi-square test for two independent samples to compare the
distributions of types in the corresponding subsamples. The null hypothesis
${\rm H}^{0}$ states that the subsamples PSS-CSO-G and ECS-G have the same
distributions of types, and likewise the subsamples PSS-CSO-Q and ECS-Q. ${\rm
H}^{0}$ was tested against the alternative hypothesis, ${\rm H}^{\rm A}$, that
the two samples have different distributions. The results indicate that there
is no reason to reject ${\rm H}^{0}$ in favour of ${\rm H}^{\rm A}$ at
$\alpha=0.05$.

Next, we focus on the proportions $f_{\rm sf}$ of SF galaxies. For this
purpose, we have summarised the templates in a coarser scheme, distinguishing
only between types with active SF ($t_{\rm SED}=2-4$) on the one hand and all
other types (i.e. either dominated by an old stellar population or by an AGN)
on the other. For this analysis, we included only sources with fluxes in at
least eight wavelength bands, where at least two data points must be in the
NIR or MIR. The resulting samples consist of 115 PSSs and CSOs and 116 ECSs,
respectively. The fraction of SF galaxies is $f_{\rm sf}^{\rm PSS}=0.46\pm
0.05$ for the PSS-CSO sample and $f_{\rm sf}^{\rm ECS}=0.28\pm 0.04$ for the
ECS sample. We applied the one-tailed two-sample Z test of proportions where
the null hypothesis, ${\rm H}^{0}:f_{\rm sf}^{\rm PSS}\leq f_{\rm sf}^{\rm
ECS}$ is tested against the alternative hypothesis ${\rm H}^{\rm A}:f_{\rm
sf}^{\rm PSS}>f_{\rm sf}^{\rm ECS}$ at an error probability $\alpha$. The
result is that ${\rm H}^{0}$ must be rejected in favour of ${\rm H}^{\rm A}$
at the 95% confidence level ($\alpha=0.05$), i.e. we can assume that the
proportion of SF galaxies in the PSS-CSO sample is larger than that in the
comparison sample ($p=0.006$). On the other hand, the two-sided Z-test for the
proportions of AGNs (including AGN-SF composites) shows that there is no
evidence of different AGN proportions in the PSS-CSO and ECS samples.

The formation of a spheroidal component seems to be important for the history
of SF in galaxies. Late-type disk-dominated spiral galaxies (Sbc, Sc, Sd) tend
to actively form stars, while E and S0 are usually quiescent. In terms of both
morphology and SF indicators, Sa galaxies lie between the passive galaxies and
SF galaxies (Bendo et al., 2002; Hameed & Devereux, 2005; Vika et al., 2015;
González Delgado et al., 2016; Wang et al., 2018). This is also evident in our
two samples. A total of 21 sources were classified as Sa. For 10 of them, the
MIR colours (Sect. 4.3) correspond to spiral galaxies with moderate SFR, while
11 are passive galaxies, similar to E and S0. Therefore, we have also
performed the above analysis for an alternative classification scheme in which
the Sa galaxies were classified as early. In this case, too, there is a clear
difference between the two samples ($f_{\rm sf}^{\rm PSS}=0.36\pm 0.05,f_{\rm
sf}^{\rm ECS}=0.23\pm 0.04,p=0.014$).

The results from the analysis of the MBSEDs agree well with those derived from
the composite spectra. In particular, they support the interpretation of the
differences between the composite spectra as a consequence of different
contributions from younger stellar components (Sect. 4.1.2). There is a
stronger association with younger star formation in the PSS-CSO sample.

###  4.3 WISE fluxes and colours

Observational data in the MIR spectral range are particularly useful for
studying the relationship between the AGN emission and the contribution from
SF in the host galaxy. We used the WISE data in the four bands W1, W2, W3, and
W4 at the central wavelengths 3.4, 4.6, 12, and 22 $\mu$m, respectively
(Wright et al., 2010).

Figure 11:  MIR-to-radio flux density ratio $q22$ vs. redshift for the PSS-CSO
(top) and the ECS (bottom) sample; only sources with reliable type
classification from the MBSED fitting and with $\mbox{S/N}>3$ for the W4 flux
density are plotted. Filled squares stand for the QSO subsample, open squares
for the G subsample. The colours indicate types from MBSED fitting (see
inset).

Figure 12:  WISE $W1-W2$ vs. $W2-W3$ diagram for the PSS-CSO (top) and the ECS
(middle) sample. The symbols and colours have the same meaning as in Fig. 11.
The dotted vertical demarcation lines are from Jarrett et al. (2017), the
horizontal line marks the AGN threshold from Stern et al. (2012). Bottom:
Histogram distributions of $W2-W3$ for the PSS-CSO-G (blue) and ECS-G (green)
galaxies with $W1-W2<0.8$. Only sources with $\mbox{S/N}>5$ in the bands W1
and W2 and with $\mbox{S/N}>3$ in the band W3 are plotted.

Figure 13:  The same as in Fig. 12, but for $W1-W2$ vs. $W3-W4$. Only sources
with $\mbox{S/N}>5$ in the bands W1 and W2 and with $\mbox{S/N}>3$ in the
bands W3 and W4 are plotted.

Jet-dominated AGNs and SF galaxies differ in the ratios of their MIR flux to
the radio flux. To quantify the MIR-to-radio ratio, Caccianiga et al. (2015)
introduced the parameter $q22=\log\,(F_{\rm 22\,\mu m}/F_{\rm 1.4\,GHz})$,
where $F_{\rm 22\,\mu m}$ is the flux density in the W4 band and $F_{\rm
1.4\,GHz}$ is the 1.4 GHz flux density. Major star formation contribution to
the radio emission can be expected in sources with $q22>1$ (Caccianiga et al.,
2015). We computed $q22$ with $k$-corrections using the individual spectral
slopes between the 12 and 22 $\mu$m (observed frame) flux densities for
$F_{\rm 22\,\mu m}$ and the radio slope $\alpha_{\rm r}=-0.7$ for $F_{\rm
1.4\,GHz}$. In both samples, the $q22$ values are distributed over a large
range, but all sources have $q22\la 1$ and, except for two low-$z$ systems,
$q22<0$ (Fig. 11). SF activity is not a dominant source in the radio band, the
observed radio flux is most likely dominated by synchrotron emission from the
relativistic jet. However, it must be taken into account that the plotted
samples are highly incomplete. Only 33% of the PSS-CSO-G subsample and 11% of
the ECS-G subsample are detected in the W4 band and meet the requirement
$\mbox{S/N}>3$ for $F_{\rm 22\,\mu m}$. In particular, there is a bias against
normal E, S0, and S galaxies ($t_{\rm SED}=1$ or 2), as can be seen in Fig.
11.

With some cautions (e.g. Gürkan et al., 2014), the WISE colours $W1-W2$ and
$W2-W3$ provide a useful tool for selecting AGNs and detecting SF activity in
galaxy samples (e.g. Stern et al., 2012; Jarrett et al., 2017; Cluver et al.,
2017). In the $W1-W2$ vs. $W2-W3$ diagram, galaxies with little hot dust
emission occupy a narrow sequence at $W1-W2\approx-0.2\ \mbox{to}+0.5$ mag,
where the colour index $W2-W3$ is a good indicator of the SF activity.
Galaxies with high SFR, including luminous infrared galaxies (LIRGs) and
ultra-luminous infrared galaxies (ULIRGs) are found at the right-hand side of
this sequence at $W2-W3>3.5$ mag (‘SF region’). Galaxies with little or absent
SF populate the left-hand side at $W2-W3<2$ mag (‘passive region’). The galaxy
population in the intermediate area is attributed to normal spiral disks with
moderate SFR. AGNs with hot dust populate the colour space above the threshold
$W1-W2=0.8$ mag (‘AGN region’).

