# INCLUSIVE DETERMINATION OF $|V_{cb}|$ AT BELLE

RAYNETTE VAN TONDER
for the Belle Collaboration  University of Bonn, Physikalisches Institut,
Nussallee 12, 53115, Bonn, Germany

###### Abstract

We present the measurement of the first to fourth order moments of the four-
momentum transfer squared, $q^{2}$, of inclusive $B\rightarrow X_{c}\ell\nu$
decays using the full Belle data set consisting of $711\,\mathrm{fb^{-1}}$ of
integrated luminosity at the $\Upsilon(4S)$ resonance for $\ell=e,\mu$. The
measured moments are crucial experimental inputs for a novel, data-driven
approach to determine inclusive $|V_{cb}|$, which we implement in order to
extract a preliminary value of $|V_{cb}|\times 10^{3}=41.7\pm 1.2$.

##  1 Introduction

Precise measurements of the absolute value of the Cabibbo-Kobayashi-Maskawa
(CKM) matrix element $V_{cb}$ are important to deepen our understanding of the
Standard Model of Particle Physics (SM) [1, 2]. The current world averages of
$V_{cb}$ from exclusive and inclusive determinations are [3]:

$\displaystyle|V_{cb}^{\textrm{excl.}}|$ $\displaystyle=$
$\displaystyle(39.25\pm 0.56)\times 10^{-3}\leavevmode\nobreak\ ,$
$\displaystyle|V_{cb}^{\textrm{incl.}}|$ $\displaystyle=$
$\displaystyle(42.19\pm 0.78)\times 10^{-3}\leavevmode\nobreak\ ,$ (1)

where the uncertainties are the sum from experiment and theory. Both world
averages exhibit a disagreement of approximately 3 standard deviations with
one another.

Inclusive determinations of $|V_{cb}|$ exploit the fact that the total decay
rate can be expanded into a manageable set of non-perturbative matrix elements
using the Heavy Quark Expansion (HQE). A novel and alternative approach [4] to
determine $|V_{cb}|$ from inclusive decays exploits reparametrization
invariance that reduces the full set of 13 non-perturbative matrix elements
present in the total rate to a set of only 8 parameters at the order of
$\mathcal{O}(1/m_{b}^{4})$. The key prerequisite giving rise to the
reparametrization invariance in the total rate holds only for the moments of
the four-momentum transfer squared, $q^{2}$, of $B\rightarrow X_{c}\ell\nu$
decays. The first measurement of the first moment of the $q^{2}$ spectrum was
reported by CLEO [5] with a lepton energy requirement of $1\,\mathrm{GeV}$.
Here, a first systematic study of the first to fourth moments, $\langle
q^{2}\rangle$, $\langle q^{4}\rangle$, $\langle q^{6}\rangle$, $\langle
q^{8}\rangle$ without a lower lepton energy cut and using a progression of
cuts $q^{2}\in[3.6,10.1]$ GeV2 is presented.

##  2 Event Reconstruction

The full Belle data set of ($772\pm 10$) $\times 10^{6}$ $B$ meson pairs, that
were produced at the KEKB accelerator complex [6] with a centre-of-mass energy
of $\sqrt{s}=10.58\,\mathrm{GeV}$, is analysed for this measurement. Monte
Carlo (MC) samples of $B$ meson decays and continuum processes
($e^{+}e^{-}\rightarrow q\bar{q}$ with $q=u,d,s,c$) are simulated using the
EvtGen generator [7], while the detector response is modelled with GEANT3 [8].
The modelling of inclusive semileptonic $B\rightarrow X_{c}\ell\nu$ signal
decays is described in detail elsewhere [9].

Collision events are reconstructed using the Full Reconstruction algorithm
[10], which reconstructs one of the two $B$ mesons by making use of hadronic
decay channels. In total 1104 decay cascades are reconstructed, resulting in
an efficiency [11] of 0.28% and 0.18% for charged and neutral $B$ meson pairs.
The output classifier score of this neural network represents the estimated
quality of the reconstructed candidates, denoted as the $B_{\textrm{tag}}$,
and the best candidates for each event are selected. In order to suppress
continuum processes the $B_{\textrm{tag}}$ candidates are required to have a
beam-constrained mass of
$M_{bc}=\sqrt{(s/2)^{2}-|\textbf{p}_{\textrm{tag}}|^{2}}>5.27\,\mathrm{GeV}$.

After the reconstruction of the $B_{\textrm{tag}}$ candidate all remaining
tracks and clusters are used to reconstruct the signal side. By using the
momentum of the $B_{\textrm{tag}}$ candidate together with the precisely known
initial beam-momentum, the signal $B$ rest frame is defined as:

$p_{\textrm{sig}}=p_{e^{+}e^{-}}-\Big{(}\sqrt{m_{B}^{2}+|\textbf{p}_{\textrm{tag}}|^{2}},\textbf{p}_{\textrm{tag}}\Big{)}\leavevmode\nobreak\
.$ (2)

Since multiple leptons are likely to originate from a double semileptonic
$b\rightarrow c\rightarrow s$ cascade, exactly one signal lepton per event is
required. In addition, the charge of the signal lepton is required to be
opposite to that of the $B_{\textrm{tag}}$ charge. The four-momentum of the
hadronic system, $p_{X}$, is reconstructed from the sum of the remaining
unassigned charged particles and neutral energy depositions. Furthermore, the
hadronic mass of the $X$ system is reconstructed as
$M_{X}=\sqrt{(p_{X})^{\mu}(p_{X})_{\mu}}$ . With the $X$ system reconstructed,
the four-momentum of the neutrino in the event is estimated by calculating the
missing four-momentum,

$P_{\textrm{miss}}=(E_{\textrm{miss}},\textbf{p}_{\textrm{miss}})=p_{\textrm{sig}}-p_{X}-p_{\ell}\leavevmode\nobreak\
.$ (3)

For correctly reconstructed semileptonic $B\rightarrow X_{c}\ell\nu$ decays
$E_{\textrm{miss}}\approx|\textbf{p}_{\textrm{miss}}|$ and events with
$E_{\textrm{miss}}-|\textbf{p}_{\textrm{miss}}|\in[-0.5,0.5]$ GeV are
selected. Finally, the four-momentum transfer squared is reconstructed as
$q^{2}=(p_{\textrm{sig}}-p_{X})^{2}$.

##  3 Calculation of Moments

Figure 1: The reconstructed $M_{X}$ distribution for
$q^{2}>3.6\,\mathrm{GeV^{2}}$ (left) and the corresponding binned signal
probabilities (right).

In order to subtract background events a two step procedure is carried out.
First, a binned likelihood fit of the $M_{X}$ distribution is performed to
determine the number of expected signal and background events. The signal and
background templates for the fit are determined from MC samples, while
systematic uncertainties are incorporated via nuisance-parameter constraints.
Next, the determined number of background events is used to construct binned
signal probabilities defined as

$w_{i}=\frac{N_{\text{Total}}^{i}-N_{\text{Bkg}}^{i}}{N_{\text{Total}}^{i}}\leavevmode\nobreak\
.$ (4)

The binned signal probabilities for each $q^{2}$ selection are fitted with a
polynomial least-square fit of a given order $n$ to determine continuous
event-wise weights, $w(M_{X})$. Figure 1 shows the $M_{X}$ spectrum and the
$w_{i}$ distribution for the selection with $q^{2}>3.6\,\mathrm{GeV^{2}}$.

The reconstructed $q^{2}$ values are corrected from reconstruction and
selection effects by performing three sequential steps: firstly, an event-wise
calibration function is applied as a function of the reconstructed $q^{2}$
value to correct for resolution distortions. This linear calibration function
is determined by comparing the reconstructed and generator-level moments and
is derived separately for each order of the moments we wish to measure.
Subsequently, the calibrated $q^{2}$ values are calculated by inverting the
calibration curve for each order $m$:
$q^{2m}_{\text{cal}}=(q^{2m}-a_{m})/b_{m}$, with $a_{m}$ and $b_{m}$ denoting
the intercept and slope.

Figure 2: The generator-level and reconstructed $B\rightarrow X_{c}\ell\nu$
moments for the first moment for different $q^{2}$ selections (left), together
with the residual bias correction factors $\mathcal{C}_{\text{cal}}$ and
$\mathcal{C}_{\text{acc}}$ (right).

Subsequently, residual bias that may arise from small non-linearities in the
extracted calibration curves is corrected with a global correction factor,
denoted as $\mathcal{C}_{\text{cal}}$. Lastly, acceptance and selection losses
are corrected by applying a global correction factor,
$\mathcal{C}_{\text{acc}}$. The residual bias correction factor
$\mathcal{C}_{\text{cal}}$ is determined by comparing the calibrated and
generator-level moments for each order and $q^{2}$ selection, while
$\mathcal{C}_{\text{acc}}$ is determined by comparing the generator-level
moments with a sample without any selection criteria. The linear calibration
functions as well as the additional correction factors are determined from MC
samples that are statistically independent from the samples used in the
background subtraction procedure and are shown in Figure 2 for the first
moment. Finally, the $q^{2}$ moments are given by

$\langle
q^{2m}\rangle=\frac{\mathcal{C}_{\text{cal}}\cdot\mathcal{C}_{\text{acc}}}{\sum^{\text{events}}_{i}w(M_{Xi})}\sum^{\text{events}}_{i}w(M_{Xi})\cdot
q^{2m}_{\text{cal}\mbox{ }i}\leavevmode\nobreak\ .$ (5)

##  4 Results

Figure 3 shows the first to fourth order measured $q^{2}$ moments for
electrons and muons. In the shown ratio many of the associated systematic
uncertainties cancel and all reported moments are compatible with the
expectation of lepton flavour universality.

A $\chi^{2}$-fit is performed [12] to the combined measured moments using a
conservative estimate for theoretical uncertainties in order to extract a
value of $|V_{cb}|\times 10^{3}=41.7\pm 1.2$.

