MNRAS 000, 1–22 (2021) Preprint 13 January 2022 Compiled using MNRAS LATEX style file v3.0
Accretion mode versus radio morphology in the LOFAR Deep Fields
B. Mingo1★, J. H. Croston1, P. N. Best2, K. J. Duncan2, M. J. Hardcastle3, R. Kondapally2, I. Prandoni4,
J. Sabater2,5, T. W. Shimwell6,7, W. L. Williams7, R. D. Baldi4,8, M. Bonato4,9,10, M. Bondi4, P. Dabhade11,
G. Gürkan12, J. Ineson8, M. Magliocchetti13, G. Miley7, J. C. S. Pierce3, and H. J. A. Röttgering7
1School of Physical Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
2Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK
3Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
4INAF-IRA, Via Gobetti 101, I-40129, Bologna, Italy
5STFC UK Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK
6ASTRON, Netherlands Institute for Radio Astronomy, Oude Hoogeveensedijk 4, 7991 PD, Dwingeloo, The Netherlands
7Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands
8School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
9Italian ALMA Regional Centre, Via Gobetti 101, I-40129, Bologna, Italy
10INAF-Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, I-35122, Padova, Italy
11Observatoire de Paris, LERMA, Collège de France, CNRS, PSL University, Sorbonne University, 75014, Paris, France
12Thüringer Landessternwarte (TLS), Sternwarte 5, D-07778 Tautenburg, Germany
13INAF - IAPS, Via Fosso del Cavaliere 100, 00133, Roma, Italy
Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
Radio-loud active galaxies have two accretion modes [radiatively inefficient (RI) and radiatively efficient (RE)], with distinct
optical and infrared signatures, and two jet dynamical behaviours, which in arcsec- to arcmin-resolution radio surveys manifest
primarily as centre- or edge-brightened structures [Fanaroff-Riley (FR) class I and II]. The nature of the relationship between
accretionmode and radiomorphology (FR class) has been the subject of long debate.We present a comprehensive investigation of
this relationship for a sample of 286well-resolved radio galaxies in the LOFARTwo-metre Sky SurveyDeep Fields (LoTSS-Deep)
first data release, for which robust morphological and accretion mode classifications have been made. We find that two-thirds
of luminous FRII radio galaxies are RI, and identify no significant differences in the visual appearance or source dynamic
range (peak/mean surface brightness) of the RI and RE FRIIs, demonstrating that both RI and RE systems can produce FRII
structures. We also find a significant population of low-luminosity FRIIs (predominantly RI), supporting our earlier conclusion
that FRII radio structures can be produced at all radio luminosities. We demonstrate that in the luminosity range where both
morphologies are present, the probability of producing FRI or FRII radio morphology is directly linked to stellar mass, while
across all morphologies and luminosities, RE accretion occurs in systems with high specific star formation rate, presumably
because this traces fuel availability. In summary, the relationship between accretion mode and radio morphology is very indirect,
with host-galaxy environment controlling these two key parameters in different ways.
Key words: galaxies: jets – galaxies: active – radio continuum: galaxies – black hole physics
1 INTRODUCTION
Jets in active galactic nuclei (AGN) are highly collimated beams of
plasma launched as part of the accretion process from the vicinity of
the central supermassive black hole, and can reach distances of up
to a few Mpc (e.g Blandford et al. 2019). The large-scale radio mor-
phology of AGN is a consequence of the interaction between the jet
and the surrounding environment, and provides fundamental insight
on the underlying physics regulating this process and on the nature
of the jet itself (see e.g. the recent review by Hardcastle & Croston
2020). The morphology of (extended) radio-loud AGN on kpc to
★ E-mail: bmingo@extragalactic.info (BM)
Mpc scales has been the subject of investigation for many decades
(e.g.Miley 1980), and typically falls into the two categories identified
by Fanaroff & Riley (1974) – centre-brightened and edge-brightened
structures (FRI and FRII). There is now extensive observational evi-
dence that this dichotomy in kpc to Mpc structures is a consequence
of jet deceleration and decollimation in FRI jets, which occurs on
∼ kpc scales (e.g. Bicknell 1994; Laing & Bridle 2002, 2014). The
long-standing scenario in which this behaviour is controlled by a
combination of jet power and environmental density on small scales
(Bicknell 1994; Ledlow & Owen 1996) has found support in recent
LOFAR work (Mingo et al. 2019, hereafter M19).
As well as morphology, radio galaxies have traditionally been
classified according to two different sets of emission line properties
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(see e.g. Hine & Longair 1979; Best & Heckman 2012), with those
displaying strong optical emission lines called high-excitation radio
galaxies (HERGs, or HEGs) and those where the lines are faint
or absent being labelled as low-excitation radio galaxies (LERGs,
or LEGs). Sometimes they are also referred to as weak- or strong-
line radio galaxies (WLRGs and SLRGs, see e.g. Tadhunter 2016).
These two classes have been linked to a dichotomy in the accretion
properties of the AGN central engine, motivated by the behaviour
of X-ray binaries (see e.g. the review by Fender & Gallo 2014).
LERGs are associated with low-rate, radiatively-inefficient accretion
(RI; Narayan & Yi 1995), effective at producing jets but generating
little radiation beyond the radio. HERGs are instead linked to high-
rate, radiatively-efficient accretion (RE; Shakura & Sunyaev 1973),
generating large amounts of radiation but only sometimes producing
jets.
In the literature, the FRI and II morphological classes have been
frequently associated with the RI and RE accretion states, respec-
tively, (e.g. Jackson & Rawlings 1997). Such a connection is sup-
ported by the fact that there are few (though not zero) known exam-
ples of FRI HERGs (see e.g. Gurkan et al., subm.), but is contradicted
by the existence of a significant population of FRII LERGs (e.g. Hine
& Longair 1979; Willott et al. 1999; Chiaberge et al. 2000; Hardcas-
tle et al. 2004; Baldi et al. 2010; Croston et al. 2018). One possible
explanation for the existence of FRII LERGs in a model where the
central engine determines the large-scale radio morphology is that
they are systems where the central engine has recently switched off,
or suffered a significant change in accretion rate, but this information
has not reached the hotspots yet (e.g. Tadhunter 2016). This model
predicts observable differences in the large-scale radio structures of
FRII LERGs and HERGs, with the former frequently presenting a
more relaxed, ‘fat double’ structure (Owen & Laing 1989). Addi-
tional support for a model in which the central engine controls the jet
physics comes from the relationship between blazar peak frequency
and luminosity (the ‘blazar envelope’), which may be explained by
intrinsic differences in jet structure on sub-parcsec scales that are
linked to accretion mode rather than jet power (e.g. Meyer et al.
2011; Keenan et al. 2021).
New facilities and deeper radio surveys are now demonstrating
that the faint radio-loud AGN population encompasses more diverse
behaviour than previously observed, which must be incorporated
into physical models, e.g. small sources with very weak jets, and
low-luminosity FRIIs (e.g. Best & Heckman 2012; Fernandes et al.
2015; Baldi et al. 2015; Capetti et al. 2017b; Miraghaei & Best
2017; Mingo et al. 2019; Jimenez-Gallardo et al. 2019; Macconi
et al. 2020; Pierce et al. 2020; Grandi et al. 2021; Vardoulaki et al.
2021). Environmental studies have also demonstrated links between
environment and both radiomorphology and accretionmode (Gendre
et al. 2013; Ineson et al. 2015; Croston et al. 2019; Vardoulaki et al.
2021). In the high accretion regime there is increasing evidence of the
presence of jets along with radiatively-driven winds in sources often
classified as radio-quiet AGN (e.g. Jarvis et al. 2019; Fawcett et al.
2020), though there seems to be evidence for an inverse correlation
between the strength of the wind and that of the jet (Mehdipour &
Costantini 2019). Many of these radio sources seem to be young or
unlikely to grow to large sizes, so it is yet unclear how they tie in with
the evolved FRI/II classes, and what impact they might have beyond
their host galaxies (e.g. Chhetri et al. 2020; Patil et al. 2020). We are
also achieving greater insight on the bright compact steep spectrum
(CSS) and gigahertz peaked spectrum (GPS) populations, with radio
powers comparable to those of extended radio galaxies, but which
are far too numerous to always evolve into FRIs and FRIIs (see e.g.
the recent review by O’Dea & Saikia 2021).
Table 1. LOFAR deep fields coverage. Columns show (l to r): field name,
sky area with available multi-wavelength coverage, RMS noise in the central
region of the field at 150 MHz, total combined exposure, and our initial
sample size (sources with catalogued sizes> 8 arcsec) prior to morphological
classification. For details on each individual field see Sabater et al. (2021);
Tasse et al. (2021).
Field Sky area RMS Exposure N sources
[sq deg] [𝜇Jy/beam] [h]
ELAIS-N1 6.74 19 164 8251
Boötes 8.63 30 80 3886
Lockman 10.28 22 112 8078
In this work we exploit the newly-released LOFAR TwoMetre Sky
Survey Deep Fields dataset (LoTSS-Deep; Tasse et al. 2021; Sabater
et al. 2021; Kondapally et al. 2021; Duncan et al. 2021) to examine
the relationship between radio morphology and accretion mode for a
large and well-resolved sample with high-quality optical and infrared
data, and to investigate in detail the role of host galaxy stellar mass,
fuel availability, and jet power as controlling influences. The LoTSS-
Deep survey covers an area of ∼ 25 square degrees with an average
noise level of ∼ 25 𝜇Jy beam−1 and the same 6-arcsec resolution as
the LOFAR Two Metre Sky Survey first data release ( LoTSS-DR1;
Shimwell et al. 2019; Williams et al. 2019; Duncan et al. 2019),
resulting in a source density of up to ∼ 5000 sources per square
degree, a factor of ∼ 7 higher than LoTSS-DR1, in a sky area ∼ 17
times smaller. The increased depth and outstanding multiwavelength
coverage of LoTSS-Deep allows us to reliably classify sources up
to redshift (𝑧) ∼ 2.5, for the first time allowing us to examine this
relationship for faint and moderately-bright sources well beyond our
local Universe. LoTSS-Deep is representative of the data quality
and source density expected for the low-frequency Square Kilometre
Array (SKA) surveys (Dewdney et al. 2009; Prandoni & Seymour
2015), whichmeans that investigating the demographics and different
physical mechanisms driving these populations will be a key step
towards their large-scale identification and classification in the SKA
surveys, and to understand their evolution.
In Section 2 we describe the data from the three LOFAR deep
fields that are the subject of this study (ELAIS-N1, Boötes, and
the Lockman Hole), our radio source morphological classification
results following the method developed by M19, and the host galaxy
spectral energy distribution (SED) classifications obtained by Best et
al. (subm.). We then analyse the properties of our sources in Section
3, comparing our results with those obtained using LoTSS-DR1.
In Section 4 we then discuss the relationship between jet power,
accretion mode, and radio galaxy morphology, while in Section 5 we
explore the role of the host galaxy in regulating both accretion and
FR class. Section 6 summarises our conclusions.
For this paper we have used a concordance cosmology with 𝐻0 =
70 km s−1 Mpc−1, Ω𝑚 = 0.3 and ΩΛ = 0.7.
2 DATA AND ANALYSIS
2.1 The LOFAR deep fields
The three fields covered in this paper – ELAIS-N1 (Oliver et al.
2000), the Lockman Hole (Lockman et al. 1986), and Boötes (Jan-
nuzi & Dey 1999) – were chosen as targets for the LoTSS-Deep
surveys because of their excellent multiwavelength coverage (see
Kondapally et al. 2021, and references therein), as well as their loca-
tion in the sky – high in declination to allow high elevation observing
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 3
and mitigating additional ionospheric thickness and beam elongation
effects, whilst allowing for good uv-coverage. The ultimate aim of
the deepest LOFAR surveys is to achieve a central noise level of ∼ 10
𝜇Jy/beam, ten times deeper than the target depth of the all-northern-
sky LoTSS survey (Shimwell et al. 2017, 2019), over a sky area of
30-50 square degrees, and with a resolution of 6 arcsec. As described
by Sabater et al. (2021), this coverage and depth is sufficient to probe
radio source populations beyond 𝑧 ∼ 1 with sufficient statistics so
as to obtain representative samples of sources. Fig. 1 by Tasse et al.
(2021) provides a direct comparison of the resolution and sensitivity
for the current and planned LoTSS-Deep surveys, in comparison to
other existing and planned surveys.
The LOFAR and multiwavelength data for the three deep fields
are slightly different, with the Lockman Hole having the largest sky
area with multiwavelength coverage, and ELAIS-N1 the deepest ra-
dio images (lowest RMS). Details of the observation strategy, data
processing and calibration, and compilation of the multiwavelength
catalogues for the three LOFAR deep fields are described by Tasse
et al. (2021), Sabater et al. (2021), Kondapally et al. (2021), and
Duncan et al. (2021). Table 1 provides a summary of the broad
characteristics of each field in terms of their LOFAR coverage. Any
implications of the differences between the three fields for our results
are noted in Sections 2.2 and 3. We note that we have observed no
field-dependent effects at a significant level in the results presented
in this work.