Figure 12 shows the $W1-W2$ vs. $W2-W3$ diagram (Vega magnitudes) for the PSS-
CSO sample (top) and the ECS sample (middle), where only sources with reliable
signal-to-noise ratios are plotted ($\mbox{S/N}>5$ for the W1 and the W2 band,
$\mbox{S/N}>3$ for W3). In the passive region, all PSSs and CSOs and the
majority of the ECSs have MBSEDs of early-type galaxies. In the AGN region, on
the other hand, the vast majority of the sources belong to the QSO subsamples
with MBSEDs classified as AGNs or composites. In both two samples, several
objects with AGN-type MBSEDs have $W1-W2$ values below the AGN demarcation.
This is most likely due to a ‘dilution’ of the AGN radiation by stellar light
in the bands W1 and W2 (Donley et al., 2012). The proportion of sources in the
AGN region is similar in both samples ($53\pm 5$ % for PSS-CSO and $59\pm 5$ %
for ECS). In the non-AGN region, however, the distribution along the $W2-W3$
axis is different: For the PSS-CSO sample, we find $9\pm 3$ % in the passive
and $38\pm 5$ % in the intermediate plus SF region, compared to $24\pm 5$ %
and $17\pm 4$ % for the ECS sample. The bottom panel of Fig. 12 shows the
distribution of $W2-W3$ for the galaxies (subsamples PSS-CSO-G and ECS-G) with
$W1-W2<0.8$ and $z<0.6$. The redshift limit was chosen because the strong
polycyclic aromatic hydrocarbon (PAH) features around $\sim 11\,\mu$m, which
are usually considered indicators for massive young stars, are no longer
covered by the W3 band at higher redshifts. With only one exception, WISE
measurements are available for all PSSs, CSOs, and ECSs with $z<0.6$. It can
be seen that the PSS-CSO-G sample has a larger contribution from SF activity.
We applied again the one-tailed two-sample Z test of proportions to test the
null hypothesis ${\rm H}^{0}:f_{\rm sf}^{\rm PSS}\leq f_{\rm sf}^{\rm ECS}$
against ${\rm H}^{\rm A}:f_{\rm sf}^{\rm PSS}>f_{\rm sf}^{\rm ECS}$ and found
that ${\rm H}^{0}$ can be rejected in favour of ${\rm H}^{\rm A}$ at a high
significance level ($p=0.003$). The Z test for all sources at $z<0.6$, i.e.
without restricting S/N, gives the same result ($p=0.01$).

The WISE colour $W3-W4$ is particularly sensitive to the strength of SF
because a high SFR leads to strong emission by a warm dust component, which
contributes more to the W4 band than to the W3 band. The colour-colour diagram
$W1-W2$ vs. $W3-W4$ is shown in Fig. 13 for the sources with $\mbox{S/N}>5$ in
the bands W1 and W2, and $\mbox{S/N}>3$ in W3 and W4. As in Fig. 12, the ‘AGN
cloud’ at $W1-W2>0.8$ is clearly seen. In the non-AGN region ($W1-W2<0.8$),
the sources cover a wider interval of $W3-W4$ than the AGN cloud. At first
glance, the distribution of the PSSs and CSOs along the $W3-W4$ axis is
similar to those in the colour-magnitude diagrams presented by Nikutta et al.
(2014) for huge samples of QSOs, AGNs, and SF galaxies, respectively. They
found the following median values $\mu$ of the $W3-W4$ distribution: $2.45\pm
0.19$ for 14 795 QSOs, $2.54\pm 0.29$ for 4 509 AGN galaxies, and $2.19\pm
0.33$ for their sample of 38 092 SF galaxies. Here we derived the median
values $\mu(W3-W4)=2.55\pm 0.05$ for the PSS-CSO and $2.53\pm 0.05$ for the
ECS sample. These values are similar to those given by Nikutta et al. (2014)
for their samples of AGN galaxies and QSOs. The bottom panel of Fig. 13 shows
the histogram distribution of $W3-W4$ for the galaxies at $z<0.6$ in the PSS-
CSO and the ECS samples. We note that reliable measurements of W4 are only
available for a small number of galaxies.

The interpretation of the $W3-W4$ distribution in the non-AGN region is not
entirely clear. When only the galaxies with $W1-W2<0.8$ and $z<0.6$ are
considered, the resuting median values are $\mu(W3-W4)=2.34\pm 0.16$ for the
PSS-CSO subsample and $2.42\pm 0.08$ for the (very small) ECS subsample. The
blue PSSs and CSOs with $W3-W4<2$ all have MBSEDs classified as early-type
galaxies ($t_{\rm SED}=1$). On the other side, the very red sources with
$W3-W4>3$ were classified as spiral or starburst galaxy. The reddest galaxy in
the top panel of Fig. 13 is SDSS J075756.71+395936.1 ($z=0.066$) with
$W3-W4=3.66$. Based on its MBSED, it was classified as an Sb galaxy, its SFR
of $\sim 16,\mathcal{M}_{\odot}$ yr-1 (Chang et al., 2015) is the highest in
our PSS-CSO sample (see Sect. 5 and Table 17). Investigating a sample of flat-
spectrum NLS1s, Caccianiga et al. (2015) argued that red colours $W3-W4>2.5$
cannot be explained by AGN template spectra alone, but require a significant
SF component. However, other effects may also contribute to a red $W3-W4$
colour: warm dust components heated by the AGN (polar dust and possible warm
dust related to the dusty torus) or the suppression of PAHs by the AGN
(Järvelä et al., 2022, and references therein).

##  5 Star formation rates and stellar masses

Figure 14:  D4000n index and sSFR. Top and middle: sSFR from Chang et al.
(2015) versus D4000n for the PSS-CSO and the ECS samples. Filled squares
signify sSFR data from good model fits (FLAG=1). The dashed line indicates the
boundary between the SF sequence (above) and passive galaxies (below). The
dotted line is a power-law approximation of the mean values from Brinchmann et
al. (2004, their Fig. 11). The spectral types $t_{\rm SED}$ from the MBSED fit
are colour coded as in Fig. 12. Bottom: Histogram distributions of the D4000n
index for the PSS-CSO (blue) and ECS (green) galaxies with $\mbox{S/N}>3$.

Figure 15:  Stellar mass and SFR. Top and middle: SFR versus stellar mass from
Chang et al. (2015) for the PSS-CSO and the ECS samples. The colour coding of
the symbols is the same as in Fig. 14, filled squares signify galaxies with
good model fits. The cyan curves are local density contours maps for $\sim
9\cdot 10^{4}$ SDSS galaxies. The black lines indicate the SF sequence (solid)
and the $3\sigma$ scatter (dashed), the blue lines are the corresponding
extrapolations to $\log M_{\ast}/M_{\odot}=12$. Bottom: Histogram
distributions of the SFR for the PSS-CSO (blue) and ECS (green) galaxies with
${\tt FLAG}=1$.

SFR values from simple model fits are unreliable for galaxies where the fluxes
are likely to be significantly affected by the AGN component. It has been
known for a long time that the 4000 Å discontinuity in the spectra of galaxies
correlates with the ratio of the present SFR to the past-averaged SFR and can
thus be used to measure SF histories (e.g. Bruzual, 1983; Kauffmann et al.,
2003b; Gallazzi et al., 2014; Haines et al., 2017; Borghi et al., 2022), where
it must be noted that there is also a correlation with metallicity (Vazdekis
et al., 2015). Brinchmann et al. (2004) suggested to use the 4000 Å break
index D4000n (Balogh et al., 1999) to estimate the in-fibre SFR in composite
and AGN galaxies. This index expresses the ratio of the average flux densities
$F_{\nu}$ in the narrow bands 4000 - 4100 Å and 3850 - 3950 Å. The values of
D4000n are available from the MPA-JHU catalogues (SDSS table galSpecIndx) for
$\sim 80\%$ of the galaxies (class = GALAXY) in our samples. For the other
galaxies, we measured D4000n from the SDSS spectra. Using reliable data only
($\mbox{S/N}>3$), the mean values of D4000n are $1.70\pm 0.04$ for the PSS-CSO
and $1.88\pm 0.02$ for the ECS sample (Table 4). The larger value for the
latter is consistent with the stronger contribution of the old stellar
population seen in the composite spectrum (Sect. 4.1).

Robust data on SFRs and stellar masses for more than $8\,10^{5}$ galaxies of
the full SDSS spectroscopic galaxy sample have been provided by Chang et al.
(2015). Based on the combination of SDSS and WISE photometry, the authors
created SEDs that cover the wavelength range 0.4 - 22 $\mu$m. They employed
MAGPHYS for SED modelling that consistently treats stellar emission along with
absorption and re-emission by interstellar dust. The differences between their
data and the MPA-JHU data is based on the addition of the MIR fluxes, the use
of updated Galactic extinction corrections, and different dust attenuation
laws. Cross-correlation with the Chang et al. (2015) catalogue yields 38
matches for our PSS-CSO-G sample and 40 matches for the ECS-G sample.
Following the recommendation of Chang et al. (2015), we focus on objects with
flag = 1, i.e. galaxies with reliable aperture corrections, good WISE
photometry and good-quality SED fits. This reduces the samples to 19 (PSS-CSO)
and 27 (ECS) galaxies, all at $z<0.2$. Among them, there is only one galaxy
with $t_{\rm SED}>2$, namely the ECS galaxy SDSS J094124.02+394441.8, which
has been classified as a starburst galaxy ($t_{\rm SED}=3$). More than 90% of
the galaxies in both samples have SFRs $\la 5\mathcal{M}_{\odot}\,{\rm
yr}^{-1}$. Larger SFRs are found only for SDSS J075756.71+395936.1 (10
$\mathcal{M}_{\odot}\,{\rm yr}^{-1}$) from the PSS/CSS sample and the two
sources SDSS J094124.02+394441.8 (93 $\mathcal{M}_{\odot}$ yr-1) and SDSS
J120732.92+335240.1 (13 $\mathcal{M}_{\odot}$ yr-1) from the ECS sample.

