# Probing the Wind Component of Radio Emission in Luminous High-Redshift
Quasars

Gordon T. Richards Department of Physics, Drexel University, 32 S. 32nd
Street, Philadelphia, PA 19104, USA  Trevor V. McCaffrey Department of
Physics, Drexel University, 32 S. 32nd Street, Philadelphia, PA 19104, USA
Amy Kimball National Radio Astronomy Observatory, 1003 Lopezville Rd, Socorro,
NM 87801, USA  Amy L. Rankine Institute of Astronomy, University of Cambridge,
Madingley Road, Cambridge, CB3 0HA  James H. Matthews Institute of Astronomy,
University of Cambridge, Madingley Road, Cambridge, CB3 0HA  Paul C. Hewett
Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge,
CB3 0HA  Angelica B. Rivera Department of Physics, Drexel University, 32 S.
32nd Street, Philadelphia, PA 19104, USA

(Received Dec. 5, 2020)

###### Abstract

We discuss a novel probe of the contribution of wind-related shocks to the
radio emission in otherwise radio-quiet quasars. Given 1) the known non-linear
correlation between UV and X-ray luminosity in quasars, 2) that such
correlation leads to higher likelihood of radiation-line-driven winds in more
luminous quasars, and 3) that luminous quasars are more abundant at high
redshift, deep radio observations of high-redshift quasars are needed to probe
potential contributions of accretion disk winds to radio emission from
quasars. To this end, we target a sample of 50 $z\simeq 1.65$ color-selected
quasars that span the range of expected accretion disk wind properties as
traced by broad C IV emission. 3-GHz observations with the Very Large Array to
an rms of $\approx 10\mu$Jy beam-1 probe to star formation rates of $\sim
400\,M_{\sun}\,{\rm yr}^{-1}$, leading to 22 detections. Supplementing these
pointed observations are survey data of 388 sources from the LOFAR Two-metre
Sky Survey Data Release 1 that reach comparable depth (for a typical radio
spectral index), where 123 sources are detected. These combined observations
reveal a radio detection fraction that is a non-linear function of C IV
emission line properties and suggest that the data may require multiple
origins of radio emission in radio-quiet quasars. Specifically, in radio-quiet
quasars we find evidence for radio emission from weak jets or coronae in
quasars with low Eddingtion ratios, with either (or both) star formation and
accretion disk winds playing an important role in luminous quasars and
correlated with increasing Eddington ratio. Additional pointed radio
observations will be needed to fully establish the nature of radio emission in
radio-quiet quasars.

##  1 Introduction

Despite the fact that the first quasars to be discovered were strong radio
sources (Schmidt, 1963; Condon et al., 2013), the origin of radio emission
from quasars (particularly radio-quiet [RQ] quasars) remains an unsolved
question. Part of the problem is that there are likely multiple origins of
radio emission, including jets, star formation, accretion disk coronae, and
shocks from winds (e.g., Simpson, 2017; Kimball, 2018; Panessa et al., 2019),
yet investigations tend to make the implicit assumption that strong radio
emission is due to jets, while weaker radio emission is dominated by star
formation. Another part of the problem is that the fraction of luminous
quasars detected in the radio is quite small. Kellermann et al. (1989)
examined 114 quasars from the Palomar Bright Quasar Survey (Schmidt & Green,
1983)—the so-called Palomar-Green (PG) quasar sample—finding a radio-loud
(RL111Herein, having high-frequency radio flux density greater than ten times
the optical flux density or $\log R>1$.) fraction of just 15–20%. However, it
is perhaps underappreciated that the ratio of RL to RQ quasars changes as a
function of both redshift and luminosity (Jiang et al., 2007; Kratzer &
Richards, 2015). At low luminosity, Ho & Ulvestad (2001) and Ho & Peng (2001)
argue that the majority of Seyfert (1943) galaxies are RL sources. At higher
luminosity, the fraction of quasars from the Sloan Digital Sky Survey (SDSS;
York et al. 2000) that are even detected (let alone formally RL) at the
$\sim$1 mJy beam-1 depth of the Faint Images of the Radio Sky (FIRST; Becker
et al. 1995) survey is only 4% (Schneider et al., 2010). The fraction of those
that are extended radio sources is only $\sim$ 10% (i.e., $\sim$0.4% of SDSS
quasars; Ivezić et al. 2002).

For the luminous quasars that show radio lobes, it is clear that the dominant
source of the radio emission is synchrotron emission from jets (Urry &
Padovani, 1995), thus it is necessary to break quasars into dichotomous RL and
RQ classes (or jetted and non-jetted, Padovani 2017). However, it is equally
important to understand that RQ quasars are not a monolithic class of objects
either in terms of optical/UV spectra (Richards et al., 2011) or radio
emission (Panessa et al., 2019), and should, at a minimum, be divided into
hard-spectrum and soft-spectrum RQ quasars (Kratzer & Richards, 2015). When
probing to fainter radio flux densities, the (generally unresolved) radio
emission is expected to be dominated by star formation (SF) processes that are
largely unrelated to the central engine, either in the form of radio from
free-free emission in H II regions produced by star formation or from
synchrotron emission produced by relativistic electrons accelerated by SNe
(Kimball et al., 2011b; Condon et al., 2013). Both AGN and SF sources of radio
emission might be expected to depend on environment (e.g., Hickox et al.,
2009; Retana-Montenegro & Röttgering, 2017). Beyond star formation, it is also
important to determine if some quasars really are radio silent, if there is
evidence for weak “frustrated jets” (e.g., Barvainis et al., 1996; Blundell &
Beasley, 1998; Herrera Ruiz et al., 2016), coronal emission (Laor & Behar,
2008; Raginski & Laor, 2016; Laor et al., 2019), or shocks from winds
interacting with gas in the host galaxy (e.g., Jiang et al., 2010a; Zakamska &
Greene, 2014; Laor et al., 2019).