##  5 Outlook

Figure 3: The first to fourth order measured moments for electrons and muons
with the total statistical and systematic errors scaled by a factor of 10.
Note that the individual electron and muon moments are strongly correlated.

Preliminary measurements of the first to the fourth moments of the
$B\rightarrow X_{c}\ell\nu$ $q^{2}$ spectrum with several selections on
$q^{2}$ are reported. The measured moments are crucial experimental inputs for
a novel, data-driven approach to determine inclusive $|V_{cb}|$, which we
implement in order to extract a first preliminary value. The final analysis
will incorporate a few modifications including additional systematics
regarding signal and background modelling, as well as improving the
calculation of the background subtraction functions. Additionally, a different
set of $q^{2}$ selections will be considered for the final measured moments.

## Acknowledgments

This research was supported by the DFG Emmy-Noether Grant No. BE 6075/1-1.

## References

## References

  * [1] N. Cabibbo, Phys. Rev. Lett. 10, 531 (1963).
  * [2] M. Kobayashi and T. Maskawa, Prog. Theor. Exp. Phys. 49, 652 (1973).
  * [3] Y. S. Amhis et al. (HFLAV), (2019), arXiv:1909.12524683 [hep-ex].
  * [4] M. Fael, T. Mannel, and K. K. Vos, JHEP 02, 177 (2019).
  * [5] S. E. Csorna et al. (CLEO), Phys. Rev. D 70, 032002713 (2004).
  * [6] S. Kurokawa and E. Kikutani, Nucl. Instrum. Methods A 499, 1 (2003).
  * [7] D. J. Lange, Nucl. Instrum. Methods A 462, 152 (2001).
  * [8] R. Brun, F. Bruyant, M. Maire, A. C. McPherson, and P. Zanarini, CERN-DD-EE-84-1 (1987).
  * [9] L. Cao et al. (Belle), (2021), arXiv:2102.00020 [hep-ex].
  * [10] M. Feindt et al., Nucl. Instrum. Methods A 654, 432 (2011).
  * [11] A. Bevan et al., Eur. Phys. J. C 74, 3026 (2014).
  * [12] K. Olschewsky, “Inclusive determination of $V_{cb}$ using a new method ” in: these Proceedings (2021).


# MIGHTEE: Are giant radio galaxies more common than we thought?

J. Delhaize1, I. Heywood2,3,4, M. Prescott5, M.J. Jarvis2,6, I. Delvecchio7,8,
I.H. Whittam2,6, S.V. White3, M.J. Hardcastle9, C.L. Hale10, J. Afonso11,12,Y.
Ao13,14, M. Brienza15,16, M. Brüggen17, J.D. Collier18,19, E. Daddi7, M.
Glowacki5,6,N. Maddox20, L.K. Morabito21, I. Prandoni16, Z. Randriamanakoto22,
S. Sekhar18,23,5, Fangxia An5, N.J. Adams2, S. Blyth1, R.A.A. Bowler2, L.
Leeuw6, L. Marchetti1,16,S.M. Randriamampandry22,24, K. Thorat25,18, N.
Seymour26 O. Smirnov3,4,A.R. Taylor18,5, C. Tasse27,28,29 M. Vaccari5,16
Affiliations are listed at the end of the paper<EMAIL_ADDRESS>
(Accepted 2020 December 9. Received 2020 November 18; in original form 2020
September 23)

###### Abstract

We report the discovery of two new giant radio galaxies (GRGs) using the
MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey.
Both GRGs were found within a $\sim$1 deg2 region inside the COSMOS field.
They have redshifts of $z=0.1656$ and $z=0.3363$ and physical sizes of 2.4 Mpc
and 2.0 Mpc, respectively. Only the cores of these GRGs were clearly visible
in previous high resolution VLA observations, since the diffuse emission of
the lobes was resolved out. However, the excellent sensitivity and uv coverage
of the new MeerKAT telescope allowed this diffuse emission to be detected. The
GRGs occupy an unpopulated region of radio power – size parameter space. Based
on a recent estimate of the GRG number density, the probability of finding two
or more GRGs with such large sizes at $z<0.4$ in a $\sim$1 deg2 field is only
$2.7\times 10^{-6}$, assuming Poisson statistics. This supports the hypothesis
that the prevalence of GRGs has been significantly underestimated in the past
due to limited sensitivity to low surface brightness emission. The two GRGs
presented here may be the first of a new population to be revealed through
surveys like MIGHTEE which provide exquisite sensitivity to diffuse, extended
emission.

###### keywords:

galaxies: active – radio continuum: galaxies

††pubyear: 2020††pagerange: MIGHTEE: Are giant radio galaxies more common than
we thought?–Author Affiliations††pubyear: 2020

##  1 Introduction

Some classes of active galactic nuclei (AGN) have jets of relativistic
plasma/particles emanating from the central region, which produce radio
synchrotron emission. In some cases, these jets propagate to extremely large
distances beyond the host galaxy and into the intergalactic medium (IGM). When
the projected linear size of the jets and lobes exceeds 0.7 Mpc, such systems
are referred to as giant radio galaxies (GRGs; e.g. Schoenmakers et al., 2001;
Lara et al., 2001; Dabhade et al., 2020a)111Adjusted to the modern cosmology
of Planck Collaboration et al. (2016) for consistency with the current
literature.. GRGs are the largest individual objects in the Universe. The
largest known has a projected linear size222Based on a redshift of $z=0.3067$,
an angular size of 17.4 arcmin, and adjusted to our chosen cosmology. of 4.89
Mpc (Machalski et al., 2008), though the majority of known GRGs are below 2
Mpc in extent (Dabhade et al., 2020b).

Several factors have been proposed to explain why the jets of GRGs are able to
extend to such large distances. One suggestion is that these systems exist in
low density environments that allow the jets to permeate easily through the
IGM (e.g. Mack et al., 1998; Malarecki et al., 2015). However, $\sim$10 per
cent of GRGs have now been found to reside in cluster environments (Komberg &
Pashchenko, 2009; Dabhade et al., 2020b; Tang et al., 2020) and Lan &
Prochaska (2020) recently found no difference between the environments of GRGs
and that of galaxy control samples. Another scenario is that the central
engines of GRGs may boast particularly powerful and/or restarted AGN activity,
though several studies including Komberg & Pashchenko (2009) and Hardcastle et
al. (2019) found little evidence that GRGs are different to normal radio
galaxies in this respect.

A prevailing idea is that these objects represent the oldest AGN systems, such
that the jets have had enough time to grow to their large sizes (e.g. Ishwara-
Chandra & Saikia, 1999). However, we may then expect the existence of many
more GRGs than are currently known (Komberg & Pashchenko, 2009).

Fewer than 1000 GRGs have been found to date. Dabhade et al. (2020b) recently
compiled a comprehensive catalogue of all 820 GRGs known. At the time of
writing, a further six have been reported by Ishwara-Chandra et al. (2020) and
five by Tang et al. (2020).

The first GRG discovery was made in the 1970s (Willis et al., 1974) and since
then GRGs have primarily been found via wide-field radio continuum surveys
such as the NRAO VLA Sky Survey (NVSS; Condon et al., 1998), the Faint Images
of the Radio Sky at Twenty-Centimeters survey (FIRST; Becker et al., 1995),
the Westerbork Northern Sky Survey (WENSS; Rengelink et al., 1997) and the
Sydney University Molonglo Sky Survey (SUMSS; Mauch et al., 2003).

The highly-extended nature of GRGs and the generally low surface brightness of
their lobes, which fade as they age, make them notoriously difficult to detect
and identify. For example, 162 GRGs have only very recently been discovered in
NVSS data, despite this survey having already existed for two decades. These
were found via the Search and Analysis of Giant radio galaxies with Associated
Nuclei (SAGAN) project, which rigorously combined newly-developed automated
pattern recognition techniques (Proctor, 2016) with careful manual inspection
(Dabhade et al., 2017, 2020b). Despite such efforts, and the existence of many
thousands of ‘normal’ sized ($<0.7$ Mpc) radio galaxies (RGs), GRGs remain
scarce (e.g. Kaiser et al., 1997).

However, the new generation of deep and wide-field radio surveys, with
sensitivity to a range of spatial scales, may provide a much clearer
understanding of the number density and physics of such sources. In
particular, low frequency surveys with new-generation instruments like the Low
Frequency Array (LOFAR; van Haarlem et al., 2013), the Murchison Widefield
Array (MWA; Tingay et al., 2013) and the upgraded Giant Metre-wave Radio
Telescope (uGMRT; Gupta et al., 2017)  are proving excellent resources for
detecting and characterising GRGs (e.g. Hurley-Walker et al., 2015; Hardcastle
et al., 2016; Clarke et al., 2017; Seymour et al., 2020; Cantwell et al.,
2020). This is due, in part, to the increasing brightness towards lower radio
frequencies often displayed by radio galaxies. Indeed, Dabhade et al. (2020a)
recently reported the highest sky density of consistently-sampled GRGs using
Data Release 1 of the LOFAR Two-metre Sky Survey (LoTSS; Shimwell et al.,
2019). They found 239 GRGs over a 424 deg2 region at 120-168 MHz.

In the GHz regime, the newly-commissioned MeerKAT telescope in South Africa
(Jonas & MeerKAT Team, 2016) is proving to be an excellent instrument for GRG
studies (e.g. Cotton et al., 2020). Although it operates at higher frequencies
than LOFAR, MWA and uGMRT (though there is some overlap in observing frequency
with the latter), MeerKAT has the excellent sensitivity and uv coverage ideal
for such work, including the simultaneous availability of long and short
baselines.

The MeerKAT International GHz Tiered Extragalactic Exploration survey
(MIGHTEE; Jarvis et al., 2016) is a galaxy evolution survey underway with
MeerKAT. Among its data products will be high-quality radio continuum data
over a relatively wide field (20 deg2). In this paper, we report the discovery
of two newly-identified GRGs in 1 deg2 MIGHTEE Early Science observations of
the COSMOS field. The discovery of these objects within such a small sky area
hints at the presence of a ‘hidden’ population of GRGs, hitherto undetected
due to observational limitations. If GRGs prove to be more common than
previously thought, this may alter our understanding of the AGN duty cycle and
the impact of AGN-induced jets on the evolution of galaxies and the IGM.