The ratio of the present to the past-averaged SFR is expressed by the specific
star formation rate sSFR = SFR/$\mathcal{M}_{\ast}$, where
$\mathcal{M}_{\ast}$ is the stellar mass. The sSFR versus D4000n diagram for
$\sim 10^{5}$ objects from the main SDSS galaxy sample with redshifts $z<0.2$
shows two clouds with the maximum population densities at $\mbox{D4000n}\sim
1.3$ and 1.9, respectively (Kauffmann et al., 2003b; Brinchmann et al., 2004).
The first cloud comprises SF galaxies, the second one consists of old
elliptical galaxies. Figure 14 shows the sSFR versus D4000n diagrams for our
samples. The dotted line marks the mean relation for the SF sequence and the
dashed line indicates the border between the SF and the passive region, both
taken from Brinchmann et al. (2004, their Fig. 11). As expected, the
population below the demarcation line is strongly dominated by galaxies
classified as early type. The diagrams look different for the two samples with
more main-sequence galaxies in the PSS-CSO sample: $37\pm 11$ % of the PSS-CSO
galaxies with flag=1 lie above the demarcation line, but only $11\pm 6$ % of
the ECS galaxies ($42\pm 8$ % and $8\pm 4$ % respectively for all galaxies).

The bottom panel of Fig. 14 shows the histogram distributions of the D4000n
index. There is a strong peak at $\mbox{D4000n}\sim 1.9$ in the comparison
sample, reflecting again the dominance of old stellar populations. This peak
is also present in the PSS-CSO sample, but there is a stronger peak at $\sim
1.6$, which reflects the clump at the end of the SF sequence seen in the top
panel. According to the two-sample KS test, the difference between both
distributions is highly significant ($p=3\cdot 10^{-5}$).

Figure 15 displays the SFR vs. stellar mass diagrams. For comparative
purposes, we overplotted the distribution of the data from Chang et al. (2015)
for $\sim 9\cdot 10^{4}$ galaxies from the SDSS spectroscopic sample with
$z<0.2$ by equally spaced local density contours.111111Following Chang et al.
(2015, their Fig. 11), we used only the 86 923 galaxies above the (redshift-
dependent) mass completeness limit for the contour map and applied the
standard $1/V_{\rm max}$ weighting (Schmidt, 1968). The contours were computed
for a grid size of
$(\Delta\log{\mathcal{M}}_{\ast},\Delta\log\,\mbox{SFR})=(0.05,0.1)$ and are
spaced by a factor of 1.5, starting at 0.66. Also plotted is a power law
approximation of the SF sequence and the $3\sigma$ limits with $\sigma=0.39$
dex, both from Chang et al. (2015). Both the PSS-CSO and the ECS samples span
a relatively narrow range of stellar masses at the high-mass end of the galaxy
distribution. The derived masses are at the low-mass end of the mass
distribution of the brightest cluster galaxies (Dalal et al., 2021), but are
typical of luminous radio galaxies. On the other hand, the SFRs cover a wide
range from close to the SF sequence down to almost zero. The diagrams again
indicate a lower proportion of SF galaxies in the ECS sample: $42\pm 10$ % of
the PSS-CSO galaxies with flag=1 lie above the lower limit of the extrapolated
SF sequence, but only $22\pm 8$ % of the ECS galaxies ($45\pm 8$ % and $18\pm
6$ % for all galaxies). The histogram distributions of the SFR in the two
samples are displayed in the bottom panel of Fig. 15 for the sources with good
quality SFR data. According to the two-sample KS test, the difference between
the two distributions is significant ($p=0.03$) when we include all galaxies,
but not for the rather small samples of sources with flag=1. A similar result
is found for the sSFR distributions. The mean values of D4000n,
$\log\,M_{\ast},\log\,\mbox{SFR}$, and $\log\,\mbox{sSFR}$ for the two samples
are listed in Table 4.

Figure 16:  Image cutouts from the HST Legacy Archive (WFPC2, ACS, WFC3/UVIS
with various filters) for 18 PSSs and CSOs (always in the image centre). The
image size is adapted to the size of the ambient structure, the bar indicates
10 kpc at the distance of the source (N up, E left).

Figure 16:  continued.

##  6 Optical morphology

This section addresses the issue of interactions between galaxies and the
merging of galaxies as one of the main drivers of core activity. Extended
tidal structures, shells, rings, strong lopsidedness, or simply close galaxy
pairs are usually taken as signposts of gravitational perturbation induced by
a close encounter or merger. Finding such structures in distant galaxies is a
challenge. Detecting extended low-surface brightness features requires deep
observations and discovering peculiar structures in the inner regions of
galaxies requires high spatial resolution. We have taken two approaches to
achieve this goal. First, we analysed archival images from the Hubble Space
Telescope (HST) for a subsample of 19 sources. Second, we performed a
statistical analysis of the subsamples of the low-$z$ galaxies on images from
major galaxy surveys. We started with the combination of the images from SDSS
and PanSTARRS-1 (PS1, Flewelling et al., 2020) for the sources up to $z=0.2$,
and then we also analysed the images from the DESI Legacy Surveys (Dey et al.,
2019) up to $z=0.3$.

If the data set is not too large, the human visual system is still the most
efficient and reliable for recognising complex patterns. To determine the
fraction of mergers or otherwise morphologically distorted galaxies, a
classification system has to be defined. Tidal structures are very diverse,
dependent on the properties of the involved galaxies and the merger parameters
(e.g. Duc & Renaud, 2013; Ren et al., 2020). With regard to the subjectivity
of an inspection by eye we prefer here the simple classification scheme that
was applied to post-SB galaxies by Meusinger et al. (2017). It classifies
galaxy morphology into one of four types that are assigned to the numerical
distortion flag $t_{\rm m}$ (index ‘m’ for merger): 0 = no apparent
peculiarities, 1 = weak indications of a structure that seems to be related to
a neighbour galaxy (Messier 51 type), 2 = weak lopsidedness, tidal structures,
such as streamers, tidal arms, fans, or shells, 3 = the same characteristics
as for $t_{\rm m}=2$, but very pronounced. Furthermore, $t_{\rm m}=-1$ was
provided if an evaluation is not possible, but this did not apply in any case.

A neighbouring galaxy (in the projection) is only considered to indicate an
interaction if the two galaxies are either embedded in a common halo or
connected by a light bridge. A lower value of $t_{\rm m}$ can, of course, be
the result of factors that make it difficult to detect low-surface brightness
structures, such as a bright AGN (e.g. J080133.55+141442.8 in Fig. 16) or
blending of the host by a nearby bright object. In general, it is difficult to
distinguish minor mergers from old and faded major mergers. A value of $t_{\rm
m}=2$ does therefore not necessarily mean that the distortions are
intrinsically weaker than for $t_{\rm m}=3$. In the following, we consider
$t_{\rm m}\geq 2$ as indicative of strong tidal interactions and mergers.

###  6.1 HST images

The high spatial resolution of the HST is a great advantage for the detection
of merger signatures, especially in the inner regions of the galaxies and at
higher redshifts. We searched the HST Legacy
Archive121212https://hla.stsci.edu/ for images of the PSS-CSO hosts from our
sample. We found images of 18 PSS-CSO galaxies from different observation
programmes. (In addition, the HST image of the discarded source SDSS
J101714.23+390121.1 is shown and discussed separately in Appendix A.) Although
this subsample is small and too inhomogeneous for the statistical analysis, it
gives an impression of the frequency of nearby neighbours and peculiar
morphologies.