Thus, when investigating the radio demographics of active galactic nuclei
(AGNs) it is crucial to make clear what part of the AGN landscape is being
probed (e.g., host-dominated AGNs vs. luminous quasars). Moreover, it must be
established if the samples being investigated are capable of probing all radio
origins. For example, given that optical luminosity is seen to correlate with
the presence of accretion disk winds (via the well-known optical to X-ray
correlation; Proga et al. 2000), low-redshift samples such as those
investigated by Kimball et al. (2011b) are not as capable of testing for wind-
based radio emission. Our approach is designed to particularly address
radiation-line-driven winds thought to be active in the parent sample of broad
absorption line quasars (BALQSOs; e.g., Rankine et al. 2020). Early work on
BALQSOs noted the possibility of an anti-correlation between the presence of
winds (as indicated by broad absorption lines, Weymann et al. 1991) and the
presence of radio jets (Stocke et al., 1992). While there are examples of
strongly radio-emitting BALQSOs (Gregg et al., 2000; Brotherton et al., 2002;
DiPompeo et al., 2011; Morabito et al., 2019), there nevertheless persist
significant differences (in properties related to accretion disk winds)
between the most RL quasars and those with the most extreme absorption troughs
(Richards et al., 2011, Figure 17).

To test for a wind contribution to radio emission, we take advantage of the
ability to recognize quasars whose spectra appear quite similar, but that have
very different physical properties (e.g., as characterized by the Eddington
ratio, $L/L_{\rm Edd}$; see Sulentic et al. 2007). These differences are
correlated with the observed radio properties of quasars (Kratzer & Richards,
2015) and with the presence of winds (Rankine et al., 2020). Richards et al.
(2011) and Kratzer & Richards (2015) used two broad-emission-line diagnostics
(the equivalent width, EW, of C IV and the “blueshift” of the peak of C IV
relative to the laboratory wavelength) to show that, while RL sources are not
uniformly distributed across this “C IV parameter space”, neither do they
occupy a distinct part of parameter space apart from radio-quiet quasars (in
contrast to Boroson 2002). In any parameter space defined by optical/UV quasar
properties, RQ quasars vastly outnumber RL quasars; however, the differences
between the optical spectral properties of RL and RQ quasars are subtle at
best (Kimball et al., 2011a; Schulze et al., 2017), spanning nowhere near the
full range of spectral properties that characterize quasar spectra (Boroson,
2002; Richards et al., 2011). The primary goal of this investigation is to
test for different possible origins of radio emission specifically in
luminous, high-redshift RQ quasars—from jets to shocks to coronoae to star
formation—using deep radio observations to levels of radio emission expected
to be dominated by star formation (at the redshift of the quasars). We focus
on exploring the changes in radio luminosity and radio detection fraction as a
function of C IV-based wind diagnostics in order to include shocks from winds
as a potential source of radio emission (e.g., Jiang et al., 2010b; Laor et
al., 2019), specifically in RQ quasars.

This paper is organized as follows. In Section 2 we describe the targets, with
Section 3 presenting the details of the radio observations. Section 4 outlines
the empirical framework for the prediction of trends presented in Section 5.
Sections 6 discusses the relationships between the observed radio emission,
emission line properties, and star formation. Finally in Sections 7 and 8 we
discuss these results in the context of different proposed origins of radio
emission and present our conclusions and suggestions for future work. The
cosmology assumed throughout the paper is $H_{0}=70$ km s-1 Mpc-1,
$\Omega_{m}=0.3$, and $\Omega_{\Lambda}=0.7$. The convention adopted for radio
spectral index, $\alpha$, is $f_{\nu}\propto\nu^{\alpha}$ and the ratio of
radio to optical emission, $R$, was calculated as the ratio between the rest-
frame 3-GHz and 2500-${\rm\AA}$ flux density.

##  2 Targets

We need a representative sample that is capable of probing multiple origins of
radio emission in RQ quasars, specifically that can address the question of
whether shocks from accretion disk winds can be contributors to radio emission
in luminous quasars. Theoretical understanding of radiation-line-driven
accretion disk winds (Murray et al., 1995; Proga et al., 2000) requires an
abundance of UV photons (responsible for driving the wind) and a dearth of
X-ray photons (that limit line driving) for strong radiation line driving. The
empirical observation of an anti-correlation between the optical/UV and X-ray
(e.g., Steffen et al., 2006) means that our investigation requires high
luminosity sources (in the optical/UV). The small volume available at low
redshift translates into the need for high-redshift ($z\gtrsim 1$) sources.
Over roughly $1.65<z<2.2$ the C IV emission line serves as a diagnostic
indicating the presence of accretion disk winds (Richards et al., 2011).

At the inception of this investigation, there was a dearth of deep radio
observations (e.g., as compared to those from the Faint Images of the Radio
Sky [FIRST; Becker et al. 1995] survey), covering large enough area to include
large numbers of high-redshift quasars. Thus we proposed and were awarded
pointed observations with the Karl G. Jansky Very Large Array (VLA; Perley et
al. 2011); see Sections 3.1–3.4. Since that time, data from the LOw-Frequency
ARray (LOFAR; van Haarlem et al. 2013) has become public and will be included
as well (Section 3.5). A complementary investigation based entirely on the
first data release of the LOFAR Two-metre Sky Survey (LoTSS; Shimwell et al.
2019) is presented by Rankine et al. (2021).

The targets were defined starting with the SDSS-DR7 quasar sample (Schneider
et al., 2010). We imposed a limit on spectral signal to noise of ${\rm
S/N}\geq 10$ per pixel over a window of 1600–3000Å in the restframe, giving a
sample of 8653 quasars. To avoid biases inherent to the fact that some SDSS
quasars were selected explicitly because of their radio emission (Richards et
al., 2002), we included only 8403 SDSS quasars that were targeted by color
selection (but not excluding quasars that were also radio selected, indeed two
of the targets are known FIRST sources). We prefer to concentrate on the SDSS-
DR7 sample, rather than the SDSS-DR16 (Lyke et al., 2020) sample that is
currently available as DR16 is based on less homogenous selection criteria.

