# Gamma-ray emission from young radio galaxies and quasars

G. Principe,1,2,3 L. Di Venere 4,5 M. Orienti, 3 G. Migliori, 3 F. D’Ammando,
3 M. N. Mazziotta, 5 and M. Giroletti 3
1Dipartimento di Fisica, Universitá di Trieste, I-34127 Trieste, Italy
2 Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste,
Italy
3 INAF - Istituto di Radioastronomia, I-40129 Bologna, Italy
4Dipartimento di Fisica “M. Merlin” dell’Universita‘ e del Politecnico di
Bari, I-70126 Bari, Italy
5 Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari, Italy
<EMAIL_ADDRESS>
(Accepted August 11, 2021. Received April 26, 2021.)

###### Abstract

According to radiative models, radio galaxies and quasars are predicted to
produce gamma rays from the earliest stages of their evolution. Exploring
their high-energy emission is crucial for providing information on the most
energetic processes, the origin and the structure of the newly born radio
jets. Taking advantage of more than 11 years of Fermi-LAT data, we investigate
the gamma-ray emission of 162 young radio sources (103 galaxies and 59
quasars), the largest sample of young radio sources used so far for such a
gamma-ray study. We separately analyze each source and perform the first
stacking analysis of this class of sources to investigate the gamma-ray
emission of the undetected sources. We detect significant gamma-ray emission
from 11 young radio sources, four galaxies and seven quasars, including the
discovery of significant gamma-ray emission from the compact radio galaxy PKS
1007+142 (z=0.213). The cumulative signal of below-threshold young radio
sources is not significantly detected. However, it is about one order of
magnitude lower than those derived from the individual sources, providing
stringent upper limits on the gamma-ray emission from young radio galaxies
($F_{\gamma}<4.6\times 10^{-11}$ ph cm-2 s-1) and quasars
($F_{\gamma}<10.1\times 10^{-11}$ ph cm-2 s-1), and enabling a comparison with
the models proposed. With this analysis of more than a decade of Fermi-LAT
observations, we can conclude that while individual young radio sources can be
bright gamma-ray emitters, the collective gamma-ray emission of this class of
sources is not bright enough to be detected by Fermi-LAT.

###### keywords:

Galaxies: evolution – galaxies: active – galaxies: jets – radio continuum:
galaxies – gamma-rays: galaxies

††pubyear: 2021††pagerange: Gamma-ray emission from young radio galaxies and
quasars–7

##  1 Introduction

Understanding the origin and evolution of the high-energy emission in
extragalactic radio galaxies and quasars is one of the greatest challenges
faced by modern astrophysics. The extragalactic gamma-ray sky is dominated by
blazars (Massaro et al., 2015; Abdollahi et al., 2020), which are a sub-class
of active galactic nuclei (AGN) for which the gamma-ray emission is favoured
by their small jet inclination to the line of sight and by relativistic
beaming. The increasing amount of data collected by the Large Area Telescope
(LAT) on board the Fermi Gamma-ray Space Telescope (Atwood et al., 2009)
allows us to investigate many classes of objects in the gamma-ray sky
(Sahakyan et al., 2018). A small percentage, $\sim 2\%$, of the fourth catalog
of gamma-ray AGN (4LAC, Ajello et al., 2020a), are radio galaxies (or
misaligned AGN), which have larger jet inclination angles ($>10^{\circ}$) and
a smaller Doppler factor ($\delta\leq 2-3$) than blazars. With their
misaligned jets, they offer a unique tool to probe some of the non-thermal
processes at work in unbeamed regions in AGN, which are usually overwhelmed by
beamed emission from the jet in blazars.

In the evolutionary scenario, the size of a radio galaxy is strictly related
to its age (Fanti et al., 1995). Extragalactic compact radio objects, with a
projected linear size (LS) smaller than about 20 kpc, i.e. they reside within
the host galaxy, are important objects because they are expected to be the
progenitors of extended radio galaxies (Readhead et al., 1996). The young
nature of these objects is strongly supported by the determination of
kinematic and radiative ages in some of the most compact sources, which were
found to be $t\sim 10^{2}-10^{5}$ years (Phillips & Mutel, 1982; Murgia et
al., 1999; Gugliucci et al., 2005; Giroletti & Polatidis, 2009), while large-
size objects have an age of $t\sim 10^{7}-10^{8}$ years (Jamrozy et al., 2005;
Harwood et al., 2017).

Extragalactic compact radio objects can be classified into GHz-peaked spectrum
(GPS) and compact steep spectrum (CSS) sources depending on their radio
spectra. GPS objects are powerful radio sources whose spectra present peak
frequencies $\nu_{p}>0.5$ GHz (O’Dea, 1998). CSS objects are similarly
powerful inverted-spectrum radio sources but with peak frequencies in a lower
frequency range when compared to the GPS population, $\nu_{p}\lesssim 0.5$
GHz. Morphologically, GPS/CSS sources may be reminiscent of a smaller version
of classical doubles (Fanaroff-Riley type-II radio galaxies), with pairs of
symmetric lobes present on opposite sides of a weak radio nucleus. In such
cases they are called compact symmetric objects (CSOs) if LS $\lesssim$ 1 kpc,
medium symmetric objects (MSOs) if LS $\sim 1-20$ kpc, and large symmetric
objects (LSOs) if LS $>20$ kpc (Fanti et al., 1990; O’Dea & Saikia, 2021). As
shown by O’Dea & Baum (1997), there seems to be a relatively tight correlation
between the peak frequency and the source’s linear size. This unifies the GPS
and CSS populations and suggests that they are both manifestations of the same
physical phenomenon.

GPS and CSS objects reside either in galaxies or quasars. For the quasars,
gamma-ray emission is favored by the smaller jet-inclination angle and beaming
effects, while the origin of gamma-ray emission in galaxies is still a matter
of debate. Young radio sources were predicted to constitute a relatively
numerous class of extragalactic objects detectable by Fermi-LAT (Stawarz et
al., 2008). They are entirely located within the innermost region of the host
galaxies, surrounded by dense and inhomogeneous interstellar medium, which may
be a rich source of UV/optical/IR photons. In compact radio sources associated
with quasars the high-energy emission could be due to inverse Compton (IC) of
the synchrotron photons by a dominant jet component, producing an emission
that can be strongly beamed (e.g. Migliori et al., 2012; Migliori et al.,
2014). However, the most compact and powerful radio galaxies are expected to
produce isotropic $\gamma$-ray emission up to the GeV band through IC
scattering of the UV/optical/IR photons by the electrons in the compact radio
lobes (Stawarz et al., 2008; Kino & Asano, 2011, see the latter for a
discussion of hadronic models). In these models the high-energy luminosity of
radio galaxies strictly depends on different source parameters such as linear
size, jet power, UV/optical/IR photon density and the equipartition condition
in the lobes.

The search for gamma-ray emission from young radio sources is crucial for
providing information on the physical conditions in the central region of the
host galaxy, the energetic processes possibly at work in such regions, as well
as the origin and the structure of the newly born radio jets. However,
systematic searches for young radio sources at gamma-ray energies have so far
been unsuccessful (D’Ammando et al., 2016). Dedicated studies have reported a
handful of detections. Migliori et al. (2016) reported the first association
of a gamma-ray source with a GPS radio galaxy, NGC 6328 ($z$ = 0.014). Beyond
the confirmation of this object in the fourth Fermi-LAT catalog (4FGL,
Abdollahi et al., 2020), detections have been reported for only five CSS
sources (3C 138, 3C 216, 3C 286, 3C 380, and 3C 309.1), all associated with
quasars, and for a second GPS radio galaxy (NGC 3894, $z$ = 0.0108) (Principe
et al., 2020). The five CSS quasars have high gamma-ray luminosity ($>10^{46}$
erg s-1) and show flaring activity at high energies, suggesting
(mildly-)beamed emission.

Taking advantage of the increased exposure provided by more than eleven years
of LAT data, we investigate the gamma-ray properties of a sample of 162 young
radio sources (103 galaxies and 59 quasars). In addition to the gamma-ray
analysis of each young radio object, we perform the first stacking analysis of
this class of sources in order to investigate the gamma-ray emission of the
young radio sources still below the detection threshold in the high-energy
regime.

Throughout this article, we assume $H_{0}=70$ km s-1 Mpc-1 , $\Omega_{M}=0.3$,
and $\Omega_{\Lambda}=0.7$ in a flat Universe.

##  2 Sample of young radio sources

To select young radio sources, we base our sample on the following resources
which contain radio galaxies and quasars with projected linear size below 50
kpc:

  * •

the sample of 51 bona-fide young radio sources created by Orienti & Dallacasa
(2014) selected on the basis of the detection of the core as a crucial
requirement for classifying a source as a genuine CSO/MSO/LSO. It contains 32
galaxies and 19 quasars with linear sizes from a few pc to tens of kpc. In
particular, for half of them, their youth is strongly supported by the
determination of kinematic and radiative ages which were found to be a few
thousand years or less.

  * •

The sample of 25 nearby ($z$ < 0.25) and compact ($\theta$ < 2”) radio
galaxies selected by de Vries et al. (2009) from the COmpact RAdio sources at
Low Redshift (CORALZ) sample (Snellen et al., 2004). For this sample the
authors investigated the size and morphological classification by means of
VLBI observations, reporting sizes spanning a couple of pc to a few kpc.

  * •

The list of sources used for the investigation of the optical properties of
young radio AGNs based on the Sloan Digital Sky Survey (SDSS) spectroscopy
created by Liao & Gu (2020), where they excluded all possible blazar-like
objects with optical spectral variability. This list contains 126 (54 galaxies
and 72 quasars) sources with different physical properties: a redshift value
between 0.001 and 3.5 and linear sizes between 0.4 pc and 30 kpc.

  * •

The sample of 17 GPS and/or CSOs with measured redshifts below 1 and linear
sizes below 1 kpc from Wójtowicz et al. (2020).

Since several sources are present in more than one of the above-listed
samples, we removed all the repetitions. In addition to these samples, we
included a handful of objects selected from the following resources:

  * •

three sources, the galaxies NGC 3894, TXS 0128+554, and the quasar 3C 380,
have been specifically selected since they have been detected at high energy
and investigated in Principe et al. (2020), Lister et al. (2020) and Zhang et
al. (2020), respectively.

  * •

The young radio galaxy 0402$+$379 ($z$ = 0.0545, LS =7.3 pc) was selected
because it has been proposed, together with other sources already included in
our sample, as a promising candidate gamma-ray source in the study by
Kosmaczewski et al. (2020), where the X-ray emission of 29 GPS and CSO objects
($t\lesssim$ 3 kyr, LS $<$ 300 pc) was investigated using high-angular
resolution X-ray telescopes.

Our final sample consists of 162 young radio sources with known position,
redshift, linear size, radio luminosity and peak frequency (see Table 5 in the
Appendix A.2). Among them, 103 are classified as galaxies and 59 as quasars.

The selected sources have redshift values between 0.001 and 3.5 and linear
sizes spanning from less than 1 pc up to a few tens of kpc. Most (129) of the
sources have redshift below 1, with seven sources located in the local
Universe ($z$ < 0.05, $D_{L}\lesssim$200 Mpc). In Fig. 1 we show the
distribution of the linear size for our sample of sources, discriminating
between galaxies and quasars.

Figure 1:  Projected linear size for the full sample of young radio sources
described in Sect. 2. Red dotted line, blue dashed line and green solid line
represent galaxies, quasars and all the sources, respectively.

Considering the morphological classification, about half (79) of the sources
are classified as CSOs (LS $<1$ kpc), 70 sources as MSO (LS $\sim 1-20$ kpc),
and 13 LSOs with LS between 20 and 50 kpc.

Concerning their radio spectra and peak frequency ($\nu_{p}$), 52 sources are
classified as GPS ($\nu_{p}>0.5$ GHz), with the remaining 110 being classified
as CSS sources ($\nu_{p}<0.5$ GHz). For several CSS sources only upper limits
on the peak frequency have been found in the literature. The radio luminosity
($\nu L_{\nu\textrm{=5\,GHz}}$) of the sources contained in our sample varies
by more than 8 orders of magnitude ($\nu L_{\nu\textrm{= 5\,GHz}}\sim
10^{38}-10^{46}$ erg s-1). Fig. 2 shows the distribution of the radio
luminosity vs redshift for all the sources selected in this work.

Figure 2:  Radio luminosity vs redshift for the galaxies (red) and quasars
(blue) contained in our sample.