Figure 16 shows the HST images, which were obtained with different cameras and
through different filters. If several images from the same camera are
available, they were stacked to increase the signal-to-noise ratio. For the
two most distant objects, SDSS J080447.96+101523.7 ($z=1.968$) and SDSS
J213638.58+004154.2 ($z=1.941$), the host galaxies are not visible. The same
applies to J133037.69+250910.9 ($z=1.055$).131313In SDSS DR16, the other star-
like object in the image is listed with AGN-typical WISE colours, which is
based however on a misidentification with J133037.69+250910.9. The search in
the IPAC Infrared Science Archive reveals the Spitzer colour
$[3.6]-[4.5]=0.1$, which is far away from the AGN wedge (Stern et al., 2005)
and is consistent with stellar origin. On the other hand, J133037.69+250910.9
has AGN-typical MIR colours $[3.6]-[4.5]=0.78$ and $[5.8]-[8.0]=1.13$. All
three objects are spectroscopically classified as quasars. The HST image of
J131739.20+411545.6 ($z=0.066$) strengthens the case for the existence of PSSs
and CSOs in galaxies with significant disk components. The edge-on view makes
it difficult to detect possible morphological distortions in this galaxy. All
other host galaxies appear distorted or have nearby neighbours
(J092405.30+141021.4 and J100800.04+073016.6), which indicates a high merger
fraction of PSS-CSO galaxies.

###  6.2 Images of low-$z$ galaxies from major surveys

We applied the above classification scheme to the subsamples of the low-$z$
host galaxies. According to our experience from previous studies (e.g.
Meusinger et al., 2017), it is difficult to recognise tidal features from
galaxy-galaxy interactions on SDSS images at redshifts larger than about 0.2.
Therefore, we restricted this analysis to $z<0.2$, which results in a list of
altogether 67 galaxies: 33 from the PSS-CSO and 34 from the ECS sample.

One disadvantage of our method is, of course, the inevitable subjective
character of the classification by eye. Reliable conclusions about the
relative frequency of distorted morphologies of PSS-CSO galaxies are only
meaningful in relation to the comparison sample. In order to reduce the risk
of a subjective bias towards over- or underestimation of peculiarities in the
target sample, it is important to ensure an unbiased assessment of the images
of both samples in a ‘blind’ approach. To do so, we stored the images from
both samples in the same archive and selected them randomly for the
assessment, whereby the examiner could not know which sample they belonged to.
Simultaneous inspection of the SDSS and the PS1 i-band image141414In general,
PS1 is similar in depth to SDSS in g and r, but slightly deeper in i.
significantly reduces the risk of false detection of weak features due to
noise or artefacts. A peculiar structure is only considered recognised if it
is visible on both images. The images were evaluated in three independent
runs. In each run, a merger flag $t_{\rm m}$ was assigned to each galaxy. For
26 PSS-CSO galaxies (81%), identical values were estimated in all three runs.

Based on these results, relative merger fractions $f_{\rm m}=N_{\rm m}/N_{\rm
tot}$ were computed, where $N_{\rm m}$ is the number of mergers and $N_{\rm
tot}$ is the total number of galaxies in the sample with $t_{\rm m}\geq 0$.
Using the merger criterion $t_{\rm m}\geq 2$, we find 16 mergers in the PSS-
CSO but only 4 in the ECS sample.

Deeper optical images were recently provided by the DESI Legacy Imaging
Surveys. For our study, using deeper images not only has the advantage of
improving the confidence in detecting peculiar morphological structures, but
also that the redshift range of the low-$z$ sample can be expanded and thus
the statistics are improved. In the second step, we therefore analysed the
image cutouts from the DESI Legacy Surveys
DR10151515https://www.legacysurvey.org/dr10/description/, which completed
three imaging projects on different telescopes in the g, r, and z bands and
incorporates additional data in the i band. The procedure is the same as
before, the $z$ range has been expanded up to $z=0.3$. This new low-$z$ sample
now consists of 43 galaxies from the PSS-CSO sample (Table 17) and 41 ECS
galaxies. Now we found 26 mergers in the PSS-CSO sample and only six in the
ECS sample. The results confirm the previous finding of a significantly higher
merger fraction for the PSS-CSO sample compared to for the ECS sample. The
mean peculiarity index is $t_{\rm m}=1.47\pm 0.19$ for the former and $t_{\rm
m}=0.45\pm 0.14$ for the latter sample. In the following we only refer to the
results from the Legacy Surveys images. The mean $t_{\rm m}$ values from the
three runs are listed in the last column of Table 17 along with other
properties of the sources from this low-$z$ subsample.161616Because this study
is focused on the host galaxies, we list the SDSS names in column 2, not the
names of the radio sources. The latter can be found in Liao & Gu (2020).

Examples of merger galaxies are shown and briefly described in Appendix B. The
resulting merger fraction $f_{\rm m}^{\rm\,PSS}=0.61\pm 0.07$ for the PSS-CSO
sample is in accordance with the range of 0.4 - 0.7 from previous systematic
studies (O’Dea & Saikia, 2021, and references therein). We note however that
the numbers are not directly comparable to each other because of the
differences in the observational material, the evaluation methods and the
merger criteria. The merger fraction of the ECS sample is much lower, $f_{\rm
m}^{\rm\,ECS}=0.15\pm 0.06$. According to the one-tailed two-sample Z test,
the difference is highly significant ($p<10^{-5}$).

Figure 17:  Number of mergers per MBSED type $t_{\rm SED}$ for PSS-CSO (blue)
and ECS galaxies (green) with $z<0.3$.  Figure 18:  Merger fraction per MBSED
type $t_{\rm SED}$ for PSS-CSO (blue) and ECS galaxies (green) with $z<0.3$.
The vertical bars indicate the standard errors of the proportions.

It is well known that gravitational interactions and merging can elevate the
SFR in galaxies. This is also seen in our data. The histograms in Figs. 17 and
18 show the distributions of the number of mergers across the different MBSED
types and the merger fraction per type, respectively. $62\pm 9$ % of the
merger galaxies in the PSS-CSO low-$z$ sample show SEDs of star forming
galaxies ($t_{\rm SED}=2,3$ or 4). For the non-merger galaxies, this
proportion is smaller, $47\pm 12$ %. All four PSS-CSO host galaxies from Table
17 with SFR $>10\,{\mathcal{M}}_{\odot}\,{\rm yr}^{-1}$ are mergers (Table
17), although the reliability flag is set to 1 for only one them (SDSS
J075756.71+395936.1). Conversely, not all disturbed galaxies with $t_{\rm
m}\geq 2$ have substantial SF, which is expected particularly for dry mergers.

Table 5: The subsample of low-redshift ($z<0.3$) PSS-CSO galaxies.