In practice another important consideration is whether the quasars are part of
the “uniform” selection criteria (which implies $i_{\rm AB}<19.1$), see
Richards et al. (2002) and Richards et al. (2006a). While we have chosen not
to include that constraint in our investigation, it could be important for
future work that attempts to explore radio emission from winds over a larger
range in optical luminosity (where sources may be fainter than the magnitude
limit of the “uniform” sample). The spectral reconstruction approach we have
taken here (see § 4) means that we have also not excluded quasars where broad
absorption features (Weymann et al., 1991) corrupt the profile of the emission
line when using more common analysis techniques.

##  3 Radio Data

###  3.1 VLA Experimental Design

For the sake of identifying a suitably sized pilot sample for deep
observations with the VLA, we further limited the VLA targets to $1.645\leq
z\leq 1.6519$, which is the smallest range of redshift at the lower end of the
distribution that yielded the desired pilot sample of 50 quasars. The small
redshift window has the added benefit of minimizing any redshift evolution in
the sample. These targets span a range of bolometric luminosities222Using
Equation 9 from Krawczyk et al. (2013). of $\log L_{\rm Bol}/[{\rm
erg\,s}^{-1}]=46.13$–46.81, which is consistent with the requirement of
probing sources capable of supporting radiation-line-driven winds (Veilleux et
al., 2013; Zakamska & Greene, 2014). Four of the 50 targets are known BAL
quasars (as identified by Shen et al. 2011). The full VLA sample is presented
in Table 1.