##  3 Analysis Description

The LAT is a gamma-ray telescope that detects photons by conversion into
electron-positron pairs and has an operational energy range from 20 MeV to 2
TeV. It comprises a high-resolution converter tracker (for direction
measurement of the incident gamma rays), a CsI(Tl) crystal calorimeter (for
energy measurement) and an anti-coincidence detector to identify the
background of charged particles (Atwood et al., 2009). The analysis procedure
applied in this work is mainly based on two steps. First, we investigate the
gamma-ray data of each individual source with a standard likelihood analysis
(see e.g. Principe et al., 2020; 2021arXiv210406855A, as described in detail
in the next subsection). Subsequently we performed a stacking analysis of the
sources which were not significantly detected in the individual study, in
order to investigate the general properties of the population of young radio
galaxies and quasars.

###  3.1 Analysis of individual sources

We analysed the Fermi-LAT data of each individual source in our sample in
order to determine whether it is detected or not (using a Test Statistic
TS111The test statistic (TS) is the logarithmic ratio of the likelihood
$\mathcal{L}$ of a model with the source being at a given position in a grid
to the likelihood of the model without the source,
TS=$2\log\frac{\mathcal{L}_{\mathrm{src}}}{\mathcal{L}_{\mathrm{null}}}$
(Mattox et al., 1996). $>25$ as a threshold). We analysed more than 11 years
of Fermi-LAT data between August 5, 2008, and November 1, 2019 (MJD 54683 –
58788). We selected events which have been reprocessed with the P8R3_Source_V2
instrument response functions (IRFs) (IRFs, Bruel et al., 2018), in the energy
range between 100 MeV and 1 TeV. The low energy threshold is motivated by the
large uncertainties in the arrival directions of the photons below 100 MeV,
leading to a possible confusion between point-like sources and the Galactic
diffuse component (see Principe & Malyshev, 2017; Principe et al., 2018, 2019,
for a different analysis implementation to solve this and other issues at low
energies with Fermi-LAT).

We reduced the contamination from the low-energy Earth limb emission (Abdo et
al., 2009) by applying a zenith angle cut to the data. We made a harder cut at
low energies by selecting event types with the best point spread function222A
measure of the quality of the direction reconstruction is used to assign
events to four quartiles. Gamma rays in Pass 8 data can be separated into 4
PSF event types: 0, 1, 2, 3, where PSF0 has the largest point spread function
and PSF3 has the best one. (PSF). For energies below 300 MeV we excluded
events with zenith angle larger than 85∘, as well as photons from PSF0 and
PSF1 event types, while between 300 MeV and 1 GeV we excluded events with
zenith angle larger than 95∘, as well as photons from the PSF0 event type.
Above 1 GeV we use all events with zenith angles less than 105∘.

The binned likelihood analysis (which consists of model optimisation, and
localisation, spectrum and variability analyses) was performed with
Fermipy333http://fermipy.readthedocs.io/en/latest/ (Wood et al., 2017), a
python package that facilitates the analysis of LAT data with the Fermi
Science Tools, of which the version 11-07-00 was used. For each source in our
sample we considered a region of interest (ROI) of about 15∘ radius centred on
the source position, and each ROI is analysed separately. In each ROI we
binned the data with a pixel size of $0.1^{\circ}$ and 8 energy bins per
decade. The model used to describe the sky includes all point-like and
extended LAT sources located at a distance $<20^{\circ}$ from the source
position and listed in the 4FGL (Abdollahi et al., 2020), as well as the
Galactic diffuse and isotropic emission. For these two latter contributions,
we made use of the same
templates444https://fermi.gsfc.nasa.gov/ssc/data/access/lat/
BackgroundModels.html adopted to compile the 4FGL.

For the analysis we first optimised the model for the ROI, then we searched
for possible additional faint sources in each ROI, not included in 4FGL, by
generating TS maps (significance maps). Subsequently, we re-localised the
sources of our sample with TS $>$ 4 ($\sim 2\sigma$). We performed the
spectral analysis during which we left free to vary the diffuse background and
the spectral parameters of the sources within 5∘ of our targets. For the
sources in a radius between 5∘ and 10∘ only the normalisation was fit, while
we fixed the parameters of all the sources within the ROI at larger angular
distances from our targets. For the spectral energy distribution (SED) plot of
the detected sources, we repeated the spectral analysis dividing the photons
into seven energy bands: six logarithmically spaced bands between 100 MeV and
100 GeV and one band between 100 GeV and 1 TeV. We modeled the spectrum of
each source with a power-law (PL) function

$\dfrac{dN}{dE}=N_{0}\times(\frac{E}{E_{b}})^{-\Gamma};$ (1)

using $E_{b}=1$ GeV. Upper limits at 95% confidence level are reported in the
Appendix A.2, for the sources with no significant gamma-ray emission (TS <
10). In order to derive the upper limits we repeated the spectral analysis
fixing the photon index ($\Gamma=2$).

Finally we extracted a light curve for each source using time bins of 1 year.
For the brightest and variable sources, the lightcurve analysis was repeated
using time intervals of 3 months in order to better characterise the emission
variability. The fluxes in each interval were obtained by leaving only the
normalisation free to vary and freezing the other spectral parameters to the
best fit values obtained from the full range analysis. Using the same method
applied in Abdollahi et al. (2020), for all the detected sources we computed
the variability index $TS_{var}$. Variability is considered probable when
$TS_{var}>23$ ($>$68), corresponding to 99% confidence in a $\chi^{2}$
distribution with $N_{int}-1=10$ (44) degrees of freedom, where $N_{int}$ is
the number of intervals corresponding in our case to the 11 yearly-length
periods (to the 45 three-months long periods). Properties of the detected
young radio sources, including SEDs and lightcurves, are described in Sect.
4.1, while the results for all the individual sources are reported in the
Appendix (Table 5).

###  3.2 Stacking analysis

As discussed in Sect. 4.1, only a few young radio sources have been
significantly detected. We analysed in detail the population of the undetected
sources looking for collective emission from these objects.

To reach this goal we performed a stacking analysis of the sources using the
spectrum results of each object as described in Sect. 3.1. For each source and
each energy bin a log-likelihood profile $\log\mathcal{L}_{i,k}$ was
calculated, i.e. the log-likelihood value as a function of the photon flux.
The indices $i$ and $k$ represent the source and the energy bin, respectively.
We assumed a spectral shape common to all sources $dN/dE$ and calculated the
corresponding log-likelihood value at a given energy. The total log-likelihood
was obtained by summing over all the energy bins and sources:

$\log\mathcal{L}=\sum_{i}\sum_{k}\log\mathcal{L}_{i,k}|_{dN/dE(E_{k})}$ (2)

We assumed a simple power-law spectrum (see Eq. 1) for the entire population.
We varied the normalisation $N_{0}$ and photon index $\Gamma$ to create a
2-dimensional likelihood profile in order to search for the parameter values
which maximise the log-likelihood. The significance of the potential detection
was checked by comparing the maximum log-likelihood value with the one of the
null hypothesis ($\log\mathcal{L}_{null}$), i.e., the hypothesis in which the
flux of the gamma-ray emitter is zero. We obtained the
($\log\mathcal{L}_{null}$) from the 2-dimensional profile by setting $k=0$ and
defined the TS = $2(\log\mathcal{L}-\log\mathcal{L}_{null}$). If the log
likelihood distribution in the null hypothesis is asymptotically Gaussian,
then we expect values of $\Delta\log\mathcal{L}>4.61$ to occur by chance only
5% of the time. We performed MCMC simulations (see Appendix A.1.1) and
verified that this threshold corresponds to a 5% false positive detection
rate.

In the case of no detection, we estimated the upper limit at 95% confidence
level on the photon flux deriving the 2-dimensional contour corresponding to a
$\Delta\log\mathcal{L}=4.61/2$, having two additional free parameters in the
model (Ciprini, Di Venere and Mazziotta, in preparation). A similar method was
applied in Ajello et al. (2020b) to study a different class of celestial
objects.

To verify the robustness of our stacking method, we simulated 11 years of Pass
8 data for 100 sources in random positions on the sky with the same spectrum.
The flux value was chosen such that the sources are below the LAT detection
threshold. The simulation confirmed the robustness of the method to detect
cumulative gamma-ray emission from sources of the same population, assuming
they have similar spectral properties (see Appendix A.1.1 for details).

As discussed in detail in Sect. 4.3, the stacking procedure was applied to
several sub-samples of the population available. The selection was based on
different parameters (e.g. linear size, distance). Since the photon flux upper
limit depends on the number of sources included in the stacking procedure, we
repeated the analysis changing the number of sources $N$ included in the
stacking procedure. See Appendix A.1.2 for more details.

##  4 Results

In this section we present the results of the analysis of each individual
source as well as the cumulative results obtained from the stacking analysis
of all the undetected (TS<25) young radio sources. Our analysis expands the
4FGL study to more than 11 years of Fermi-LAT data. For the individual sources
we report here the results of those with a TS value $\geq$ 25 555TS = 25 with
2 degrees of freedom, as in the case of a simple power-law model, corresponds
to an estimated statistical significance of $\sim 4.6\sigma$ assuming that the
null-hypothesis TS distribution follows a ${\chi}^{2}$ distribution (see
Mattox et al. (1996))., and those with a marginal detection, 10 $\leq$ TS <
25\. The high-energy properties of all the sources of our sample are listed in
Appendix A.2.

###  4.1 Young radio sources: individual detections

From our analysis we detect significant gamma-ray emission (TS $>$ 25) at the
positions of 11 young radio sources (see Fig. 3), four galaxies and seven
quasars, whose characteristics are reported in Table 1.

Figure 3:  Sky map, in Galactic coordinates and Mollweide projection, showing the young radio sources in our sample. The detected sources are labelled in the plot. All the 4FGL sources (Abdollahi et al., 2020) are also plotted, with grey points, for comparison. Table 1:  List of young radio sources detected in our analysis. We report the name, morphological/spectral type, redshift, projected linear size (LS) [kpc], peak frequency ($\nu_{p}$) [GHz], radio power at 5 GHz [W Hz-1], gamma-ray significance (TS), gamma-ray flux (0.1–1000 GeV) in units of 10-9 ph cm-2 s-1, power-law photon index, gamma-ray luminosity [1044 erg s-1] and gamma-ray variability (TSvar) estimated with the one-year time intervals. Sources with variable gamma-ray emission are marked with †. The table is divided into two blocks: the upper one lists the galaxies, while the lower part reports the quasars. The parameter TSvar indicates the significance of the variability. The newly detected source, PKS 1007+142 is marked with ‘*’.  Name | type | $z$ | LS | $\nu_{p}$ |  log L${}_{5\,\textrm{GHz}}$ | TS |  Fγ | $\Gamma$ |  Lγ |  TSvar
---|---|---|---|---|---|---|---|---|---|---
|  |  | kpc | GHz |  W Hz-1 |  |  10-9 cm-2 s-1 |  |  1044 erg s-1 |
Galaxies
NGC 6328 | CSO/GPS | 0.014 | 0.002 | 4 | 24.28 | 36 |  5.30$\pm$1.45 |  2.60$\pm$0.14 | 0.011 | 5
NGC 3894 | CSO/GPS | 0.0108 | 0.010 | 5 | 24.60 | 95 |  2.03$\pm$0.48 |  2.05$\pm$0.09 | 0.006 | 11
TXS 0128+554 | CSO/GPS | 0.0365 | 0.012 | 0.66 | 23.69 | 178 |  8.03$\pm$1.46 |  2.20$\pm$0.07 | 0.19 | 9
PKS 1007+142* | MSO/GPS | 0.213 | 3.3 | 0.5-2 | 25.71 | 31 |  4.65$\pm$1.55 |  2.56$\pm$0.18 | 2.8 | 4
Quasars
3C 138† | MSO/CSS | 0.759 | 5.9 | 0.176 | 27.97 | 34 |  2.09$\pm$0.89 |  2.05$\pm$0.12 | 64 | 68
3C 216† | LSO/CSS | 0.6702 | 56 | 0.066 | 27.23 | 153 |  7.78$\pm$0.98 |  2.60$\pm$0.09 | 97 | 24
3C 286 | LSO/CSS | 0.85 | 25 | <0.05 | 28.41 | 67 |  5.60$\pm$1.10 |  2.52$\pm$0.12 | 110 | 8
3C 309.1† | MSO/CSS | 0.905 | 17 | <0.076 | 28.08 | 207 |  6.33$\pm$0.74 |  2.47$\pm$0.07 | 180 | 215
3C 380† | MSO/CSS | 0.692 | 11 | <0.05 | 27.68 | 2274 |  36.44$\pm$1.48 |  2.41$\pm$0.03 | 510 | 68
PKS 0056-00 | MSO/CSS | 0.719 | 15 | <0.14 | 27.50 | 52 |  5.21$\pm$1.48 |  2.30$\pm$0.15 | 74 | 11
PKS B1413+135† | CSO/GPS | 0.247 | 0.03 | 8.4-15 | 26.19 | 1198 |  14.72$\pm$1.02 |  2.10$\pm$0.03 | 28 | 321

Nine out of the 11 detected sources were present in previous Fermi-LAT
catalogs, while PKS 0056-00 has been recently reported in the latest release
of LAT sources 4FGL-DR2666https://fermi.gsfc.nasa.gov/ssc/data/access/
lat/10yr_catalog/ (Abdollahi et al., 2020).