n | Name | $z$ | $R_{\rm i}$ | Type | Class | BPT | Bft | $t_{\rm SED}$ | $t_{\rm WISE}$ | logSFR | log$\mathcal{M}_{\ast}$ | $t_{\rm m}$
---|---|---|---|---|---|---|---|---|---|---|---|---
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) |  (12) | (13)
1 | J002833.42+005510.9 | 0.104 | 2.52 | CSS | LERG | Sy | Sc | 2 | inter |  0.317 | 10.58 | 3.00
2 | J075756.71+395936.1 | 0.066 | 1.42 | CSS | HERG | Sy | Sb | 2 | SF |  1.017 | 11.00 | 3.00
3 | J081323.75+073405.6 | 0.112 | 2.17 | CSS | LERG | L | Sa | 2 | inter | -3.398 | 11.55 | 2.00
4 | J082504.56+315957.0 | 0.265 | 1.94 | CSS |  LERG: |  L: | Ell5 | 1 | pass | -2.988: | 11.93: | 2.00
5 | J083139.79+460800.8 | 0.131 | 1.76 | GPS | LERG | L | Ell13 | 1 | pass | -3.388 | 11.58 | 0.33
6 | J083411.09+580321.4 | 0.093 | 1.07 | GPS | LERG | L | Ell13 | 1 | pass | -3.508: | 11.48: | 0.00
7 | J083637.84+440109.6 | 0.055 | 1.46 | CSS | HERG | S | S0 | 1 | inter | -2.808 | 11.17 | 2.00
8 | J090615.53+463619.0 | 0.085 | 1.98 | GPS | LERG | C | Sa | 2 | inter |  0.272 | 11.35 | 0.00
9 | J092405.30+141021.4 | 0.136 | 2.38 | CSS | LERG | L | Sy2 | 5 | pass |  |  | 0.00
10 | J093430.68+030545.3 | 0.225 | 2.71 | CSS | LERG | L | Sb | 2 | pass |  0.322 | 11.34 | 3.00
11 | J093609.36+331308.3 | 0.076 | 0.99 | GPS | LERG | L | Ell13 | 1 | pass | -3.513 | 11.45 | 2.00
12 | J094525.90+352103.6 | 0.208 | 2.54 | CSS | QSO | S | Sdm | 2 | inter |  |  | 2.00
13 | J100955.51+140154.2 | 0.213 | 3.22 | CSS | LERG | L | Sy18 | 5 | inter |  1.007: | 11.51: | 0.00
14 | J102618.25+454229.3 | 0.152 | 2.00 | CSO | LERG | L | Sa | 2 | pass | -3.533 | 11.43 | 0.00
15 | J103719.33+433515.3 | 0.025 | 0.61 | CSS | LERG | L | S0 | 1 | pass | -3.758: | 11.17: | 3.00
16 | J104029.94+295757.7 | 0.091 | 2.11 | CSS | LERG | L | Sa | 2 | inter |  0.572 | 11.13 | 3.00
17 | J105731.17+405646.1 | 0.025 | 0.01 | GPS | LERG | C | Ell5 | 1 | pass | -3.603: | 11.26: | 3.00
18 | J114339.59+462120.4 | 0.116 | 2.01 | CSS | LERG | L | Ell13 | 1 | pass | -3.248 | 11.65 | 0.00
19 | J115000.08+552821.3 | 0.139 | 2.17 | CSS |  LERG: | L | Sb | 2 | inter |  0.122 | 11.33 | 2.00
20 | J115727.60+431806.3 | 0.230 | 2.73 | CSS | QSO | C | QSO1 | 6 | QSO |  |  | 0.00
21 | J120902.79+411559.2 | 0.096 | 1.56 | CSS |  LERG: | L | Ell13 | 1 | pass |  |  | 2.33
22 | J124419.96+405136.8 | 0.249 | 2.85 | CSS | QSO | Sy | Arp220 | 4 | inter |  |  | 0.00
23 | J124733.31+672316.4 | 0.107 | 2.19 | CSO | LERG | L | Ell13 | 1 | pass | -3.563 | 11.40 | 0.00
24 | J131739.20+411545.6 | 0.066 | 1.47 | GPS | LERG | L | Ell13 | 1 | pass | -3.423 | 11.56 | 0.00
25 | J132419.67+041907.0 | 0.263 | 2.08 | CSS | LERG | C | Ell2 | 1 | pass | -3.088: | 11.87: | 0.00
26 | J132513.37+395553.2 | 0.076 | 0.97 | GPS | LERG | L | Ell5 | 1 | pass | -3.543 | 11.42 | 2.00
27 | J134035.20+444817.3 | 0.065 | 1.63 | GPS | HERG | Sy | I20551 | 3 | QSO |  1.022: | 10.79: | 3.00
28 | J134733.36+121724.2 | 0.120 | 3.02 | GPS | QSO | Sy | N6090 | 3 | QSO |  |  | 3.00
29 | J140051.58+521606.5 | 0.118 | 2.01 | CSS | LERG | L | Sa | 2 | inter |  0.137 | 11.31 | 0.00
30 | J140700.39+282714.6 | 0.077 | 2.10 | GPS | QSO | C | QSO2 | 5 | QSO |  |  | 3.00
31 | J140942.44+360415.9 | 0.149 | 2.15 | CSS | QSO | Sy | Sb | 2 | inter |  |  | 2.00
32 | J141327.22+550529.2 | 0.282 | 2.63 | CSS |  LERG: | L | Sc | 2 | inter |  0.502: | 11.27: | 2.00
33 | J141558.81+132023.7 | 0.247 | 3.89 | CSO |  HERG: |  Sy: | Mrk231 | 4 | QSO |  |  | 0.00
34 | J143521.67+505122.9 | 0.100 | 1.83 | CSS | LERG | L | Sb | 2 | inter | -3.773 | 11.20 | 2.33
35 | J144712.76+404744.9 | 0.195 | 2.98 | CSS | LERG | Sy | Sb | 2 | inter |  0.467 | 11.10 | 0.00
36 | J151141.26+051809.2 | 0.084 | 1.62 | HFP | QSO | Sy | I19254 | 4 | QSO |  |  | 2.00
37 | J152349.34+321350.2 | 0.110 | 2.29 | CSS | HERG | Sy | Ell5 | 1 | pass | -0.588 | 10.79 | 2.00
38 | J155927.67+533054.4 | 0.179 | 2.56 | CSS | LERG | Sy | Sc | 2 | inter |  1.712: | 11.39: | 2.00
39 | J160246.39+524358.3 | 0.106 | 2.24 | CSS | LERG | L | Sa | 2 | inter |  0.707 | 11.41 | 2.00
40 | J160335.16+380642.8 | 0.241 | 3.12 | CSS | HERG | Sy | Sb | 2 | inter |  |  | 0.00
41 | J161148.52+404020.9 | 0.151 | 2.77 | CSS | QSO | L | Sa | 2 | inter |  |  | 0.67
42 | J164348.60+171549.4 | 0.163 | 3.21 | CSS | G | SF: | Sb | 2 | inter |  |  | 2.33
43 | J225025.34+141952.0 | 0.235 | 3.31 | CSS | QSO | C: | QSO1 | 6 | QSO |  |  | 2.00

171717 (1) running number; (2) SDSS name; (3) redshift; (4) radio loudness
parameter (see Sect. 2); (5) radio source type from Liao & Gu (2020); (6)
optical spectral class; (7) spectral type based on BPT diagram (Sy = Seyfert,
L = LINER, C = composite, SF = star forming); (8) best-fitting SWIRE template;
(9) spectral type from MBSED (Table 4); (10) type based on WISE $W1-W2$ vs.
$W2-W3$ diagram; (11) SFR from Chang et al. (2015) in units of
$\mathcal{M}_{\odot}$ yr-1; (12) stellar mass from Chang et al. (2015) in
units of $\mathcal{M}_{\odot}$; (13) merger index based on images from the
DESI Legacy Surveys. A colon marks uncertain data.

###  6.3 Discussion of the different merger fractions

The merger-AGN connection is the subject of a long-running debate. Both
theoretical arguments and numerical simulations suggest that ‘wet’ galaxy
mergers (i.e. with at least one gas-rich component) are capable of efficiently
triggering streams of cold gas towards the innermost regions of galaxies, thus
in principle creating the conditions for triggering nuclear activity by
feeding the central SMBH (e.g. Toomre & Toomre, 1972; Sanders & Mirabel, 1996;
Kauffmann et al., 2000; Di Matteo et al., 2005; Hopkins et al., 2008; Holt,
2009; Blecha et al., 2018; Weigel et al., 2018; McAlpine et al., 2020; Zhang
et al., 2021). However, despite intensive efforts, the observational evidence
is inconsistent. Several studies do not point to a close connection between
AGNs and (observable evidence for) recent galaxy mergers (Malkan et al., 1998;
McLure et al., 1999; Grogin et al., 2005; Georgakakis et al., 2009; Cisternas
et al., 2011; Schawinski et al., 2011; Hewlett et al., 2017; Villforth et al.,
2017; Lambrides et al., 2021; Sharma et al., 2021). In contrast, the view that
the phenomenon of luminous AGNs is essentially related to interactions between
galaxies is supported by other studies (Hutchings & Campbell, 1983; Gehren et
al., 1984; Keel et al., 1985; Heckman et al., 1986; Stanghellini et al., 1993;
Bennert et al., 2008; Karouzos et al., 2010; Ramos Almeida et al., 2011;
Bessiere et al., 2012; Villar-Martín et al., 2012; Sabater et al., 2013;
Chiaberge et al., 2015; Weston et al., 2017; Goulding et al., 2018; Gao et
al., 2020). In particular, galaxy mergers may play a role in triggering
powerful radio AGNs (Ramos Almeida et al., 2012; Chiaberge et al., 2015).
These seemingly contradictory results could be partly due to differences in
the depth of the images used to classify mergers (Bennert et al., 2008;
Bessiere et al., 2012) and due to different techniques of selecting the AGN
sample (e.g. Ellison et al., 2019, and references therein). Furthermore,
strong AGN variability could have an impact if the variability time scale is
much shorter than the characteristic lifetime of merger relics (Hickox et al.,
2014; Goulding et al., 2018; French et al., 2023).