Similar to the value-added cataloguing of LoTSS-DR1 (Williams
et al. 2019), the basic sizes and shapes of our extended sources have
been catalogued using PyBSDF (Mohan & Rafferty 2015) and for
the largest sources PyBDSF was complemented by Zooniverse com-
ponent association. Host identification was achieved through a com-
bination of maximum likelihood and Zooniverse visual identification
(Kondapally et al. 2021). We used the value-added and component
catalogues for each deep field for our analysis, in a similar manner as
that described by M19. As our science aims require morphological
classification, we imposed an initial cut at a catalogued size of 8
arcsec to discard any sources that would be too small to classify, but
did not impose an initial flux density cut. Table 1 shows the initial
number of sources for each field that we have used for our analysis.
The total numbers listed represent ∼ 26 per cent of all the sources in
ELAIS-N1 and the Lockman Hole, and ∼ 20 per cent of the sources
in Boötes.
Thanks to the excellent multiwavelength coverage available in our
deep fields we were able to rely on spectral energy distribution (SED)
fitting to separate galaxies from AGN, to derive star formation rates
and stellar masses for each source, and to classify AGN based on
their radio loudness and accretion mode. Details of this process are
given in Section 2.3.
2.2 Morphological classification
Our aim in classifying the extended sources from LoTSS-Deep was
to obtain clean samples of FRIs and FRIIs. To do so we used a
two-fold approach, similar to that described by M19, in which the
sources were first automatically classified (as described in Section
2.2.1 below) and then further refinements to the samples were carried
out via visual inspection (Section 2.2.2).
While we had enough information from the SEDfitting results (de-
scribed in Section 2.3) to pre-filter star-forming galaxies, we decided
to automatically classify and visually inspect the radio sources first,
in order to provide additional validation for the SED results. This
independent estimate on the potential level of contamination is also
useful for comparison with other automatic classification methods,
andwith samples which do not have themultiwavelength information
needed to easily filter out star-forming galaxies.
2.2.1 Automatic classification
We used the morphological code LoMorph1, described in detail by
M19, to automatically classify all the resolved sources in each field.
The morphological categories are defined by the same process as in
our earlier work, to which we refer the reader for details. Sources
whose peaks of emission are closer to the centre (optical host posi-
tion) than to the edges were classified as FRIs, and those with the
brightest emission closer to the edges of the source than the host
position as FRIIs. Sources with different classifications on each side
were classified as ‘Hybrids’ and excluded from FRI/II comparisons;
however, we know from M19 that a combination of data resolution
and source projection can produce this appearance, and that true
hybrid sources are very rare (Harwood et al. 2020).
We carried out some minor improvements to the masking of unas-
sociated components in LoMorph to minimise problems caused by
the higher source density present in LoTSS-Deep compared to the
main LoTSS survey (Shimwell et al. 2019) – these changes are de-
scribed in full in the online repository. As for M19, we found that
the best noise threshold compromise was again achieved at 4× the
local RMS noise. The local RMS was estimated directly from the
PyBSDF RMS image, as it provides good noise estimates for these
deeper datasets, even considering the high source density (Tasse et al.
2021; Sabater et al. 2021).
We retained the ‘too faint’ filter in LoMorph requiring that at
least five pixels in each source be above the 4×RMS threshold, and
that its total flux (post-RMS filtering) be above 1 mJy. We visually
inspected samples of ‘too faint’ sources, and verified that they did
not contain enough information to be reliably classified, despite the
increased image depth. In contrast, sources in the ‘unresolved’ class
were sufficiently bright, but with too small an angular size to identify
individual structures.
We also separated the sources into large and small classes, for
which themorphological classifications are expected to have different
reliability, using the resolution criteria of M19, where the small
sources have sizes ≤27 arcseconds, or sizes below 40 arcseconds but
a combined distance between the peaks of emission (d1+d2) smaller
than 20 arcseconds.
The automatic classification results are summarised for the three
fields in Table 2. The results shown in Table 2 are prior to any sam-
ple cleaning, so they include misclassified sources and star-forming
galaxies. Sample cleaning is described in the following Section.
2.2.2 Visual inspection adjustments
As discussed by M19, the (large, > 27 arcsec) FRI and FRII classes
are the cleanest andmost reliable, so we will focus almost exclusively
on these for the remainder of this work. The FRI class in particular
suffers from contamination from sources that fulfil the automatic
classification criteria for FRIs, but are physically distinct from this
population (M19). Given the higher source density and the necessity
for the cleanest possible samples, we visually inspected both the FRIs
and the FRIIs for the three LOFAR deep fields analysed in this work,
using the same criteria described by M19. The results are shown in
Table 3, with a final, securely-classified sample of 161 FRIs and 126
1 https://github.com/bmingo/LoMorph/
MNRAS 000, 1–22 (2021)
4 B. Mingo et al.
Table 2. Automatic classification statistics. See Section 2.2.1 for details and M19 for a full description of the individual classes. The second set of columns
includes only the sources with well-defined redshifts, and 𝑧 ≤ 2.5, used throughout the rest of this work. The last column includes in square brackets the
percentage of sources remaining for each class after the 𝑧 cut. As detailed in the main text, we have focused primarily on the (large) FRI and FRII classes for
the rest of the analysis.
All sources 𝑧 ≤ 2.5
Morphology EN1 Boötes Lockman total EN1 Boötes Lockman total [%]
FRI 109 74 123 306 108 73 120 301 [98%]
FRII 55 48 58 161 49 47 51 147 [91%]
Hybrid 36 30 50 116 36 29 47 112 [97%]
Small FRI 181 134 258 573 174 125 238 537 [94%]
Small FRII 18 25 24 67 17 21 24 62 [93%]
Small hybrid 30 35 24 89 30 31 24 85 [96%]
Unresolved 185 218 338 741 168 206 304 678 [91%]
Too faint 7637 3322 7203 18162 6540 2987 6165 15692 [86%]
Table 3. Visual classification statistics. See the main text and M19 for a detailed description of the individual classes. For each field the columns list, from left
to right, the number of straight (lobed/tailed) FRIs, narrow-angle tails, wide-angle tails, FRIIs, double-doubles, ‘core-dominated’ sources, ‘fuzzy blobs’, hybrid
candidates, star-forming galaxies, and sources with technical issues. The top four rows correspond to the sources automatically classified as FRIs, and the bottom
four to those automatically classified as FRIIs. Numbers in bold show the sources for which the visual inspection matches the automatic classification
Field Straight FRI NAT WAT FRII D-D Core-D Blob Hybrid SF galaxy Bad
EN1 - FRI 27 9 22 9 0 11 15 2 5 8
Boötes - FRI 28 3 10 1 5 16 2 1 3 4
Lockman - FRI 31 6 25 16 3 13 14 2 7 3
Total - FRI 86 18 57 26 8 40 31 5 15 15
EN1 - FRII 0 0 1 39 0 0 0 0 0 9
Boötes - FRII 0 0 0 42 0 0 0 2 0 3
Lockman - FRII 0 1 3 45 1 0 0 1 0 0
Total - FRII 0 1 4 126 1 0 0 3 0 12
FRIIs. Galleries of these two classes can be found in Sections 4.1
and 4.2.
We did not incorporate into our final sample the 26 visually-
identified FRIIs that were automatically classified as FRIs by Lo-
Morph (due to issues with deconvolution, noise, and projection ef-
fects, as described in M19), nor the five visually-identified FRIs
automatically classified as FRIIs. The double automated and visual
test guarantees that we have the cleanest, most unambiguous sam-
ple obtainable with our sample, directly comparable to the results of
M19, and useful for future work on automated classification. The im-
pact of prioritising reliability over completeness is discussed where
relevant in Sections 3, 4, and 5.
As in M19 we subdivided the FRIs into straight (lobed/tailed),
narrow-angle tails (NATs, Rudnick & Owen 1976; O’Dea & Owen
1985), and wide-angle tails (WATs, Owen & Rudnick 1976). Fol-
lowing our M19 definitions, we classified sources with very bright
cores surrounded by a drop and subsequent rise in brightness as
‘core-dominated’, and those with bright cores surrounded by halo-
like extended emission not resembling lobes or tails as ‘fuzzy blobs’.
We classified as ‘Bad’ any sources with technical issues: suspected
bad host IDs, bad region associations (including suspected blends),
or problems with masking or flood-filling. We again kept the double-
double sources (Schoenmakers et al. 2000b; Mahatma et al. 2019),
mostly classified as FRIs by LoMorph (Table 2), separate from the
FRIIs, since they cannot be treated on the same terms as sources
where only one activity cycle is visible. For the same reason, we
tried to avoid including visually-obvious remnants in either of our
clean FRI/II classes.
We also visually inspected the optical images for all sources to
filter out obvious nearby galaxies where the radio emission is arising
from star formation processes, rather than an AGN. These sources
are labelled as ‘SF galaxy’ in Table 3.
The percentage of sources correctly classified by LoMorph (com-
pared to the visual inspection) is slightly worse than that we obtained
for LoTSS, due to not pre-filtering star-forming galaxies and a higher
incidence of deblending complications in the deeper images. ELAIS-
N1, being the deepest field, was particularly problematic in terms of
visually disentangling blends and overlapping sources in the Galaxy
Zoo, as described by Kondapally et al. (2021), but the higher sky
density compared to LoTSS-DR1 also caused some overlaps and
complications in the other two fields, which were not completely re-
solved by the LoMorphmasking modifications. While the small sky
areas involved mean that the three fields are not entirely comparable
due to cosmic variance, it is interesting to note that the classification
statistics are generally better for the two shallower fields compared
to ELAIS-N1. This may point to future challenges with applying
automated classification methods to increasingly deep surveys at this
imaging resolution.
In terms of morphology, it is interesting to see a much higher
fraction of FRIIs compared to M19: FRIIs constitute 44 per cent of
all sources, versus 25 per cent in LoTSS-DR1, although the fraction
changes slightly for each field. This is a direct consequence of having
both deeper radio data and deeper multiwavelength coverage. We
discuss this in more detail in Section 3.1. Within the FRIs we also
found a slightly higher fraction of bent sources (NATs and WATs)
compared to our earlier work (47 vs 37 per cent). This is likely due
to the increased depth of the radio data, making the tails of these
sources easier to find.
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 5
Table 4.Table of SED classifications produced byBest et al. (subm.). The first
two columns refer to the labels output by the fitting and consensus process,
with values 1=True, 0=False, and -1=undetermined (when the classification
could not be determined within the uncertainty margin). The last column
indicates the type of source selected based on combinations of values from
the two labels.
AGN Radio AGN Sources selected
0 0 Star-forming galaxy
1 0 ‘Radio-quiet’ AGN
0 1 LERG (‘Jet-mode’ radio AGN)
1 1 HERG (‘Quasar mode’ radio AGN)
-1 (Any) No secure classification
(Any) -1 No secure classification
2.3 SED classification
Spectral energy distribution (SED) fitting works by fitting a series
of model templates to the broad-band photometry and/or spectra of
astronomical sources (galaxies, in this case). Since the focus of our
work is on radio AGN, our goal in using the SED fitting information
was threefold: i) to identify which of our sources fell into different
accretion regimes (RI/RE – HERG/LERG); ii) to eliminate contami-
nants from our sample (star-forming galaxies with no radio jets); iii)
to constrain the stellar masses and star formation rates in the host
galaxies of our radio sources.
The three LOFAR Deep Fields datasets covered in this work have
deep multiwavelength coverage, spanning the far-IR (Herschel; Pil-
bratt et al. 2010), mid-IR (Spitzer, WISE Fazio et al. 2004; Mainzer
et al. 2011; Cutri 2014), near-IR (including UKIDSS – UK Infrared
Telescope Deep Sky Survey – extragalactic survey for Lockman Hole
and ELAIS-N1; Lawrence et al. 2007), optical (Pan-STARRS, Sub-
aru HSC-SSP – Hyper Suprime-Cam Subaru Strategic Program –
and NDWFS – NOAO Deep Wide-Field Survey; Kaiser et al. 2010;
Aihara et al. 2018; Jannuzi & Dey 1999), and UV (GALEX;Morris-
sey et al. 2007). Herschel observations were deblended using XID+
(Hurley et al. 2017), to avoid issues with contamination caused by
companion late-type galaxies blendingwith the point-spread function
(PSF) of early-type radio galaxy hosts (Drouart et al. 2014; Falkendal
et al. 2019). Full details on these data and how they were combined
into the value-added catalogues are given by Kondapally et al. (2021)
andMcCheyne et al. (subm.). Photometric redshifts were determined
by Duncan et al. (2021), following a similar procedure to that used
for LoTSS (Duncan et al. 2019), and fed into the SED fitting codes
(along with spectroscopic redshifts where available).