In addition to the sources already included in the 4FGL-DR2, we report here
the discovery of gamma-ray emission from the young radio galaxy PKS 1007+142
($z$ = 0.213). We significantly (TS = 31) detected gamma-ray emission from the
compact radio galaxy PKS 1007$+$242\. The LAT best-fit position of PKS
1007$+$142 (R.A., Dec. (J2000)= 152.43${}^{\circ}\pm 0.05^{\circ}$,
14.08${}^{\circ}\pm 0.06^{\circ}$), 68% confidence-level uncertainty
$R_{68}=0.08^{\circ}$, is compatible with its radio counterpart (see left
panel of Fig. 4). We note that the 4FGL-DR2 catalog reports the detection of
the source 4FGL J1010.0+1416 (TS$=31$, $\Gamma=2.73\pm 0.19$) located in the
vicinity of PKS 1007+142 and less than 0.2∘ away from the source we report
here. The source, detected in the 4FGL-DR2 catalog which is based on 10 years
of data, presents a similar significance, however it does not present a clear
association with PKS 1007+142. For a crosscheck, we repeated the analysis
using 11.3 years starting from the position of the 4FGL-DR2 source obtaining a
position compatible, considering the statistical error, with the one reported
above. This can indicate a possible better localization achieved in this work
thanks to the dedicated analysis and the longer exposure.

Figure 4: Left: Fermi-LAT TS map (in sigma units) above 100 MeV of the region
around PKS 1007$+$142\. The red star and circle represent the central position
and the 68% confidence-level uncertainty R${}_{68}=0.08^{\circ}$ of the gamma-
ray source, respectively. White dots show radio sources from the NVSS survey
whose sizes are arbitrarily scaled depending on their radio flux density.
Right: Fermi-LAT SED of the galaxy PKS 1007$+$142\. The SED has been fit with
a PL (blue line). The $1\sigma$ upper limit is reported when TS $<4$.

PKS 1007$+$142 presents a soft gamma-ray spectrum (see right panel of Fig. 4)
with best-fit results $\Gamma=2.56\pm 0.18$ and $F=(4.65\pm 1.55)\times
10^{-9}$ ph cm-2 s-1.

In order to verify the association of the newly detected gamma-ray source with
its radio counterpart we used a Bayesian method (Nolan et al., 2012) which is
based only on spatial coincidence between the gamma-ray source and its
potential counterpart. A uniform threshold of P $>0.8$ is applied to the
posterior probability for the association to be retained. The resulting
association probability confirms the association of the new Fermi-LAT galaxy
with the radio counterpart PKS 1007+142 (P = 0.92).

Considering the detected sources, all the galaxies are GPS ($\nu_{p}$ > 0.5
GHz), while all the quasars are CSS, with the exception of the peculiar GPS
PKS B1413$+$135.

####  4.1.1 Galaxies

In our analysis we observe significant gamma-ray emission from four young
radio galaxies: NGC 3894, NGC 6328, TXS 0128$+$554 and the newly detected
source PKS 1007$+$142\. Compared to galaxies previously reported in the 4FGL
catalog, NGC 3894 presents a relatively flat spectrum ($\Gamma=2.05\pm 0.05$),
while NGC 6328 has a softer spectrum ($\Gamma=2.60\pm 0.14$). The spectral
results obtained for NGC 3894 and NGC 6328 are compatible with those found in
the 4FGL as well as those obtained in dedicated studies (Principe et al.,
2020; Migliori et al., 2016).

A recent multi-frequency radio radio very long baseline array (VLBA) study of
the galaxy TXS 0128$+$554 ($z$ = 0.0365), presented in Lister et al. (2020),
provided new information on this radio source, which was previously classified
as blazar candidate of uncertain type (BCU) in the 4FGL catalog. They measured
the compact size (LS $\sim$ 12 pc), misaligned nature
(43${}^{\circ}<\theta<59^{\circ}$) and the advanced speed of the jet
separation ($v=0.32\pm\,0.07c$) of the source, classifying it as a young radio
galaxy with kinematic age of only 82 $\pm$ 17 years. In our analysis, the
source is significantly detected with a TS = 178, and presents spectral
results ($\Gamma=2.20\pm 0.07$, $F_{\gamma}=8.03\pm 1.46\,\times 10^{-9}$ ph
cm-2 s-1) which are compatible with the 4FGL results. No significant gamma-ray
emission variability has been found for the detected young radio galaxies
($TS_{var}<23$; see Appendix A.2 for the light curve plots).

####  4.1.2 Quasars

In our analysis we detect significant gamma-ray emission from seven quasars
already reported in earlier works: 3C 138, 3C 216, 3C 286, 3C 309.1, 3C 380,
PKS 0056-00, PKS B1413$+$135\. For all the detected quasars, with the
exception of 3C 138, the spectral parameters obtained in this work are
compatible with those reported in the 4FGL catalog. Five quasars have a
relatively soft photon index ($\Gamma>$ 2.3), while 3C 138 and PKS B1413$+$135
present a flatter spectrum with photon indexes $\Gamma\sim$ 2 (see Table 1,
and the SED plots in the Appendix A.2).

Five quasars (3C 138, 3C 216, 3C 309.1, 3C 380 and PKS B1413$+$135) present
significant variability (TSvar > 23) of the gamma-ray emission. As can be seen
from their light curves (see the Appendix A.2 for the light curve plots) the
sources underwent strong flares during the LAT observations. In particular,
the quasar 3C 138 underwent a strong gamma-ray flare in 2012, during which the
emission spectrum was quite soft $\Gamma_{2012}\gtrsim 2.5$. Thereafter, the
activity quickly decreased: the source is only marginally detected until 2016,
after which the flux falls below the detection threshold. Similarly the
measured flux and photon index also decrease with the time interval considered
for the analysis, reaching a flux of $F=2.1\pm 0.9\times 10^{-9}$ ph cm-2 s-1
and a photon index $\Gamma=2.05\pm 0.12$, as obtained in this work.

PKS B1413$+$135 is significantly detected (TS = 1198) in our analysis, showing
extremely bright gamma-ray emission, with an average flux $F=(14.7\pm
1.0)\times 10^{-9}$ ph cm-2 s-1. Its light curve reveals a strong gamma-ray
flare in the latest period of observation considered for this work (August –
November 2019), when the source reached a flux $F=(126\pm 10)\times 10^{-9}$
ph cm-2 s-1, an order of magnitude above the averaged one (see Fig. 5 for the
light curve of the source).

Figure 5: Fermi-LAT three-month binned light curve of PKS B1413$+$135\. The
$1\sigma$ upper limit is reported when TS $<4$. The dashed line represents the
averaged flux for the entire period. The flux values have been estimated for
the energy range 100 MeV – 300 GeV.

We analysed the gamma-ray emission at the time of the flare with daily time-
bins. The flux reached the peak on August 29, 2019, when the source was
detected with a significance of TS = 67. We measured a daily flux of
$F_{E>100\,\textrm{MeV}}=(5.4\pm 1.9)\times 10^{-7}$ ph cm-2 s-1 and a
significant hardening of the spectrum: $\Gamma=2.0\pm 0.2$
($\Gamma_{\rm\,4FGL}=2.41\pm 0.07$), in agreement with the preliminary results
reported by Angioni et al. (2019). PKS B1413$+$135 has long been considered an
unusual object, with a BL-Lac-like AGN hosted in a spiral galaxy at redshift
$z$ = 0.247 (Carilli et al., 1992; Vedantham et al., 2017). The detection of
the flaring activity supports the idea that the gamma-ray emission is beamed
and produced by a relativistic jet at a relatively small viewing angle,
similar to the case of the AGN PKS 0521$-$36 (D’Ammando et al., 2015).
Alternatively, it can represent a particular case of high-activity episode of
a misaligned AGN as seen in 3C84 (Brown & Adams, 2011; Sahakyan et al., 2018;
Fukazawa et al., 2018). A recent study by Readhead et al. (2021) argues that
the association with the spiral host galaxy is just due to a chance alignment,
and instead supports the hypothesis that PKS B1413+135 is a background blazar-
like object lying in the redshift range 0.247 $<$ z $<$ 0.5.

The presence of gamma-ray flares in the other quasars suggests that their
high-energy emission is due to a relativistic jet and beaming effect,
confirming their non-misaligned nature.

###  4.2 Results on individual sources

In order to investigate the possible origin of the high-energy emission in the
population of young radio sources, we compared the radio and gamma-ray
properties of the investigated sources. Figure 6 shows the distribution of the
gamma-ray luminosity vs redshift for all the sources in our sample.

Figure 6:  Gamma-ray luminosity vs redshift for all the sources in our sample.
The circle (triangles) represents the detected (undetected) sources,
distinguishing between galaxies (in green) and quasars (blue). The dashed line
represents the averaged Fermi-LAT ten-year sensitivity
($F_{E>100\,\textrm{MeV}}=1.8\times 10^{-9}$ MeV cm-2 s${-1}$, $\Gamma=2.0$)
for an isolated point source outside the Galactic plane ($\mid
b\mid>25^{\circ}$), as a function of the redshift.

The dashed line represents the averaged Fermi-LAT ten-year sensitivity as a
function of the redshift ($F_{E>100\,\textrm{MeV}}=1.5\times 10^{-9}$ MeV cm-2
s-1, $\Gamma=2.0$) for an isolated point source outside the Galactic plane
($\mid b\mid>25^{\circ}$). All the detected sources have a gamma-ray
luminosity above, or in the proximity, of the Fermi-LAT sensitivity, while we
derived upper limits mainly below the Fermi-LAT sensitivity for the remaining
ones. In addition, nine sources, eight galaxies and one quasar (3C 147), were
marginally detected. This means that, although below the TS $=$ 25 threshold
to formally claim a detection, they present a non-negligible gamma-ray
emission (TS > 10, corresponding to a significance > 3$\sigma$, see Table 2).
Of these, the radio galaxies are at redshift between 0.093 and 0.763, i.e.
more distant than the radio galaxies detected by Fermi-LAT so far, with the
exception of PKS 1007$+$142\. Their gamma-ray luminosity is between $\sim$
2$\times$1043 and 2.2$\times$1045 erg s-1, with the quasar 3C 147 reaching a
luminosity of about 5$\times$1045 erg s-1. It is likely that the detection of
these sources, or a fraction of them, will be confirmed with the increase of
statistics in the coming years.