Another controversial connection is that between the triggering of an AGN and
the quenching of SF in the host galaxy. Because of the strong energetic and
mechanical feedback, AGNs are assumed to play a decisive role in this process
(Fabian, 2012; Heckman & Best, 2014). However, only a few post-SB galaxies
show signs of AGNs in the form of strong AGN emission lines or bright emission
in the MIR, X-ray or radio domain (Nielsen et al., 2012; Meusinger et al.,
2017; Greene et al., 2020). Recently, Wu et al. (2023) reported a high
occurrence rate of AGN-driven [O iii] 5007Å emission derived from ultra-deep
spectra of a small sample of post-starburst galaxies, suggesting a higher
abundance of AGNs than previously thought.

It is not our aim to evaluate the merger fractions from Sect. 6.2 in the
context of the debate on the merger-AGN-SF connection. However, we think it is
important to understand the difference between the rates for the PSS-CSO and
the comparison sample. If the PSS-CSO sample represents young radio galaxies
in which a fraction $f_{\rm m}^{\rm\,PSS}$ was triggered by mergers, it seems
reasonable to explain the lower fraction in the comparison sample by
evolutionary or environmental differences in the observability of the tidal
structures. In the following, we examine in more detail the extent to which
these two explanations could apply to our samples.

####  6.3.1 Evolutionary effects

A simple evolutionary scenario is based on the fact that the tidal structures
disappear and become invisible after a certain time. If a significant
proportion of young radio galaxies is associated with galaxy mergers and the
radio sources are still present for a considerable period after the tidal
structures are no longer detectable, a sample of young radio galaxies can be
expected to have a higher percentage of mergers than a comparison sample of
extended sources. The main requirements for this scenario are that (i) the
comparison sample represents the more evolved stages of the PSSs and CSOs and
(ii) the radio source lifetime is comparable to or longer than the lifetime of
the observable morphological merger signatures.

Observations suggest that the merger-AGN association is particularly clear for
luminous AGNs obscured by dust (Urrutia et al., 2008; Satyapal et al., 2014;
Kocevski et al., 2015; Fan et al., 2016; Weston et al., 2017; Ricci et al.,
2017; Goulding et al., 2018; Steinborn et al., 2018; Ellison et al., 2019),
which are predominantly found in the late stages of mergers. Patil et al.
(2020) studied radio galaxies with extremely red MIR-optical colour ratios and
found that these sources are ‘consistent with a population of newly triggered,
young jets caught in a unique evolutionary stage in which they still reside
within the dense gas’ and might become GPS or CSS sources. Based on the
analysis of cosmological hydrodynamical simulations, McAlpine et al. (2020)
investigated the connection between galaxy-galaxy mergers and enhanced SMBH
growth. They concluded that, at low redshift, the enhancement of AGNs peaks at
$\sim 0.3$ Gyr after coalescence. In the late stage of mergers, tidal
structures disappear quickly. Barnes (2016) computed the evolution of the
tidal fraction $f_{\rm tid}$, i.e. the fraction of disk particles in the tidal
tails, in numerical simulations of major mergers of disk/bulge/halo systems
for a set of different merger parameters. From their Fig. 14, we derive a mean
decline rate of $\Delta f_{\rm tid}/f_{\rm tid}=-1.5\pm 0.13$ per Gyr in the
late stage of the mergers, where we adopted the value $\Delta t_{\rm p12}=0.5$
Gyr for the mean time between the first and the second pericentric passage
(Mihos & Hernquist, 1996). We assume that the tidal fraction can be taken as a
proxy of the observability of the tidal features. The typical lifetimes of FR
II galaxies are usually assumed to be $\sim 10^{6}-10^{8}$ yr (Allen et al.,
2006; O’Dea et al., 2009; Bird et al., 2008; Antognini et al., 2012; Garofalo
et al., 2018). Thus, if we assume a mean age of $\la 0.05$ Gyr for the evolved
radio galaxies in the comparison sample, the merger fraction of the comparison
sample is expected to be only less than 8% smaller than that of the PSS-CSO
sample, which is far from sufficient to account for the observed difference
between $f_{\rm m}^{\rm\,PSS}=0.61$ and $f_{\rm m}^{\rm\,ECS}=0.15$.

The above evolution scenario is based on the assumption that all PSSs and CSOs
evolve into extended radio galaxies represented by the ECS sample. However, if
not all PSSs and CSOs evolve (O’Dea & Saikia, 2021, and references therein),
the merger fraction of evolved radio sources may be lower than that of the
young sources due to another effect: If a significant fraction of the merger-
triggered PSSs and CSOs get stuck in the dense interstellar medium of the
merger core and suffers the ‘dying type 3 source’ fate described by An & Baan
(2012), the counterparts of these merger galaxies are missing from the
comparison sample. Let us assume that the proportion $f_{\rm m}^{\rm\,y}$ of
the young sources (upper index ‘y’ for young) are triggered by mergers and
only a fraction $f_{\rm m,1}^{\rm\,y}<f_{\rm m}^{\rm\,y}$ can evolve, while
the fraction $f_{\rm m,2}^{\rm\,y}=f_{\rm m}^{\rm\,y}-f_{\rm m,1}^{\rm\,y}$
are episodic sources or ‘impostors’ which do not evolve. The ratio of the
merger fraction to the non-merger fraction for the evolved sources is
$R_{1}=f_{\rm m}^{\rm\,e}/f_{\rm nm}^{\rm\,e}=f_{\rm m,1}^{\rm\,y}/f_{\rm
nm}^{\rm\,y}$, and thus the merger fraction of the ECS sample results in
$f_{\rm m}^{\rm\,ECS}=f_{\rm m,1}^{\rm\,y}+f_{\rm m,2}^{\rm\,y}\cdot
R_{1}/(1+R_{1})$. Using the abbreviations $R_{2}:=f_{\rm m,2}^{\rm\,y}/f_{\rm
m,1}^{\rm\,y}$ and $S_{1}:=R_{1}/(1+R_{1})$, we get $f_{\rm
m}^{\rm\,ECS}=f_{\rm m}^{\rm\,y}\cdot(1+R_{2}\cdot S_{1})/(1+R_{2})$. If we
set $f_{\rm m}^{\rm\,y}=0.61\pm 0.07$, i.e. equal to the observed merger
fraction of the PSS-CSO sample, we find that the low observed merger fraction
of the comparison sample, $f_{\rm m}^{\rm\,ECS}=0.15\pm 0.06$, can be
reproduced if $R_{2}=7.5\pm 1.5$. This means that $88\pm 3\,\%$ of the merger-
triggered sources, or at least $\sim 54\pm 2\,\%$ of all young radio sources,
do not evolve to extended sources represented by the ECS sample.

####  6.3.2 Cluster environment

Current evidence suggests that both the persistence of tidal structures and
the properties of AGNs depend on the environment of the galaxies (e.g. Mihos,
2004; Sabater et al., 2013; Marshall et al., 2018). In particular, tidal
structures tend to be weaker and disappear more rapidly in the dense
environment of galaxy cluster cores. As a result, the merger fraction of a
sample of cluster galaxies would be smaller than that of field galaxies. O’Dea
& Saikia (2021) summarised previous studies on small samples of powerful GPSs
and CSSs. They concluded that their environment appears to be similar to that
of powerful large radio sources and suggested that follow-up studies be
carried out on larger samples.

We have used the following large catalogues of galaxy clusters to check the
cluster membership. The GMBCG Galaxy Cluster Catalog (Hao et al., 2010) is a
catalogue of galaxy clusters selected from SDSS DR7 by identifying the red-
sequence plus the brightest cluster galaxies. It contains 55 424 rich galaxy
clusters in the redshift range $0.1<z<0.55$. Wen et al. (2012, WHL) identified
132 684 clusters with $0.05\la z\la 0.75$ on the basis of photometric
redshifts of galaxies from SDSS DR8. Wen & Han (2015) published an updated
version of the WHL catalogue, using the SDSS DR12 spectroscopic data and
additionally a list of 25 419 complementary clusters at high redshifts around
bright galaxies. For about 89% of the identified clusters in the SDSS
spectroscopic survey region, spectroscopic redshifts are available in the SDSS
DR12. The cluster redshifts cover the range $0.05<z<0.75$. Banerjee et al.
(2018) presented a galaxy cluster catalogue constructed from the SDSS DR9,
which comprises 46 479 galaxy clusters at $z\la 0.64$. Complementary to the
above data sources, the galaxy cluster catalogues of Burenin (2017), Wen et
al. (2018), and Kirkpatrick et al. (2021) were included in our search. The
extension of the Planck galaxy cluster catalogue by Burenin (2017) comprises
$\sim 3\,000$ galaxy clusters detected in the Planck all-sky Compton parameter
maps and identified using data from the WISE and SDSS surveys. Wen et al.
(2018) presented a compilation of $\sim 47\,600$ clusters identified from
photometric data of 2MASS, WISE, SuperCOSMOS, and detection by ROSAT and XMM-
Newton. Finally, the catalogue of Kirkpatrick et al. (2021) was included,
which is the largest compilation of visually confirmed X-ray selected
spectroscopic galaxy clusters. The latter lists 2 740 galaxy clusters
spectroscopically confirmed using SDSS DR16 spectroscopic data for 33 340
individual galaxy members. In all the catalogues used, the redshift
distributions of the clusters drop significantly at $z\sim 0.6$.