The SED-fitting procedure and resulting classifications are de-
scribed in detail by Best et al. (subm.). The broadband spectra were
individually fitted with each of magphys (Multi-wavelength Anal-
ysis of Galaxy Physical Properties, da Cunha et al. 2008), cigale
(Code Investigating Galaxy Evolution, Burgarella et al. 2005; Yang
et al. 2020), agnfitter (Calistro Rivera et al. 2016), and bagpipes
(Bayesian Analysis of Galaxies for Physical Inference, Carnall et al.
2018, 2019). The information from the different fits was then used
to determine whether the source was classified as an (optical) AGN
(understood as radiatively-efficient sources, with detectable AGN
emission in the FIR through X-rays) or not. Both cigale and agnfit-
ter include AGN templates, which allowed Best et al. to calculate an
‘AGN fraction’ with each code per source, and estimate the relative
goodness of fit of these compared with magphys and bagpipes.
If the source was not classified as an AGN, then magphys and
bagpipes generally provided consistent star formation rates (SFRs)
to +/- 0.2 dex. cigale generally provided values which were system-
atically the same, but with larger scatter. This might be due to the
less complete range of galaxy SEDs included. agnfitter performed
slightly worse, presenting a systematic offset and more scatter. In the
vast majority of these cases (see Best et al. for details of the excep-
tions), Best et al. adopted the logarithmic median of the bagpipes
and magphys values as the consensus measurement, to maximise
accuracy.
If the source was classified as an AGN, the SFRs derived by
cigale were used instead. These were mostly consistent with the
bagpipes/magphys values, except for a subset of sources which had
significant AGN contribution in the optical or the far-IR, where the
lack of AGN templates in bagpipes/magphys caused inconsistencies,
making the cigale values the safest option. Again, agnfitter tended
to have greater scatter (though less so than for the non-AGN sources).
This approach established a consensus estimate of physical galaxy
properties, such as star formation rate and stellar mass, for every
source. Best et al. also determined which sources had a radio excess
over the emission expected purely from star formation (see e.g. Read
et al. 2018; Smith et al. 2021) at a ∼ 3𝜎 level (0.7 dex), similarly to
Hardcastle et al. (2019).
Sources were then assigned a series of labels depending on the
result of the SED classification, as detailed in Table 4. It is worth
noting that the RI/RE (LERG/HERG) classifications derived from
this SED-fitting approach are primarily based on the overall contin-
uum shape (although contributions of emission lines within given
filters are included in the SED models), and are therefore not strictly
comparable to classifications based purely on line intensity ratios and
line breaks, such as those used by Best & Heckman (2012). However,
for a small proportion of the sources (5.1, 21.1, and 4.7 per cent of all
sources in, respectively, the ELAIS-N1, Boötes and Lockman Hole
fields) spectroscopic information is also available and folded in to
the classifications.
As the SED classifications are most reliable below 𝑧 ∼ 2.5 (Best
et al., subm.) we restricted all our analysis to this redshift range. As
shown in Table 2, this had minimal impact on the sample size.
3 RESULTS
3.1 Redshift, luminosity and size
Fig. 1 shows the 𝑧 distribution of FRIs and FRIIs for the three com-
bined LOFAR deep fields in comparisonwith the LoTSS-DR1 results
presented byM19. Asmentioned in Section 2.2.2 it is striking that the
FRI/II proportions are substantially different from what was found in
the LoTSS-DR1 morphological analysis. However, the distribution
of sources up to 𝑧 ∼ 0.8 is not substantially different from that of
the LoTSS-DR1 FRIs and FRIIs, which is reassuring. It is mostly the
deeper multiwavelength data that enable host identification to higher
redshift in our LoTSS-Deep sample. LoTSS-Deep has host identifi-
cations for > 97 per cent of sources (Kondapally et al. 2021), a much
larger fraction than for LoTSS-DR1 (73 per cent; see Williams et al.
2019). The differences in redshift distribution between the FRIs and
FRIIs are also more apparent, with the latter peaking at higher 𝑧 val-
ues. Aswe pointed out in our earlier work (M19) this is a combination
of selection and evolution (see also e.g. Willott et al. 2001; Wang &
Kaiser 2008; Donoso et al. 2009; Gendre et al. 2010; Kapińska et al.
2012; Williams & Röttgering 2015; Williams et al. 2018).
Fig. 1 also shows the overall redshift distribution of the LERGs and
HERGs in our sample. LERGs are the dominant population, as also
discussed by Kondapally et al. (subm.), and overall HERGs tend to
be found at slightly higher redshifts (the median values are 𝑧 = 0.602
MNRAS 000, 1–22 (2021)
6 B. Mingo et al.
0
200
n
DR1 FRI
DR1 FRII
0
20
n
DF FRI
DF FRII
0.001 0.01 0.1 1 10
z
0
20
40
n
DF LERGs
DF HERGs
Figure 1. Top two panels: redshift distribution comparison between the three
combined deep fields and the LoTSS-DR1 sources from M19. Following the
same colour convention, the histograms for the FRIs are in yellow and those
for the FRIIs in blue. Bottom panel: redshift distribution for the LERG
(orange) and HERG (purple) sources in LoTSS-Deep. Note that our LoTSS-
Deep sample is limited to 𝑧 ≤ 2.5, as mentioned in Section 2.3. See Section
3.2 for detailed statistics.
and 𝑧 = 0.922 for LERGs and HERGs, respectively, though there is
large scatter).We note that the absence of low-𝑧HERGs in our sample
is likely due to the small sky area covered (LERGs are the dominant
population at low-𝑧). A detailed breakdown of the accretion statistics
for each morphological class is presented in Section 3.2, and the
implications are discussed in detail in Section 4.
Fig. 2 illustrates the distribution of redshifts across the three fields.
Given the different noise levels in the three fields (Table 1), this plot
helps us understand how different flux density and surface bright-
ness limits preferentially include or exclude different populations.
The fact that the highest overlap between the FRI and FRII distri-
butions happens in the deepest field (EN1), and the lowest overlap
in the shallowest (Boötes) hints at the fact that FRIs at high red-
shift do exist, and in large numbers, but they are the first victims
of surface brightness limits. However, we are working with small
number statistics, and there might be small differences in terms of
host identification due to the different optical catalogues used in the
three fields (see Kondapally et al. 2021, for details). There are some
low-significance differences in the 𝑧 distributions of FRIs and FRIIs
for the three fields, likely caused by a combination of effects (bin-
ning, underlying differences in themultiwavelength catalogues, radio
survey depth, etc.), but these have no impact on our conclusions. We
checked whether our main results showed any differences across the
three fields and found no evidence of discrepancies at a statistically
significant level. As such, all the analysis and results described in the
following Sections were carried out using the combined dataset.
The luminosity and size distribution of our LoTSS-Deep sources is
very similar to that of the morphological catalogue for LoTSS-DR1
(Fig. 3). The median sizes for the FRI and FRII in our LoTSS-Deep
sample are 380 ± 20 and 540 ± 50 kpc, respectively, and the median
150-MHz luminosities 1.5 ± 1.7 × 1025 and 1.2 ± 2.7 × 1026 W
Hz−1. The distributions are roughly consistent with the M19 DR1
sample – in both samples the dominant population is large, mature
systems (partly due to our angular size cut), though we note that in
our LoTSS-Deep sample we find no FRII sources with sizes below
100 kpc. These sources are quite rare, and the combination of angular
0
10
20
n
EN1 FRI
EN1 FRII
0
5
10
n
Bootes FRI
Bootes FRII
0.001 0.01 0.1 1 10
z
0
10
20
n
Lockman FRI
Lockman FRII
Figure 2. Redshift distribution histograms for the three deep fields. Colours
as in Fig. 1.
size and surface brightness limits only allows us to identify them at
low 𝑧. Our DR1 sample covered a much larger sky area, making it
easier to catch some examples of rarer populations.
We have again drawn a horizontal line to represent the canonical
FR luminosity break at L150 ∼ 1026 W Hz−1 (Fanaroff & Riley
1974; Ledlow & Owen 1996). The number of FRIs above this line
is rather small: 19 across the three fields, representing a fraction
of 13 per cent, identical to that found for LoTSS-DR1. We again
found a large number of sources with FRII morphology below the
canonical line, which we will refer to as FRII-Low throughout this
work, after M19 (similarly, we will refer to the luminous FRIIs above
the line as FRII-High). There are 50 FRII-Low across the three deep
fields (respectively 21, 14, and 15 in EN1, Boötes, and Lockman),
representing a fraction of ∼ 40 ± 9 per cent, lower than that found
in LoTSS-DR1 (∼ 51 per cent), but consistent within the errors.
Notably, we only found 9 of these sources at very low luminosities
(L150 < 1025 W Hz−1), or 7 per cent of all FRIIs, which is a
much smaller fraction than that found in LoTSS-DR1 (21 per cent),
though still consistent within the errors. These differences are likely
driven by our ability to detect more distant, high luminosity FRIIs in
LoTSS-Deep, thanks to the lower flux density limit and better host
identification, thus lowering the overall fraction of low-luminosity
sources. The properties of our FRII-Low are discussed in detail in
Section 4.2.1.
In terms of size distributionwe also found 21 sources (19 are FRIIs,
of which 5 are FRII-Low) with sizes greater than 1 Mpc, and thus
giant radio galaxies (GRG, see e.g. Ishwara-Chandra & Saikia 1999;
Schoenmakers et al. 2000a; Machalski et al. 2001, 2008; Dabhade
et al. 2017, 2020a,b, and references therein), a larger proportion than
we found in our earlier work, indicating that this population greatly
benefits from the increased survey depth.
As in our LoTSS-DR1 work, it is crucial to emphasise that the ap-
parent correlation between size and luminosity in our sample is pri-
marily driven by selection effects (see also the discussion by Turner
& Shabala 2015; Hardcastle et al. 2019). The top-left corner of both
plots in Fig. 3 is empty because our angular size cut at 27 arcsec
eliminates the small, bright sources. The bottom-right corner of both
plots could contain physically large yet faint sources, falling below
the surface brightness limits of either sample. Similarly to M19, this
makes our sample selection quite different from that of several older
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 7
101 102 103 104
Size (kpc)
1021
1022
1023
1024
1025
1026
1027
1028
1029
L
15
0
(W
H
z−
1
)
DF FRI
DF FRII
101 102 103 104
Size (kpc)
DR1 FRI
DR1 FRII
Figure 3. Luminosity vs size distribution of the FRIs (yellow circles) and FRIIs (blue squares) for the combined deep fields (left) and the LoTSS-DR1 sources
from M19 (right). The dashed line shows the ‘traditional’ boundary between FRIs and FRIIs, at 𝐿150 ∼ 1026 WHz−1 (Fanaroff & Riley 1974; Ledlow & Owen
1996).
surveys, which contain more compact, luminous sources – we return
to the implications of this point in later sections.
3.2 Accretion mode vs radio morphology
As introduced in Section 2.3 we used the SED classifications from
Best et al. (subm.) to classify our sources according to their accretion
properties (see Table 4). In Table 5 we summarise the SED-based
AGN/SF classifications for each of our morphological classes. We
removed the FRI source with an ‘undefined’ SED classification from
the sample for any further analysis, leaving the final number of FRIs
at 160. Other than for this source, our results show that the sample
cleaning via visual inspection (Section 2.2.2) was highly consistent
with the SED-fitting filters, and that the automatic classification for
the large FRI and FRII categories is robust even in the absence of
any pre-selection.
In terms of accretion mode we found the overwhelming majority
(95 per cent) of FRI sources to be LERGs, a percentage that is in
broad agreement with those found in earlier surveys (e.g. Best &
Heckman 2012; Best et al. 2014; Capetti et al. 2017a), and with
no significant variations in the fractions of straight or bent sources.
Of the 7 FRI HERGs at least 4 have been confirmed as such by
optical, X-ray, and/or IR surveys, including two sources identified as
broadline QSOs in the Sloan Digital Sky Survey as of data release
16 (SDSS DR16, Blanton et al. 2017; Ahumada et al. 2020).
The FRII-Low are also mostly LERGs (91 per cent). This is con-
sistent with recent results by Miraghaei & Best (2017) and Grandi
et al. (2021), who show that at low luminosities and fluxes FRIIs are
predominantly LERGs (80 and 89 per cent, respectively at 𝑧 < 0.1
and 𝑧 < 0.15). Most importantly, our sample covers extra parameter
space in terms of luminosity and redshift compared to FIRST (Becker
et al. 1995; Helfand et al. 2015), as LoTSS-Deep is ∼ 50 − 70 times
more sensitive.
Surprisingly, we found that nearly two thirds (65 per cent) of
the FRII-High are LERGs. This is not consistent with the fractions
derived from older surveys, though the LOFAR work of Williams
et al. (2018) already showed a larger fraction of LERGs at high radio
luminosities compared to existing surveys. We discuss these results
and their implications in more depth in Section 4.
The core-dominated sources and fuzzy blobs are also LERG-
dominated, though the percentage of HERGs in these populations (25
and 19.5 per cent, respectively) is only slightly smaller than that of
the FRII-High. It is thus unlikely that these classes are predominantly
populated by QSOs, though a fraction of them are clearly luminous
AGN at other wavelengths. As suggested in M19 they are likely to
contain a mixture of populations, including restarting sources.