Throughout this paper we assume a $\Lambda$CDM cosmology with $H_{0}$$=67.8$
km s-1 Mpc-1, $\Omega_{\Lambda}=0.692$ and $\Omega_{{\rm M}}=0.308$ (Planck
Collaboration et al., 2016). We assume a Chabrier (2003) initial mass
function, unless otherwise stated.

##  2 Radio continuum surveys

###  2.1 MIGHTEE L-band data

Table 1: Summary of the MeerKAT MIGHTEE observations used for this study. The COSMOS field centre is J2000 10h00m28.6s +02d12m21s, and the COSMOS_8 pointing is at J2000 10h00m29.0s +02d33m33.79s. The primary calibrator for all observations was PKS B1934-638, and the secondary was 3C 237. The observations used MeerKAT’s $L$-band system, 900 – 1670 MHz. Date | Block ID | Field | Antennas | Track (h) | On-source (h) | Channels
---|---|---|---|---|---|---
2018-04-19 | 1524147354 | COSMOS | 64 | 8.65 | 6.1 | 4096
2018-05-06 | 1525613583 | COSMOS | 62 | 8.39 | 5.1 | 4096
2020-04-02 | 1585844155 | COSMOS_8 | 60 | 8 | 6.25 | 32768
2020-04-26 | 1587911796 | COSMOS | 59 | 8 | 6.25 | 32768

The MIGHTEE survey targets four extragalactic deep fields, namely the European
Large Area ISO Survey - South 1 (ELAIS-S1), COSMOS, the XMM-Newton Large Scale
Structure (XMM-LSS) field, and the Extended Chandra Deep Field South (E-CDFS),
for a total sky area of approximately 20 deg2. The survey uses total
intensity, polarised intensity and spectral line data products to achieve a
range of science goals. It has components using MeerKAT’s $L$-band (900 – 1670
MHz) and $S$-band (1.75 – 3.5 GHz) receivers, and a primary design requirement
is to reach the $L$-band classical confusion limit in total intensity at about
2 $\mathrm{\mu}$Jy beam-1 (for a resolution of $\sim 8$ arcsec).

The initial release of total intensity continuum within the MIGHTEE consortium
included a single pointing in the COSMOS field (J2000 10h00m28.6s +02d12m21s;
see Table 1). The two GRGs we present in this paper were discovered in this
pointing. Since one of the GRGs was towards the edge of the field, we have
also imaged an extra MIGHTEE pointing (COSMOS_8; J2000 10h00m29.0s
+02d33m33.79s) for which this object was closer to the centre of the primary
beam.

Full details of the initial MIGHTEE continuum data release and the data
processing method333The calibration and imaging scripts are available online
here: https://www.github.com/IanHeywood/oxkat and through the Astrophysics
Source Code Library record ascl:2009.003 (Heywood, 2020) will be presented in
Heywood et al. (in prep.), however we provide a brief overview here.

  * •

The data were converted from their native format into a MeasurementSet format
by the South African Radio Astronomy Observatory (SARAO)
archive444https://archive.sarao.ac.za/, and averaged from their original
L-band frequency resolution to 1024 channels in the process. Flags generated
by the telescope control and monitoring system were applied.

  * •

Basic flagging commands were applied to all fields using CASA (McMullin et
al., 2007). Frequency ranges containing persistent radio frequency
interference (RFI) were flagged on spacings shorter than 600 m. The auto-
flagging algorithms tfcrop and rflag were used on the calibrator fields.

  * •

The standard calibrator PKS B1934-638 was used to derive delay and bandpass
solutions using the relevant CASA tasks. This was an iterative process, with
rounds of autoflagging on residual visibilities in each iteration.

  * •

The gain solutions derived from the primary were applied to the secondary
calibrator (3C 237), and an intrinsic spectral model was derived for the
latter. Time-dependent complex gains were then derived from the observations
of the secondary using this intrinsic model with the gaincal task. This
process compensates for the effects of the large fractional bandwidth of
MeerKAT, coupled with the fact that the data arrive in MeasurementSet format
that only has a single spectral window. The flux scale may be biased if this
effect is not taken into account.

  * •

All the gain corrections were applied to the target data, which is then
flagged using the tricolour555https://github.com/ska-sa/tricolour package.
Removing the edges of the bandpass where the gain sharply rolls off results in
770 MHz of usable bandwidth. A loss of about 50 per cent of the data in this
region is typical following RFI removal, although the RFI occupancy is
strongly baseline dependent, and most of this loss occurs on spacings shorter
than 1 km.

  * •

The target data were imaged using wsclean (Offringa et al., 2014). The data
were imaged blindly with 100,000 iterations, and a clean mask was derived from
the resulting image, excluding regions below a local threshold of 6$\sigma$
where $\sigma$ is the pixel standard deviation. The data were re-imaged using
this mask.

  * •

The multi-frequency clean components from the masked image were used to
predict a visibility model for self-calibration using the casa gaincal task.
Phase corrections were derived for every 64 seconds of data, and an amplitude
and phase correction was derived for every target scan, with the solutions for
the former applied while solving for the latter. The self-calibrated data were
re-imaged using wsclean and the mask is refined if necessary.

  * •

Direction-dependent corrections were made by imaging the data with ddfacet
(Tasse et al., 2018). The resulting model was partitioned into $\sim$10
directions, constrained by the location of off-axis problem sources, and the
need to retain suitable flux in the sky model per direction. The killms
package (e.g. Smirnov & Tasse, 2015) was then used to solve for a complex gain
correction for each direction with a time / frequency interval of 5 minutes /
128 channels. Another run of ddfacet reimaged the data, applying the
directional corrections.

  * •

Finally, the images were primary-beam corrected by dividing them by an
azimuthally averaged Stokes I model, evaluated at 1284 MHz using the eidos
(Asad et al., 2019) package.

MIGHTEE continuum data is imaged twice, with a Briggs’ robust parameter of 0.0
and -1.2. This is to deliver a higher sensitivity image as well as a higher
angular resolution image, the trade-off for which is a loss of sensitivity due
to the down-weighting of the many short spacings that MeerKAT’s dense core
provides. The COSMOS pointing for a total on-source time of 17.45 h reaches a
thermal noise (measured away from the main lobe of the primary beam) of 1.9
$\mu$Jy beam-1 in the robust 0.0 image, with an angular resolution of 8.4
arcsec $\times$ 6.8 arcsec (position angle -11.2 deg). The robust -1.2 image
reaches 6 $\mu$Jy beam-1 with an angular resolution of 4.8 arcsec $\times$ 4.0
arcsec (position angle -12.63 deg).

We imaged the 6.25 h of COSMOS_8 data with less aggressive weighting (robust =
0.3) in order to increase the sensitivity to the diffuse emission from the
lobes. This image has a thermal noise of 2.5 $\mu$Jy beam-1 and an angular
resolution of 11.6 arcsec $\times$ 7.4 arcsec (position angle -12.6 deg). The
higher resolution map made with COSMOS_8 data was imaged using a robust
parameter of -1.2 and has a thermal noise of 8.5 $\mu$Jy beam-1 and an angular
resolution of 5.0 arcsec $\times$ 4.0 arcsec (position angle -15.44 deg).

Note that in practice the central region of the lower resolution images (both
COSMOS and COSMOS_8) are limited to root mean squared (rms) ‘noise’ levels of
$\sim$3 $\mu$Jy beam-1 by a combination of thermal noise and classical
confusion. The lower resolution images are used for the work presented here,
unless otherwise stated.

###  2.2 VLA-COSMOS 3 GHz Large Project

In this paper, we also use data from the VLA-COSMOS 3 GHz Large Project
(hereafter VLA-3 GHz). This project was presented by Smolčić et al. (2017a)
and is a continuum survey with the Karl G. Jansky Very Large Array (VLA;
Perley et al. 2011) covering 2.6 deg2 over the full COSMOS field. This 384 h
survey was centred at 3 GHz with a 2 GHz bandwidth using three sets of 64
pointings in A and C array. It reached an rms sensitivity of 2.3$\mu$Jy beam-1
with 0.75 arcsec resolution. This is similar in sensitivity to the Early
Science MIGHTEE data, accounting for the different central frequencies and
assuming a standard spectral index for radio galaxies ($\alpha=-0.8$; where
$S_{\nu}\propto\nu^{\alpha}$).

While the angular resolution of the VLA-3 GHz data is superior to that of
MIGHTEE, it has poorer sensitivity to large-scale emission. The VLA has up to
351 baselines with a minimum baseline length of 36 m, while MeerKAT has up to
$2,016$ baselines with a minimum length of 29 m. The larger number of short
baselines, combined with the better instantaneous uv coverage provided by its
configuration, therefore makes MeerKAT the better instrument for detecting
diffuse, extended emission.

##  3 GRG properties

Figure 1: GRG1 (left) and GRG2 (right) as seen in MIGHTEE. The magenta contour
(for GRG-associated emission only) highlights the full extent of the GRGs. The
contour level is $7\,\mu$Jy beam-1 for GRG1 and $5\,\mu$Jy beam-1 for GRG2.
Notable features of the GRGs are labelled. Unrelated continuum sources are
marked by cyan circles. The MIGHTEE maps shown are COSMOS_8 (robust=0.3,
thermal noise 2.5$\mu$Jy beam-1) for GRG1, and COSMOS (robust=0.0, thermal
noise 1.9$\mu$Jy beam-1) for GRG2. The beam is shown in the bottom left corner
of each image.  Figure 2: GRG1 as seen in MIGHTEE (yellow contours), overlaid
on a composite optical image of HSC _g_ , _r_ and _i_ bands. Contours are
shown at intervals of $30\sqrt{3}^{n}\,\mu$Jy beam-1, where $n=0,1,2...$20\.
The lowest contour level is $7\,\mu$Jy beam-1 and is shown in magenta, as in
Figure 1, to highlight the full extent of the GRG. The inset shows an
enlargement of the core area, where the host galaxy can be seen.  Figure 3:
GRG1 as seen in the VLA-3 GHz data (background map) and in the MIGHTEE data
(black contours). Contour levels are as in Figure 2 and the lowest contour
level is again shown in magenta. The hot-spot in the northern lobe is seen
faintly at 3 GHz. The inset shows an enlargement of the core region, where a
double-lobed/jetted structure can be seen in the VLA-3GHz image.