We consider a galaxy as belonging to a cluster if (a) the projected distance
from the catalogued cluster position is $d_{\rm c}\leq 1$ Mpc at the redshift
of the cluster and (b) the radial component of the relative velocity with
respect to the cluster is $v_{\rm rel}\leq 10^{3}$ km s-1. The latter was
determined from the relation $v_{\rm rel}/c=(z_{\rm g}-z_{\rm cl})/(1+z_{\rm
cl})$ (Harrison, 1974), where $z_{\rm g}$ and $z_{\rm cl}$ are the redshifts
of the galaxy and the cluster, respectively. We identified host clusters in
all used catalogues, with the sole exception of the X-ray cluster catalogue by
Kirkpatrick et al. (2021). If a nearby cluster was identified in two or more
catalogues, we used the following priority list: (1) Wen & Han (2015), (2)
Banerjee et al. (2018), (3) Burenin (2017), (4) Hao et al. (2010), (5) Wen et
al. (2018). The catalogue by Wen & Han (2015) contains by far the most sources
from our samples. The order of the other catalogues was chosen arbitrarily.

The criterion of cluster membership is fulfilled by 15 sources from the PSS-
CSO and 13 from the ECS sample. This corresponds to $19\pm 4$ % (PSS-CSO) and
$17\pm 4$ % (ECS) cluster members for the redshift range $z<0.6$. The
difference is not significant according to the two-sided Z-test ($p=0.67$).
More revealing is the proportion of sources located in the cluster core
regions, where the strongest effects are to be expected. Of the PSSs and CSOs,
$15\pm 4$% (12/78) are found within $d_{\rm c}=0.2$ Mpc, and a similar
percentage of $12\pm 4$ % (9/78) is found for the ECS sample. With only two
exceptions, all identified clusters are listed in the Wen & Han (2015)
catalogue, where the estimated cluster radius $r_{500}$ is also given. The
mean values are $r_{500}=0.76\pm 0.04$ Mpc for the PSS-CSO and $0.72\pm 0.09$
Mpc for the ECS sample. These results suggest that the cluster environment
does not play a decisive role in the difference between the merger fractions
of the two samples.

Finally, we note that the merger fraction among cluster galaxies is
significantly lower than in general. Among the PSS-CSOs in clusters, we find
four mergers ($f_{\rm m}=0.27\pm 0.11$), among the ECS none at all. The mean
merger index is $t_{\rm m}=0.64\pm 1.08$ (PSS-CSO) and $0.15\pm 0.38$ (ECS),
respectively.

##  7 Summary and conclusions

We compared a sample of compact and presumably young extragalactic radio
sources with a sample of evolved, extended radio galaxies and QSOs. To
construct the former, we used the sample from (Liao & Gu, 2020) with available
SDSS spectra, here referred to as the ‘PSS-CSO sample’. The extended
comparison sources (ECSs) were selected from a larger sample of extended radio
galaxies and QSOs with steep radio spectra. The selection was made in such a
way that each object from the PSS-CSO sample has one counterpart in the ECS
sample with a similar redshift, 1.4 GHz luminosity, optical luminosity, and
SDSS spectral class, where the linear size, measured in VLASS, must be greater
than 50 kpc.

This study focuses in particular on spectra, SEDs and morphological properties
and is based on archival data in the spectral range from the far UV to MIR. We
divided both samples into the subsamples of galaxies and QSOs and generally
compared the corresponding subsamples with each other using statistical tests.
The main results are the following.

(i) We compared the distributions of the radio galaxies in three different
diagnostic diagrams. There are no significant differences in the [N ii]-based
BPT diagram. In the KEx diagram, the PSS-CSO sample shows a higher mean line
dispersion of the [O iii] line. Both diagrams suffer from a significant bias
because a substantial portion of galaxies have weak or missing H$\beta$ lines.
The more complete WHAN diagram, on the other hand, shows a significantly
higher percentage of passive and retired galaxies in the ECS compared to the
PSS-CSO sample.

(ii) In the SDSS composite spectra of the galaxy subsamples, the continuum is
sufficiently well described by a mixture of old early-type galaxies and SF
galaxies, with a significantly larger contribution from SF galaxies in the
PSS-CSO sample. Such a difference is not visible between the continua of the
QSO composites. All four composite spectra show relatively strong forbidden
emission lines [O iii] and [O ii] in combination with weak Fe blends, as
expected for luminous radio galaxies. The PSS-CSO composites show a larger
line ratio [O ii]/[Ne v] which can be attributed to SF or to the interaction
of the radio jets with the dense interstellar medium of the host galaxy.

(iii) We classified the individual MBSEDs using a set of template spectra.
$44\pm 7$ % of the galaxies from the ECS-G sample are best matched by
templates of early-type galaxies, compared to only $31\pm 6$ % for the PSS-
CSO-G sample. The latter is largely in agreement with Nascimento et al.
(2022). The proportion of SF galaxies is significantly larger for PSSs and
CSOs than for ECSs, both in the entire sample and in the two subsamples of
galaxies and QSOs. The colour-colour diagram based on the first three WISE
bands also shows a significantly lower proportion of SF galaxies in the ECS
compared to the PSS-CSO sample.

(iv) The distributions of the D4000n index are different for PSS-CSO and ECS
galaxies. The mean values are D4000n = $1.70\pm 0.04$ for the PSS-CSO-G and
$1.88\pm 0.02$ for the ECS-G sample. The larger value for the latter is
consistent with the stronger contribution of old stellar populations and is
consistent with the previous points. The distributions of the SFR and the sSFR
from Chang et al. (2015) also differ for the two samples in such a way that
they indicate a higher proportion of SF galaxies in the PSS-CSO sample. In the
SFR - stellar mass diagram, $42\pm 10$ % of the PSS-CSO galaxies with
available SFR data lie on the main sequence of SF galaxies, compared to only
$22\pm 8$ % for the ECS-G sample. For the vast majority, the SFRs and stellar
masses of PSS-CSO-G host galaxies cover the ranges from $\sim 0$ to 5
$\mathcal{M}_{\odot}$ yr-1 and $3\cdot 10^{11}$ to
$10^{12}\mathcal{M}_{\odot}$, respectively, in good agreement with Nascimento
et al. (2022).

(v) We analysed archival HST images of 18 PSSs and CSOs from our sample. In
all cases where the host galaxy can be studied, we found indications of
gravitational interactions and mergers. Motivated by this high merger
fraction, we used the DESI Legacy Surveys images to analyse the morphologies
of all objects with $z\leq 0.3$ from both samples. We found a higher
proportion of mergers in the PSS-CSO ($0.61\pm 0.07$) compared with the ECS
sample ($0.15\pm 0.06$). This highly significant difference ($p<10^{-5}$) can
be explained by assuming that 61% of the PSSs and CSOs were triggered by
mergers, but $88\pm 3$ % of the merger-triggered or $54\pm 2$ % of all young
radio sources do not evolve and are thus not represented in the comparison
sample of extended radio sources. This interpretation is in line with the
recent finding by Kiehlmann et al. (2023) that most CSOs belong to a distinct
class of jetted AGNs.

(vi) To look for possible differences in the environments of the PSSs-CSOs and
the ECSs, we matched the positions and redshifts of the sources with $z<0.6$
from both samples with several large galaxy cluster catalogues. The results do
not suggest a significant difference.

(vii) We provide further evidence that the host galaxy of the CSS source 4C
+39.29 is most likely not SDSS J101714.23+390121.1 at $z=0.211$ but SDSS
J101714.10+390123.8 at $z=0.526$ as suggested already by Gandhi et al. (2006).
The observations are consistent with an ongoing jet–cloud interaction in that
source.

We conclude that our PSS-CSO sample consists of both young radio sources that
evolve into extended radio structures and frustrated or hindered sources that
do not. Galaxy-galaxy interactions and mergers appear to play an important
role in triggering the radio AGN. These results need to be verified with a
larger PSS-CSO sample.