Among the other morphological classes obtained with LoMorph,
only the (large - as defined in Section 2.2.1) hybrids contain a no-
ticeable fraction of HERGs (12 per cent), consistent with the likely
scenario that at least some of them might be FRII sources in projec-
tion (Harwood et al. 2020).
3.3 Mid-IR diagnostics
We investigated how the accretion and morphology classifications
relate to AGN sample selections at other wavelengths, so as to un-
derstand how reliably LOFAR sources can be classified when the
sophisticated accretion mode classification method employed here is
unfeasible.
Mid-infrared colour-colour plots are commonly used as a diagnos-
tic tool for radiatively efficient AGN activity and sample selections
(see e.g. Mateos et al. 2012; Stern et al. 2012; Donley et al. 2012;
Assef et al. 2013; Secrest et al. 2015; Chhetri et al. 2020), though
their usefulness for selecting AGN is limited to very bright radia-
tively efficient sources (quasars and bright Seyfert galaxies), where
the torus component dominates, and often fainter AGN are missed
(see e.g. Rovilos et al. 2014; Gürkan et al. 2014, 2018, 2019; Mingo
et al. 2016; Retana-Montenegro & Röttgering 2020). Given that all
three fields have Spitzer IRAC (Werner et al. 2004; Fazio et al. 2004)
and WISE (Wright et al. 2010; Mainzer et al. 2011) coverage, these
are the most useful diagnostics to test with our sample (see also the
discussion by Best et al., subm.).
Fig. 4 shows the Spitzer IRAC colour-colour distribution of our
sources, including theAGN selectionwedge of Stern et al. (2005) and
Assef et al. (2013), which is less restrictive than that of Donley et al.
(2012). Nearly all sources (> 97 per cent) were detected by IRAC.
The selection wedge encompasses 61 per cent of the HERGs, but 47
per cent of the sources inside it are LERGs. This is not unexpected,
as both Assef et al. (2013) and Donley et al. (2012) highlight the
likely presence of contaminants in this area for data covering a large
redshift range.
Wematched our sample in topcat (Taylor 2005)with theAllWISE
catalogue ofCutri (2014), using a 2 arcsecmatching radius (following
the SDSS DR12 matching criteria of Pâris et al. 2017, which should
yield a false positive rate of just 2 per cent) around the optical host
positions for each source. The detection fraction was lower forWISE
than IRAC (93/75/82 per cent of FRI/FRII-High/FRII-Low), and
particularly bad for the FRII-High LERGs, of which only 64 per cent
MNRAS 000, 1–22 (2021)
8 B. Mingo et al.
Table 5. SED-fitting classifications by morphological class (see Section 2.2.1 and Table 2 for details on the individual classes). The second column (n) shows the
number of sources in each category; the other columns show the number [percentage] of sources in each category. The first set of rows shows the classification
results for the source classes that are the main focus of this work (FRII-High are FRIIs with 𝐿150 > 1 × 1026 WHz−1; FRII-Low are those below the boundary,
as introduced by M19). Our morphological classifications are summarised in Tables 2 and 3, and the SED labels (Best et al., subm.) can be found in Table 4.
Note that the FRI source with ‘undefined’ classification was excluded from the rest of our analysis.
Morphology n SF HERG LERG RQ AGN undefined
Straight FRI 86 0 [0%] 5 [6%] 81 [94%] 0 [0%] 0 [0%]
NAT 18 0 [0%] 1 [5.5%] 16 [89%] 0 [0%] 1 [5.5%]
WAT 57 0 [0%] 1 [2%] 56 [98%] 0 [0%] 0 [0%]
Total FRI 161 0 [0%] 7 [4%] 153 [95%] 0 [0%] 1 [<1%]
FRII-High 69 0 [0%] 24 [35%] 45 [65%] 0 [0%] 0 [0%]
FRII-Low 57 0 [0%] 5 [9%] 52 [91%] 0 [0%] 0 [0%]
Total FRII 126 0 [0%] 29 [23%] 97 [77%] 0 [0%] 0 [0%]
Core-D 40 0 [0%] 10 [25%] 30 [75%] 0 [0%] 0 [0%]
Blobs 31 1 [3%] 6 [19.5%] 24 [77.5%] 0 [0%] 0 [0%]
Hybrid 111 3 [3%] 13 [12%] 95 [85%] 0 [0%] 0 [0%]
Small FRI 536 52 [10%] 54 [10%] 407 [76%] 4 [1%] 19 [3%]
Small FRII 62 3 [5%] 6 [9.5%] 52 [84%] 0 [0%] 1 [1.5%]
Small Hybrid 85 5 [6%] 7 [8%] 71 [84%] 0 [0%] 2 [2%]
Unresolved 677 249 [37%] 35 [5%] 326 [48%] 17 [3%] 50 [7%]
Too faint 15688 11663 [74%] 211 [1.5%] 2809 [18%] 813 [5%] 192 [1.5%]
−0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
[5.8]-[8.0] (Vega)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
[3
.6
]-
[4
.5
]
(V
eg
a)
FRI
FRII-H
FRII-L
HERGs
Figure 4. Spitzer IRAC colour-colour diagnostic plot for the FRI (yellow
circles), FRII-High (blue squares), and FRII-Low (red triangles) in the com-
bined LOFAR deep fields, including the AGN selection wedge from Stern
et al. (2005) andAssef et al. (2013). HERGs are outlinedwith black diamonds,
all the other sources are LERGs.
hadWISE counterparts. Fig. 5 illustrates these results. We have used
the rough divisions of Mingo et al. (2016) and Mingo et al. (2019),
based on the synthetic SED results of Wright et al. (2010) and Lake
et al. (2012), to categorise the sources. The ‘AGN’ box encompasses
44 per cent of the WISE-detected HERGs, while 53 per cent of the
sources in the box are LERGs. A wedge like that of Mateos et al.
(2012) would still only contain ∼ 65 per cent of the HERGs.
These results confirm the fact (e.g. Gürkan et al. 2014;Mingo et al.
2016) that neither diagnostic plot offers a reliable accretion mode
diagnostic or selection tool for radio AGN, and both – but especially
WISE, being shallower – must be used with caution, particularly for
samples with a wide redshift range.
Luminous FRII LERGs, which constitute nearly 2/3 of all our
FRII-High and are not being picked up by shallower radio surveys,
0 1 2 3 4 5
W2-W3
−0.50
−0.25
0.00
0.25
0.50
0.75
1.00
1.25
1.50
W
1-
W
2
Ell SF ULIRG
AGN ULIRG/AGN
FRI
FRII-High
FRII-Low
HERGs
Figure 5.WISE colour-colour diagnostic plot. Symbols and colours as in fig.
4. Rough class divisions as in Mingo et al. (2016, 2019). W3 upper limits are
represented with grey arrows.
are not well detected by WISE (and likely other surveys selecting
AGN at wavelengths other than radio). We discuss the implications
of this result in more detail in Section 4.
4 IS THERE A LINK BETWEEN ACCRETION MODE AND
FR CLASS?
Section 3.2 and Table 5 show that our samples are dominated by
LERGs across all populations, though in different proportions de-
pending on luminosity. The LERG fraction for different luminosity
and FR class subsets of our sample is shown in Table 6. These re-
sults strongly argue that radio morphology on 100-kpc scales is not
directly controlled by either accretion mode, given the considerable
fraction of FRII LERGs, or by jet power, given the presence of the
FRII-Low.
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 9
Table 6. Breakdown of LERG/HERG fractions and comparison with other samples. Agresti-Coull confidence intervals (last column) were calculated at the
2-sigma level, and shown here multiplied by 100 to compare with the percentages for each sample. The first group of rows shows the results for all the sources
in our sample, across the combined Deep Fields and various luminosity intervals. The second set of rows shows the results broken down by morphology. The
third group shows the results from the 𝑧 < 1 FRIIs in the 3CRR sample (Laing et al. 1983; Hardcastle et al. 2007, 2009; Mingo et al. 2014). The last group of
rows shows a comparison with the results of Williams et al. (2018), with three subsets of our sources matched in terms of luminosity and redshift distribution.
Sample Total LERG HERG % LERG AC error
All DF FRI/FRII 286 250 36 87.4 3.8
All DF, 𝐿150 > 1026 88 62 26 72.6 9.3
All DF, 𝐿150 ≤ 1026 198 188 10 95.0 2.8
All DF, 1025 < 𝐿150 ≤ 1026 117 108 9 92.3 4.3
All DF, 𝐿150 ≤ 1025 81 80 1 98.8 (-)
All FRI 160 153 7 95.6 2.7
All FRII 126 96 29 76.2 3.7
FRII-High 69 45 24 65.2 10.9
FRII-Low 57 52 5 91.2 5.8
All 3CRR FRII (𝑧 ≤ 1) 136 18 118 13.2 6.0
3CRR FRII (𝑧 ≤ 1), 𝐿178 ≤ 2 × 1028 62 13 49 21.0 10.5
Boötes sample from Williams et al. (2018) 47 24 23 51.1 13.9
All DF, broad cut (𝐿150 > 5.65 × 1025, 𝑧 ≤ 2) 111 82 29 73.9 8.0
DF FRII, matched 78 52 26 66.7 10.2
All DF, strict cut (𝐿150 > 3.16 × 1026, 0.5 < 𝑧 ≤ 2) 42 26 16 61.9 13.9
101 102 103 104
Size (kpc)
1021
1022
1023
1024
1025
1026
1027
1028
1029
L
1
50
(W
H
z−
1
)
FRI
FRII-H
FRII-L
HERGs
Figure 6. Luminosity versus size distribution of FRI, FRII-Low, and FRII-
High, including accretionmode. Colours and symbols as in Fig. 4. The dashed
line shows the ‘traditional’ boundary between FRI and FRII, at 𝐿150 ∼ 1026
W Hz−1 (Fanaroff & Riley 1974; Ledlow & Owen 1996).
We do find some dependence of accretion mode on luminosity
(see Table 6), but as we found in M19, this is not necessarily linked
to morphology. Fig. 6 shows the luminosity vs size distribution of
FRI, FRII-Low, and FRII-High, with black diamonds surrounding
all sources classified as HERGs; this clearly shows that LERGs are
the dominant population at low radio luminosities, regardless of
morphology. HERGs are most likely to be found above the traditional
FRI/II boundary, though even in this area of the parameter space they
are a minority. As mentioned in Section 3.1, we note that the bottom-
right corner of the parameter space is inaccessible due to surface-
brightness limits (see e.g. Hardcastle et al. 2019), so we might be
missing further FRII-Low at large sizes.
It is clear that the HERG/LERG ratio in our sample is very differ-
ent compared to older surveys (e.g. 3CRR, 6C, 7C, 2Jy, see Laing
et al. 1983; Mingo et al. 2014; Fernandes et al. 2015, and references
therein), particularly at higher luminosities (LERGs have long been
known to dominate at low luminosities out to at least 𝑧 ∼ 1, e.g.
Best et al. 2014; Pracy et al. 2016). This is illustrated in the left-
hand panel of Fig. 7, which shows the luminosity dependence of the
HERG/LERG fraction for our sample and all the sources in 3CRR,
regardless of their FR classification. While at lower luminosities
(FRI-dominated in 3CRR) the HERG/LERG ratios appear compat-
ible, the high fraction of LERGs among the FRII-High sources in
particular is different to that found for earlier surveys, as is clearly
shown in the right-hand panel of Fig. 7. It is likely that our lower
flux density limit drives the discrepancy with older, shallower sur-
veys. For instance, as shown in Table 6, the LERG fraction for 3CRR
FRIIs is only 13 per cent. However, the LOFAR Boötes results of
Williams et al. (2018) already showed a trend in the direction that we
see in our work. Splitting our sample in several ways to match that
of Williams et al. (2018), as illustrated in the last section of Table
6, yields HERG/LERG percentages that are different for both sam-
ples, but consistent within the errors. Kondapally et al. (subm.) note
that the small discrepancy between our Boötes results and those of
Williams et al. (2018) is likely due to a combination of the lower flux
density limit and the differences in the SED classification method.
It is very unlikely that these very bright radio sources could be
associated with radiatively-efficient but optically-faint AGN, mis-
classified as LERGs via the SED fitting process. Even considering
the large scatter, and the caveats on how jet power translates into
radio luminosity (e.g. Hardcastle & Krause 2013), there is a broad
correlation between the radiative and kinetic (jet) output of RE AGN
(see e.g. Mingo et al. 2016). If the FRII-High LERGs were RE
they would be outliers in this correlation, and thus still intrinsically
different, in terms of their underlying physics, from the luminous,
radiatively efficient FRIIs observed in older surveys.