Table 1: Target Quasar Properties Name | RA | Dec | Redshift | $M_{i}(z=2)$ | $\nu\log L_{1350{\rm\AA}}$ | BAL? | $S_{\mathrm{3GHz}}$ | $\log L_{\mathrm{3GHz}}$
---|---|---|---|---|---|---|---|---
(SDSSJ) | (J2000) | (J2000) |  |  | ($\mathrm{W}$) |  | ($\mu$Jy) | ($\mathrm{W\ Hz^{-1}}$)
001342.45$-$002412 | 3.426880 |  $-$0.403514 | 1.6506 |  $-$27.52 | 39.08 | 0 | $163.0\pm 17.0$ | 24.47
014023.83+141151 | 25.099303 | 14.197709 | 1.6504 |  $-$26.09 | 39.21 | 1 | $516.0\pm 27.0$ | 24.84
014658.21$-$091505 | 26.742564 |  $-$9.251470 | 1.6509 |  $-$26.34 | 39.05 | 0 | $<30.0$ | $<23.61$
015720.27$-$093809 | 29.334464 |  $-$9.635872 | 1.6508 |  $-$26.30 | 38.89 | 0 | $<42.9$ | $<23.76$
081656.84+492438 | 124.236843 | 49.410604 | 1.6508 |  $-$26.14 | 39.02 | 0 | $<26.1$ | $<23.55$
082334.60+213917 | 125.894208 | 21.654881 | 1.6515 |  $-$27.32 | 39.51 | 0 | $<31.5$ | $<23.63$
082423.61+260656 | 126.098409 | 26.115647 | 1.6501 |  $-$26.16 | 39.17 | 0 | $<25.8$ | $<23.54$
082928.79+401608 | 127.369984 | 40.269003 | 1.6507 |  $-$26.20 | 38.94 | 0 | $<29.7$ | $<23.60$
090502.96+154553 | 136.262365 | 15.764731 | 1.6510 |  $-$26.17 | 39.03 | 0 | $<60.0$ | $<23.91$
092933.99+300240 | 142.391663 | 30.044674 | 1.6517 |  $-$26.84 | 39.15 | 0 | $83.2\pm 11.0$ | 24.05
092959.42+270541 | 142.497601 | 27.094903 | 1.6515 |  $-$27.03 | 39.36 | 0 | $111.4\pm 7.5$ | 24.18
095648.48+534713 | 149.202005 | 53.787094 | 1.6514 |  $-$26.58 | 38.82 | 0 | $<36.0$ | $<23.69$
100723.31+162842 | 151.847151 | 16.478509 | 1.6510 |  $-$26.86 | 39.24 | 0 | $<40.5$ | $<23.74$
101946.98+494848 | 154.945784 | 49.813513 | 1.6513 |  $-$26.63 | 39.22 | 0 | $2570.0\pm 150.0$ | 25.54
104031.38+362611 | 160.130766 | 36.436579 | 1.6504 |  $-$26.60 | 39.16 | 0 | $100.0\pm 12.0$ | 24.13
104852.52+003230 | 162.218859 | 0.541673 | 1.6506 |  $-$27.10 | 39.22 | 0 | $<36.3$ | $<23.69$
105814.69+015230 | 164.561246 | 1.875186 | 1.6519 |  $-$26.45 | 39.08 | 0 | $<660.0$ | $<24.95$
110041.31+315018 | 165.172157 | 31.838363 | 1.6505 |  $-$27.17 | 39.27 | 0 | $294.0\pm 17.0$ | 24.60
110729.57+270800 | 166.873237 | 27.133554 | 1.6501 |  $-$26.46 | 39.08 | 1 | $<27.9$ | $<23.57$
113047.11+190325 | 172.696313 | 19.057130 | 1.6509 |  $-$26.40 | 39.19 | 0 | $<25.5$ | $<23.54$
113244.74+172629 | 173.186458 | 17.441512 | 1.6504 |  $-$26.74 | 39.22 | 0 | $63.8\pm 6.7$ | 23.93
113532.86+001411 | 173.886946 | 0.236518 | 1.6517 |  $-$26.60 | 39.17 | 0 | $<27.0$ | $<23.56$
113548.66$-$022617 | 173.952777 |  $-$2.438297 | 1.6502 |  $-$26.66 | 39.19 | 0 | $<38.4$ | $<23.71$
114408.55+020221 | 176.035649 | 2.039225 | 1.6506 |  $-$26.15 | 39.00 | 0 | $93.0\pm 10.0$ | 24.10
120015.35+000553 | 180.063964 | 0.098102 | 1.6506 |  $-$26.40 | 38.88 | 1 | $29.6\pm 6.4$ | 23.60
120845.22+653344 | 182.188432 | 65.562414 | 1.6512 |  $-$26.82 | 39.26 | 0 | $<34.5$ | $<23.67$
121753.12+294304 | 184.471363 | 29.717947 | 1.6507 |  $-$26.98 | 39.21 | 0 | $128.1\pm 7.4$ | 24.24
122225.34+414117 | 185.605621 | 41.688191 | 1.6511 |  $-$26.67 | 39.20 | 0 | $63.0\pm 8.4$ | 23.93
124118.12+624606 | 190.325501 | 62.768374 | 1.6510 |  $-$27.65 | 39.46 | 0 | $316.0\pm 18.0$ | 24.63
130243.08+130039 | 195.679505 | 13.010924 | 1.6505 |  $-$27.82 | 39.43 | 1 | $91.5\pm 6.1$ | 24.09
130545.73+462417 | 196.440578 | 46.404898 | 1.6506 |  $-$26.71 | 39.12 | 0 | $<25.5$ | $<23.54$
132249.49+324826 | 200.706248 | 32.807450 | 1.6513 |  $-$26.81 | 39.17 | 0 | $<48.3$ | $<23.81$
133802.31+084638 | 204.509649 | 8.777358 | 1.6502 |  $-$26.42 | 39.15 | 0 | $<47.7$ | $<23.81$
135348.69+071850 | 208.452913 | 7.314084 | 1.6502 |  $-$26.51 | 39.19 | 0 | $203.0\pm 15.0$ | 24.44
140612.82+292303 | 211.553449 | 29.384175 | 1.6513 |  $-$26.41 | 39.05 | 0 | $<38.4$ | $<23.71$
144326.01+305657 | 220.858396 | 30.949340 | 1.6513 |  $-$26.71 | 39.19 | 0 | $69.5\pm 4.4$ | 23.97
144510.01+524538 | 221.291710 | 52.760759 | 1.6515 |  $-$27.23 | 39.38 | 0 | $55.4\pm 3.9$ | 23.87
144925.90+311041 | 222.357935 | 31.178179 | 1.6509 |  $-$26.46 | 38.79 | 0 | $55.1\pm 8.3$ | 23.87
150951.87+075658 | 227.466148 | 7.949674 | 1.6517 |  $-$26.86 | 39.02 | 0 | $<60.9$ | $<23.91$
151450.99+544609 | 228.712463 | 54.769198 | 1.6510 |  $-$26.49 | 39.22 | 0 | $36.2\pm 5.4$ | 23.69
153512.10+540215 | 233.800417 | 54.037567 | 1.6501 |  $-$26.64 | 38.77 | 0 | $50.8\pm 2.5$ | 23.83
153745.03+481502 | 234.437634 | 48.250815 | 1.6517 |  $-$26.75 | 39.07 | 0 | $<22.5$ | $<23.48$
154631.77+191407 | 236.632400 | 19.235513 | 1.6512 |  $-$26.58 | 38.98 | 0 | $<28.8$ | $<23.59$
160025.19+302751 | 240.104995 | 30.464225 | 1.6511 |  $-$26.14 | 38.94 | 0 | $<26.1$ | $<23.55$
163021.64+411147 | 247.590194 | 41.196401 | 1.6511 |  $-$27.12 | 39.35 | 0 | $41.4\pm 5.2$ | 23.75
172057.26+284745 | 260.238597 | 28.795948 | 1.6507 |  $-$26.28 | 39.06 | 0 | $<19.5$ | $<23.42$
213109.57+104714 | 322.789885 | 10.787284 | 1.6517 |  $-$26.25 | 38.96 | 0 | $<44.1$ | $<23.77$
215800.38+002724 | 329.501589 | 0.456736 | 1.6504 |  $-$26.96 | 39.15 | 0 | $<42.0$ | $<23.75$
234607.49$-$002908 | 356.531222 |  $-$0.485763 | 1.6511 |  $-$26.26 | 38.65 | 0 | $<22.5$ | $<23.48$
235321.03$-$085930 | 358.337649 |  $-$8.991838 | 1.6509 |  $-$26.73 | 39.09 | 0 | $56300.0\pm 150.0$ | 26.88
|  |  |  |  |  |  |  |