###### Acknowledgements.

We thank the referee for useful feedback, which has improved this manuscript.
We would like to thank Gülay Gürkan for comments and suggestions on the
manuscript and the other members of the TLS Working Group on Extragalactic
Radio Astronomy for numerous discussions. This research has made use of data
products from the Sloan Digital Sky Survey (SDSS). Funding for the SDSS and
SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating
Institutions (see below), the National Science Foundation, the National
Aeronautics and Space Administration, the U.S. Department of Energy, the
Japanese Monbukagakusho, the Max Planck Society, and the Higher Education
Funding Council for England. The SDSS Web site is http://www.sdss.org/. The
SDSS is managed by the Astrophysical Research Consortium (ARC) for the
Participating Institutions. The Participating Institutions are: the American
Museum of Natural History, Astrophysical Institute Potsdam, University of
Basel, University of Cambridge (Cambridge University), Case Western Reserve
University, the University of Chicago, the Fermi National Accelerator
Laboratory (Fermilab), the Institute for Advanced Study, the Japan
Participation Group, the Johns Hopkins University, the Joint Institute for
Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and
Cosmology, the Korean Scientist Group, the Los Alamos National Laboratory, the
Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for
Astrophysics (MPA), the New Mexico State University, the Ohio State
University, the University of Pittsburgh, University of Portsmouth, Princeton
University, the United States Naval Observatory, and the University of
Washington.
The Legacy Surveys consist of three individual and complementary projects: the
Dark Energy Camera Legacy Survey (DECaLS; Proposal ID #2014B-0404; PIs: David
Schlegel and Arjun Dey), the Beijing-Arizona Sky Survey (BASS; NOAO Prop. ID
#2015A-0801; PIs: Zhou Xu and Xiaohui Fan), and the Mayall z-band Legacy
Survey (MzLS; Prop. ID #2016A-0453; PI: Arjun Dey). DECaLS, BASS and MzLS
together include data obtained, respectively, at the Blanco telescope, Cerro
Tololo Inter-American Observatory, NSF’s NOIRLab; the Bok telescope, Steward
Observatory, University of Arizona; and the Mayall telescope, Kitt Peak
National Observatory, NOIRLab. Pipeline processing and analyses of the data
were supported by NOIRLab and the Lawrence Berkeley National Laboratory
(LBNL). The Legacy Surveys project is honored to be permitted to conduct
astronomical research on Iolkam Du’ag (Kitt Peak), a mountain with particular
significance to the Tohono O’odham Nation. NOIRLab is operated by the
Association of Universities for Research in Astronomy (AURA) under a
cooperative agreement with the National Science Foundation. LBNL is managed by
the Regents of the University of California under contract to the U.S.
Department of Energy. This project used data obtained with the Dark Energy
Camera (DECam), which was constructed by the Dark Energy Survey (DES)
collaboration. Funding for the DES Projects has been provided by the U.S.
Department of Energy, the U.S. National Science Foundation, the Ministry of
Science and Education of Spain, the Science and Technology Facilities Council
of the United Kingdom, the Higher Education Funding Council for England, the
National Center for Supercomputing Applications at the University of Illinois
at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the
University of Chicago, Center for Cosmology and Astro-Particle Physics at the
Ohio State University, the Mitchell Institute for Fundamental Physics and
Astronomy at Texas A&M University, Financiadora de Estudos e Projetos,
Fundacao Carlos Chagas Filho de Amparo, Financiadora de Estudos e Projetos,
Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro,
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the
Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche
Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy
Survey. The Collaborating Institutions are Argonne National Laboratory, the
University of California at Santa Cruz, the University of Cambridge, Centro de
Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the
University of Chicago, University College London, the DES-Brazil Consortium,
the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH)
Zurich, Fermi National Accelerator Laboratory, the University of Illinois at
Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the
Institut de Fisica d’Altes Energies, Lawrence Berkeley National Laboratory,
the Ludwig Maximilians Universitat Munchen and the associated Excellence
Cluster Universe, the University of Michigan, NSF’s NOIRLab, the University of
Nottingham, the Ohio State University, the University of Pennsylvania, the
University of Portsmouth, SLAC National Accelerator Laboratory, Stanford
University, the University of Sussex, and Texas A&M University. BASS is a key
project of the Telescope Access Program (TAP), which has been funded by the
National Astronomical Observatories of China, the Chinese Academy of Sciences
(the Strategic Priority Research Program “The Emergence of Cosmological
Structures” Grant # XDB09000000), and the Special Fund for Astronomy from the
Ministry of Finance. The BASS is also supported by the External Cooperation
Program of Chinese Academy of Sciences (Grant # 114A11KYSB20160057), and
Chinese National Natural Science Foundation (Grant # 12120101003, # 11433005).
The Legacy Survey team makes use of data products from the Near-Earth Object
Wide-field Infrared Survey Explorer (NEOWISE), which is a project of the Jet
Propulsion Laboratory/California Institute of Technology. NEOWISE is funded by
the National Aeronautics and Space Administration. The Legacy Surveys imaging
of the DESI footprint is supported by the Director, Office of Science, Office
of High Energy Physics of the U.S. Department of Energy under Contract No. DE-
AC02-05CH1123, by the National Energy Research Scientific Computing Center, a
DOE Office of Science User Facility under the same contract; and by the U.S.
National Science Foundation, Division of Astronomical Sciences under Contract
No. AST-0950945 to NOAO. This publication has made use of the VizieR catalogue
access tool, CDS, Strasbourg, France, and of the NASA/IPAC Infrared Science
Archive (IRSA), operated by the Jet Propulsion Laboratories/California
Institute of Technology, founded by the National Aeronautic and Space
Administration. In particular, this publication makes use of data products
from the Wide-field Infrared Survey Explorer, which is a joint project of the
University of California, Los Angeles, and the Jet Propulsion
Laboratory/California Institute of Technology, funded by the National
Aeronautics and Space Administration. In addition, we used data products from
the Two Micron All Sky Survey, which is a joint project of the University of
Massachusetts and the Infrared Processing and Analysis Center/California
Institute of Technology, funded by the National Aeronautics and Space
Administration and the National Science Foundation. We also used observations
made with the NASA Galaxy Evolution Explorer, GALEX, which is operated for
NASA by the California Institute of Technology under NASA contract NAS5-98034.
We also made use of data products from the United Kingdom Infrared Telescope
(UKIRT) Infrared Deep Sky Survey (UKIDSS). UKIRT is operated by the Joint
Astronomy Centre on behalf of the Science and Technology Facilities Council of
the U.K..
This work is also based in part on observations made with the NASA/ESA Hubble
Space Telescope, obtained from the Hubble Legacy Archive, which is a joint
project of the Space Telescope Science Institute (STScI), the Space Telescope
European Coordinating Facility (ST-ECF), and the Canadian Astronomy Data
Centre (CADC). We also used observations made with Subaru Telescope which is
operated by the National Astronomical Observatory of Japan and obtained from
the SMOKA, which is operated by the Astronomy Data Center, National
Astronomical Observatory of Japan. We also used observations made with
Hectospec at the MMT Observatory, a joint facility of the Smithsonian
Institution and the University of Arizona, and obtained from the CfA
Optical/Infrared Science Archive.
We also used images from Pan-STARRS1 (PS1). The PS1 Surveys and the PS1 public
science archive have been made possible through contributions by the Institute
for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the
Max-Planck Society and its participating institutes, the Max Planck Institute
for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial
Physics, Garching, The Johns Hopkins University, Durham University, the
University of Edinburgh, the Queen’s University Belfast, the Harvard-
Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global
Telescope Network Incorporated, the National Central University of Taiwan, the
Space Telescope Science Institute, the National Aeronautics and Space
Administration under Grant No. NNX08AR22G issued through the Planetary Science
Division of the NASA Science Mission Directorate, the National Science
Foundation Grant No. AST-1238877, the University of Maryland, Eotvos Lorand
University (ELTE), the Los Alamos National Laboratory, and the Gordon and
Betty Moore Foundation.
This research has made use of the CIRADA cutout service at URL
cutouts.cirada.ca, operated by the Canadian Initiative for Radio Astronomy
Data Analysis (CIRADA). CIRADA is funded by a grant from the Canada Foundation
for Innovation 2017 Innovation Fund (Project 35999), as well as by the
Provinces of Ontario, British Columbia, Alberta, Manitoba and Quebec, in
collaboration with the National Research Council of Canada, the US National
Radio Astronomy Observatory and Australia’s Commonwealth Scientific and
Industrial Research Organisation.

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