Table 2:  List of the young radio sources with marginal detection (TS > 10). We report name, type, redshift, projected linear size (LS) [kpc], radio peak frequency ($\nu_{p}$) [GHz], radio luminosity at 5 GHz [W Hz-1], gamma-ray significance (TS), gamma-ray flux (0.1–1000 GeV) in units of 10-9 ph cm-2 s-1, power-law photon index ($\gamma$) and gamma-ray luminosity [1044 erg s-1].  Name | type | $z$ | LS | $\nu_{p}$ |  log L${}_{5\,\textrm{GHz}}$ | TS |  Fluxγ | $\Gamma$ |  Lumγ
---|---|---|---|---|---|---|---|---|---
|  |  | kpc | GHz |  W Hz-1 |  |  10-9 cm-2 s-1 |  |  1044 erg s-1
Galaxies
0404+768 | CSO/GPS | 0.598 | 0.866 | 0.55 | 27.53 | 12 |  2.70$\pm$0.81 |  2.61$\pm$0.29 | 22.2
1323+321 | CSO/GPS | 0.369 | 0.305 | 0.68 | 27.07 | 19 |  1.36$\pm$0.41 |  2.15$\pm$0.23 | 4.0
3C346 | LSO/CSS | 0.162 | 22.056 | <0.045 | 25.99 | 13 |  1.23$\pm$0.43 |  2.07$\pm$0.20 | 0.82
1843+356 | CSO/GPS | 0.763 | 0.022 | 2 | 27.32 | 11 |  0.59$\pm$0.24 |  1.93$\pm$0.24 | 22.6
J140051+521606 | CSO/CSS | 0.116 | 0.32 | <0.15 | 24.36 | 17 |  0.12$\pm$0.05 |  1.64$\pm$0.32 | 0.20
J083411.09+580321.4 | CSO/CSS | 0.093 | 0.0086 | <0.4 | 24.13 | 15 |  3.53$\pm$0.96 |  2.66$\pm$0.20 | 0.30
J092405.30+141021.4 | CSO/CSS | 0.136 | 0.74 | <0.4 | 24.18 | 13 |  2.15$\pm$0.69 |  2.33$\pm$0.24 | 0.58
J155235.38+441905.9 | MSO/CSS | 0.452 | 6.93 | <0.4 | 25.56 | 17 |  0.78$\pm$0.26 |  2.07$\pm$0.19 | 6.0
Quasar
3C147 | MSO/CSS | 0.545 | 4.454 | 0.231 | 27.92 | 22 |  6.89$\pm$1.51 |  2.69$\pm$0.16 | 47.120

In the leptonic scenario at the basis of the expectation of gamma-ray emission
from young radio sources, the relativistic electrons producing the gamma rays
via IC scattering are the same ones responsible for the radio emission via
synchrotron radiation (Maraschi et al., 1992; Ghisellini & Madau, 1996; Abdo
et al., 2010b). Therefore, we investigate the presence of a possible
correlation between the radio and gamma-ray emission. Comparing the intrinsic
gamma-ray luminosity and the total radio luminosity (5 GHz) of the detected
young radio sources a correlation is readily apparent, $\log
L_{\gamma}=(0.86\pm 0.18)\times\log L_{\textrm{5\,GHz}}+(7.71\pm 1.31)$, with
Spearman correlation $\rho$ = 0.88.

However, the use of luminosity introduces a redshift bias in samples that have
a dynamic range in luminosity distance which is much larger than that in
fluxes. For this reason, we investigated a possible correlation between the
radio flux density and the Fermi-LAT flux, a method that unveiled a
significant correlation for the gamma-ray blazar population reported by
Ackermann et al. (2011); Lico et al. (2017). Considering the 1.4-GHz flux
density (from the NRAO VLA Sky Survey, NVSS) and the gamma-ray flux for the
sub-sample of young radio sources, we do not find any obvious correlation,
with Spearman and Pearson correlation coefficient of $\rho=-0.12$ and
$r=0.05$, respectively. With all the caveats due to the small number of
objects, it seems that no direct correlation is present between the radio and
gamma-ray emission in our sample, in contrast to that found in the studies
previously done using all the AGN detected by Fermi-LAT.

###  4.3 Undetected young radio sources: stacking analysis results

We searched for a signal from the population of the 151 undetected young radio
sources by applying the stacking procedure described in Sect. 3.2. Fig. 7
shows the 2-dimensional log-likelihood profile obtained from the stacking
analysis of the undetected galaxies and quasars. The blue region in the plots
indicates the most probable parameter values for the considered sample. If a
detection was found, a circumscribed blue area should have appeared in the
plot, corresponding to the most probable parameter values (see for example
Fig. 13). Instead, the plots in Fig. 7 indicate that no significant emission
is observed.

Upper limits on the gamma-ray flux for the young radio sources associated with
galaxies and quasars are calculated from the black dashed line in Fig. 7,
corresponding to the 95% confidence level upper limit, as described in Sect.
3.2. Similar results are found both when the stacking analysis is performed on
all galaxies and quasars together and when quasars and galaxies are considered
separately. The photon flux upper limits are reported in Table 3. The photon
indices reported correspond to the values for which the photon flux upper
limit is calculated.

Figure 7: Likelihood profile assuming a simple power-law spectrum for all the undetected galaxies (left panel) and quasars (right panel) separately. The black dashed line represents contour for which a $\Delta\log\mathcal{L}=4.61/2$ is obtained, corresponding to the 95% confidence level upper limit. Table 3:  Results of the stacking analysis for all the undetected sources contained in our sample, as well as for the galaxies and quasars only. $F^{*}_{\gamma}$: upper limits on the flux in units of 10-11 ph cm-2 s-1, $\Gamma$: photon index corresponding to the upper limit value. Select. | N | TS | $F^{*}_{\gamma}$ | $\Gamma$
---|---|---|---|---
All | 151 | 0.3 | 3.29 | 2.53
Galaxies | 99 | 0.1 | 4.62 | 2.40
Quasars | 52 | 0.2 | 10.09 | 2.64

The upper limits obtained from the stacking analysis are about one order of
magnitude below the averaged upper limits of the individual undetected sources
($F^{*}_{av}=7.1\times 10^{-10}$ ph cm-2 s-1). A factor of 10 is compatible
with the prediction derived from the analysis of background fluctuations, as
discussed in section A.1.2.

In order to estimate the upper limits for different energy bins of the
undetected sources, we repeat the stacking analysis for seven separate energy
ranges, six bands logarithmically spaced between 100 MeV and 100 GeV, and a
single one between 100 GeV and 1 TeV. The photon index was kept fixed to the
value found from the full energy range analysis, i.e. the value reported in
Table 3. Fig. 8-left (right) shows the upper limits for the SED of the
undetected sources associated with galaxies (quasars), derived with the
stacking analysis, compared to the averaged upper limits on the individual
undetected galaxies (quasars) and to the SED of the detected ones.

Figure 8:  Left panel: Upper limits for the undetected young radio galaxies as
determined from the stacking analysis, compared to the averaged upper limits
of the individual undetected radio galaxies (grey band) and the SEDs of the
detected ones. Right panel panel: the same as for the left panel but for
quasars.

####  4.3.1 Stacking analysis of selected sub-samples

We searched for a possible detection with the stacking analysis using
different sub-samples defined by selections of the physical properties. We
considered the stacking analysis for several sub-samples of nearby ($z$ <
0.07, 0.15, 0.4, and 1) and compact (LS < 0.35, 0.5 and 1 kpc) sources.
Additionally, we also performed the stacking analysis on the sample of bona-
fide young radio sources from Orienti & Dallacasa (2014), but no significant
gamma-ray emission was observed. Table 4 contains the results of the stacking
analysis for each sub-sample based on the different parameters selected.

Table 4:  Results of the stacking analysis for different sub-samples defined by selections on the linear size and redshift ranges. In the last row, labeled as ’O14’, are reported the results of the stacking analysis for the sample of bona-fide young radio sources created by Orienti & Dallacasa (2014). The results of young radio galaxies are reported on the left while on the right those ones of the quasars. $F^{*}_{\gamma}$: upper limits on the flux in units of 10-11 ph cm-2 s-1.  | Galaxies | Quasars
---|---|---
Select. | N | TS | $F^{*}_{\gamma}$ | $\Gamma$ | N | TS | $F^{*}_{\gamma}$ | $\Gamma$
LS < 0.35 | 48 | 0.1 | 7.2 | 2.36 | 9 | 0.0 | 58.6 | 2.62
LS < 0.5 | 52 | 0.1 | 9.4 | 2.38 | 13 | 0.1 | 46.1 | 2.58
LS < 1 | 58 | 0.1 | 11.3 | 2.36 | 17 | 0.1 | 68.7 | 2.68
$z$ < 0.07 | 10 | 0.5 | 108 | 2.66 | 0 | - | - | -
$z$ < 0.15 | 36 | 0.1 | 58.7 | 2.60 | 0 | - | - | -
$z$ < 0.4 | 63 | 0.1 | 10.4 | 2.54 | 4 | 0.0 | 23.7 | 2.48
$z$ < 1 | 93 | 0.1 | 5.5 | 2.38 | 25 | 0.1 | 21.3 | 2.54
|  $z$ < 0.07
---
LS < 0.15
8 | 0.8 | 172.9 | 2.78 | 0 | - | - | -
|  $z$ < 0.15
---
LS < 0.35
27 | 0.1 | 31.2 | 2.52 | 0 | - | - | -
|  $z$ < 0.4
---
LS < 0.5
39 | 0.1 | 15.4 | 2.50 | 1 | 0.0 | 297.5 | 2.76
O14 | 37 | 0.1 | 4.6 | 2.50 | 15 | 0.1 | 44.8 | 2.80

Despite restricting the stacking analysis to the closest and most compact
sources (two among the required criteria for a promising gamma-ray emitter
according to Stawarz et al. 2008), no significant gamma-ray emission has been
observed in the different sub-samples.

####  4.3.2 Stacking analysis: comparison with model predictions

The next test that we made for the analysis was to compare the expectation
from the model proposed by Stawarz et al. (2008) with the results of the
stacking analysis. To this aim, we repeated the stacking analysis in energy
bins for the sample of sources with $d_{L}\leq 300$ Mpc. In Fig. 9 we compare
the results of this stacking analysis with the model expectation, assuming a
source at a luminosity distance $d_{L}=300$ Mpc. In the model, the gamma-ray
emission of the source is due to up-scattering of the UV photons of the disc
by the relativistic electrons in the lobes. The predicted luminosity was
estimated following Stawarz et al. (2008):

$\displaystyle\frac{\left(\epsilon{\rm L}_{\epsilon}\right)_{\rm
IC/UV}}{10^{42}{\rm erg\ s^{-1}}}\sim$ $\displaystyle
2\frac{\eta_{e}}{\eta_{B}}\left(\frac{{\rm L_{j}}}{{\rm 10^{45}erg\
s^{-1}}}\right)^{1/2}\left(\frac{{\rm LS}}{{\rm 100pc}}\right)^{-1}$
$\displaystyle\left(\frac{{\rm L}_{\rm UV}}{{\rm 10^{46}erg\
s^{-1}}}\right)\left(\frac{\epsilon}{{\rm 1GeV}}\right)^{-0.25}$ (3)

where $L_{j}$ is the jet power, $L_{UV}$ the UV luminosity and
$\eta_{e}/\eta_{B}$ expresses the particle to magnetic field energy density
ratio. The luminosity was calculated for a source with LS = 100 pc,
$\eta_{e}/\eta_{B}=1$, $\rm L_{j}=\rm 10^{45}\,erg\ s^{-1}$ and $\rm
L_{UV}=\rm 10^{46}\,erg\ s^{-1}$. We emphasize that the parameters used are
very conservative and any larger deviation from the energy equipartition
($\eta_{e}/\eta_{B}>>1$) will substantially increase the expected IC/UV flux.
For these parameter values, we found that the predicted gamma-ray emission is
in tension with the upper limits obtained from the stacking analysis. One
possible explanation is that the assumed model parameters are too extreme in
terms of e.g. jet power and/or UV luminosity.

Figure 9:  Upper limits for the undetected young radio galaxies located at z
$<0.07$ ($d_{L}<300$ Mpc), as determined from the stacking analysis, compared
to the expectations for a luminosity distance $d_{L}=300$ Mpc and projected
linear size LS = 100 pc (dashed line) taken from Stawarz et al. (2008). The
averaged upper limits of the individual undetected radio galaxies (grey band)
and the SEDs of the detected young radio galaxies are also plotted.

Admittedly, the procedure described above is somewhat simplistic as it assumes
that all sources have similar fluxes and does not take into account the
different values of the physical parameters, such as the linear size, of the
sources in the sample.

Following Eq. 3, we converted the gamma-ray flux upper limits into constraints
on the physical parameters of the sources. In particular, we set the known
values of $d_{L}$ and LS and derived information on the UV luminosity and jet
power from each source. Since we have two free parameters, we repeated the
stacking procedure for several fixed values of the UV luminosity and derived
an upper limit on the jet power for each case. This procedure results in an
exclusion region in the two-parameter space. In Fig. 10 the shaded area shows
the allowed parameter space in jet power and UV luminosity. For jet powers of
1042–1043 erg s-1 (see Sect. 5), the UV luminosity must be 1045 erg s-1 or
below to be in agreement with the lack of detection in gamma rays.

NGC 3468 and UGC 5771777also known as FIRST J105731.1+405646 and FIRST
J103719.3+433515, respectively. have been excluded from this analysis. In
fact, due to their proximity and small size, they would dominate the stacking
results.

Figure 10:  Constraint on the jet power and UV luminosity, estimated with the
stacking analysis on undetected galaxies assuming the gamma-ray expectations
derived by Stawarz et al. (2008). The blue region defines the allowed values
for the two parameters.