The two GRGs were serendipitously discovered in the MIGHTEE-COSMOS map
(original pointing) during the process of cross-matching the radio sources
with their optical counterparts, via visual inspection. Details of this
process will be presented in Prescott et al., (in prep), and are similar to
those of Prescott et al. (2018). Following IAU conventions, we name these
sources MGTC J095959.63+024608.6 (hereafter GRG1) and MGTC J100016.84+015133.0
(hereafter GRG2). The prefix MGTC indicates the discovery of the sources in
the MIGHTEE-continuum survey. The basic properties of each GRG are summarised
in Table 2 and flux densities and radio powers are presented in Table 3. Note
that only statistical uncertainties are quoted in Table 2, but that systematic
uncertainties are found to be less than 3 per cent (Heywood et al., in prep).

Table 2: Properties of the two GRGs discovered in MIGHTEE-COSMOS. Columns: (1) Object name (2) Right Ascension (J2000) (3) Declination (J2000) (4) Spectroscopic redshift (5) Projected angular size (6) Projected linear size.  1 | 2 | 3 | 4 | 5 | 6
---|---|---|---|---|---
Name | R.A. | Dec | $z$ | $d$ | D
| (h:m:s) | (d:m:s) |  | (arcmin) | (Mpc)
MGTC J095959.63+024608.6 (GRG1) | 09:59:59.63 | +02:46:08.6 | 0.1656 | 13.8 | 2.42
MGTC J100016.84+015133.0 (GRG2) | 10:00:16.84 | +01:51:33.0 | 0.3363 | 6.8 | 2.04
Table 3: Integrated flux and radio power of the two GRGs. Columns: (1) Object name (2) Effective frequency of MIGHTEE map at position of GRG (3) Peak brightness of core (4-6) Integrated flux density of core, northern lobe and southern lobe (7) Total integrated flux density of GRG, combining all components (8) Spectral index of core (9) Radio power at $\nu_{{\rm eff}}$ ($\sim$1.2 GHz), based on columns 7 and 8. ∗See Table 2 for official names. ∗∗May include significant levels of unrelated emission from coincident continuum sources. See text for details.  1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
---|---|---|---|---|---|---|---|---
Name∗ | $\nu_{{\rm eff}}$ | $S_{{\rm p,core}}$ | $S_{{\rm int,core}}$ | $S_{{\rm int,NL}}$ | $S_{{\rm int,SL}}$ | $S_{{\rm int,all}}$ | $\alpha_{3{\rm GHz}}^{\nu_{{\rm eff}}}$ | $P_{1.2\,\rm{GHz}}$
| (GHz) | (mJy beam-1) | (mJy) | (mJy) | (mJy) | (mJy) |  | (W Hz-1)
GRG1 | 1.217 | $3.73\pm 0.01$ | 5.92$\pm 0.01$ | 8.59$\pm 0.02$ |  ∗∗21.07$\pm 0.01$ |  ∗∗35.59$\pm 0.02$ | $-0.51\pm 0.03$ | ${}^{**}(2.438\pm 0.002)\times 10^{24}$
GRG2 | 1.256 | $0.72\pm 0.01$ | 0.778$\pm 0.002$ | 2.04$\pm 0.01$ | 0.92$\pm 0.01$ | 3.74$\pm 0.02$ | $0.29\pm 0.04$ | $(1.329\pm 0.007)\times 10^{24}$

###  3.1 MGTC J095959.63+024608.6 (GRG1)

####  3.1.1 MIGHTEE data

GRG1 is shown in Figures 1, 2 and 3. The two diffuse outer lobes and jets are
detected for the first time in MIGHTEE, thus newly identifying this object as
a GRG. The core of GRG1 is located at ${\rm R.A.}={\rm 09h59m59.63s}$ and
${\rm Dec}={\rm+02d46m08.6s}$ and has an elongated structure in MIGHTEE. It is
associated with a host galaxy identified in optical and near-infrared data
(see Figure 2 inset) with a spectroscopic redshift of $z=0.1656$, according to
the Sloan Digital Sky Survey Data Release 14 (SDSS DR14; Abolfathi et al.,
2018). Summing the distance between the outer edges of each lobe and the
central core, the total projected angular size of GRG1 is 13.8 arcmin. It
therefore has a physical size of 2.42 Mpc, which places it solidly in the
regime of giant radio galaxies.

The northern lobe contains a potential hot-spot which is extended and is not
associated with any point source (indicated in Figure 1). This is confirmed
using the higher-resolution (4.8 arcsec $\times$ 4.0 arcsec) MIGHTEE image. A
comparison with optical data (Prescott et al., in prep) likewise reveals no
counterpart. Diffuse emission extends to the north-east of this hot-spot, in a
direction perpendicular to that of the jet. The reason for this is unclear,
but could indicate interactions with the surrounding IGM, allowing plasma to
flow in that direction (e.g. Subrahmanyan et al., 2008). This lobe appears to
be edge-brightened, which is typical of a Fanaroff and Riley type II (FRII;
Fanaroff & Riley 1974) radio galaxy. Some collimated jet emission is present
between the core and the lobes.

There is also evidence of a potential hot-spot towards the centre of the
southern lobe and the presence of a jet. The latter is bent with respect to
the northern jet alignment. Again, this could be due to interactions with the
IGM and surrounding environment. For example, Malarecki et al. (2015) suggest
that the lobes of GRGs could bend to avoid high density regions.

MIGHTEE flux densities in Table 3 are measured by integrating within the
region indicated by the magenta contour shown in Figure 1. The contributions
from coincident compact continuum sources not associated with the GRG have
been removed. These unrelated sources (presumably fore- or background
galaxies) are marked in Figure 1 and were identified in the counterpart cross-
matching procedure of Prescott et al., (in prep). This made use of the higher-
resolution MIGHTEE and VLA-3GHz data. Except for continuum sources larger than
the beam, we assume they are unresolved point sources and approximate their
total flux density to be equal to their peak brightness in the higher-
resolution MIGHTEE map. This is to minimise the amount of diffuse GRG-related
emission removed.

However, some ‘contaminating’ emission from these unrelated sources may still
remain, meaning that the flux density measurements in Table 3 are conservative
upper limits, particularly for the southern lobe. A lower limit for the flux
density of the southern lobe (avoiding areas with bright continuum sources) is
$3.60\pm 0.01$ mJy, giving a total flux density for GRG1 of $18.11\pm 0.02$
mJy.

####  3.1.2 VLA-3GHz data

Only the core of GRG1 is clearly detected in the VLA-3GHz data and the total
flux density reported for the object by Smolčić et al. (2017a) was $3.02\pm
0.15$ mJy.

In these high-resolution VLA data, the core of the GRG is resolved and
displays a double-lobed/jetted structure (see inset of Figure 3). These could
be the inner-most part of the jets, or could be a separate set of inner
lobes/jets. In the latter case, this object could be classified as a double-
double radio galaxy (e.g. Schoenmakers et al., 2000; Mahatma et al., 2019) and
a candidate restarted AGN (e.g. Jurlin et al., 2020; Brienza et al., 2020 and
references therein). However, no hot-spots are evident in this region, as
might be expected from inner lobes/jets.

The northern lobe/hot-spot of GRG1 is detected in the 3 GHz data, as seen in
Figure 3. However, it has a low signal-to-noise ratio (SNR) and therefore does
not appear in the SNR$\geq 5$ source catalogue presented by Smolčić et al.
(2017a). There is only a vague hint of the southern lobe in the 3 GHz map,
being highly diffuse and therefore predominantly resolved out of the VLA data.

####  3.1.3 Radio power

To determine the spectral index of the core, we first smooth the VLA-3 GHz map
to the resolution of the MIGHTEE map. We then measure the spectral index using
the MIGHTEE peak brightness ($3.73\pm 0.01$ mJy beam-1; to minimise
contamination from the jets) and the 3 GHz peak brightness from the smoothed
map ($2.93\pm 0.032$ mJy beam-1). This gives $\alpha_{3{\rm GHz}}^{\nu_{{\rm
eff}}}=-0.51\pm 0.03$. Here, $\nu_{{\rm eff}}=1.217\,$ GHz and is the
effective frequency of the MIGHTEE map at the position of this GRG. The
relative flatness of the core’s spectrum is expected due to synchrotron self-
absorption.

GRG1 has a 1.2 GHz radio power of $\sim 10^{24.4}$ W Hz-1 (lower limit of
$\sim 10^{24.1}$ W Hz-1). This was calculated using the measured spectral
index of the core and assuming a spectral index of -0.8 for the lobes. This
assumption is reasonable for an optically-thin lobe, and also given that
Dabhade et al. (2020a) find the mean spectral index distribution of their
sample of GRGs to be $\alpha^{1.4}_{0.144}=-0.79$, which is similar to that of
RGs.

Although the morphology of GRG1 somewhat resembles an FRII-like structure, its
radio power is more typical of an FRI-type galaxy (Fanaroff & Riley, 1974).
However, this is consistent with the results of Mingo et al. (2019) who have
recently discovered a population of low-luminosity666Up to several orders of
magnitude below the traditional FR break, which is $\sim 10^{24.5}$ W Hz-1 in
_L_ -band. FRII-type RGs in LoTSS. They find that radio luminosity does not
reliably predict whether a source has an FRI or an FRII-type morphology.
Furthermore, GRG1 has a lower radio power than that of most known GRGs.
According to Dabhade et al. (2020b), known GRGs at $z<1.0$ have mean 1.4 GHz
radio powers of $\sim 10^{25.3}$ W Hz-1. In many ways, GRG1 is reminicent of
NGC6251, a borderline FRI/FRII GRG. Both have weak hot-spots, FRI-like jets
and radio powers below the FR break (Cantwell et al., 2020).