Two of the 50 potential VLA targets were bright enough ($f_{\nu}>1\,$mJy
beam-1) to be detected in the FIRST survey (Helfand et al., 2015), while the
other 48 required new, deeper radio observations, using the VLA. We observed
these targets in the VLA C configuration and in the S band ($\sim$2–4 GHz) in
order to optimize the angular resolution of our images, as well as to leverage
archival data from past and ongoing/future radio sky surveys (e.g., FIRST and
the VLA Sky Survey [VLASS; Lacy et al. 2020]). Since our investigation entails
testing the possible origins of radio emission, it was necessary to observe
down to limits consistent with processes driving the radio emission in radio-
quiet quasars. Magliocchetti et al. (2016) evaluate the typical break
luminosity between star-forming galaxies and AGN in the 1.4-GHz radio
luminosity function (RLF) for $z>1$ to be $\log(L_{\rm 1.4GHz}(z)[\mathrm{W\
Hz^{-1}sr^{-1}}])\approx 21.7+z$. For the mean redshift of our sample,
$z\simeq 1.65$, (and changing to units of $\mathrm{W\ Hz^{-1}}$), that
corresponds to $\log{L_{\rm 1.4GHz}/[\mathrm{W\ Hz^{-1}}]}=24.45$, which is
roughly consistent with other estimates of the break luminosity at this
redshift (Jarvis & Rawlings, 2004; Simpson, 2017). Using a typical radio
spectral index for a star-forming galaxy ($\alpha=-0.7$; Condon 1992) and
aiming to probe at least a factor of five deeper than the observed break in
the RLF with a 3-$\sigma$ detection, the desired sensitivity limit was
approximately 7.5$\mu$Jy333In practice 15 of the 28 nondetections had rms
sensitivity $>10\mu$Jy, owing to issues such as crowded fields and the Clarke
Belt.. With 25% loss of bandwidth due to radio frequency interference (RFI) in
the S-band, 28 minutes of on-source observing time per target were required to
meet the depth criterion. Such a depth provided a good balance between the
total observing time for a large sample and the ability to attribute
nondetections to processes different from those that drive radio emission in
radio-loud quasars.

Similar depth “pencil-beam” surveys are insufficient to conduct this
investigation. For example, of the 449 sources detected to 7.5$\mu$Jy at 1.4
GHz by Seymour et al. (2004), only 1 has a known redshift in the range of
$1.5<z<1.8$. Similarly, of the more than 10,000 sources detected in 2.6 deg2
of the COSMOS field at 3 GHz, only two have $1.6<z<1.7$ (Smolčić et al.,
2017). Thus, pointed observations such as those presented here have an
effective area more than an order of magnitude larger than even moderately
wide surveys to deep radio limits.

###  3.2 VLA Data Reduction

We processed the VLA data using the Common Astronomy Software Application
(CASA, version 5.4.2; McMullin et al., 2007). Raw data from the VLA were first
calibrated by the standard VLA calibration pipeline, and the calibration data
later fine tuned by us. We first reviewed the calibrations of our data from
the initial pipeline run to check if any further flagging of our data must be
done manually (e.g., for RFI excision or poorly behaved antennas). If
significant flagging of one or more of the calibrators was necessary (as
opposed to just flagging the target), we re-ran the pipeline on the flagged
dataset. Once we performed/reviewed preliminary calibrations on our target, we
were able to begin the imaging process with the object’s fully calibrated
measurement set.

To create all images, we utilized the tclean task in CASA with natural
weighting (which is the weighting scheme that provides the best sensitivity to
point sources). For images that contain other very bright contaminating
sources within the field of view, or exhibit clear tropospheric phase errors,
we applied up to three rounds of phase-only self-calibration.

While we designed our observations to reach a target sensitivity of
7.5$\mu\mathrm{Jy}$, the actual noise level of an interferometric image
depends on weather during the observation, the amount of flagged data, quality
of calibration, and the proximity of bright sources within the field-of-view.
For 21 of our targets (13/21 are non-detections), we successfully reached
noise levels of $\approx$7.5–10$\mu$Jy; a further 16 targets yielded
sensitivity levels of $<15\mu$Jy (with 10/16 undetected). Of the 11 remaining
VLA targets, 10 were constrained to noise levels $<20\mu$Jy, owing mostly to
augmented interference in regions coinciding with the Clarke belt. Four of
these 10 are non-detections; for these non-detections a 20$\mu$Jy rms gives a
3-$\sigma_{\mathrm{rms}}$ upper limit of $\log(L_{\rm
3GHz}[\mathrm{W\,Hz^{-1}}])=23.91$. Finally, the image of SDSS 1058$+$0152 is
severely dynamic-range limited, owing to a powerful 120mJy source at
105829.56$+$013401.02 (offset $\simeq 18^{\prime}$ from the central target)
and making it difficult to obtain useful radio data for this target. We
nevertheless retain its $\approx 220\mu$Jy rms, which corresponds to a
3-$\sigma_{\mathrm{rms}}$ upper limit on its radio luminosity at $\log(L_{\rm
3GHz}[\mathrm{W\,Hz^{-1}}])<24.94$.

###  3.3 VLA Detections and Limits

Of our 50 targets, 22 are detected at peak flux densities $f_{\nu}>30\mu$Jy
beam-1, while 3-$\sigma_{\mathrm{rms}}$ upper limits are obtained for the peak
flux densities of the 28 non-detections. All radio data taken from our sample
are reported in Table 1. Images of the 22 detections (including two from
FIRST) are shown in Figure 1, while a histogram of total radio flux density is
shown in the left panel of Figure 2. Measured 3-GHz radio luminosities are
converted to 1.4 GHz assuming $\alpha=-0.7$ and also to $\log R$ in the middle
panel assuming the median optical luminosity of the sample
$\log{L_{2500\AA}\mathrm{[W\,Hz^{-1}]}}=23.81$.