In the two subsections that followwe consider the radio morpholo-
gies of the FRI and FRII subsamples and their relation to accretion
mode, including exploring the reasons for differences with shallower
surveys, inmore detail. Section 5 then builds on these results to incor-
porate host-galaxy parameters (stellar mass and star formation rate
MNRAS 000, 1–22 (2021)
10 B. Mingo et al.
0
50
100
%
n=5 n=6 n=33 n=36 n=85 n=32 n=46 n=14 n=21 n=5 n=2
n=0 n=0 n=0
1023 1024 1025 1026 1027 1028 1029 1030
L150 (W Hz
−1)
0
50
100
%
n=1
n=0 n=0
n=1 n=13 n=4 n=15 n=8 n=23 n=12 n=40 n=20 n=33 n=1
all DF LERGs
all DF HERGs
all 3CRR LERGs
all 3CRR HERGs
0
50
100
%
n=0
n=2 n=5 n=3 n=30 n=17 n=29 n=14 n=19 n=5 n=2
n=0 n=0 n=0
1023 1024 1025 1026 1027 1028 1029 1030
L150 (W Hz
−1)
0
50
100
%
n=0 n=0 n=0 n=0 n=0
n=1 n=11 n=6 n=22 n=12 n=35 n=17 n=30 n=1
DF FRII LERGs
DF FRII HERGs
3CRR FRII LERGs
3CRR FRII HERGs
Figure 7. Left: stacked histograms showing the percentile fraction of LERGs and HERGs in our LoTSS-Deep dataset (top) and the 3CRR sample (bottom) as a
function of luminosity, regardless of their FR classification. Right: same plot, restricted to FRII sources in both samples. In all panels LERGs are shown in shades
of orange and HERGs in shades of purple. 3CRR Data obtained from M. Hardcastle’s updated version of the Laing et al. (1983) 3CRR catalogue (available at
https://3crr.extragalactic.info/). 3C 343.1 was excluded from the sample (Arp et al. 2004).
obtained from SED fitting) into our understanding of what controls
radio morphology and accretion mode.
4.1 FRI
As introduced in previous sections, our FRI sources are mostly con-
sistent with results from previous surveys in terms of their size and
luminosity distribution (Fig. 3). In addition, their hosts are predomi-
nantly found in the red elliptical locus in mid-IR colour-colour plots
(Figs. 4 and 5), and they are overwhelmingly LERGs (Figs. 6 and 8,
Tables 5 and 6).
The deeper radio and optical data in LoTSS-Deep have allowed
us to identify FRI sources at higher 𝑧, particularly in the ELAIS-N1
field (Fig. 2), but due to the smaller sky area limiting our sample
size it is not possible to investigate any trends in terms of evolution
of their accretion properties. This will become clearer as LOFAR
samples grow, and with future surveys carried out with MeerKAT
and the SKA.
As mentioned in Section 3.2, our results are consistent with the
established picture in which FRI HERGs are very rare (e.g. Best &
Heckman 2012; Best et al. 2014; Miraghaei & Best 2017; Capetti
et al. 2017a; Gurkan et al., subm.). Given the very small number
of FRI HERGs in our sample (7 sources, all shown in Fig. 9) it is
not possible to draw firm conclusions as to what distinguishes these
sources from the more numerous FRI LERGs shown in Fig. 8, other
than the fact the core seems to be the brightest structure in all but one
of them, and that none of them seem to be WATs or NATs (although
most do present some bends and twists). Table 6 shows that the
fraction of FRI HERGs must be below 8 per cent. The key as to why
these FRIs are HERGs likely lies in the combination of their host
masses and gas availability, as we will discuss in Section 5.2.
4.2 FRII
A key question this work was designed to address is whether mor-
phology and accretionmode have any relation for the FRII population
in particular, where both accretionmodes have long been known. Fig.
10 demonstrates that it is not possible to visually identify whether an
FRII drawn at random from our sample is a LERG or a HERG, and
whether it is in the ‘High’ or ‘Low’ luminosity category. Morpho-
logically, our FRIIs are quite uniform across the board.
Conversely, our LoTSS-Deep results do show a difference in the
accretion properties of FRII-Low and FRII-High, with the latter
having a much higher HERG fraction than the former (see Table 5).
Table 6 shows that the HERG/LERG statistics for both populations
do not overlap accounting for uncertainties. Fig 6 suggests there is
no obvious size trend in HERG fraction, but Fig. 7 does suggest a
trend with luminosity, although it is unclear whether this continues
to the highest luminosities. There appears to be a gradual transition
in accretion properties between the FRII-Low and FRII-High, rather
than a sharp dichotomy. It is thus not possible to unequivocally
determine whether an FRII is an FRII-High or -Low based on either
its accretion properties or its morphology.
In the following subsections we examine the morphology and
accretion behaviour of the two FRII subclasses, including examining
source morphology quantitatively using the source dynamic range
(SDR) – defined as the peak pixel flux in a source (smoothed over
a 4-pixel area, as described by M19) divided by the mean pixel flux
(after the 4 RMS cut) – as a proxy for radio structure. Sources with
high SDR show a large flux difference between the brightest and
faintest structures, while those with low SDR have very uniform
surface brightness.
4.2.1 Low-luminosity FRII (FRII-Low)
While Fig. 10 shows that it is not possible to visually distinguish FRII-
High and FRII-Low, in our sample the FRII-Low do have lower mean
SDRs than the FRII-High at a level that is statistically significant
(3.9 ± 0.6 and 6.3 ± 0.7, respectively, with 2𝜎 errors). This is not
surprising as the population on average will have lower total flux, so
that the ratio between the peak flux density and the survey RMS noise
is likely to be systematically lower. A fraction of the low SDR FRII-
Lows seem to have more relaxed (‘fluffier’) structures, with irregular
lobe edges, not something we clearly observed for the FRII-Low in
LoTSS-DR1. This could indicate that with the lower flux density
limit we might be picking up more fading sources in LoTSS-Deep.
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 11
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Figure 8. Gallery of FRI LERGs, showing two of each FRI class, in order: lobed, tailed, NAT and WAT. All images have been drawn at random within their
respective classes, so as to be representative of the overall subsets. The plots are produced as output by LoMorph based on the radio maps filtered at the
desired RMS level. The symbols indicate the positions of the optical host (red X), the first and second-brightest peak of emission beyond the core (d1 and d2 –
inverted and non-inverted cyan Y, respectively), and the maximum extent of the source in both directions (D1 and D2 – up and down pointing orange triangles,
respectively for the directions to the brightest and second-brightest peak). The scale is in pixel coordinates, with a scale of 1.5 arcsec per pixel. See M19 for
more details.
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8113, ILTJ162112.94+543910.5, d1=8.5, d2=13.5, maxd1=14.2, maxd2=16.3, FR=1, dRange=5.6, Size=47.3
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Figure 9. Gallery of all the FRI HERGs. Symbols and colours as in Fig. 8. The scale is in pixel coordinates, with a scale of 1.5 arcsec per pixel.
A spectral index analysis would be necessary to establish the frac-
tion of fading sources in our sample and will be carried out in a future
analysis. We argued in M19 based on spectral index and population
demographics that the majority of FRII-Low are unlikely to be fading
sources – given that sources fade quickly it is implausible that such
a large fraction of the total FRII population would be in this stage of
evolution. The fact that nearly all FRII-Low are LERGs reinforces
the hypothesis that these sources are likely to be more similar to
their FRI counterparts in terms of accretion properties than to the
powerful, high-accretion-rate, ‘traditional’ FRIIs from the 2Jy and
3CRR surveys, as originally noted by Hardcastle & Looney (2008)
and recently highlighted by Grandi et al. (2021). Our results also
show that these radiatively-inefficient, low-luminosity FRIIs can be
found well beyond the local Universe, with 44 per cent of our FRII-
Low at 0.5 < 𝑧 ≤ 1, and 18 per cent at 𝑧 > 1. This, as well as the
presence of FRII-Low across a wide range of source sizes, seems to
contradict the idea proposed by Grandi et al. (2021) that the FRII-
Low are a late-stage evolution of luminous, high-accretion FRIIs and
a phenomenon of the local Universe.
As mentioned in Section 3.1 we have found a slightly higher frac-
tion of FRII-Low giant radio galaxies in our LoTSS-Deep sample
compared to LoTSS-DR1. With the low statistics it is impossible to
tell if this indicates any redshift evolution, but further studies using
future larger LoTSS datasets of the FRII-Low giants in particular will
MNRAS 000, 1–22 (2021)
12 B. Mingo et al.
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1577, ILTJ143012.27+351922.5, d1=53.3, d2=60.0, maxd1=83.0, maxd2=76.8, FR=2, dRange=4.1, Size=242.1
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2107, ILTJ143152. 6+323215.5, d1=7.6, d2=14.1, maxd1=11.7, maxd2=19.2, FR=2, dRange=5.9, Size=48.8
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7981, ILTJ110114.88+572408.9, d1=67.2, d2=66.5, maxd1=86.3, maxd2=72.2, FR=2, dRange=2.0, Size=239.7
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451496, ILTJ142953.29+330536.4, d1=19.8, d2=12.8, maxd1=24.8, maxd2=17.2, FR=2, dRange=2.5, Size=64.8
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Figure 10. Gallery of randomly-selected (within each subset) FRII sources. Rows 1-2 show FRII-Low LERGs, row 3 FRII-Low HERGs, rows 4-5 FRII-High
LERGs, and rows 6-7 FRII-High HERGs. Symbols and colours as in Fig. 8. Scale in pixel coordinates, with 1.5 arcsec per pixel.
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 13
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
dynamic range
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Figure 11. Source dynamic range and size comparison for the FRII-High
HERG (cyan bars) and LERG (magenta outlines) subsets.
be key to understanding the impact, and evolution of the FRII-Low
population.
It is clear that accretion mode or jet power on their own do not
determine whether a low-power, low-accretion-rate source becomes
an FRI or an FRII. We will address whether the host properties can
help us find the answer to this question in Section 5. Given the lack of
radiative signatures at other wavelengths and their low radio fluxes,
meaning that before LOFAR the FRII-Low were only identified in
small numbers, their contribution to cosmic evolution might have
been underestimated. The importance of their role in galaxy and
cluster evolution will be greatly dependent on the duration of their
life cycles, which we will be able to constrain with lower frequency,
LOFARLBAobservations in the near future (deGasperin et al. 2021;
Williams et al., subm.).
4.2.2 Luminous FRII (FRII-High)
The FRII-High are particularly key to understanding the relation-
ship between accretion mode and morphology. We found no visually
identifiable difference between LERGs and HERGs among the lu-
minous FRIIs (Fig. 10); however, our data also allow us to examine
this question quantitatively by comparing the source dynamic range
(SDR) for the FRII-High HERGs and LERGs (Fig 11). Based on a
Kolmogorov-Smirnov (KS) test, the hypothesis that the two SDR dis-
tributions are drawn from the same parent population cannot be ruled
out at the 95 per cent confidence level (Table 7)– this confirms our
visual impression that across our large sample of 69 high-luminosity
FRIIs, the accretion mode does not significantly affect the large-scale
radio morphology.
The resolution of our LOFAR images is not sufficient to com-
pare the inner jet morphology or the compactness of hotspots for the
FRII-High HERGs and LERGs. We therefore cannot directly rule
out differences in these structures. However, we note that travel time
100 101 102 103 104
Size (kpc)
1025
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1029
1030
1031
L
1
7
8
(W
H
z−
1
)
3CRR FRII
3CRR HERGs
DF FRII
Figure 12. Luminosity vs size distribution for all the FRII sources in the
3CRR survey. Empty black circles surround the HERGs. The plot also shows
a density map of the DF FRIIs (regardless of accretion mode), with darker
shades of gray denoting larger numbers of sources in a given hexagonal bin.
for fluid along a collimated highly relativistic FRII jet, even out to
500 kpc distances, is ∼ a few million years (e.g. Mullin & Hard-
castle 2009) – one to two orders of magnitude shorter than typical
lobe evolution timescales (i.e. overall source ages) (e.g. Hardcastle
et al. 2019). It is therefore unlikely that systematic changes in the
nucleus would not lead to identifiable differences in the radio bright-
ness distribution of the two populations as a whole. We defer further
investigation of FRII life cycles to a future paper. We already know
from simulations and population statistics (e.g. Hardcastle & Krause
2013, 2014; Hardcastle et al. 2019; Shabala et al. 2020) and our spec-
tral index results from M19 that the bulk of the FRII-High LERGs
cannot be explained away as fading sources.
Fig 11 and Table 7 also show a lack of significant difference in
the size distributions of HERGs and LERGs. This argues against a
model in which there is a general tendency for sources to evolve from
high accretion rate HERGs to low accretion rate LERGs over their
lifetimes. We note that (as shown in the Table) there is evidence of a
systematic difference in flux for the two subsamples, which cannot be
fully explained by redshift effects. A systematic failure to identify the
hosts of HERGs with low radio fluxes (but high enough radio lumi-
nosities to be FRII-High) could explain the observed flux difference
between FRII-High HERGs and LERGs but this very unlikely, given
the high host identification rate for LoTSS-Deep (97 per cent), and
the fact that HERGs, by definition, are more luminous than LERGs
beyond the radio. Our sample is also free of the issues with SED-
template fitting for bright QSOs described by Duncan et al. (2019),
given the improved infrared coverage in LoTSS-Deep compared to
LoTSS-DR1 (Duncan et al. 2021).