As a further investigation, we checked whether the nearby detected galaxies,
NGC 3894 and NGC 6328, represent special cases. We simulated 11 years of Pass
8 data for 100 sources at random positions (see Appendix A.1.1) assuming the
same luminosity and spectral shape as NGC 3894 ($\Gamma=2.05$). The simulated
sources were located within the nearby Universe at a distance of 300 Mpc ($z$
= 0.07), six times larger than the redshift of NGC 3894 ($z$ = 0.0108), and
therefore with a simulated flux of $5.5\times 10^{-11}$ ph cm-2 s-1. We
performed the stacking analysis on the simulation and found no detection. We
repeated the procedure for NGC 6328, simulating sources with $\Gamma=2.60$, a
flux of $2.0\times 10^{-10}$ ph cm-2 s-1 and located at a distance of 300 Mpc.
Similarly no detection was found with the stacking analysis. This indicates
that we need a sample of more than a hundred objects with the characteristics
of NGC 3894 and NGC 6328 up to a distance of 300 Mpc in order to detect
significant gamma-ray emission from the stacking analysis.

Finally, we estimated the number of sources needed to detect (TS$\geq$25) a
gamma-ray signal assuming the prediction of Stawarz et al. (2008). As
described in Appendix A.1.1, we generated five simulated datasets, each one
consisting of 100 sources with a fixed input flux in the range ($2-20\times
10^{-10}$ ph cm-2 s-1) and spectral index of $\Gamma=2.25$ (as in the
prediction of Stawarz et al. 2008). For each simulated dataset we applied the
stacking procedure (as described in Sect. 3) for an increasing number of
sources and estimated the minimum number of sources necessary to reach a
detection. Fig. 11 shows these values as a function of the input flux. We
compare these results with the gamma-ray flux expected from galaxies located
at luminosity distances $d_{L}=$ 300 and 700 Mpc. While a young radio galaxy
located at $d_{L}=$ 100 Mpc will be clearly detected ($F_{exp}\sim 1\times
10^{-8}$ ph cm-2 s-1), when moving to higher luminosity distances, the
expected flux of an individual source is below the LAT sensitivity. Therefore,
with the stacking procedure, a sample of $\gtrsim$ 2 (60) sources at 300 Mpc
(700 Mpc) would be needed to detect a gamma-ray signal. This estimate is in
agreement with the results obtained in our analysis of LAT data, indicating
that only the closest sources could be detected by Fermi-LAT (see the
detection of NGC 3894, NGC 6328 and TXS 0128+554). At higher distances ($\geq$
300 Mpc), a larger sample of young radio galaxies with characteristics similar
to those assumed by Stawarz et al. (2008), e.g. LS $\leq$ 100 pc, would be
needed to test the lobe scenario.

Figure 11:  Number of sources necessary to reach a detection with the stacking
procedure as a function of the simulated flux. The vertical black lines show
the gamma-ray flux predicted by Stawarz et al. (2008) for different luminosity
distances. See Appendix A.1.1 for more details. The vertical red dashed line
represents the averaged Fermi-LAT ten-year sensitivity
($F_{E>100\,\textrm{MeV}}=1.8\times 10^{-9}$ MeV cm-2 s${-1}$, $\Gamma=2.0$)
for an isolated point source outside the Galactic plane ($\mid
b\mid>25^{\circ}$).

##  5 Discussion

Before the launch of Fermi-LAT, young radio sources were predicted to emerge
as a new class of gamma-ray emitting objects. However, after more than ten
years of observations, only a handful of sources have been unambiguously
detected (Ackermann et al., 2015; Migliori et al., 2016; Ajello et al., 2020a;
Principe et al., 2020; Lister et al., 2020), with the quasars playing a major
role.

We analysed 11.3 yr of gamma-ray data collected by Fermi-LAT for a sample of
162 bona-fide young radio sources. We detect significant gamma-ray emission
for 12 per cent (7/59) of quasars, and for 4 per cent (4/103) of galaxies.
With a Lγ between 2.8$\times$1045 and 5.1$\times$1046 erg s-1, quasars are
orders of magnitude more luminous than galaxies, whose Lγ ranges between
6.1$\times$1041 and 2.8$\times$1044 erg s-1. Selection effects likely play a
role, in fact quasars are usually at larger redshift with respect to galaxies.
However, we note that this difference holds true also for objects at similar
redshift, such as the galaxy PKS 1007$+$142 and the quasar PKS B1413$+$135,
with the latter being an order of magnitude more luminous in gamma rays than
the former888For this comparison we conservatively assumed a redshift of
0.247.. One possible explanation is that gamma rays in quasars and radio
galaxies have a different origin. In the former, the emission could be
produced in the jet, while in the latter in the radio lobes. In young radio
quasars, effects due to small angles between the jet axis and our line of
sight and relativistic jet speed boost the gamma-ray emission (see Migliori et
al. 2014). If we plot the photon index vs Lγ of the detected sources (Fig.
12), we see that young quasars lie in the same region occupied by flat
spectrum radio quasars (FSRQs), whereas young galaxies are in the locus of
misaligned radio galaxies. Among the misaligned objects there is 3C 84
(z=0.01756 Falco et al., 1999), which presents intermittent jet activity and
whose last active phase started around 2007 and is still ongoing both in radio
and high-energy bands (Nagai et al., 2010; Brown & Adams, 2011; Sahakyan et
al., 2018; Fabian et al., 2015; Fukazawa et al., 2018; Hodgson et al., 2018).

Figure 12:  Diagram of the $\gamma$-ray luminosity vs. photon index for the
extragalactic sources with known redshift contained in the 4LAC (Ajello et
al., 2020a). Detected young radio galaxies (YRDG) are represented as red
stars, while quasars (YQSO) are represented as blue crosses.

Further support for boosting effects in quasars comes from the gamma-ray light
curves which show significant variability in five quasars (3C 138, 3C 216, 3C
309.1, 3C 380, and PKS B1413$+$135). In particular, for the quasar PKS
B1413$+$135 we observed a gamma-ray flare at the end of 2019 with a flux
increase of a factor of 8.5 with respect to the average value, and up to a
factor of 35 if we consider the daily peak flux. These kinds of flux increases
have been observed during high activity states in many FSRQs and BL Lacs (Abdo
et al., 2010c).
A beamed, jet-origin of gamma-ray emission in quasars is also supported by
their radio morphology. The radio emission of the gamma-ray detected quasars
mainly comes from the core and the approaching jet. Sometimes the counter-jet
is detected at low frequencies only, like in PKS B1413$+$135 (Perlman et al.,
1996; Lister et al., 2019) and 3C 138 (Dallacasa et al. in preparation).
Superluminal motion of jet features has been observed in 3C 380, 3C 309.1
(Lister et al., 2019), and marginally in PKS B1413$+$135, confirming the
presence of Doppler boosting effects (Gugliucci et al., 2005; Lister et al.,
2019). Conversely, gamma-ray detected young radio galaxies show a double lobe-
like morphology with minor contribution from the jet (Tingay et al., 1997;
Kunert-Bajraszewska et al., 2010; Lister et al., 2019), with the possible
exception of NGC 3894 (Taylor et al., 1998). However, no significant beaming
effect seems to take place in this source (Principe et al., 2020).

Radio lobes may be possible loci for the production of significant gamma-ray
emission in unbeamed sources, although a contribution from the jet cannot be
ruled out, as suggested for TXS 0128+554 (Lister et al. 2020). Centaurus A was
the first radio source in which gamma-ray emission was unambiguously observed
in the lobes (Abdo et al., 2010a), followed some years later by the detection
of gamma rays in the lobes of Fornax A (Ackermann et al., 2016). It is
difficult to detect high-energy emission from lobes due to their faintness and
the challenge of disentangling the lobe contribution from the emission arising
from other source regions.

In young radio galaxies, the emission from highly relativistic regions in the
jet is de-beamed due to their large jet-angles to our lines of sight. Hence,
the lobe emission could dominate in some situations. Stawarz et al. (2008)
predicted different levels of gamma-ray luminosity depending on the radio
source size, the jet power and the radiation field of the seed photons which
could be Compton-scattered by the relativistic electrons in the lobes. The
fact that young radio galaxies are still elusive in gamma rays suggests that
the range of values of parameters assumed in the model were too optimistic.

For the radio galaxies in our sample, we estimate the minimum jet power,
$P_{\rm j}$ following Wójtowicz et al. (2020):

$\displaystyle P_{\rm j}\sim$ $\displaystyle 1.5\times
10^{45}\times\left(\frac{{\rm LS}}{{\rm
100\,pc}}\right)^{9/7}\left(\frac{\tau_{\rm j}}{{\rm 100\,yr}}\right)^{-1}$
$\displaystyle\times\left(\frac{L_{\rm 5\,GHz}}{10^{42}{\rm
erg\,s^{-1}}}\right)^{4/7}{\rm erg\,s^{-1}}$ (4)

where LS is the linear size, $\tau_{\rm j}$ is the source age, and L${}_{\rm
5\,GHz}$ is the luminosity at 5 GHz. In Eq. 4 we consider the linear size and
the luminosity at 5 GHz reported in Tables 5,6 and 7. We assume ages between
about 100 yr, for the most compact sources, and 105 yr for sources with LS of
several kpc, as derived from radiative and kinematic ages of sources (e.g.
Murgia et al., 1999; Fanti & Fanti, 2002; Murgia, 2003; Polatidis & Conway,
2003; Giroletti & Polatidis, 2009). We end up with minimum jet powers for
galaxies between 1040 and 1046 erg s-1. The higher values are obtained for
sources at higher redshift, and for 3C 346, which is among the marginally
detected sources from our analysis. However, the majority of the galaxies have
L${}_{\rm\,5GHz}<10^{43}$ erg s-1 and estimated minimum jet power P${}_{\rm
j}<10^{44}$ erg s-1. As shown in Fig. 11, this requires a UV luminosity above
1045 erg s-1 in order to detect a cumulative signal by the stacking analysis.
This is far from the expectation from Stawarz et al. (2008), in which the
optimal conditions occurred for sources with jet power $\sim$1046 erg s-1, LS
$<$ 100 pc, and at redshift $<$0.2 ($\sim$1 Gpc). For our estimated jet power,
the highest expected gamma-ray luminosity for sources with LS $<$ 100 pc is
$<$ 1044 erg s-1. The fact that young radio galaxies are faint emitters of
gamma rays is also suggested by the results of the stacking analysis, which
set the upper limit to their emission, as a whole population, an order of
magnitude below the Fermi-LAT threshold. This indicates that only the closest
sources could be detected by Fermi-LAT, while if we consider objects at higher
and higher redshift, boosting effects are necessary for their detection.

##  6 Conclusions

The goal of our study was to investigate the gamma-ray properties of young-
radio sources. To this end we analysed 11.3 years of Fermi-LAT data for a
sample of 162 sources. First, we analysed the gamma-ray data of each source
individually to search for a significant detection. Then, we performed a
stacking analysis of all undetected sources in order to search for a
cumulative signal. For the purpose of our study, having a large sample was the
priority, hence we combined different compilations of young radio sources.
This resulted in the largest sample of young radio sources used so far for a
gamma-ray study (see e.g. Migliori et al. 2014; D’Ammando et al. 2016). The
main results can be summarised as follows:

  * •

11 sources were significantly (TS $>25$) detected, and we obtained a marginal
detection (11 $<$ TS $<$ 25) for nine other sources. In addition to three
radio galaxies and seven quasars already present in the 4FGL, we report the
discovery of gamma-ray emission associated with the young radio galaxy PKS
1007+142;

  * •

young quasars and radio galaxies appear to have distinct gamma-ray properties.
On one hand, the former are luminous, typically variable and share the same
location as blazar sources in the photon index vs. gamma-ray luminosity plot.
Hence, this strongly favors a jet origin for their gamma-ray emission. On the
other hand, the latter are compact, gamma-ray faint, not significantly
variable and have photon index and Lγ similar to misaligned sources. Such
features are in principle compatible with an origin of the gamma-ray emission
(or a fraction of it) in the lobes;

  * •

stacking the LAT data of the remaining below-threshold sources did not result
in the detection of a statistically significant signal, neither when the
sample were subdivided into bins by redshift nor by linear size. The upper
limits obtained with this procedure are, however, tighter than those derived
from the individual sources. This enabled a comparison with the model proposed
by Stawarz et al. (2008), predicting isotropic gamma-ray emission from the
compact lobes of young radio galaxies. As a result we can rule out jet powers
$\gtrsim$1042–1043 erg s-1 coupled with UV luminosities $>$ 1045 erg s-1;

  * •

a challenge in the comparison between data and model was presented by the
small number of sources at low redshift in our sample. We showed that, already
at $\sim$700 Mpc, at least $\gtrsim$60 young radio galaxies would be needed to
test the lobe scenario;

  * •

similarly, on the basis of this study we cannot establish whether the
undetected sources are gamma-ray analogues of NGC 3894 and NGC 6328, the two
closest gamma-ray detected radio galaxies. In fact, at 300 Mpc 100 sources
with similar gamma-ray fluxes would still not be sufficient to obtain a
stacked signal above the background level.