####  3.1.4 Host galaxy and AGN characterisation

The host galaxy of this radio source appears elliptical in Hyper Suprime-Cam
imaging (Aihara et al., 2019; see Figure 2 inset). The top panel of Figure 4
shows the rest-frame SDSS DR14 optical spectrum of the host galaxy. Features
typical of an early-type elliptical galaxy can be seen in this spectrum, such
as a prominent 4000Å break, the presence of strong absorption lines like MgI
and NaD, as well as the lack of nebular emission lines. It contains no
prominent narrow or broad emission lines associated with high excitation radio
galaxies or quasars, such as [Oiii].

To further examine whether there is any evidence of radiatively-efficient AGN
activity, we fit the spectral energy distribution (SED) of the host galaxy
with various templates. The SED is constructed using the photometric catalogue
of Laigle et al. (2016) (hereafter COSMOS15), along with mid-infrared
(including 24$\mu$m) to sub-millimetre data from the "super-deblended"
catalogue of Jin et al. (2018). Note that the only significant detection (to
the 3$\sigma$ level) of GRG1 in Herschel bands777Data from the Photoconductor
Array Camera and Spectrometer Evolutionary Probe (PEP; Lutz et al., 2011)
survey and Herschel Multi-tiered Extragalactic Survey (HerMES; Oliver et al.,
2012). is at 100$\mu$m. The 3$\sigma$ upper limits were used in cases of non-
detection (see Delvecchio et al., 2017 for details).

The SED is fit with: (i) the MAGPHYS code of da Cunha et al. (2008)
considering only star-formation, and (ii) the SED3FIT code of Berta et al.
(2013) which also incorporates a set of AGN templates. The results are shown
in Figure 5 and further details of the SED-fitting approach can be found in
Delvecchio et al. (2017). The fit obtained in the latter case, with an AGN
component, does not significantly (<99 per cent confidence level) improve the
reduced $\chi^{2}$ of the fit, on the basis of a Fisher-test. Therefore, we
conclude that SED fitting reveals no evidence of radiatively-efficient AGN
activity.

From the best-fitting parameters of the SED model (without AGN), we find that
the host galaxy has a stellar mass of $M_{*}=10^{11.42}$ M⊙ and a (combined IR
and UV) star formation rate (SFR) of 1.97 M⊙/yr. It is therefore only weakly
star-forming and lies below the main sequence of star formation (Schreiber et
al., 2015) by a factor of $\sim$2\. This is consistent with the fact that it
is undetected in the far-infrared.

In the X-ray regime, the galaxy has a [2–10] keV luminosity of $L_{X}\sim
10^{41.6}$ erg s-1, based on data from the Chandra-COSMOS (Elvis et al., 2009;
Civano et al., 2012) and COSMOS-Legacy catalogues (Civano et al., 2016;
Marchesi et al., 2016). Since this is roughly five times in excess of that
predicted by the SFR-LX-ray relation of star-forming galaxies (Lehmer et al.,
2016), it is possible that concomitant low-luminosity AGN activity is present
in the X-ray. The AGN bolometric luminosity predicted by the fit with AGN
would be consistent within 1$\sigma$ with the observed $L_{X}$, if assuming a
set of [2–10] keV bolometric corrections from Lusso et al. (2012). However,
the total X-ray emission from the core follows the correlation for unabsorbed
jet-related emission in Hardcastle et al. (2009). Therefore, this is likely a
pure jet system with no evidence for additional accretion-related X-ray
emission.

Therefore, no evidence of (obscured or unobscured) radiatively-efficient AGN
activity is found in any regime. Rather, GRG1 can be classified as a low-
excitation radio AGN (LERG; e.g. Best & Heckman, 2012) powered by radiatively-
inefficient Bondi accretion of hot gas from the IGM (Hardcastle & Croston,
2020 and references therein) hosted by a massive, weakly star-forming
elliptical galaxy.

The host of GRG1 is the most massive galaxy in a group of eight according to
the zCOSMOS group catalogue of Knobel et al. (2012) (group ID 606).
Furthermore, Giodini et al. (2010) associated the core of this galaxy with an
X-ray cluster. This is unsurprising since GRGs are commonly found to reside in
small groups of galaxies (Malarecki et al., 2015). This environmental
information supports the scenario in which the bending of the southern jet,
and the extended emission of the northern lobe, result from interactions with
the surrounding intergalactic and intragroup/intracluster medium of the GRG.

Figure 4: Rest-frame optical spectra of the GRG host galaxies. Top: SDSS DR14
spectrum for GRG1. Bottom: zCOSMOS spectrum for GRG2.

Figure 5: SED of the GRG1 (top) and GRG2 (bottom) host galaxies showing
detections (closed points) or 3$\sigma$ upper limits (open circles) in UV to
sub-millimetre bands. The best-fitting template considering only star-
formation (blue line) was determined using MAGPHYS (left) and SED3FIT (right).
SED3FIT also fits an AGN template (dashed red line), and the combined (AGN +
star-forming) template (black line). The MAGPHYS fit on the left was preferred
for both GRGs, based on a Fisher-test of the reduced $\chi^{2}$. See text for
further details.

###  3.2 MGTC J100016.84+015133.0 (GRG2)

Figure 6: GRG2 as seen in MIGHTEE (yellow contours), overlaid on a composite
optical image of HSC _g_ , _r_ and _i_ bands. Contours are shown at intervals
of $20\sqrt{3}^{n}\,\mu$Jy beam-1, where $n=0,1,2...20$. The lowest contour
level is $5\,\mu$Jy beam-1 and is shown in magenta, as in Figure 1, to
highlight the full extent of the GRG. The inset shows an enlargement of the
core area.  Figure 7: GRG2 as seen in the VLA-3 GHz data (background map) and
in the MIGHTEE data (black contours). Contour levels as in Figure 2. At 3 GHz
a compact core is seen and diffuse emission in the northern hot-spot is
faintly visible.

####  3.2.1 MIGHTEE data

GRG2 is shown in Figures 1, 6 and 7. The core is situated at R.A.=10h00m16.84s
and Dec=+01d51m33.0s. The associated host galaxy has a spectroscopic redshift
of $z=0.3363$ according to the zCOSMOS Bright catalogue (Lilly et al., 2007,
Lilly et al., 2009).

Both lobes and a potential hot-spot within the northern lobe are clearly
detected for the first time in MIGHTEE, as is the entire northern jet and some
weak emission from the southern jet. Based on these data, GRG2 has a projected
angular size of 6.8 arcmin and therefore a projected linear size of 2.04 Mpc.
Some elongated emission is seen in MIGHTEE between the core and the southern
lobe, which may be attributable to the jet in that direction.

The unequal strength of detection of the two jets may imply that they are
relativistic on these scales and lie away from the plane of the sky, so that
their apparent surface brightness is being affected by relativistic beaming
(Blandford & Königl, 1979).

Flux densities have been measured within the region outlined by the magenta
contour shown in Figure 1.

####  3.2.2 VLA-3GHz data

As seen in Figure 7, there is a hint of a VLA-3 GHz detection of the northern
lobe/hot-spot, though it does not appear in the source catalogue of Smolčić et
al. (2017a) due to its low SNR in these data. No hot-spot is seen in the
southern lobe in MIGHTEE and this lobe is entirely undetected in the VLA-3 GHz
data.

The core is detected in the VLA-3 GHz observations and is reported in the
source catalogue of Smolčić et al. (2017b) with a 3 GHz flux density of
$0.878\pm 0.044$ mJy. There is no evidence of any extended structure or
unusual morphology of the core in these VLA data (see Figure 7 inset).

####  3.2.3 Radio power

The MIGHTEE peak brightness of the GRG2 core ($0.72\pm 0.01$ mJy beam-1) and
the peak brightness from the smoothed VLA-3 GHz map ($2.43\pm 0.03$ mJy
beam-1) are used to find a spectral slope of $\alpha_{3{\rm GHz}}^{1.256{\rm
GHz}}\sim 0.29\pm 0.04$. This is relatively flat, although slightly positive
and is again consistent with typical cores of radio galaxies. Considering this
and again assuming a spectral index of -0.8 for the lobes, the 1.2 GHz radio
power of GRG2 is $\sim 10^{24.1}$ W Hz-1. This is lower than most known GRGs
(Dabhade et al., 2020b) and slightly lower than that of GRG1.

####  3.2.4 Host galaxy and AGN characterisation

The host galaxy appears elliptical in HSC imaging (see Figure 6 inset).
Despite having a zCOSMOS optical spectrum of relatively poor quality (bottom
panel of Figure 4), it is typical of the absorption spectrum of a red, dead
elliptical galaxy, with no emission features present.

GRG2 displays no signatures of AGN activity in the X-ray or mid-IR and has no
significant detection in the super-deblended Herschel photometry of Jin et al.
(2018), with a combined SNR of all Herschel bands of only $\sim 0.8$. It is
not identified as an AGN via SED fitting (see Figure 5). Note that the SED fit
is not improved by the inclusion of an AGN template despite the improvement at
24$\mu$m, since the outcome of the Fisher-test is guided much more by the
worsening of the fit at IRAC-8$\mu$m. If the fit with AGN was preferred, the
predicted AGN bolometric luminosity would correspond to an expected $L_{X}\sim
10^{42.6}$ erg s-1, which is about 10 times higher than the formal X-ray limit
at [2–10] keV (Civano et al., 2016). Since GRG2 is not X-ray detected, this
check further supports the relatively low significance of the AGN component
obtained from the Fisher-test.