We now revisit the question of why the population seen in LoTSS-
deep differs from older surveys of luminous radio galaxies, such
as 3CRR, 2Jy, and others, whose FRII populations were dominated
by luminous, radiatively-efficient FRIIs (e.g. Hardcastle et al. 2007,
2009; Best & Heckman 2012; Gendre et al. 2013; Mingo et al. 2014;
Ineson et al. 2015, 2017; Tadhunter 2016). Fig. 12 shows all the
FRIIs from the updated version of the 3CRR radio survey (Laing
et al. 1983). The luminosities and sizes were taken from the 3CRR
database2, with sizes measured uniformly from the best available
2 https://3crr.extragalactic.info/
MNRAS 000, 1–22 (2021)
14 B. Mingo et al.
Table 7.Median and mean values, and KS test statistics that the two samples are drawn from the same distribution, for the parameters shown in Fig. 11.
Variable HERG median LERG median HERG mean LERG mean KS stat p value
SDR 7.5 5.1 7.6 5.6 0.367 0.021
Size (kpc) 708 551 865 741 0.233 0.318
Total flux (mJy) 151 61 1356 167 0.422 0.005
Redshift 1.30 1.41 1.22 1.44 0.236 0.305
published low-frequency map (typically a multi-configuration Very
Large Array map at 1.4 or 5 GHz). As above, 3C 343.1 was excluded
from the sample. 79 per cent of all 3CRR sources are FRII, of which
87 per cent are HERGs. Comparing the 3CRR and DF FRII source
distributions (the latter being shown in the density map and in Fig.
6), it is immediately obvious that the populations are different, even
accounting for the different redshift ranges.
As we highlighted in M19, surveys prior to LoTSS covered a
very different part of the parameter space, which explains why the
FRII-Low lay undiscovered as a population until recently. How-
ever, luminosity differences alone cannot explain the discrepancy
in LERG/HERG fractions: Table 6 shows that restricting the 3CRR
FRIIs to the luminosity range comparable to our sample only in-
creases the LERG fraction to 21 per cent. The difference in the
LERG/HERG ratio for both samples is stark even when a depen-
dence with luminosity is taken into account, as shown in the right-
hand panel of Fig. 7.
One possible explanation relates to the significantly different size
distributions of 3CRR and our sample: if HERGs are more common
for small source sizes (perhaps because of the evolution of accretion
over a source’s lifetime) this could explain the discrepancy. We do
not see a dependence of accretion mode on size within our FRII
sample, but this consists primarily of physically large sources. We
therefore looked at the accretion mode statistics at 𝐿150 > 1 × 1026
W Hz−1 (where sources have a high probability of being FRII) and
𝑧 < 2.5 for sources (i) in our small, unresolved, and faint categories
(see Tables 2, 3, and 5), and (ii) pre-filtered from the catalogue before
the classification procedure (catalogued sizes below 8 arcseconds).
These smaller source samples cover physical sizes of ∼ 50−400 kpc
and up to ∼ 50 kpc, respectively.
We found 176 sources in the first group of small, unresolved and
faint sources (overwhelmingly in the small FRI, small FRII, and
small Hybrid categories), of which 145 are LERGs (∼ 82 per cent)
and 31 HERGs (∼ 18 per cent). We found 278 pre-filtered sources in
the same redshift and luminosity range, of which 217 (∼ 78 per cent)
are LERGs, and 61 (∼ 22 per cent) HERGs. Although we do not
have reliable morphological classifications for these smaller sources
we know from our earlier work and that of Gürkan et al. (2019) that
even with LOFAR we are unlikely to observe a substantial enough
number of FRIs in this luminosity regime to cause contamination
concerns (see also Fig. 6).
We therefore find no evidence that the HERG fraction increases to
smaller sizes, and so the different size distributions cannot explain
our discrepancy with 3CRR. This additional analysis of our smaller
source categories also further supports the argumentmade earlier that
the lack of significant difference in the size distribution of HERGs
and LERGs in our sample contradicts a scenario in which all FRII
sources start their lives asHERGs and then switch to a lower accretion
rate once they have exhausted their initial supply of gas. This is also
consistent with the small HERG fraction in the LOFAR galaxy-scale
jet sample of Webster et al. (2021a) and Webster et al. (2021b). We
note that of course this doesn’t rule out a small fraction of the FRII
LERGs being older/fading systems, but this scenario cannot explain
the population as a whole.
A more viable explanation for the discrepancy in HERG fraction
with 3CRR is the strong redshift dependence in 3CRR caused by
its shallow flux density limit, which means that (beyond the local
Universe) 3CRR sources will have the highest radio luminosities of
any sources at their redshift, and therefore are likely to have among
the highest jet powers. While accretion rate is not the only factor
controlling jet power (black hole mass and spin are also important),
it is nevertheless easier to produce a higher jet power at a high
accretion rate (Hardcastle 2018b, e.g.). It therefore seems plausible
to expect HERGs to be particularly prevalent in a population that
comprises the most powerful jets of their epoch; the fact that HERGs
are less prevalent in a more representative sample spanning a wider
range of luminosities at each epoch is perhaps then unsurprising.
Although there are still many open questions the more complete
perspective on the radio-loud AGN population from our wide area
deep survey enables several firm conclusions about the FRII popula-
tion:
• It is not possible to tell whether an FRII is luminous (FRII-
High) or less luminous (FRII-Low), or whether it is accreting at a
low (LERG) or high (HERG) rate based solely on radio morphology
inferred from LoTSS-Deep quality data (see Fig. 10). While we
cannot examine the jet structures of the two classes directly with
our images, we consider it implausible that systematic differences in
the inner jets and/or hotspot regions would not lead to a detectable
systematic difference in the large-scale brightness distributions of
the two classes.
• Luminous FRIIs are not predominantly HERGs, although con-
versely a majority (∼ 2/3) of the minority HERG population are
luminous FRIIs.
• There is no significant difference in the size distribution of FRII
HERGs and LERGs, with many examples of both accretion modes
in the giant radio galaxy regime (> 1 Mpc), which argues against
models in which there is a general tendency for individual sources to
transition from the HERG to LERG regime as they age.
• While population studies based on luminosity functions include
LERGs in general terms, the impact of FRII LERGs (at all lumi-
nosities) on cosmic evolution and cosmic magnetism might need to
be reassessed, given that they are more numerous than previously
thought and their evolution and physical conditions are expected to
be different to those of FRI LERGs (e.g. Croston et al. 2018).
5 THE ROLE OF THE HOSTS: STELLAR MASS AND
STAR FORMATION RATE
From Section 4, we conclude that radio morphology is not primarily
controlled by accretion mode, since we cannot identify systematic
differences in the radio structures of FRII HERGs and LERGs. We
also conclude from this work and from M19 that radio morphology
is not solely controlled by jet power, because of the existence of the
FRII-Low. Therefore, in this section we incorporate the high-quality
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 15
MKs (mag)
0.0
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DR1 FRII-High
DR1 FRII-Low
−25.0−24.5−24.0−23.5−23.0−22.5
MK (mag)
0.0
0.2
0.4
0.6
0.8
1.0
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d
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DF FRII-High
DF FRII-Low
Figure 13. Histogram of the near-IR K-band absolute magnitude distribution
for the FRII-high (blue) vs FRII-Low (red, hatched). The top plot shows
the rest-frame 𝑀𝐾𝑠 LoTSS-DR1 results from M19, with slightly different
binning. The bottom plot shows the rest-frame 𝑀𝐾 results for the three
combined deep fields.
host-galaxy information we have available for this sample to examine
the role that host-galaxy properties play in determining both radio
structures and accretion modes.
In the pc to kpc-scale jet disruption paradigm for the FR dichotomy
(e.g. Bicknell 1994; Laing &Bridle 2002, 2014; Kaiser & Best 2007;
Perucho &Martí 2007), jet deceleration to form FRI jets is primarily
the result of interaction with the environment and entrainment of
material from it. The primary source of material to be entrained is
thought to be the central hot-gas environment, rather than winds from
young stellar populations (e.g. Laing & Bridle 2014; Anglés-Castillo
et al. 2021), though stellar mass loading can play an important role
for weaker jets (Perucho et al. 2014). For massive ellipticals, the
typical hosts of radio-loud AGN, the hot-gas environment richness
is expected to scale with galaxy mass (e.g. Kim & Fabbiano 2013).
Therefore if the jet disruption paradigm is correct, we would predict
that stellar mass is the best easily measurable proxy for the environ-
mental conditions that, along with jet power, determine the FR class
of a source. We test this model in Section 5.1.
Accretion mode, in contrast, is thought to be most closely linked
to the availability of a supply of dense cold gas close to the nucleus,
which can increase the accretion rate above that available only from
fuelling linked to the hot gas environment (Gaspari et al. 2013, 2015;
Hardcastle & Croston 2020). In AGN the mass of the black hole also
plays a more important role than in X-ray binaries in determining
how much gas is needed to cross the threshold between RI and RE
accretion (e.g. Hardcastle 2018a). We might expect that the specific
star formation rate (sSFR, defined as the total star formation rate di-
vided by the stellar mass), which is a better indicator of the fractional
gas content of the galaxy than the global star formation rate (e.g.
Abramson et al. 2014; Ilbert et al. 2015), would be a good proxy for
short-term cold gas availability, and hence accretion mode. This idea
is investigated in Section 5.2.
5.1 What host galaxy properties control FR class?
In our previous work with LoTSS-DR1 we observed a significant
difference between the host absolute magnitudes of FRII-High and
FRII-Low. This holds true for our LoTSS-Deep sample as well, as
9.0 9.5 10.0 10.5 11.0 11.5 12.0
Stellar mass (log10(M¯))
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FRI
FRII-High
FRII-Low
Figure 14.Normalised histogramdistribution of stellarmasses for the LoTSS-
Deep FRI (yellow, solid), FRII-H (blue solid outline, dotted) and FRII-L (red
dashed outline, hashed).
shown in Fig. 13, where we have plotted a comparison histogram
of K𝑠 and K absolute magnitudes for both samples. Unlike in M19
we did not filter and match the populations in size and redshift for
LoTSS-Deep, as the statistics would have been too low, which is also
why we used wider bins for this comparison. We again filtered out
any QSOs to avoid contamination. M𝐾𝑆 is a reliable proxy for stellar
mass (e.g. Ziparo et al. 2016), but with our SED fitting approach in
LoTSS-Deep (covered in sections 2.1 and 2.3) we have much better,
more direct estimates of this parameter.
The histogram in Fig. 14 therefore shows the hostmass comparison
between FRI, FRII-High and FRII-Low with values derived through
the SED fitting procedure. For all three populations most hosts have
masses 10.5 < log10 (𝑀∗) < 11.5 M , but it is clear that the FRII-
Low distribution has a tail of hosts with lower stellar masses. There
are three FRII-Low sources with log10 (𝑀∗) < 10M , two of which
are at relatively high 𝑧 (0.9, 1.2) and have larger uncertainties on their
stellarmasses, as these sources are below themass completeness limit
of the sample (Duncan et al. 2021). The lower-mass tail of FRII-Lows
is present even ignoring these outliers.
The trends in Fig. 14 are consistent with what theM𝐾𝑆 histograms
in Fig. 13 show, and our conclusions in M19. The greater overlap
between the FRII-High and FRII-Low in stellar masses compared
to the M𝐾 distribution could be due to some AGN contribution in
this band for radiatively-efficient sources (which are overwhelmingly
FRII-High).
It is possible to take this analysis further without this sample
to consider only the luminosity ranges in which the FRII and FRI
populations show most overlap. In Fig. 15 we show a matched subset
of FRII-Low and FRI in both luminosity and size, eliminating giant
radio galaxies, which aremore likely to be fading or restarting sources
(Dabhade et al. 2020a). The difference in host stellar masses between
both populations becomes very clear. The left-hand panel shows the
luminosity range where FRI and FRII coexist, highlighting that both
distributions have significantly different dependences on stellar mass.