Figure 1: Images of radio detections in our sample ($\simeq
90^{\prime\prime}\times 90^{\prime\prime})$. All of the sources detected by
our VLA observations (rainbow colormap) at 3 GHz are on the same linear flux
density scale up to $\approx 500\mu$Jy, indicated by the colorbar at the top
of the figure. The 1.4-GHz images of the two FIRST sources (red-to-white
colormap) are scaled linearly as well, from zero up to the peak flux density
of each source ($\approx$50mJy beam-1 for J2353$-$0859; $\approx$2.5mJy beam-1
for J1019$+$4948).  Figure 2: Left: Distribution of measured total 3-GHz flux
densities (blue) and upper limits (open histogram with arrows) in our VLA
sample and corresponding luminosity distribution. Sources with pre-existing
FIRST detections are shown in orange. The top axis converts the measured flux
densities to luminosity assuming a source redshift of $z=1.65$. Middle:
Histogram of radio luminosity for our pointed observations converted to 1.4
GHz assuming $\alpha=-0.7$. The grey shaded region marks $\pm 2$-$\sigma$
bounds of the overall distribution of forced photometry on our parent sample
(§3.4). Vertical lines indicate the expected median radio luminosity from star
formation (dash-dot and dotted; see §6), the intended radio luminosity depth
of our pointed observations (dashed), and the break luminosity at $z\simeq
1.65$ (solid; above which AGN contributions to the radio are thought to
dominate over SF). The top axis converts the displayed radio luminosity to
$\log{R}$ assuming a constant optical luminosity
$\log{L_{2500\AA}\mathrm{[W\,Hz^{-1}]}}=23.81$ (the median optical luminosity
of the parent sample). All quasars with
$\log{L_{1.4GHz}\mathrm{[W\,Hz^{-1}]}}>25$ have $\log{R}>1$ (the traditional
divide between RL and RQ). Right: Stacked histogram of the 123/388 LoTSS-
detected sources selected from our parent sample (see §3.5). Sources detected
only in LoTSS are colored blue while those with known FIRST detections are
orange. In computing luminosities, measured 144-MHz flux densities are
converted to 1.4 GHz assuming a constant radio spectral index $\alpha=-0.7$;
for FIRST-detected quasars, we use their measured 1.4-GHz FIRST flux
densities.

Overall, 12 objects (24%) are detected at high significance
($\mathrm{S/N}>6$), while the other 10 detections (20%) are more marginal at
$\mathrm{S/N}=3$–6. SDSS 2353$-$0859 (a FIRST source) is the only object to
exhibit clear signs of extended morphology (what appears to be a core with two
lobes, one brighter than the core, one fainter) with just one other object
that may have weak extended emission (SDSS 0013$-$0024). It is possible some
sources with wide separation lobes, that are not easily associated with the
core, exist. However, most of the potential candidate lobes either have
optical counterparts or would be at separations of $>500$ kpc.

We also performed a median stacking analysis on the 28 undetected sources,
finding a median peak flux density of 22$\mu$Jy beam-1 with rms noise in the
stacked image of 2.9$\mu$Jy beam-1. Thus, while the majority of our sources
are formally undetected, we would expect them to be detected at $3\sigma$ on
average had we been able to achieve our goal of 7.5 $\mu$Jy rms uniformly
across the full sample.

###  3.4 Forced Photometry of Parent Sample

Our sample of 50 targets was drawn from a parent sample of 8403 known, color-
selected, high S/N SDSS quasars as discussed in Section 2. For the sake of
comparison, we performed forced photometry for the parent sample using the
radio images from the NRAO-VLA Sky Survey (NVSS; Condon et al. 1998),
measuring the image peak flux density at the known position of each
object.444https://www.cv.nrao.edu/nvss/NVSSPoint.shtml A Gaussian fit to the
distribution, sigma-clipping the tail of bright sources, yields a median of
0.09 mJy beam-1 with a standard deviation of $\sigma$ = 0.49 mJy beam-1. The
error on the median peak is just $\sigma/\sqrt{8403}=0.005$ mJy beam-1, which
strongly indicates that the average undetected source has non-zero radio
emission. The range of radio luminosities for this forced photometry is shown
by the grey shading in the middle panel of Figure 2. This comparison enables
us to see that only the known FIRST sources populate the extreme tail of the
distribution that is almost certainly dominated by jetted emission (e.g.,
Jarvis & Rawlings, 2004; Simpson, 2017).

###  3.5 LOFAR Data

While focused on a very different part of the radio spectrum (144 MHz) than
our VLA S-band observations, the median depth (71 $\mu$Jy beam-1 median) of
the LoTSS data is similarly sensitive to quasars with radio spectral indices
of $\approx-0.7$. Thus LoTSS provides an additional source of data for our
experiment. We specifically make use of the data from the first data release
(DR1; Shimwell et al. 2019), covering over 400 deg2 with optical
identifications and morphological classifications provided by Williams et al.
(2019).

Rankine et al. (2021) demonstrate that radio-loud LOFAR detections (using a
defintion of radio loud that is adjusted for the differences in frequency
coverage between VLA and LOFAR) are likely drawn from the same parent
population as radio-loud sources identified by FIRST. Thus it is possible to
combine the 3GHz and 144MHz data, despite the order of magnitude difference in
frequency. Of the 8403 color-selected sources in our parent sample, there are
388 sources for which LoTSS DR1 should reach comparable depth (for a typical
radio spectral index). Of those 388, 123 sources are detected. These
detections are shown in the right-hand panel of Figure 2. Sources with
existing FIRST detections are shown in orange as for the VLA data.

While the LoTSS DR1 sample covers only a small area of sky, the detection
fractions for both our VLA observations and in the LoTSS area make it clear
that, for the immediate future (until the Square Kilometer Array
precursors555https://www.skatelescope.org/precursors-pathfinders-design-
studies/ begin data releases), pointed observations will continued to be
needed for investigations into the origin of radio emission in RQ quasars.
Neither VLASS nor LoTSS reach depths that enable detections of the majority of
$z\approx 1.5$–2 sources that can best probe a wind origin for radio emission
and do so for large areas of sky.

##  4 Defining the C IV Parameter Space

What we seek to learn herein is to what extent the radio properties of quasars
are uniform across a parameter space sensitive to the presence of accretion
disk winds, or if the radio properties are systematically changing across that
space. Ideally the parameter space would be defined by physical properties
such as black-hole mass, accretion rate, spin, and orientation. However, each
of those properties is difficult to determine robustly for individual objects.
Thus, we adopt the approach of Rivera et al. (2020) and Rankine et al. (2021)
and explore the radio properties in an empirical parameter space characterized
by the C IV $\lambda$1550 emission line.