The host galaxy of GRG2 has a stellar mass of $M_{*}=10^{10.8}$ M⊙ and a SFR
of $\sim 0.5$ M⊙ yr-1, placing it well below the main sequence of star
formation. Its quiescent nature is further supported by its classification as
‘red’ based on its (_NUV-r_) and (_r-J_) colours in COSMOS2015.

We therefore conclude that GRG2, like GRG1, is a radiatively-inefficient LERG
with a massive, red, passive elliptical host. This is in line with
expectations since LERGs tend to be hosted by galaxies which have redder
colours, larger stellar masses and lower SFRs than HERG hosts (e.g. Best &
Heckman 2012; Hardcastle et al. 2013). Little information currently exists
about the SFRs of GRGs specifically, however Clarke et al. (2017) and Dabhade
et al. (2020c) have found evidence for moderate SFRs in several individual GRG
hosts.

GRG2 resides in a smaller, less rich group than GRG1. The GRG2 host is the
most massive of the five galaxies in the group (group ID 753; Knobel et al.
2012).

We note that the compact source to the southwest of centre (labelled in Figure
1) was also investigated as a potential position of the GRG core. However, it
is not situated close to the equidistant centre of the lobes and the
associated optical counterpart has a photometric redshift of $z=2.015$ in
COSMOS15, so is less likely to be the GRG host. It is considered an unrelated
compact source and its contribution to the total flux density of the southern
lobe has been removed.

##  4 Discussion

###  4.1 Statistics and GRG sky density

Figure 8: The position in the 1.2 GHz power-size (P-D) diagram of almost all
known GRGs. Objects in the compilation catalogue of Dabhade et al. (2020b) are
shown as green ($z<0.5$) and black ($z\geq 0.5$) points. GRG1 and GRG2 are
shown as red stars and are located in a previously unoccupied part of this
diagram. For comparison, dotted lines show evolutionary tracks as determined
by Hardcastle et al. (2019) for radio galaxies with jet powers of (bottom to
top) $10^{37}$, $10^{37.5}$, $10^{38}$, $10^{38.5}$ and $10^{39}$ W. The
tracks show a lifetime of up to 1 Gyr with blue triangles marking each hundred
Myr. GRG1 and GRG2 are consistent with the evolution of a $\sim 10^{37.5}$ W
jet after $\sim 700$ Myr.

Dabhade et al. (2020b) find that GRGs with sizes $>2$ Mpc are extremely rare
and comprise only 9 percent of the GRG population at $z<1$. We now estimate
the probability of finding two $>2$ Mpc GRGs in such a small (1 deg2) sky
area. For this purpose, our selection criteria can be considered to be GRGs
with projected linear sizes $>2$ Mpc out to a redshift of $z=0.5$ (and
therefore with angular sizes of $>5.3$ arcmin).

We compare with the GRG sample of Dabhade et al. (2020a). They provide the
most extensive survey of GRGs to-date, and therefore the most robust sky
density estimate presently available. Dabhade et al. (2020a) found 239 GRGs
over a 424 deg2 sky area using the LoTSS survey. Only 7 of these have sizes
larger than $2$ Mpc, of which only one has $z<0.5$. This gives a sky density
of 0.0023 GRGs per deg2 for such objects.

We firstly assume that this sky density is the "true" GRG sky density of the
Universe. We also assume independence of repeated events. That is, that the
presence of one GRG in the sky area does not affect whether or not another is
present, which is likely given the very different redshifts of the two GRGs
presented here. We can thus employ Poisson statistics to estimate the
probability ($P$) of finding $k$ number of GRGs in a 1 deg2 sky area:

$P(X=k)=\frac{\lambda^{k}e^{-\lambda}}{k!},$ (1)

where $\lambda$ is the expected number of occurrences and is equal to 0.0023
(which is the GRG sky density expected from the results of Dabhade et al.
2020a, as above.) For the case of $k=2$, we find $P(X\geq 2)=2.7\times
10^{-6}$. Therefore, there is a probability of $2.7\times 10^{-6}$ of finding
two or more GRGs with projected linear sizes $>2$ Mpc at $z<0.5$ within a 1
deg2 field, given our initial assumptions.

If the observed sky region is not strongly affected by cosmic (sample)
variance or other significant selection effects, the small p-value we find
implies that either we have been exceptionally lucky in finding these two
objects, or that our initial assumption does not hold and the true GRG sky
density is significantly higher than previously known.

The most likely explanation for an under-estimated GRG sky density is the
limited sensitivity to extended, diffuse emission of GRG lobes in past
surveys. For example, LoTSS is incomplete to low-luminosity giants due to
surface brightness sensitivity limitations, as illustrated in Figure 8 of
Hardcastle et al. (2019). While the components of GRG1 and GRG2 would exceed
the 5$\sigma$ sensitivity limit of LoTSS, it would be difficult to identify
these components as belonging to the same system if the low surface brightness
emission joining them is not visible.

While the uv coverage and angular resolution of LOFAR and MeerKAT are
comparable, the point source sensitivity of MIGHTEE ($\sim 11\,\mu$Jy at 144
MHz, for $\alpha=-0.8$) is significantly deeper than LoTSS ($\sim 71\,\mu$Jy
at 144 MHz). While it is non-trivial to directly compare surface brightness
sensitivities of the two surveys, MIGHTEE is expected to be superior due to
its depth. Low frequency surveys do benefit from being more sensitive to the
steepest spectrum lobe emission of GRGs, however LoTSS would only surpass
MIGHTEE in sensitivity where the spectral index is steeper than
$\alpha\approx-1.7$.

A further explanation for a previously under-estimated GRG sky density is a
dearth of highly sensitive, ancillary multi-wavelength observations. This
would make it more difficult to determine whether or not lobes and hot-spots
are associated with separate near-infrared/optical counterparts, particularly
at higher redshifts. This issue is largely avoided by MIGHTEE-COSMOS thanks to
the wealth of high-quality multi-wavelength data available in the field.

Therefore, GRGs may be yet more numerous than presently known, despite the
rapid increase in the number of these giants discovered in recent years (e.g.
Dabhade et al. 2020a, b). This has also been suggested by other works, such as
Ishwara-Chandra et al. (2020) who discovered six new $\sim 1$ Mpc-sized GRGs
out to $z\sim 1.3$ in the ELAIS North 1 field with the GMRT.

###  4.2 GRG parameter space

GRG1 and GRG2 do not seem atypical of large radio galaxies in that they are
LERGs hosted by “red and dead” elliptical galaxies, have flat spectrum cores
and exist in unremarkable environments (see Hardcastle & Croston, 2020 for a
review of radio AGN properties).

They do, however, have larger sizes and lower radio powers than most known
GRGs. In fact, they lie in a heretofore unoccupied part of the GRG power-size
(P-D) diagram. This is shown in Figure 8, in comparison to the 780 GRGs from
the compilation catalogue of Dabhade et al. (2020b) for which $L$-band flux
measurements are available from the NVSS or the literature (P. Dabhade, priv
comm). For these, 1.2 GHz radio powers have been calculated using the measured
spectral index where available, or otherwise assuming a value of
$\alpha=-0.8$.

Note that the lower limit (and likely more realistic) radio power of GRG1
($P_{1.2}=10^{24.1}$ W Hz-1; see Section 3.1.3) has been used in Figure 8.
However, GRG1 would still sit slightly below all other GRG of the same size on
this diagram if the conservative upper limit to its radio power was used.

It is therefore possible that these new MIGHTEE observations are uncovering an
unexplored realm of GRG parameter space, hitherto invisible to previous
surveys due to the limitations discussed in Section 4.1. These may be the low-
luminosity giants predicted by the P-D tracks of, for example, Kaiser et al.
(1997), Shabala et al. (2008) and Hardcastle et al. (2019).

To illustrate this, Figure 8 also shows the evolutionary tracks of radio
galaxies of various jet powers, as determined by Hardcastle et al. (2019).
These are shown for an environment of halo mass $10^{13}$ M⊙, observing
frequency 1.2 GHz and $z=0.25$. GRG1 and GRG2 are consistent with the
evolution of a $\sim 10^{37.5}$ W jet after $\sim 700$ Myr, under these
conditions.

###  4.3 Implications

While our analysis has considered only enormous ($>2$ Mpc) objects, if radio
galaxies must grow to reach this size, then we may expect to similarly uncover
in our data previously undetected GRGs with smaller sizes. The full 20 deg2
MIGHTEE survey will provide an excellent resource for such studies when it is
completed in the near future. Based on the GRG sky density we observe in
COSMOS (2 per deg2), we could uncover $\sim 40$ GRGs with the full survey.
With MeerKAT simultaneously facilitating excellent sensitivity, sky coverage
and $uv$ coverage with relatively high resolution, and the upcoming Square
Kilometre Array (SKA) providing even better angular resolution, we may expect
to discover many more diffuse, extended GRGs in the near- to mid-future.

The existence of a larger population of GRGs may have implications for our
understanding of the AGN duty cycle. Low frequency follow-up observations of
these objects can provide spatially-resolved spectral information, which will
help us to characterise the intermittency of the radio AGN activity. Such
information is crucial for our understanding of whether AGN feedback across
subsequent accretion episodes remains constant or decreases with time,
enabling predictions of the overall life-cycle of AGN activity relative to the
host. This is essential for advancing our current understanding of the extent
to which AGN-driven mechanical feedback can alter the star-forming content of
the host galaxy (Croton et al., 2006).

##  5 Conclusions

Thanks to the impressive capabilities of the MeerKAT telescope, two new giant
radio galaxies have been identified in a 1 deg2 MIGHTEE survey of the COSMOS
field. Both are "red and dead" LERGs and are among the largest known GRGs with
sizes of $>2$ Mpc. However, they have low radio powers which place them in a
previously unpopulated part of GRG parameter space. Due to the diffuse nature
of the jets and lobes, these objects were resolved out and undetected in
previous surveys of the COSMOS field, including sensitive 3 GHz observations
with the VLA.