The right-hand panel shows only the decade of luminosity around the
canonical FR break, demonstrating that the absolute ratio of FRI/II
depends on stellar mass. Using three bins in stellar mass, we used a
𝜒2 fit to a line of slope zero and expected y value corresponding to
the weighted mean percentage of FRII-Lows to confirm that the null
hypothesis that the percentage of FRII-Low is independent of stellar
MNRAS 000, 1–22 (2021)
16 B. Mingo et al.
9.5 10.0 10.5 11.0 11.5 12.0
log10(stellar mass) (M¯)
0.00
0.25
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2.00
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FRI
FRII-Low
9.5 10.0 10.5 11.0 11.5 12.0
log10(stellar mass) (M¯)
0
5
10
15
20
25
n
FRI
FRII-Low
Figure 15. Stellar mass distributions for a subset of matched FRI (yellow solid bars) and FRII-Low (red hatched bars, dashed outline). Left (normalised): samples
matched in size and luminosity (200-1000 kpc, 24.3 < log10 (𝐿150) < 26 W Hz−1). Right: narrower luminosity range, focusing on the sources just below the
traditional FRI/II boundary (25 < log10 (𝐿150) < 26W Hz−1).
mass can be ruled out at the 95 per cent confidence level. The binned
FRII-Low percentages were 86 ± 28, 45 ± 13, and 19 ± 12 for the
three mass ranges log10 (𝑀∗) < 10.5, 10.5 < log10 (𝑀∗) < 11.25,
log10 (𝑀∗) > 11.25 M , showing a decreasing fraction of FRII-
Low with increasing stellar mass. In other words, for intermediate
jet powers, the probability of a galaxy producing an FRI or FRII
is strongly dependent on stellar mass; for masses ≥ 1011 M the
probability of forming an FRI becomes higher than the probability
of forming an FRII-Low. This confirms the long-standing theory that
morphology for jets of similar power is determined by interaction
with the host galaxy, likely on pc to kpc scales, consistent with the
conclusions of M19.
There is nevertheless a non-negligible number of FRII-Low in
high-mass galaxies. It is possible that this minority of FRII-Low are
the fading remains of former FRII-High, or that different gas density
and pressure distributions in the inner few kpc of these host galaxies
could enable the jets to remain collimated at low powers.
5.2 What host galaxy properties control accretion mode?
As introduced earlier, we consider the (specific) star formation rate
to be a good proxy for gas availability near the black hole. Stellar
mass may also be relevant for accretion mode due to its tight scaling
with black hole mass. The left-hand panel of Fig. 16 shows 150-
MHz luminosities against stellar masses for our FRI, FRII-High,
and FRII-Low. Most FRIs are found towards the bottom-right of
the plot (lower luminosities, higher stellar masses), as expected. For
comparison, star-forming galaxies are known to span a wide range
of sSFR (Gürkan et al. 2018) with significant redshift dependence –
values around 10−10 to 10−9 yr−1 are typical of star-forming galaxies
from the nearby Universe to the median redshift of our sample (e.g.
Damen et al. 2009).
We found no statistically significant difference between the stellar
mass distributions of all LERG and HERG hosts (𝑝 value of 0.203),
nor between those of FRII-High LERG and HERG hosts (𝑝 value of
0.141). Our results appear to contradict the idea that HERGs, overall,
favour systems with lower host masses than LERGs (e.g. Tasse et al.
2008; Smolčić 2009; Best & Heckman 2012; Hardcastle et al. 2013),
but selection effects andmeasurementmethods are crucial to consider
(see e.g. the discussions by Fernandes et al. 2015;Gürkan et al. 2018).
Unlike in previous surveys, our LERG population encompasses not
just FRIs, but a large number of FRIIs (across the entire luminosity
range, 1023 <L150 < 1029 W Hz−1) in lower-mass hosts, while our
HERGs are likely more dominated by QSOs than Seyfert galaxies.
As the survey is not statistically complete up to 𝑧 = 2.5, at the high-𝑧
end our sample is dominated by bright sources.
The right-hand panel of Fig. 16 shows the distribution of our
sources in terms of luminosity versus specific star formation rate.
Unlike with the stellar mass, it is very clear from the plot that there
is a very strong preference for HERGs to be in star-forming systems
(sSFR> −10 yr−1), regardless of their radio morphology (see also
the right-hand panel of Fig. 17). This is consistent with what has
been found in the past (e.g. Baldi & Capetti 2008; Janssen et al.
2012; Hardcastle et al. 2013; Gürkan et al. 2014; Karouzos et al.
2014; Ineson et al. 2015, 2017; Mingo et al. 2016; Miraghaei & Best
2017; Weigel et al. 2017; Williams et al. 2018), and despite some
caveats in terms of the timescales required to fuel star formation
versus powering the AGN (see e.g. Wild et al. 2010) it seems very
likely that both mechanisms are fuelled from the same gas.
In terms of FR populations, the FRII-High show a very broad
distribution of specific star formation rates, but they are more star-
forming than the FRIs, as can be seen in the left-hand panel of Fig. 17.
The histogram also shows that, as a whole, the FRII-Low tend to have
specific star formation rates intermediate between those of the FRIs
and the FRII-High, but they show a very large scatter.Most of the star-
forming FRII-Low are quite close to the traditional FRI/II boundary
(Fig. 16), hinting that they might belong to a similar population to
the FRII-High above the line, just fainter. A large fraction (40–50 per
cent) of the FRII-Lowoccupy the sameL150/sSFRparameter space as
the FRIs even when the environmental dependence of the luminosity
is considered (Hardcastle &Krause 2013; Croston et al. 2018). These
FRIIs therefore appear to have been produced in different host-galaxy
conditions to their more luminous counterparts.
The right-hand panel of Fig. 17 shows a fairly bimodal sSFR distri-
bution between LERGs and HERGs. There is, however, a noticeable
tail of LERGs in star forming systems, as also noted by Kondapally
et al. (subm.). This seems to be driven mostly by a subgroup of
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 17
9.0 9.5 10.0 10.5 11.0 11.5 12.0
Stellar mass (log10(M¯))
1023
1024
1025
1026
1027
1028
1029
L
15
0
(W
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DF FRI
DF FRII-High
DF FRII-Low
HERGs
−14 −13 −12 −11 −10 −9 −8
sSFR (log10(yr
−1))
1023
1024
1025
1026
1027
1028
1029
L
15
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(W
H
z−
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DF FRI
DF FRII-High
DF FRII-Low
HERGs
Figure 16. Left: luminosity vs stellar mass. Right: luminosity vs specific star formation rate. Symbols and colours as in fig. 4.
−14 −13 −12 −11 −10 −9 −8
sSFR (log10(yr
−1))
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sSFR (log10(yr
−1))
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LERGs
HERGs
Figure 17. Left: normalised histogram distribution of specific star formation rates for the LoTSS-Deep FRI, FRII-Low, and FRII-High. Colours and symbols
as in Fig. 14. The median sSFRs are -11.7, -11.4, and -9.8, respectively. Right: same histogram for all LERGs (orange, solid bars) and HERGs (purple outline).
The median sSFRs are -11.6 and -9.8, respectively. Note that LERGs outnumber HERGs by a factor of 7; see Table 6 for detailed statistics.
FRII sources, both above and below (but close to) the traditional
luminosity boundary (see the right-hand panel of Fig. 16).
Another conclusion we can draw from the right-hand panel of Fig.
17 is that AGN host galaxies can have high sSFR without having
a high accretion rate (tail of high sSFR LERGs), but not the other
way around: the HERG distribution drops very sharply as sSFR
decreases. It is thus clear that sSFR is the host-galaxy property most
closely linked to accretion mode; in other words: a gas supply (traced
by ongoing star formation) is necessary to produce a HERG, but
crucially, gas availability somewhere in the host does not guarantee
that a HERG will be present (tail of LERGs). We note that this result
is compatible with star formation and RE AGN activity occurring on
different physical and temporal scales, as the HERG phase is shorter
than most star formation episodes (e.g. Wild et al. 2010).
If the integrated star formation rate is a proxy for the black hole
accretion rate, and the stellar mass is a proxy for the black hole mass
(Magorrian et al. 1998), thenwewould expect that the sSFR is a proxy
for the key accretion switch quantity ¤𝑀/ ¤𝑀Edd. We can substitute in
some representative values to test this. The Eddington accretion rate
is defined as ¤𝑀Edd = (4𝜋𝐺𝑀BH𝑚𝑝)/(𝜖𝑐𝜎𝑇 ), where MBH is the
black hole mass, m𝑝 the mass of the proton, c the speed of light, 𝜖
the radiative efficiency (typically assumed to be ∼ 0.1), and 𝜎𝑇 the
Thomson scattering cross-section for the electron. Substituting in all
values except the black holemass gives anEddington accretion rate of
2.3×10−9 (𝑀BH/𝑀)M yr−1. If we considered a critical accretion
rate for the transition between the RI and RE regimes of 0.03 ¤𝑀Edd
(as observed for e.g. the 2Jy sample, see Mingo et al. 2014), we
would obtain a specific accretion rate of ¤𝑀crit/𝑀BH = 6.9 × 10−11
yr−1. Assuming a ratio between the black hole mass and the galaxy
stellar mass of 0.025 per cent (e.g. Reines & Volonteri 2015), we
would get ¤𝑀crit/𝑀galaxy = 1.7 × 10−14 yr−1. And assuming that the
AGN accretion rate is roughly a factor of 10−3 smaller than the star
formation rate (e.g. Mullaney et al. 2012), we would obtain a critical
sSFR value for the transition between the RI and RE regimes of
1.7× 10−11 yr−1. While this is a very rough calculation, and there is
a broad range of possible values for most of the parameters involved,
it is very interesting that the result is in the same order of magnitude
as the transition seen in Fig. 17.
MNRAS 000, 1–22 (2021)
18 B. Mingo et al.
Despite these interesting insights into the nature of an accretion
mode ‘switch’, there are nevertheless some LERGs in star-forming
systems (∼ 17 per cent have sSFR> −10 yr−1), so that sources cannot
be classified by accretion mode based only on sSFR. A significant
fraction of LERGs in star-forming systems have lower-mass hosts:
53 per cent of LERGs with sSFR> −11 yr−1 have log10 (𝑀∗) < 11.0
M , compared with just 17 per cent of LERGs with sSFR< −11
yr−1. However, it is not possible to determine how much of this
difference is driven by selection effects. Both host mass and star
formation rate are also linked to the larger-scale environment, which
we know also plays a role in directly fuelling not just LERGs (see e.g.
Ineson et al. 2015; Miraghaei & Best 2017; Morganti & Oosterloo
2018; Massaro et al. 2019; Croston et al. 2019) but also less powerful
HERGs, through mergers and interactions (Pierce et al. 2019).
In summary, we have found a clear link between accretion mode
and sSFR in our sample, which provides insight into the nature of
the accretion mode ‘switch’ in radio-loud AGN. There remain gaps
in our understanding of the relationship between host properties and
accretion behaviour, however, which await larger samples and better
understanding of activity timescales for different source populations
as a function of epoch.
5.3 The overall picture
In Sections 4, 5.1 and 5.2 we were able to draw a number of firm
conclusions about the relationships between radio morphology (FR
class), accretion mode and host galaxy properties. Here we briefly
summarise how these results fit together.
We have shown that the observed radio morphology (FR class) of
a radio-loud AGN is not a direct consequence of its accretion mode,
because edge-brightened, FRII lobe structures are found across all
radio luminosities and both accretion modes. This led us to take
a detailed look at the influence of host galaxy properties on both
radio morphology and accretion mode in the two preceding subsec-
tions. We have found several important relationships that help us to
understand what controls radio-loud AGN behaviour.
Firstly, we have used our large sample numbers in the radio lumi-
nosity range where both FRI and FRII morphologies are observed to
demonstrate that for systems that – broadly – are expected to have
similar jet powers, the probability of a jet forming FRI or FRII struc-
tures on large scales is strongly linked to stellar mass (Fig. 15). As
discussed above, this provides the best evidence to date that host-
galaxy environment (likely inner hot-gas density) controls whether
jets of similar power are disrupted. In contrast, the previous section
shows that accretion mode is not primarily determined by the same
parameter, but instead is closely linked with specific star-formation
rate (Fig. 17), which we interpret as tracing situations of high fuel
availability enabling high accretion rates.
By separating out the influence of different host galaxy parameters,
our analysis has demonstrated for the first time that accretion mode
and radio morphology are controlled in different ways by their host-
galaxy environment, consistent with the observational landscape in
which accretion mode and radio morphology have patterns of con-
nection but not a simple one-to-one link.
5.4 Sample completeness considerations
Sample completeness is always a concern, but particularly so when
working at the intersection of flux-limited samples and complex
selection techniques. In this section we briefly summarise the key
considerations we have addressed in this work.
In terms of radio flux densities, LoTSS-Deep is highly complete
down to values well below those that we consider in our sample (see
Section 2). The main survey papers cover this in more detail (Tasse
et al. 2021; Sabater et al. 2021). As mentioned in Section 2.1, ex-
tended sample completeness (association of components via Galaxy
Zoo) is also very high in our sample, as any sources that were flagged
up either by conflicting associations from the volunteers, classified
as blends, or identified as non-standard were checked multiple times
(Kondapally et al. 2021). This process also flagged up any issues with
host identification in extended sources, and those identified via the
maximum likelihood process were also checked repeatedly (Duncan
et al. 2021). The host identification completeness is greater than 97
per cent for LoTSS-Deep.