At low redshift, the principle component analysis (PCA) of Boroson & Green
(1992)—primarily involving the H$\beta$, [O III] and Fe II emission
lines—concisely describes the diversity of the quasar population in a 2-D
space and this type of analysis has been extended by many authors (e.g.,
Brotherton & Francis, 1999; Marziani et al., 2018). At high redshift, the C IV
emission line by itself defines a 2-D space—equivalent width (EW) and
blueshift (a measure of line asymmetry)—that can be used to empirically
distinguish quasars with very different physical properties, even without
measuring those properties directly (Richards et al., 2011; Rivera et al.,
2020). Thus we ask how the radio properties are changing across as a function
of C IV EW and blueshift, guided by expectations as to how the extrema in this
parameter space translate to extrema in physical properties. A key assumption
being that the anti-correlatied behavior of the C IV EW and blueshift (Figure
3) are indicative of the strength of an accretion disk wind.

Figure 3: Top: Distribution of sources in C IV parameter space. Larger points
represent the 50 targets observed with the VLA while smaller points and
contours plot the 388 LoTSS objects as discussed in §3.5. The location of each
point’s orthogonal projection onto the best-fit line (light blue; see Rivera
et al. 2020) defines a “C IV distance” metric that we use in comparing each
object’s location in C IV space (see text in §4). Dashed lines (perpendicular
to the curve in a scaled data space) show an illustrative division into low,
moderate, and high C IV distance regimes. Bottom: Radio detection fraction as
a function of C IV distance. Fractions are computed in seven bins with equal
numbers of quasars, including both the VLA and LoTSS objects; 1$\sigma$
Poisson errors are shown by the shaded region.

Core to our analysis is spectral reconstruction based on independent component
analysis (ICA) as discussed in more detail by Allen et al. (2011), Rankine et
al. (2020), and Rivera et al. (2020). ICA serves as a tool to provide a nearly
noise-free reconstruction of the spectral features, enabling the most robust
measurements of the C IV line parameters. We use the ICA components adopted in
the analysis of Rankine et al. (2020) and compare our results to a similar
analysis of a sample of 133 diverse quasars from the SDSS “Reverberation
Mapping” program (SDSS-RM; Shen et al. 2015), as investigated by Rivera et al.
(2020).

Just as a spectrum can be reconstructed using PCA eigenvectors that are common
to a sample with eigenvalues specific to each object (Francis et al., 1992;
Yip et al., 2004), so too can a spectrum be reconstructed using ICA
“components” and the component “weights” that are specific to that object. The
ICA components derived by Rankine et al. (2020), analyzed in the same manner
as Rivera et al. (2020), are employed and the Appendix presents the spectral
reconstructions for all 50 quasars in the VLA sample. We use these
reconstructions to extract the EW and blueshift from the spectra (both the VLA
and LoTSS quasars) in a manner that results in higher S/N than measurements
made directly from the SDSS spectra themselves (as a result of the
reconstructions incorporating information from the entire optical/UV spectrum,
rather than just the C IV emission line itself).

Figure 3 shows the distribution of C IV EW and blueshift for three sources of
data: our color-selected VLA targets (large blue/red points), color-selected
LoTSS quasars (blue/red contours and small points) and the SDSS-RM sample from
Rivera et al. (2020), as represented by the light blue track. Throughout we
will refer to the “C IV distance”, which we define using the best-fit curve
from the SDSS-RM sample as a “control”, projecting each point in the VLA and
LoTSS samples to the nearest orthogonal location on the curve. Quasars that
project onto the start of the curve in the upper-left-hand corner are defined
to have zero C IV distance, with increasing values of C IV distance towards
the bottom right. Thus, high-blueshift quasars have large C IV distance, with
increasing blueshift being correlated with Eddington ratio (Rankine et al.,
2020, Fig. 14). The top and side panels show the marginal distributions, while
the bottom panel combines the EW and blueshift information to reveal that the
radio detection fraction is a non-linear function of C IV distance.

It is instructive to look at the data more closely to see if the two
subsamples find the same non-linear trend. The overall radio-detection
fraction for our two color-selected samples is 33.1$\pm$2.8% and for VLA and
LoTSS objects separately are 44.0$\pm$9.4% and 31.7$\pm$2.9%, respectively. At
low-blueshift and high-EW (C IV distance less than 2.0) we find that the
detection fraction is instead 41.7$\pm$7.6% and is higher for both the VLA and
LoTSS quasars separately (50$\pm$22% and 41$\pm$8%, respectively).

While those detection fractions are less than 2$\sigma$ above the average,
moving to moderate blueshift and EW (moderate C IV distance), we find a
significantly lower radio-detection fraction. Overall it drops to
27.0$\pm$3.0% and is lower for both the VLA and LoTSS quasars (38$\pm$11% and
26$\pm$3%, respectively).

Then moving to high blueshift and low EW (C IV distance larger than 3.2), the
radio-detection fraction again increases. The overall fraction is
50.0$\pm$8.5% and is higher for both the VLA and LoTSS quasars (67$\pm$33% and
48$\pm$9%, respectively). These results are summarized in Table 2.

As the goal of this work is to probe the origin of radio emission in RQ
quasars, it is important to also consider these fractions after excluding
FIRST sources, which are likely to be dominated by jet emission at the
redshifts probed herein. Doing so leaves the overall trend in radio-detection
fraction unchanged: 35.4$\pm$7.4% at small C IV distance, 24.5$\pm$3.0% at
moderate C IV distance and 45.0$\pm$8.7% at large C IV distance. Again the
trend is consistent for both the VLA and LoTSS data.