Our results suggest that young radio sources can be gamma-ray emitters.
However, it is likely that young radio galaxies have typically low gamma-ray
luminosities. In the framework of a lobe-origin of such emission, this would
rule out sources with powerful jets and bright accretion disks. For a single
source, the detection of gamma-ray emission, either from the jet or the lobes,
confirms the presence of a non-thermal component at high energies. This
overcomes the limitations that we face in the X-ray band, where the radiation
from the accretion and ejection processes cannot be easily disentangled (see
e.g. Migliori et al. 2012; Ostorero et al. 2010; Siemiginowska et al. 2016 and
references therein). In the future, multi-wavelength studies of the gamma-ray
detected sources in our sample could be helpful to (i) constrain the source
parameters in the first stages of evolution and (ii) to identify the channel
through which the bulk of the young source’s energy is released in the ambient
environment. The advent of wide-sky surveys will allow a more complete test of
the models of high-energy emission from this class of source. A drastic
increase in the number of young radio sources will be achieved thanks to the
Square Kilometre Array and the next generation VLA, which will expand the
samples of young radio sources in redshift and luminosity and improve the
characterisation of their radio properties (Kapinska et al., 2015; Afonso et
al., 2015; Patil et al., 2020).

## Data Availability

The data 999Fermi-LAT data: https://fermi.gsfc.nasa.gov/ssc/data/access/ and
tools (Fermitools101010https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/
and fermipy 111111https://fermipy.readthedocs.io/en/latest/) underlying this
article are publicly available.

### ACKNOWLEDGMENTS

The effort of the LAT-team Calibration & Analysis Working Group to develop
Pass 8, and also the LAT AGN Working Group for the support given to this
project, are gratefully acknowledged.

The Fermi LAT Collaboration acknowledges generous ongoing support from a
number of agencies and institutes that have supported both the development and
the operation of the LAT as well as scientific data analysis. These include
the National Aeronautics and Space Administration and the Department of Energy
in the United States, the Commissariat à l’Energie Atomique and the Centre
National de la Recherche Scientifique / Institut National de Physique
Nucléaire et de Physique des Particules in France, the Agenzia Spaziale
Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry
of Education, Culture, Sports, Science and Technology (MEXT), High Energy
Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency
(JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research
Council and the Swedish National Space Board in Sweden.

Additional support for science analysis during the operations phase is
gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy
and the Centre National d’Etudes Spatiales in France. This work is performed
in part under DOE Contract DE-AC02-76SF00515.

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##  Appendix A Appendix

###  A.1 Validation of the stacking analysis method

####  A.1.1 Validation on simulated sources

To verify the robustness of the stacking method, we simulated 11 years of Pass
8 LAT data for 100 sources at $\mid b\mid>10^{\circ}$, in random positions
which lie at a minimum distance of 1∘ from any other source in the 4FGL
catalog (Abdollahi et al., 2020). The spectra of the simulated sources are
modeled using a power law with spectral index $\Gamma=2.25$, normalisation
$N_{0}=3\times 10^{-14}$ (MeV-1 s-1 cm-2) and energy scale $E_{0}=1$ GeV,
corresponding to an integrated photon flux of $4.3\times 10^{-10}$ (ph cm-2
s-1). The simulations include the diffuse (Galactic and isotropic) emission as
well as all the point sources from the 4FGL catalog. The normalisation of the
simulated sources was chosen such that their resulting significance after the
analysis lies below 5 $\sigma$. In fact, none of the simulated sources are
detected. A stacking analysis was performed for the simulated dataset as
described in Sect. 3.2. We repeated this analysis for an increasing number of
simulated sources and calculated the TS as defined in Sect. 3. The results are
presented in Fig. 13 (left), showing that the TS increases linearly with the
number of sources included in the stacking procedure. Fig. 13 (right) shows
the likelihood profile obtained with the full sample of simulated sources,
showing a detection with a TS $\sim$ 116.

Figure 13: Left: TS value obtained when stacking an increasing number of
simulated sources. Right: Likelihood profile assuming a simple PL spectrum for
the 100 simulated sources using a PL with $\Gamma=2.25$, normalisation
$N_{0}=3\times 10^{-14}$ (MeV-1 s-1 cm-2) and an energy scale of $E_{0}=1$
GeV.

[h!]

The best-fit photon index and normalisation are $\Gamma=2.26\pm 0.13$ and
$N_{0}=(3.5\pm 1.0)\times 10^{-14}$ MeV-1 s-1 cm-2, respectively, which are in
agreement with the input values of the simulated sources. From this test we
conclude that the stacking analysis provides a powerful method to detect
sources of the same population, assuming they have similar spectral
properties.

Similarly, we repeated the simulation and stacking procedure for five flux
values, varying $N_{0}$ in the range ($(1-10)\times 10^{-14}$ MeV-1 s-1 cm-2)
and using a PL with index $\Gamma=2.25$. For each simulation we calculated the
number of sources necessary to have a detection (TS=25).

####  A.1.2 Validation on the background

In order to verify the quality of our results from applying the stacking
analysis and ensure that they could be distinguished from simple background
fluctuations, we performed the same analysis for the background. The analysis
was performed using 100 random positions that fulfill the following criteria:
at least at 1∘ away from all 4FGL sources and from the sources in our sample
and outside of the Galactic plane, i.e. with Galactic latitude $|b|>10$. The
result of the stacking on the background sources is presented in Fig. 14 and
it shows no significant detection with a TS $\sim$ 0.3. The flux upper limit
is calculated from the contour corresponding to the 95% confidence level, as
described in Sect. 3.2, resulting in $2.82\times 10^{-11}$ ph cm-2 s-1.

Figure 14:  Likelihood profile assuming a simple power-law spectrum for 100
random positions where no gamma-ray sources are present.

In order to check the consistency of the results obtained from real data with
background fluctuations, we calculated the photon flux upper limit for a
smaller number of sources. Indeed, the upper limit value decreases with the
increasing number of sources included in the stacking analysis. In particular,
we performed the stacking analysis on a selection of sources with the same
number of below-threshold galaxies (99) and quasars (52) as in our sample. The
resulting photon flux upper limits are $2.85\times 10^{-11}$ ph s-1 and
$8.25\times 10^{-11}$ ph cm-2 s-1, respectively.

###  A.2 Results on individual young radio sources

In this section we present the gamma-ray SED and lightcurves of the detected
galaxies and quasars. Fig. 16, 17,18 show the SED and light-curve plots of the
detected sources. In Tables 5, 6, and 7, we report the physical and radio
parameters as well as the resulting gamma-ray characteristics obtained in this
work for all sources selected for this study.

During the analysis we found a new gamma-ray source (TS = 26) in the vicinity
($\sim 0.16^{\circ}$) of the quasar B3 1242$+$410 (see Fig. 15), included in
our sample. The LAT best-fit position of the new gamma-ray source is (R.A.,
Dec.(J2000)= 191.01${}^{\circ}\pm\,0.04^{\circ}$,
40.75${}^{\circ}\pm\,0.04^{\circ}$), with a 68% confidence-level uncertainty
R68 = 0.06∘. The gamma-ray source is not significantly associated with the
radio object B3 1242$+$410 (P $<$ 0.8). The nearest and most plausible
counterpart of the new gamma-ray source is the blazar 5BZQ J1243$+$4043, which
is located $0.03^{\circ}$ away. When we add a source to the model at the
location of the blazar and rerun the likelihood analysis we obtain a TS $=1$
for the quasar B3 1242$+$410\. We report, therefore, only an upper limit for
that source.

Figure 15: Fermi-LAT TS map (in sigma units) above 1 GeV of the region around
B3 1242$+$410, the red star, dashed and dotted circles represent the central
position and the 68% and 95% confidence-level uncertainty
(R${}_{68}=0.07^{\circ}$, R${}_{95}=0.12^{\circ}$) of the gamma-ray source,
respectively. White dots show radio sources from the NVSS survey arbitrarily
scaled depending on their radio flux.

Figure 16: Left panels: Fermi-LAT flux points obtained between 100 MeV and 1
TeV. The arrows refer to the $1\sigma$ upper limit on the source flux. The SED
has been fitted with PL (blue line). Right panels: Fermi-LAT one-year binned
light curve. The arrows refer to the $1\sigma$ upper limit on the source flux.
The dashed line represents the averaged flux for the entire period. The flux
values have been estimated for the energy range 100 MeV–300 GeV. Upper limits
are computed when TS $<$ 4 for both the SED and light curve plots. The plots
are for the sources: NGC 3894 (top), NGC 6328 (middle) and TXS 0128+445
(bottom).

Figure 17: The labels are the same as in Fig.16. The plots are for and the
newly detected galaxy PKS 1007+142 (first row) and the quasars: 3C 138 (second
row), 3C 216 (third row) and 3C 309.1 (fourth row).