Identification of faint radio AGN, particularly in heavily star-
forming systems, is an important concern. Our selection requires
a radio excess for any sources to be identified as radio AGN (see Sec-
tion 2.3). However, for our morphological classification we require
the radio emission to be extended on large angular scales, and for
sources to be included in our sample of FRIs and FRIIs they must
present a morphology that is not consistent with that of radio emis-
sion in star forming galaxies. Proof of our success in selecting clean
samples is presented in Section 3.2. Our sample ismissing radioAGN
with small angular scales (as discussed in detail in section 4.2.2), in-
cluding those with high star formation rates, and radio-quiet AGN.
These systems, and sample completeness at low radio luminosities,
are covered in more detail by Best et al. (subm.) and Kondapally et
al. (subm.).
We have also discussed completeness in terms of HERG/LERG
classifications in Section 4, concluding that we are unlikely to have
missed faint HERGs among luminous FRIIs because of the broad cor-
relation between kinetic and radiative output in radio AGN (Mingo
et al. 2016). We might have missed faint HERGs at lower radio lu-
minosities, but given the consensus approach at the SED fitting stage
the fraction is likely to be very low in our sample (see Section 2.3 and
the discussions by Best et al., subm. and Kondapally et al., subm.).
Hybrid AGN/star-forming systems with small jets are a population
where this selection bias might be of key importance, however.
There are also considerations regarding cosmic variance and small
sample sizes, which we havemostly covered in Section 4.2.2. LoTSS-
Deep is a pencil-beam survey, and as such it does not contain many
examples of rare populations, such as the extremely bright FRIIs
found in e.g. the 3CRR and 2Jy surveys. These issues will be ad-
dressed as the LOFAR samples grow in depth and coverage, and
eventually by the advent of the SKA surveys. As stated in Section
2.1, we have observed no systematic or statistically significant differ-
ences between our three fields.
6 CONCLUSIONS
In this work we have carried out an investigation into the relationship
between black hole accretion mode and large-scale radio morphol-
ogy, using new data from the LOFARDeep Fields. The deep LOFAR
data and exquisite multiwavelength coverage have enabled us to anal-
yse the properties of sources up to z=2.5, much farther than we were
able to achieve for LoTSS-DR1 (M19), and to use broad-band SED
fitting to constrain the host and black hole accretion properties for
all our sources. While our samples are not complete, they are repre-
sentative of the general radio-galaxy population.
Our results have led to the following conclusions:
• In contrast with what has been found in most previous surveys,
MNRAS 000, 1–22 (2021)
Accretion mode vs radio morphology in LoTSS-Deep 19
the majority (65 per cent) of the luminous FRIIs (FRII-High, 𝐿150 >
1026 WHz−1) in our sample are LERGs. We attribute this difference
to the impact of higher flux density limits on selection of older
samples.
• Using our large samples of luminous, well-resolved FRII
HERGs and LERGs we identify no significant morphological dif-
ferences between 100-kpc-scale FRIIs of different accretion modes,
demonstrating that the FR class is not primarily controlled by the
central engine. While the quality of our data does not allow us to
rule out kpc- and/or pc- scale differences in the jet structures of FRII
LERGs and HERGs, we consider it unlikely that such differences
would lead to no systematic difference in the large-scale brightness
distributions.
• As in our earlier work, a significant population of low-
luminosity FRIIs (FRII-Low, 𝐿150 ≤ 1026 WHz−1) is present. Even
accounting for the large scatter in relating luminosity to jet power,
this demonstrates that FR class is also not controlled solely by jet
power.
• The overwhelming majority of low-luminosity FRIIs (> 91 per
cent) and FRIs (> 95 per cent) are LERGs, demonstrating that radia-
tively efficient accretion in low-power sources is very rare, regardless
of their FR class.
• By examining FRIs and low-luminosity FRIIs in the luminosity
range where the populations most overlap, we demonstrate conclu-
sively that the probability of a low-power jet becoming an FRI or
an FRII depends strongly on the host galaxy stellar mass, consistent
with the kpc-scale jet disruption model for FRIs.
• Accretion mode is not closely linked to stellar mass in our
sample, but across all morphologies and luminosities HERGs are
found at high specific star formation rates, demonstrating a close link
between fuel availability and accretion behaviour.
FRIIs play a larger role in the low-power and low-accretion rate
AGN population than previously anticipated. To determine whether
we need to adjust our AGN feedback recipes for large-scale cosmo-
logical simulations to account for this we will need to consider the
luminosity functions of low-power and low-accretion rate AGN (e.g.
Kondapally et al., subm.), energetic and particle content constraints
on different FR populations (e.g. Hardcastle & Krause 2013; Croston
et al. 2005, 2018), and the duty cycles of these sources (e.g Brienza
et al. 2017; Sabater et al. 2019; Jurlin et al. 2020; Shabala et al. 2020).
We are now also moving into a regime in which we have more than
just morphological (FR) and accretion (LERG/HERG) information
to allow us to investigate the underlying physics of these sources,
thanks to the increased depth and resolution of polarisation surveys
(e.g. O’Sullivan et al. 2017; Mahatma et al. 2021).
Given that LoTSS-Deep (as well as the MeerKAT surveys, see
e.g. Jarvis et al. 2016; Fanaroff et al. 2021) is representative of
what future all-sky radio surveys with the SKA will accomplish in
terms of depth, achieving good classifications for SKA data with
our simple approach would be challenging, as visually inspecting
millions of sources is not desirable. It is likely that machine learning
approaches will suffer similarly, but training using both the visually-
filtered results from LoTSS and LoTSS-Deep as truth sets might
mitigate this. Incorporating intensity profile methods such as that
described by Barkus et al. (2022) could produce a valuable initial
filter to minimise the number of sources that need to be visually
inspected, and provide some direction to machine learning methods.
ACKNOWLEDGEMENTS
We thank the anonymous referee for their comments, which have
helped improve the paper. BM and JHC acknowledge support from
the Science and Technology Facilities Council (STFC) under grants
ST/R00109X/1, ST/R000794/1, and ST/T000295/1. PNB and JS are
grateful for support from theUKSTFCvia grant ST/R000972/1. PNB
is also grateful for support via grant ST/V000594/1. KJD acknowl-
edges funding from the European Union’s Horizon 2020 research
and innovation programme under the Marie Skłodowska-Curie grant
agreement No. 892117 (HIZRAD). MJH and JP acknowledge sup-
port from STFC [ST/V000624/1]. RK acknowledges support from
the Science and Technology Facilities Council (STFC) through an
STFC studentship via grant ST/R504737/1. IP and MB acknowledge
support from INAF under the SKA/CTA PRIN ‘FORECaST’ and
the PRINMAIN STREAM ‘SAuROS’ projects.WLWacknowledges
support from the CAS-NWO programme for radio astronomy with
project number 629.001.024, which is financed by the Netherlands
Organisation for Scientific Research (NWO). MB also acknowledges
support from the Ministero degli Affari Esteri e della Cooperazione
Internazionale - Direzione Generale per la Promozione del Sistema
Paese Progetto di Grande Rilevanza ZA18GR02.
This paper is based (in part) on data obtained with the Interna-
tional LOFAR Telescope (ILT) under project codes LC0 015, LC2
024, LC2 038, LC3 008, LC4 008, LC4 034, and LT10 01. LOFAR
(van Haarlem et al. 2013) is the LOw Frequency ARray designed and
constructed by ASTRON. It has observing, data processing, and data
storage facilities in several countries, which are owned by various
parties (each with their own funding sources), and are collectively
operated by the International LOFAR Telescope (ILT) foundation
under a joint scientific policy. The ILT resources have benefited from
the following recent major funding sources: CNRS-INSU, Obser-
vatoire de Paris and Université d’Orléans, France; BMBF, MIWF-
NRW, MPG, Germany; Science Foundation Ireland (SFI), Depart-
ment of Business, Enterprise and Innovation (DBEI), Ireland; NWO,
The Netherlands; the Science and Technology Facilities Council,
UK; Ministry of Science and Higher Education, Poland. Part of this
work was carried out on the Dutch national e-infrastructure with
the support of the SURF Cooperative through grant e-infra 160022
& 160152. The LOFAR software and dedicated reduction packages
on https://github.com/apmechev/GRID_LRTwere deployed on
the e-infrastructure by the LOFAR einfragroup, consisting of J. B. R.
Oonk (ASTRON & Leiden Observatory), A. P. Mechev (Leiden Ob-
servatory) and T. Shimwell (ASTRON) with support from N. Danezi
(SURFsara) and C. Schrijvers (SURFsara).
Part of this researchmade use of theDutch national e-infrastructure
with support of the SURF Cooperative (e-infra 180169) and the LO-
FAR e-infra group. The Jülich LOFAR Long Term Archive and the
German LOFAR network are both coordinated and operated by the
Jülich SupercomputingCentre (JSC), and computing resources on the
Supercomputer JUWELS at JSC were provided by the Gauss Centre
for Supercomputing e.V. (grant CHTB00) through the John von Neu-
mann Institute for Computing (NIC). This research has made use of
the University of Hertfordshire high-performance computing facility
(http://uhhpc.herts.ac.uk/) and the LOFAR-UK computing
facility located at the University of Hertfordshire and supported by
STFC [ST/P000096/1].
This research made use of Astropy, a community-developed core
Python package for astronomy (Astropy Collaboration et al. 2018)
hosted at http://www.astropy.org/, of Matplotlib (Hunter
2007) hosted at https://matplotlib.org/,NumPy (van derWalt
et al. 2011; Harris et al. 2020) hosted at https://numpy.org/,
MNRAS 000, 1–22 (2021)
20 B. Mingo et al.
IPython (Pérez & Granger 2007) hosted at https://ipython.
org/, and of TOPCAT (Taylor 2005) hosted at http://www.star.
bris.ac.uk/~mbt/topcat/.
This publication made use of data products from the Widefield
Infrared Survey Explorer, which is a joint project of the Univer-
sity of California, Los Angeles, and the Jet Propulsion Labora-
tory/California Institute of Technology, and NEOWISE, which is a
project of the Jet Propulsion Laboratory/California Institute of Tech-
nology.WISE andNEOWISE are funded by theNational Aeronautics
and Space Administration.
The Pan-STARRS1 Surveys (PS1) have been made possible
through contributions by the Institute for Astronomy, the University
of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society
and its participating institutes, the Max Planck Institute for Astron-
omy, Heidelberg and the Max Planck Institute for Extraterrestrial
Physics, Garching, The Johns Hopkins University, Durham Univer-
sity, the University of Edinburgh, the Queen’s University Belfast,
the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres
Observatory Global Telescope Network Incorporated, the National
Central University of Taiwan, the Space Telescope Science Institute,
and the National Aeronautics and Space Administration under Grant
No. NNX08AR22G issued through the Planetary ScienceDivision of
the NASA Science Mission Directorate, the National Science Foun-
dation Grant No. AST-1238877, the University of Maryland, Eotvos
LorandUniversity (ELTE), the Los Alamos National Laboratory, and
the Gordon and Betty Moore Foundation.
This work has made use of data from the European Space
Agency (ESA) mission Gaia (https://www.cosmos.esa.int/
gaia), processed by the Gaia Data Processing and Analysis Consor-
tium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/
consortium). Funding for the DPAC has been provided by national
institutions, in particular the institutions participating in the Gaia
Multilateral Agreement.
This work is based on observations obtained with
MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA,
at the Canada-France-Hawaii Telescope (CFHT) which is operated
by the National Research Council (NRC) of Canada, the Institut
National des Sciences de l’Univers of the Centre National de la
Recherche Scientifique (CNRS) of France, and the University of
Hawaii. This research used the facilities of the Canadian Astronomy
Data Centre operated by the National Research Council of Canada
with the support of the Canadian Space Agency. RCSLenS data
processing was made possible thanks to significant computing
support from the NSERC Research Tools and Instruments grant
program. This work is based in part on observations made with the
Spitzer Space Telescope, which was operated by the Jet Propulsion
Laboratory, California Institute of Technology under a contract with
NASA.
Herschel is an ESA space observatory with science instruments
provided by European-led Principal Investigator consortia and with
important participation from NASA. This research has made use
of data from the HerMES project. HerMES is a Herschel Key Pro-
gramme utilising Guaranteed Time from the SPIRE instrument team,
ESAC scientists and a mission scientist. The HerMES data was
accessed through the Herschel Database in Marseille (HeDaM –
http://hedam.lam.fr) operated by CeSAM and hosted by the
Laboratoire d’Astrophysique de Marseille. This work is based in part
on observations made with the Galaxy Evolution Explorer (GALEX).
GALEX is aNASASmall Explorer, launched in 2003April.We grate-
fully acknowledge NASA’s support for construction, operation, and
science analysis for the GALEX mission, developed in cooperation
with the Centre National d’Etudes Spatiales of France and theKorean
Ministry of Science and Technology.
DATA AVAILABILITY
The datasets used for the analysis in this paper, and a catalogue listing
the morphological and accretion mode classes, are publicly available
from the LOFAR surveys website at https://lofar-surveys.
org/deepfields.html and can be freely accessed. The radio mor-
phology classification code, LoMorph, is publicly available on
Github, at https://github.com/bmingo/LoMorph/.
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