The probability of finding at least two such GRGs in a small, 1 deg2 field is
only $2.7\times 10^{-6}$, based on wide-field observations with LOFAR.
Therefore, our findings provide strong evidence that GRGs may be far more
numerous than previously thought. It is only with new radio surveys such as
MIGHTEE, providing excellent extended brightness sensitivity, that this
‘hidden’ population of GRGs can be revealed. Systematic searches across the
full MIGHTEE survey are expected to yield detections of many more GRGs and low
frequency follow-up can help reveal important information about the AGN duty
cycle. These are tantalizing hints of what the future SKA will ultimately
uncover with its simultaneously excellent angular resolution and surface
brightness sensitivity.

## Acknowledgements

We thank the anonymous referee for providing useful comments which have
strengthened the paper. We thank Pratik Dabhade for helpful discussions and
for generously providing their GRG catalogue. The MeerKAT telescope is
operated by the South African Radio Astronomy Observatory (SARAO;
www.ska.ac.za), which is a facility of the National Research Foundation (NRF),
an agency of the Department of Science and Innovation. JD, SVW, IH and MP
acknowledge the financial assistance of SARAO. MP, SMR and LL acknowledge the
financial assistance of the NRF. Opinions expressed and conclusions arrived
at, are those of the authors and are not necessarily to be attributed to the
NRF.

MJJ and IH acknowledge support from the UK Science and Technology Facilities
Council [ST/N000919/1]. MJJ, IH and IHW acknowledge support from the Oxford
Hintze Centre for Astrophysical Surveys which is funded through generous
support from the Hintze Family Charitable Foundation. IH thanks the Rhodes
University Centre for Radio Astronomy Techniques and Technologies (RATT) for
the provision of computing facilities. ID is supported by the European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie
grant agreement No 788679. MP, JC, FXA, MV, KT and MG acknowledge financial
support from the Inter-University Institute for Data Intensive Astronomy
(IDIA). The research of OS is supported by the South African Research Chairs
Initiative of the NRF. YA acknowledges support by NSFC grant 11933011. RB
acknowledges support from the Glasstone Foundation. LM, MV and IP acknowledge
financial support from the Italian Ministry of Foreign Affairs and
International Cooperation (MAECI Grant Number ZA18GR02) and the South African
NRF (Grant Number 113121) as part of the ISARP RADIOSKY2020 Joint Research
Scheme. JA acknowledges financial support from the Science and Technology
Foundation (FCT, Portugal) through research grants PTDC/FIS-AST/29245/2017,
UID/FIS/04434/2019, UIDB/04434/2020 and UIDP/04434/2020. NA acknowledges
funding from the Science and Technology Facilities Council (STFC) Grant Code
ST/R505006/1. ZR acknowledges financial support from the South African
Astronomical Observatory which is a facility of the South African NRF. NM
acknowledges support from the Bundesministerium für Bildung und Forschung
(BMBF) D-MeerKAT award 05A2017. KT acknowledges support from the Inter-
University Institute for Data Intensive Astronomy (IDIA). MB acknowledges
support from the ERC-Stg DRANOEL, no 714245.

We acknowledge the use of the Ilifu cloud computing facility -
www.ilifu.ac.za, a partnership between the University of Cape Town, the
University of the Western Cape, the University of Stellenbosch, Sol Plaatje
University, the Cape Peninsula University of Technology and the South African
Radio Astronomy Observatory. The Ilifu facility is supported by contributions
from the Inter-University Institute for Data Intensive Astronomy (IDIA - a
partnership between the University of Cape Town, the University of Pretoria
and the University of the Western Cape), the Computational Biology division at
UCT and the Data Intensive Research Initiative of South Africa (DIRISA). This
work made use of the CARTA (Cube Analysis and Rendering Tool for Astronomy)
software (DOI 10.5281/zenodo.3377984 - https://cartavis.github.io).

The Hyper Suprime-Cam (HSC) collaboration includes the astronomical
communities of Japan and Taiwan, and Princeton University. The HSC
instrumentation and software were developed by the National Astronomical
Observatory of Japan (NAOJ), the Kavli Institute for the Physics and
Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High
Energy Accelerator Research Organization (KEK), the Academia Sinica Institute
for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University.
Funding was contributed by the FIRST program from Japanese Cabinet Office, the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), the
Japan Society for the Promotion of Science (JSPS), Japan Science and
Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK,
ASIAA, and Princeton University.

##  6 Data availability

The data underlying this article were accessed from the South African Radio
Astronomy Observatory (SARAO; www.ska.ac.za). The derived data generated in
this research will be shared on reasonable request to the corresponding
author.