Figure 18: The labels are the same as in Fig.16. The plots are for the quasars: 3C 380 (first row), PKS B1413$+$135 (second row), PKS 0056-00 (third row) and 3C 286 (fourth row). Table 5:  List of all young radio galaxies contained in our sample. We report in this table name, type (galaxy/quasar), redshift, projected linear size (LS) [kpc], radio turnover frequency ($\nu_{p}$) [GHz], radio luminosity at 5 GHz [W Hz-1], reference for radio information, gamma-ray significance (TS), gamma-ray flux (0.1–1000 GeV) in units of 10-9 ph cm-2 s-1, power-law photon index ($\gamma$) and gamma-ray luminosity [1044 erg s-1] of each detected source. We used a threshold of TS=10 for reporting upper limits on the gamma-ray flux and luminosity. References: dV09: de Vries et al. (2009), K20: Kosmaczewski et al. (2020), L20: Liao & Gu (2020), Li20: Lister et al. (2020), O04: Orienti et al. (2004) O08: Orienti & Dallacasa (2008), O14: Orienti & Dallacasa (2014), P20: Principe et al. (2020), R06: (Rossetti et al., 2006), W20: Wójtowicz et al. (2020), Z20: Zhang et al. (2020).  Name | type | z | LS | $\nu_{p}$ |  log L${}_{5\,\textrm{GHz}}$ | Ref. | TS |  Fluxγ | $\Gamma$ |  Lumγ
---|---|---|---|---|---|---|---|---|---|---
|  |  | kpc | GHz |  W Hz-1 |  |  |  10-9 cm-2 s-1 |  |  1044 erg s-1
OQ 208 | G | 0.076 | 0.007 | 4.0 | 25.59 | K20 | 0 | <0.27 | 2.00 | <0.050
J0111$+$3906 | G | 0.688 | 0.056 | 7.0 | 27.36 | O14 | 0 | <0.25 | 2.00 | <6.8
J1335$+$5844 | G | 0.58 | 0.105 | 8.8 | 26.94 | O14 | 0 | <0.11 | 2.00 | <1.9
J1735$+$5844 | G | 0.835 | 0.061 | 8.2 | 27.43 | O14 | 0 | <0.24 | 2.00 | <10.5
J1511$+$0518 | G | 0.084 | 0.01 | 10.9 | 24.97 | O14 | 0 | <0.20 | 2.00 | <0.045
J0428$+$3259 | G | 0.479 | 0.016 | 5.9 | 26.59 | O14 | 0 | <0.22 | 2.00 | <2.5
J0951$+$3451 | G | 0.29 | 0.021 | 7.7 | 25.43 | O14 | 0 | <0.14 | 2.00 | <0.49
0316$+$161 | G | 0.907 | 4.384 | 1.5 | 27.93 | O14 | 0 | <0.10 | 2.00 | <5.0
0404$+$768 | G | 0.598 | 0.866 | 0.55 | 27.53 | O14 | 12 |  2.70 $\pm$ 0.81 |  2.61 $\pm$ 0.29 | 22.0
0428$+$205 | G | 0.219 | 0.351 | 1.4 | 26.42 | O14 | 0 | <1.10 | 2.00 | <2.0
1323$+$321 | G | 0.369 | 0.305 | 0.68 | 27.07 | O14 | 19 |  1.36 $\pm$ 0.40 |  2.15 $\pm$ 0.23 | 4.8
1358$+$624 | G | 0.431 | 0.28 | 0.79 | 27.0 | O14 | 0 | <0.26 | 2.00 | <2.3
0710$+$439 | G | 0.518 | 0.136 | 2.88 | 27.22 | O14 | 0 | <0.14 | 2.00 | <1.9
2352$+$495 | G | 0.2379 | 0.187 | 0.87 | 26.37 | O14 | 0 | <0.18 | 2.00 | <0.39
1943$+$546 | G | 0.263 | 0.181 | 0.75 | 26.27 | O14 | 0 | <0.49 | 2.00 | <1.4
B1819$+$6707 | G | 0.22 | 0.112 | 0.976 | 25.28 | O14 | 0 | <0.24 | 2.00 | <0.44
B1946$+$7048 | G | 0.1 | 0.087 | 1.98 | 25.18 | O14 | 0 | <0.42 | 2.00 | <0.14
B3 0039$+$391 | G | 1.006 | 2.738 | <0.2 | 26.66 | O14 | 1 | <0.48 | 2.00 | <32.7
B3 0120$+$405 | G | 0.84 | 18.345 | <0.18 | 26.68 | O14 | 0 | <0.27 | 2.00 | <11.7
B3 0213$+$412 | G | 0.515 | 12.358 | 0.2 | 26.3 | O14 | 0 | <0.41 | 2.00 | <5.5
B3 0744$+$464 | G | 2.926 | 11.04 | <0.39 | 27.85 | O14 | 0 | <0.15 | 2.00 | <145.2
B3 0814$+$441 | G | 0.12 | 8.556 | <0.112 | 24.41 | O14 | 0 | <0.21 | 2.00 | <0.10
B3 0935$+$428A | G | 1.291 | 10.969 | <0.46 | 26.97 | O14 | 0 | <0.33 | 2.00 | <42.3
B3 0955$+$390 | G | 0.52 | 29.813 | <0.15 | 26.15 | O14 | 0 | <0.29 | 2.00 | <3.9
B3 1025$+$390B | G | 0.361 | 16.022 | <0.14 | 26.18 | O14 | 1 | <0.41 | 2.00 | <2.4
B3 1027$+$392 | G | 0.56 | 10.323 | 0.23 | 26.24 | O14 | 0 | <0.27 | 2.00 | <4.4
B3 1157$+$460 | G | 0.7428 | 5.852 | 0.44 | 26.89 | O14 | 0 | <0.18 | 2.00 | <6.0
B3 1201$+$394 | G | 0.4448 | 11.951 | <0.14 | 26.08 | O14 | 0 | <0.13 | 2.00 | <1.2
B3 1458$+$433 | G | 0.927 | 12.608 | <0.19 | 26.72 | O14 | 0 | <0.15 | 2.00 | <8.5
B3 2358$+$406 | G | 0.978 | 0.799 | 0.59 | 27.32 | O14 | 0 | <0.30 | 2.00 | <19.1
3C 49 | G | 0.621 | 6.781 | 0.194 | 27.16 | O14 | 1 | <0.55 | 2.00 | <11.5
3C 237 | G | 0.877 | 9.301 | 0.094 | 27.88 | O14 | 0 | <0.30 | 2.00 | <14.8
3C 241 | G | 1.617 | 10.268 | 0.104 | 27.65 | O14 | 0 | <0.13 | 2.00 | <29.8
3C 346 | G | 0.162 | 22.056 | <0.045 | 25.99 | O14 | 13 |  1.23 $\pm$ 0.43 |  2.07 $\pm$ 0.20 | 0.82
TXS 0128$+$554 | G | 0.0365 | 0.012 | 0.66 | 23.695 | Li20 | 178 |  8.03 $\pm$ 1.46 |  2.20 $\pm$ 0.07 | 0.19
B3 1049$+$384 | G | 1.018 | 0.14 | 0 | 25.82 | O04 | 0 | <0.30 | 2.00 | <21.2
1345$+$125 | G | 0.122 | 0.152 | 0.67 | 26.06 | O14 | 0 | <0.22 | 2.00 | <0.11
B3 0034$+$444 | G | 2.79 | 11.9 | 0 | 27.9 | R06 | 0 | <0.21 | 2.00 | <179.9
0035$+$227 | G | 0.096 | 0.022 | 0 | 24.716 | W20 | 0 | <0.10 | 2.00 | <0.030
0116$+$319 | G | 0.0602 | 0.12 | 0.55 | 25.09 | W20 | 3 | <0.74 | 2.00 | <0.083
1031$+$567 | G | 0.46 | 0.109 | 1.3 | 26.97 | W20 | 0 | <0.24 | 2.00 | <2.5
1245$+$676 | G | 0.107 | 0.01 | 1.42 | 24.716 | W20 | 0 | <0.25 | 2.00 | <0.094
1607$+$26 | G | 0.473 | 0.24 | 1.0 | 27.16 | W20 | 0 | <0.37 | 2.00 | <4.0
1843$+$356 | G | 0.763 | 0.022 | 2 | 27.32 | W20 | 11 |  0.59 $\pm$ 0.24 |  1.93 $\pm$ 0.24 | 22.6
1934-638 | G | 0.183 | 0.085 | 1.4 | 26.75 | W20 | 0 | <0.14 | 2.00 | <0.17
2021$+$614 | G | 0.227 | 0.016 | 5 | 26.63 | W20 | 0 | <0.13 | 2.00 | <0.26
NGC 6328 | G | 0.014 | 0.002 | 4 | 24.28 | W20 | 41 |  5.30 $\pm$ 1.86 |  2.54 $\pm$ 0.17 | 0.011
NGC 3894 | G | 0.0108 | 0.01 | 5 | 23.21 | P20 | 95 |  2.03 $\pm$ 0.48 |  2.05 $\pm$ 0.09 | 0.006
0402$+$379 | G | 0.0545 | 0.0073 | 10 | 24.88 | K20 | 6 | <3.90 | 2.00 | <0.19
Table 6:  Continued from Table 5. ∗PKS 1007$+$142 is also known as J100955.51$+$140154.2. Name | type | z | LS | $\nu_{p}$ |  log L${}_{5\,\textrm{GHz}}$ | Ref. | TS |  Fluxγ | $\Gamma$ |  Lumγ
---|---|---|---|---|---|---|---|---|---|---
|  |  | [kpc] | [GHz] |  [W Hz-1] |  |  |  [10-9 ph cm-2 s-1] |  |  [1044 erg s-1]
J073328$+$560541 | G | 0.104 | 0.09 | 0.46 | 24.68 | dV09 | 1 | <0.36 | 2.00 | <0.13
J073934$+$495438 | G | 0.054 | 0.002 | 0.95 | 23.63 | dV09 | 8 | <1.69 | 2.00 | <0.051
J083139$+$460800 | G | 0.127 | 0.02 | 2.2 | 24.62 | dV09 | 0 | <0.27 | 2.00 | <0.15
J083637$+$440109 | G | 0.054 | 1.7 | <0.15 | 23.66 | dV09 | 0 | <0.35 | 2.00 | <0.031
J090615$+$463618 | G | 0.085 | 0.049 | 0.68 | 24.49 | dV09 | 0 | <0.12 | 2.00 | <0.027
J102618$+$454229 | G | 0.153 | 0.045 | 0.18 | 24.55 | dV09 | 0 | <0.16 | 2.00 | <0.13
J103719$+$433515 | G | 0.023 | 0.009 | <0.15 | 22.96 | dV09 | 0 | <0.20 | 2.00 | <0.003
J115000$+$552821 | G | 0.139 | 0.1 | <0.23 | 24.57 | dV09 | 2 | <0.49 | 2.00 | <0.32
J120902$+$411559 | G | 0.095 | 0.035 | 0.37 | 24.26 | dV09 | 0 | <0.52 | 2.00 | <0.15
J131739$+$411545 | G | 0.066 | 0.005 | 2.3 | 24.37 | dV09 | 5 | <5.49 | 2.00 | <0.098
J140051$+$521606 | G | 0.116 | 0.32 | <0.15 | 24.36 | dV09 | 17 |  0.12 $\pm$ 0.05 |  1.64 $\pm$ 0.32 | 0.20
J140942$+$360416 | G | 0.148 | 0.07 | 0.33 | 24.45 | dV09 | 0 | <0.32 | 2.00 | <0.25
J143521$+$505122 | G | 0.099 | 0.27 | <0.15 | 24.2 | dV09 | 0 | <0.16 | 2.00 | <0.052
J150805$+$342323 | G | 0.045 | 0.15 | <0.23 | 23.35 | dV09 | 0 | <0.11 | 2.00 | <0.007
J160246$+$524358 | G | 0.106 | 0.35 | <0.15 | 24.75 | dV09 | 2 | <0.52 | 2.00 | <0.19
J161148$+$404020 | G | 0.152 | 3.4 | <0.15 | 25.03 | dV09 | 2 | <0.43 | 2.00 | <0.35
J171854$+$544148 | G | 0.147 | 0.175 | 0.48 | 24.86 | dV09 | 0 | <0.14 | 2.00 | <0.10
J093609$+$331308 | G | 0.076 | 0.002 | 2.2 | 23.84 | dV09 | 0 | <0.08 | 2.00 | <0.015
J101636$+$563926 | G | 0.232 | 0.89 | <0.15 | 24.91 | dV09 | 0 | <0.11 | 2.00 | <0.23
J105731$+$405646 | G | 0.008 | 0.0001 | 1.25 | 21.59 | dV09 | 1 | <0.41 | 2.00 | <0.001
J115727$+$431806 | G | 0.229 | 2.3 | <0.15 | 25.25 | dV09 | 0 | <1.44 | 2.00 | <1.2
J132513$+$395552 | G | 0.074 | 0.014 | 1.9 | 23.69 | dV09 | 2 | <0.53 | 2.00 | <0.091
J134035$+$444817 | G | 0.065 | 0.0041 | 2.3 | 23.89 | dV09 | 1 | <0.44 | 2.00 | <0.058
J155927$+$533054 | G | 0.178 | 4.5 | <1.5 | 24.67 | dV09 | 0 | <0.34 | 2.00 | <0.39
J002225.42$+$001456.1 | G | 0.306 | 0.27 | 0.8 | 26.52 | L20 | 1 | <0.57 | 2.00 | <2.2
J002833.42$+$005510.9 | G | 0.104 | 3.35 | <0.4 | 24.35 | L20 | 0 | <0.52 | 2.00 | <0.19
J002914.24$+$345632.2 | G | 0.517 | 0.2 | 0.8 | 27.13 | L20 | 2 | <0.74 | 2.00 | <10.0
J075756.71$+$395936.1 | G | 0.066 | 0.25 | <0.4 | 23.5 | L20 | 0 | <0.31 | 2.00 | <0.042
J081323.75$+$073405.6 | G | 0.112 | 2.81 | <0.4 | 24.71 | L20 | 1 | <0.59 | 2.00 | <0.25
J082504.56$+$315957.0 | G | 0.265 | 7.75 | <0.4 | 24.82 | L20 | 1 | <0.18 | 2.00 | <0.50
J083411.09$+$580321.4 | G | 0.093 | 0.0086 | <0.4 | 24.13 | L20 | 15 |  3.53 $\pm$ 0.96 |  2.66 $\pm$ 0.20 | 0.30
J083825.00$+$371036.5 | G | 0.396 | 1.02 | <0.4 | 25.92 | L20 | 0 | <0.28 | 2.00 | <2.0
J085408.44$+$021316.1 | G | 0.459 | 3.43 | <0.4 | 25.34 | L20 | 0 | <0.20 | 2.00 | <2.0
J090105.25$+$290146.9 | G | 0.194 | 19.33 | 0.06 | 25.96 | L20 | 1 | <0.50 | 2.00 | <0.70
J092405.30$+$141021.4 | G | 0.136 | 0.74 | <0.4 | 24.18 | L20 | 13 |  2.15 $\pm$ 0.69 |  2.33 $\pm$ 0.24 | 0.584
J093430.68$+$030545.3 | G | 0.225 | 1.62 | <0.4 | 25.25 | L20 | 0 | <0.15 | 2.00 | <0.29
PKS 1007$+$142∗ | G | 0.213 | 3.29 | <0.4 | 25.71 | L20 | 31 |  4.64 $\pm$ 1.55 |  2.56 $\pm$ 0.18 | 2.8
J101251.77$+$403903.4 | G | 0.506 | 32.8 | <0.4 | 27.01 | L20 | 1 | <0.44 | 2.00 | <5.6
J101714.23$+$390121.1 | G | 0.211 | 20.97 | <0.4 | 25.8 | L20 | 0 | <0.26 | 2.00 | <0.43
J104029.94$+$295757.7 | G | 0.091 | 3.67 | <0.4 | 24.34 | L20 | 1 | <0.49 | 2.00 | <0.13
114339.59$+$462120.4 | G | 0.116 | 17.06 | <0.4 | 24.66 | L20 | 1 | <0.57 | 2.00 | <0.26
J115919.97$+$464545.1 | G | 0.467 | 5.0 | <0.4 | 26.4 | L20 | 0 | <0.36 | 2.00 | <3.7
131057.00$+$445146.2 | G | 0.391 | 3.87 | <0.4 | 25.26 | L20 | 1 | <0.22 | 2.00 | <1.5
J132419.67$+$041907.0 | G | 0.263 | 17.04 | <0.4 | 24.99 | L20 | 0 | <0.29 | 2.00 | <0.80
J140416.35$+$411748.7 | G | 0.36 | 1.01 | <0.4 | 25.51 | L20 | 0 | <0.12 | 2.00 | <0.71
J141327.22$+$550529.2 | G | 0.282 | 0.81 | <0.4 | 24.92 | L20 | 0 | <0.25 | 2.00 | <0.81
J142104.24$+$050844.7 | G | 0.445 | 1.68 | <0.4 | 25.91 | L20 | 0 | <0.21 | 2.00 | <1.9
J144712.76$+$404744.9 | G | 0.195 | 26.28 | <0.4 | 25.23 | L20 | 0 | <0.26 | 2.00 | <0.37
J152349.34$+$321350.2 | G | 0.11 | 0.4 | <0.4 | 24.2 | L20 | 0 | <0.13 | 2.00 | <0.052
J154609.52$+$002624.6 | G | 0.558 | 0.04 | 0.6 | 27.02 | L20 | 0 | <0.27 | 2.00 | <4.3
J155235.38$+$441905.9 | G | 0.452 | 6.93 | <0.4 | 25.56 | L20 | 17 |  0.78 $\pm$ 0.26 |  2.07 $\pm$ 0.19 | 6.0
J160335.16$+$380642.8 | G | 0.241 | 6.09 | <0.4 | 27.03 | L20 | 0 | <0.15 | 2.00 | <0.35
J161823.57$+$363201.7 | G | 0.733 | 0.44 | <0.4 | 26.82 | L20 | 5 | <0.69 | 2.00 | <21.8
J165822.18$+$390625.6 | G | 0.425 | 0.97 | <0.4 | 27.1 | L20 | 2 | <0.67 | 2.00 | <5.6
Table 7:  List of all young radio quasars contained in our sample. For parameters description and references see Table 5. ∗PKS 0056-00 and ∗∗PKS B1413$+$135, are also known as J005905.51$+$000651.6 and J141558.81$+$132023.7, respectively. Name | type | z | LS | $\nu_{p}$ |  log L${}_{5\,\textrm{GHz}}$ | Ref. | TS |  Fluxγ | $\Gamma$ |  Lumγ
---|---|---|---|---|---|---|---|---|---|---
|  |  | [kpc] | [GHz] |  [W Hz-1] |  |  |  [10-9 ph cm-2 s-1] |  |  [1044 erg s-1]
J0650$+$6001 | Q | 0.455 | 0.04 | 8.0 | 26.93 | O14 | 3 | <0.55 | 2.00 | <5.4
1225$+$368 | Q | 1.973 | 0.509 | 5.2 | 28.17 | 014 | 0 | <0.36 | 2.00 | <129
B3 0137$+$401 | Q | 1.62 | 35.939 | <0.26 | 27.09 | O14 | 1 | <0.34 | 2.00 | <76.2
B3 0701$+$392 | Q | 1.283 | 15.167 | 0.34 | 27.13 | O14 | 0 | <0.26 | 2.00 | <32.7
B3 1242$+$410 | Q | 0.813 | 0.34 | 0.65 | 27.35 | O14 | 1 | <0.97 | 2.00 | <24.9
B3 2311$+$469 | Q | 0.745 | 11.575 | <0.17 | 27.18 | O14 | 2 | <0.68 | 2.00 | <22.3
B3 2349$+$410 | Q | 2.046 | 10.146 | <0.3 | 27.44 | O14 | 0 | <0.18 | 2.00 | <70.0
3C 43 | Q | 1.459 | 22.17 | <0.05 | 28.09 | O14 | 0 | <0.32 | 2.00 | <54.1
3C 48 | Q | 0.367 | 6.579 | 0.109 | 27.38 | O14 | 0 | <0.12 | 2.00 | <0.73
3C 119 | Q | 1.023 | 1.617 | 0.303 | 28.23 | O14 | 0 | <0.14 | 2.00 | <9.9
3C 138 | Q | 0.759 | 5.9 | 0.176 | 27.97 | O14 | 34 |  2.09 $\pm$ 0.89 |  2.05 $\pm$ 0.12 | 64.2
3C 147 | Q | 0.545 | 4.454 | 0.231 | 27.92 | O14 | 22 |  6.89 $\pm$ 1.51 |  2.69 $\pm$ 0.16 | 47.1
3C 186 | Q | 1.067 | 17.959 | 0.082 | 27.26 | O14 | 0 | <0.28 | 2.00 | <22.1
3C 190 | Q | 1.1944 | 33.356 | 0.088 | 27.86 | O14 | 2 | <0.56 | 2.00 | <58.6
3C 216 | Q | 0.6702 | 56.12 | 0.066 | 27.23 | O14 | 153 |  7.78 $\pm$ 0.98 |  2.60 $\pm$ 0.09 | 97.4
3C 286 | Q | 0.85 | 24.51 | <0.05 | 28.41 | L20 | 67 |  5.60 $\pm$ 1.10 |  2.52 $\pm$ 0.12 | 111.3
3C 309.1 | Q | 0.905 | 17.215 | <0.076 | 28.08 | O14 | 207 |  6.33 $\pm$ 0.74 |  2.47 $\pm$ 0.07 | 178.5
3C 380 | Q | 0.692 | 10.8 | <0.3 | 27.68 | Z20 | 2274 |  36.43 $\pm$ 1.48 |  2.41 $\pm$ 0.02 | 504.5
PKS 0056-00∗ | Q | 0.719 | 15.099 | <0.14 | 27.5 | L20 | 52 |  5.31 $\pm$ 1.48 |  2.30 $\pm$ 0.15 | 64.6
J080413.88$+$470442.8 | Q | 0.51 | 6.18 | <0.4 | 26.52 | L20 | 0 | <0.14 | 2.00 | <1.8
J080442.23$+$301237.0 | Q | 1.45 | 10.98 | <0.4 | 27.72 | L20 | 0 | <0.26 | 2.00 | <43.3
J080447.96$+$101523.7 | Q | 1.968 | 31.02 | <0.02 | 28.12 | L20 | 0 | <0.12 | 2.00 | <43.3
J081253.10$+$401859.9 | Q | 0.551 | 7.72 | <0.4 | 26.73 | L20 | 0 | <0.18 | 2.00 | <2.9
J084856.57$+$013647.8 | Q | 0.35 | 6.16 | <0.4 | 25.15 | L20 | 0 | <0.33 | 2.00 | <1.7
J085601.22$+$285835.4 | Q | 1.084 | 6.53 | <0.4 | 27.09 | L20 | 0 | <0.15 | 2.00 | <12.1
J091734.79$+$501638.1 | Q | 0.632 | 4.87 | <0.4 | 25.82 | L20 | 1 | <0.47 | 2.00 | <10.2
J092608.00$+$074526.6 | Q | 0.442 | 7.98 | <0.4 | 25.6 | L20 | 0 | <0.32 | 2.00 | <2.9
J094525.90$+$352103.6 | Q | 0.208 | 4.45 | <0.4 | 24.72 | L20 | 0 | <0.16 | 2.00 | <0.27
J095412.57$+$420109.1 | Q | 1.787 | 16.89 | <0.4 | 27.56 | L20 | 0 | <0.21 | 2.00 | <58.6
J105628.25$+$501952.0 | Q | 0.82 | 8.18 | <0.4 | 26.01 | L20 | 0 | <0.61 | 2.00 | <25.4
J112027.80$+$142055.0 | Q | 0.363 | 0.4 | 1.0 | 26.65 | L20 | 0 | <0.29 | 2.00 | <1.7
J113138.89$+$451451.1 | Q | 0.398 | 4.88 | <0.4 | 26.59 | L20 | 0 | <0.30 | 2.00 | <2.1
J114311.02$+$053516.0 | Q | 0.497 | 17.03 | <0.4 | 25.76 | L20 | 0 | <0.32 | 2.00 | <3.9
J114856.56$+$525425.2 | Q | 1.632 | 0.0068 | 8.7 | 27.84 | L20 | 1 | <1.35 | 2.00 | <201.0
J115618.74$+$312804.7 | Q | 0.417 | 4.96 | 0.1 | 26.8 | L20 | 0 | <0.31 | 2.00 | <2.5
J120321.93$+$041419.0 | Q | 1.224 | 0.6 | 0.4 | 27.74 | L20 | 1 | <0.81 | 2.00 | <90.2
J120624.70$+$641336.8 | Q | 0.372 | 6.98 | <0.08 | 26.72 | L20 | 0 | <0.63 | 2.00 | <3.9
J125325.72$+$303635.1 | Q | 1.314 | 4.62 | <0.4 | 27.31 | L20 | 0 | <0.35 | 2.00 | <46.7
J130941.51$+$404757.2 | Q | 2.907 | 0.01 | 2.0 | 27.97 | L20 | 0 | <0.20 | 2.00 | <182.9
J131718.64$+$392528.1 | Q | 1.563 | 0.29 | <0.4 | 27.55 | L20 | 0 | <0.19 | 2.00 | <38.9
J133037.69$+$250910.9 | Q | 1.055 | 0.39 | <0.04 | 28.28 | L20 | 0 | <0.36 | 2.00 | <27.5
J134536.94$+$382312.5 | Q | 1.852 | 0.93 | <0.4 | 27.98 | L20 | 0 | <0.40 | 2.00 | <125.0
J140028.65$+$621038.5 | Q | 0.429 | 0.39 | 0.6 | 27.08 | L20 | 0 | <0.25 | 2.00 | <2.2
J140319.30$+$350813.3 | Q | 2.291 | 10.21 | <0.4 | 26.12 | L20 | 3 | <0.51 | 2.00 | <263.1
J141414.83$+$455448.7 | Q | 0.458 | 0.17 | 1.4 | 26.22 | L20 | 0 | <0.40 | 2.11 | <3.1
PKS B1413$+$135∗∗ | Q | 0.247 | 0.03 | 10 | 26.19 | L20 | 1193 |  14.72 $\pm$ 1.02 |  2.10 $\pm$ 0.03 | 27.8
J144516.46$+$095836.0 | Q | 3.541 | 0.15 | 0.9 | 29.14 | L20 | 0 | <0.12 | 2.00 | <179.4
J150426.69$+$285430.5 | Q | 2.285 | 0.35 | <0.4 | 28.02 | L20 | 0 | <0.44 | 2.00 | <227.0
J152005.47$+$201605.4 | Q | 1.572 | 8.9 | <0.02 | 28.11 | L20 | 4 | <0.65 | 2.00 | <133.7
J153409.90$+$301204.0 | Q | 0.929 | 35.86 | <0.4 | 26.03 | L20 | 0 | <0.21 | 2.00 | <12.1
J154349.50$+$385601.3 | Q | 0.553 | 8.1 | <0.4 | 25.77 | L20 | 0 | <0.31 | 2.00 | <5.0
J154525.48$+$462244.3 | Q | 0.525 | 6.59 | <0.4 | 25.79 | L20 | 0 | <0.16 | 2.00 | <2.2
J162111.27$+$374604.9 | Q | 1.271 | 6.28 | <0.4 | 27.27 | L20 | 1 | <0.35 | 2.00 | <42.8
J163402.95$+$390000.5 | Q | 1.083 | 6.53 | <0.4 | 27.38 | L20 | 0 | <0.27 | 2.00 | <22.2
J164311.34$+$315618.4 | Q | 0.587 | 10.58 | <0.4 | 25.76 | L20 | 2 | <0.43 | 2.00 | <7.8
J213638.58$+$004154.2 | Q | 1.941 | 0.02 | 5.2 | 29.36 | L20 | 0 | <0.02 | 2.00 | <6.2
J225025.34$+$141952.0 | Q | 0.235 | 0.75 | <0.178 | 26.3 | L20 | 1 | <0.64 | 2.00 | <1.4