## References

  * Abolfathi et al. (2018) Abolfathi B. et al., 2018, The Astrophysical Journal Supplement Series, 235, 42
  * Aihara et al. (2019) Aihara H. et al., 2019, Publications of the Astronomical Society of Japan, 71, 114
  * Asad et al. (2019) Asad K. M. B. et al., 2019, arXiv e-prints, arXiv:1904.07155
  * Becker et al. (1995) Becker R. H., White R. L., Helfand D. J., 1995, The Astrophysical Journal, 450, 559
  * Berta et al. (2013) Berta S. et al., 2013, Astronomy & Astrophysics, 551, A100
  * Best & Heckman (2012) Best P. N., Heckman T. M., 2012, Monthly Notices of the Royal Astronomical Society, 421, 1569
  * Blandford & Königl (1979) Blandford R. D., Königl A., 1979, The Astrophysical Journal, 232, 34
  * Brienza et al. (2020) Brienza M. et al., 2020, Astronomy and Astrophysics, 638, A29
  * Cantwell et al. (2020) Cantwell T. M. et al., 2020, Monthly Notices of the Royal Astronomical Society
  * Chabrier (2003) Chabrier G., 2003, Publications of the Astronomical Society of the Pacific, 115, 763
  * Civano et al. (2012) Civano F. et al., 2012, The Astrophysical Journal Supplement Series, 201, 30
  * Civano et al. (2016) Civano F. et al., 2016, The Astrophysical Journal, 819, 62
  * Clarke et al. (2017) Clarke A. O. et al., 2017, Astronomy and Astrophysics, 601, A25
  * Condon et al. (1998) Condon J. J., Cotton W. D., Greisen E. W., Yin Q. F., Perley R. A., Taylor G. B., Broderick J. J., 1998, The Astronomical Journal, 115, 1693
  * Cotton et al. (2020) Cotton W. D. et al., 2020, Monthly Notices of the Royal Astronomical Society, 495, 1271
  * Croton et al. (2006) Croton D. J. et al., 2006, Monthly Notices of the Royal Astronomical Society, 365, 11
  * da Cunha et al. (2008) da Cunha E., Charlot S., Elbaz D., 2008, Monthly Notices of the Royal Astronomical Society, 388, 1595
  * Dabhade et al. (2020c) Dabhade P., Combes F., Salomé P., Bagchi J., Mahato M., 2020c, Astronomy and Astrophysics, 643, A111
  * Dabhade et al. (2017) Dabhade P., Gaikwad M., Bagchi J., Pand ey-Pommier M., Sankhyayan S., Raychaudhury S., 2017, Monthly Notices of the Royal Astronomical Society, 469, 2886
  * Dabhade et al. (2020b) Dabhade P. et al., 2020b, Astronomy and Astrophysics, 642, A153
  * Dabhade et al. (2020a) Dabhade P. et al., 2020a, Astronomy and Astrophysics, 635, A5
  * Delvecchio et al. (2017) Delvecchio I. et al., 2017, Astronomy and Astrophysics, 602, A3
  * Elvis et al. (2009) Elvis M. et al., 2009, The Astrophysical Journal Supplement Series, 184, 158
  * Fanaroff & Riley (1974) Fanaroff B. L., Riley J. M., 1974, Monthly Notices of the Royal Astronomical Society, 167, 31P
  * Giodini et al. (2010) Giodini S. et al., 2010, The Astrophysical Journal, 714, 218
  * Gupta et al. (2017) Gupta Y. et al., 2017, Current Science, 113, 707
  * Hardcastle et al. (2013) Hardcastle M. J. et al., 2013, Monthly Notices of the Royal Astronomical Society, 429, 2407
  * Hardcastle & Croston (2020) Hardcastle M. J., Croston J. H., 2020, New Astronomy Reviews, 88, 101539
  * Hardcastle et al. (2009) Hardcastle M. J., Evans D. A., Croston J. H., 2009, Monthly Notices of the Royal Astronomical Society, 396, 1929
  * Hardcastle et al. (2016) Hardcastle M. J. et al., 2016, Monthly Notices of the Royal Astronomical Society, 462, 1910
  * Hardcastle et al. (2019) Hardcastle M. J. et al., 2019, Astronomy and Astrophysics, 622, A12
  * Heywood (2020) Heywood I., 2020, oxkat: Semi-automated imaging of MeerKAT observations, ascl:2009.003
  * Hurley-Walker et al. (2015) Hurley-Walker N. et al., 2015, Monthly Notices of the Royal Astronomical Society, 447, 2468
  * Ishwara-Chandra & Saikia (1999) Ishwara-Chandra C. H., Saikia D. J., 1999, Monthly Notices of the Royal Astronomical Society, 309, 100
  * Ishwara-Chandra et al. (2020) Ishwara-Chandra C. H., Taylor A. R., Green D. A., Stil J. M., Vaccari M., Ocran E. F., 2020, Monthly Notices of the Royal Astronomical Society, 497, 5383–5394
  * Jarvis et al. (2016) Jarvis M. et al., 2016, in Proceedings of MeerKAT Science: On the Pathway to the SKA. 25-27 May, p. 6
  * Jin et al. (2018) Jin S. et al., 2018, The Astrophysical Journal, 864, 56
  * Jonas & MeerKAT Team (2016) Jonas J., MeerKAT Team, 2016, in MeerKAT Science: On the Pathway to the SKA, p. 1
  * Jurlin et al. (2020) Jurlin N. et al., 2020, Astronomy and Astrophysics, 638, A34
  * Kaiser et al. (1997) Kaiser C. R., Dennett-Thorpe J., Alexander P., 1997, Monthly Notices of the Royal Astronomical Society, 292, 723
  * Knobel et al. (2012) Knobel C. et al., 2012, The Astrophysical Journal, 753, 121
  * Komberg & Pashchenko (2009) Komberg B. V., Pashchenko I. N., 2009, Astronomy Reports, 53, 1086
  * Laigle et al. (2016) Laigle C. et al., 2016, The Astrophysical Journal Supplement Series, 224, 24
  * Lan & Prochaska (2020) Lan T.-W., Prochaska J. X., 2020, arXiv e-prints, arXiv:2009.04482
  * Lara et al. (2001) Lara L., Cotton W. D., Feretti L., Giovannini G., Marcaide J. M., Márquez I., Venturi T., 2001, Astronomy and Astrophysics, 370, 409
  * Lehmer et al. (2016) Lehmer B. D. et al., 2016, The Astrophysical Journal, 825, 7
  * Lilly et al. (2009) Lilly S. J. et al., 2009, The Astrophysical Journal Supplement Series, 184, 218
  * Lilly et al. (2007) Lilly S. J., Le Fevre O., Renzini A., Zamorani G., Scodeggio M., Contini T., 2007, The Astrophysical Journal Supplement Series, 172, 70
  * Lusso et al. (2012) Lusso E. et al., 2012, Monthly Notices of the Royal Astronomical Society, 425, 623
  * Lutz et al. (2011) Lutz D. et al., 2011, Astronomy and Astrophysics, 532, A90
  * Machalski et al. (2008) Machalski J., Kozieł-Wierzbowska D., Jamrozy M., Saikia D. J., 2008, The Astrophysical Journal, 679, 149
  * Mack et al. (1998) Mack K. H., Klein U., O’Dea C. P., Willis A. G., Saripalli L., 1998, Astronomy and Astrophysics, 329, 431
  * Mahatma et al. (2019) Mahatma V. H. et al., 2019, Astronomy and Astrophysics, 622, A13
  * Malarecki et al. (2015) Malarecki J. M., Jones D. H., Saripalli L., Staveley-Smith L., Subrahmanyan R., 2015, Monthly Notices of the Royal Astronomical Society, 449, 955
  * Marchesi et al. (2016) Marchesi S. et al., 2016, The Astrophysical Journal, 830, 100
  * Mauch et al. (2003) Mauch T., Murphy T., Buttery H. J., Curran J., Hunstead R. W., Piestrzynski B., Robertson J. G., Sadler E. M., 2003, Monthly Notices of the Royal Astronomical Society, 342, 1117
  * McMullin et al. (2007) McMullin J. P., Waters B., Schiebel D., Young W., Golap K., 2007, in Astronomical Society of the Pacific Conference Series, Vol. 376, Astronomical Data Analysis Software and Systems XVI, Shaw R. A., Hill F., Bell D. J., eds., p. 127
  * Mingo et al. (2019) Mingo B. et al., 2019, Monthly Notices of the Royal Astronomical Society, 488, 2701
  * Offringa et al. (2014) Offringa A. R. et al., 2014, Monthly Notices of the Royal Astronomical Society, 444, 606
  * Oliver et al. (2012) Oliver S. J. et al., 2012, Monthly Notices of the Royal Astronomical Society, 424, 1614
  * Perley et al. (2011) Perley R. A., Chandler C. J., Butler B. J., Wrobel J. M., 2011, The Astrophysical Journal, 739, L1
  * Planck Collaboration et al. (2016) Planck Collaboration et al., 2016, Astronomy and Astrophysics, 594, A13
  * Prescott et al. (2018) Prescott M. et al., 2018, Monthly Notices of the Royal Astronomical Society, 480, 707
  * Proctor (2016) Proctor D. D., 2016, The Astrophysical Journal Supplement Series, 224, 18
  * Rengelink et al. (1997) Rengelink R. B., Tang Y., de Bruyn A. G., Miley G. K., Bremer M. N., Roettgering H. J. A., Bremer M. A. R., 1997, Astronomy and Astrophysics Supplement Series, 124, 259
  * Schoenmakers et al. (2001) Schoenmakers A. P., de Bruyn A. G., Röttgering H. J. A., van der Laan H., 2001, Astronomy and Astrophysics, 374, 861
  * Schoenmakers et al. (2000) Schoenmakers A. P., de Bruyn A. G., Röttgering H. J. A., van der Laan H., Kaiser C. R., 2000, Monthly Notices of the Royal Astronomical Society, 315, 371
  * Schreiber et al. (2015) Schreiber C. et al., 2015, Astronomy & Astrophysics, 575, A74
  * Seymour et al. (2020) Seymour N. et al., 2020, Publications of the Astronomical Society of Australia, 37, e013
  * Shabala et al. (2008) Shabala S. S., Ash S., Alexander P., Riley J. M., 2008, Monthly Notices of the Royal Astronomical Society, 388, 625
  * Shimwell et al. (2019) Shimwell T. W. et al., 2019, Astronomy and Astrophysics, 622, A1
  * Smirnov & Tasse (2015) Smirnov O. M., Tasse C., 2015, Monthly Notices of the Royal Astronomical Society, 449, 2668
  * Smolčić et al. (2017b) Smolčić V. et al., 2017b, Astronomy and Astrophysics, 602, A2
  * Smolčić et al. (2017a) Smolčić V. et al., 2017a, Astronomy and Astrophysics, 602, A1
  * Subrahmanyan et al. (2008) Subrahmanyan R., Saripalli L., Safouris V., Hunstead R. W., 2008, The Astrophysical Journal, 677, 63
  * Tang et al. (2020) Tang H., Scaife A. M. M., Wong O. I., Kapińska A. D., Rudnick L., Shabala S. S., Seymour N., Norris R. P., 2020, Monthly Notices of the Royal Astronomical Society, 499, 68
  * Tasse et al. (2018) Tasse C. et al., 2018, Astronomy and Astrophysics, 611, A87
  * Tingay et al. (2013) Tingay S. J. et al., 2013, Publications of the Astronomical Society of Australia, 30, e007
  * van Haarlem et al. (2013) van Haarlem M. P. et al., 2013, Astronomy and Astrophysics, 556, A2
  * Willis et al. (1974) Willis A. G., Strom R. G., Wilson A. S., 1974, Nature, 250, 625

#### Author Affiliations

1Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch
7701, South Africa
2Astrophysics, Department of Physics, University of Oxford, Keble Road, Oxford
OX1 3RH, UK
3Department of Physics and Electronics, Rhodes University, PO Box 94,
Makhanda, 6140, South Africa
4South African Radio Astronomy Observatory, 2 Fir Street, Black River Park,
Observatory, Cape Town 7925, South Africa
5Inter-University Institute for Data Intensive Astronomy, and Department of
Physics and Astronomy,
University of the Western Cape, Robert Sobukwe Road, 7535 Bellville, Cape
Town, South Africa
6Department of Physics and Astronomy, University of the Western Cape, Robert
Sobukwe Road, 7535 Bellville, Cape Town,
South Africa
7CEA, IRFU, DAp, AIM, Université Paris-Saclay, Université de Paris, CNRS,
F-91191 Gif-sur-Yvette,
France
8INAF - Osservatorio Astronomico di Brera, via Brera 28, I-20121, Milano,
Italy & via Bianchi 46, I-23807, Merate, Italy
9Centre for Astrophysics Research, Department of Physics, Astronomy and
Mathematics, University of Hertfordshire, College Lane,
Hatfield AL10 9AB, UK
10CSIRO Astronomy and Space Science, PO Box 1130, Bentley WA 6102, Australia
11Instituto de Astrofísica e Ciências do Espaço, Universidade de Lisboa, OAL,
Tapada da Ajuda, PT1349-018 Lisboa, Portugal
12Departamento de Física, Faculdade de Ciências, Universidade de Lisboa,
Edifício C8, Campo Grande
13Purple Mountain Observatory and Key Laboratory for Radio Astronomy, Chinese
Academy of Sciences, Nanjing, China
14School of Astronomy and Space Science, University of Science and Technology
of China, Hefei, Anhui, China
15 Dipartimento di Fisica e Astronomia, Universitá di Bologna, via P. Gobetti
93/2, 40129, Bologna, Italy
16INAF, Istituto di Radioastronomia, Via Gobetti 101, 40129, Bologna, Italy
17Hamburger Sternwarte, University of Hamburg, Gojenbergsweg 112, 21029
Hamburg, Germany
18The Inter-University Institute for Data Intensive Astronomy (IDIA),
Department of Astronomy, University of Cape Town,
Private Bag X3, Rondebosch, 7701, South Africa
19School of Science, Western Sydney University, Locked Bag 1797, Penrith, NSW
2751, Australia
20Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr. 1, 81679
Munich, Germany
21Centre for Extragalactic Astronomy, Department of Physics, Durham
University, Durham, DH1 3LE, UK
22South African Astronomical Observatory, P.O. Box 9, Observatory 7935, Cape
Town, South Africa
23National Radio Astronomy Observatory, 1003 Lopezville Road, Socorro, NM
87801, USA
24A&A, Department of Physics, Faculty of Sciences, University of Antananarivo,
B.P. 906, Antananarivo 101, Madagascar
25Department of Physics, University of Pretoria, Private Bag X20, Hatfield
0028, South Africa
26International Centre for Radio Astronomy Research, Curtin University,
Bentley, WA 6102, Australia
27 GEPI, Observatoire de Paris, CNRS, Université Paris Diderot, 5 place Jules
Janssen, 92190 Meudon, France
28 Centre for Radio Astronomy Techniques and Technologies, Department of
Physics and Electronics, Rhodes University,
Grahamstown 6140, South Africa
29 USN, Observatoire de Paris, CNRS, PSL, UO, Nançay, France