An obvious concern is that dust-reddening could be playing a role in the
radio-detection fraction given known trends between radio emission and both
intrinsic quasar colors in the optical and the presence of dust reddening
(Richards et al., 2003; White et al., 2007; Kratzer & Richards, 2015; Klindt
et al., 2019; Fawcett et al., 2020; Rosario et al., 2020). The color-selected
nature of our samples should mitigate against a bias towards more easily
finding RL quasars in dust-reddened sources explicitly due to being targeted
as radio sources. However, we find that the trends with C IV distance remain
even if we exclude the most likely dust-reddened sources ($\Delta(g-i)>0.3$;
Richards et al. 2003): 37.9$\pm$7.6% at small C IV distance, 25.6$\pm$3.0% at
moderate C IV distance and 50.0$\pm$8.6% at large C IV distance.

Rankine et al. (2021, Fig. 3) see a similar distribution in radio-detection
fraction with blueshift for LoTSS quasars that are not restricted to being
color selected—but without a well-defined rise at low values. This difference
(fall then rise of the C IV detection fraction with C IV distance in our work
versus a consistent rise with C IV blueshift in Rankine et al. 2021) may point
to the C IV distance being a better metric. For example, in addition to being
correlated with blueshift at high blueshift, $L/L_{\rm Edd}$ is also seen to
be anti-correlated with C IV EW (Baskin & Laor, 2004; Shemmer & Lieber, 2015).
Indeed, there is other evidence for the type of nonlinearity that the C IV
distance captures. For example, Rankine et al. (2020) find a gradient of He II
EW along what we define as the C IV distance, which distinguishes moderate and
high EW sources at similar blueshift. We further note that White et al. (2007,
Fig. 13) found a similar non-linear distribution in radio loudness as a
function of optical color. These trends will be discussed in more detail in
Section 7.1.

Table 2: Detection Fractions as a Function of C IV Distance

C IV Distance | VLA | LoTSS | Combined
---|---|---|---
Low |  $50\pm 22$% |  $41\pm 8$% |  $41.7\pm 7.6$%
Moderate |  $38\pm 11$% |  $26\pm 3$% |  $27.0\pm 3.0$%
High |  $67\pm 33$% |  $48\pm 9$% |  $50.0\pm 8.5$%

Note. — Radio detection fractions for VLA (50 quasars) and LoTSS (388)
samples. “Low” and “High” regions of C IV distance are illustrated by the
dashed lines in Figure 3, marking points with C IV Distance $<2.0$ and $>3.2$,
respectively.

##  5 Possible Origins of Radio Emission

An experiment that fully explores the diversity of quasars and thus the
occurence of the four potential sources of radio emission from quasars (e.g.,
Panessa et al., 2019) might be expected to reveal one (or more) of the
following trends with respect to C IV distance.

If the radio emission in RQ quasars is dominated by winds, then we might
expect the radio-detection fraction to increase with C IV distance—due to
objects with stronger winds (as indicated by larger C IV blueshifts) being
more likely to have shock-related radio emission (e.g., Laor et al., 2019).
The correlation need not be one-to-one, as said radio emission would require
the presence of dense material for the wind to run into; therefore not all
sources with winds would be expected to exhibit radio emission.

If RQ quasars are instead dominated in the radio by coronal emission, the
radio detection fraction would decrease (Raginski & Laor, 2016; Laor et al.,
2019; Giustini & Proga, 2019) with C IV distance. X-rays provide the indicator
of the expected direction of the trend assuming that changes in the X-ray are
correlated with the overall size of the corona, and that the coronal X-ray and
radio emission are correlated. X-ray emission gets weaker with increasing
$L/L_{\rm Edd}$, both as found empirically (Sulentic et al., 2007; Kruczek et
al., 2011; Timlin et al., 2020) and as predicted by accretion disk wind models
(Giustini & Proga, 2019). Thus, we expect radio emission of coronal origin to
result in less radio emission with increasing C IV distance.

In terms of a star formation origin, a naïve prediction might be that there
would be no trend in C IV distance. At the redshifts investigated herein the
parent sample of SDSS quasars are all brighter than the “characteristic”
luminosity, $L_{*}$, that defines the break in the quasar luminosity function
(Richards et al., 2006b; Shen et al., 2020). That being the case, we might
expect the SDSS-DR7 sample to lack the diversity of the full AGN population
(i.e., objects both brighter and fainter than $L_{*}$)—in terms of properties
such as host-galaxy morphology and environment—that are potentially related to
star formation rate (SFR) and SFR-related radio emission (see Section 6).
Moreover, all of our targets herein are drawn from a very narrow range of both
redshift and optical luminosity. If, however, star formation is influenced by
“feedback” processes (e.g., Silk & Rees, 1998), including accretion disk
winds, then a trend with C IV distance might be expected—given the known
correlation between accretion-disk winds and C IV distance (Richards et al.,
2011; Wang et al., 2011). Specifically, if winds generate positive feedback—in
the sense of increasing star formation—then a higher radio detection fraction
would be expected with increasing C IV distance. For negative feedback, the
opposite would be expected. Of course, there is no a priori reason why SF and
accretion processes would have to be contemporaneous and we are implicitly
assuming that the gas content and star-formation efficiencies of quasar hosts
remained unchanged with C IV distance.

As for a possible jet origin, we do not make a prediction so much as point out
that strong radio sources are anti-correlated with C IV distance666Apparently
as are radio-quiet—but otherwise high radio luminosity—flat-spectrum sources
(Timlin et al., 2021)., and that, while our VLA data probe $\approx 20\times$
deeper than FIRST, we might not expect more than a single source with jetted
emission: FIRST’s depth was chosen in order to probe the break in the radio
luminosity function, below which star formation starts to dominate over the
jetted population. Therefore we do not expect to find significant jet emission
among sources that were not already detected by FIRST. However, we acknowledge
that our observations (indeed most radio observations) lack the resolution to
identify small-scale jets. In Section 7 we dicuss some insights on whether the
presence of such small-scale jets may increase or decrease with C IV distance.

In short, our experiment is about looking for trends in radio emission with C
IV distance. Our VLA pilot study, even when combined with LoTSS-DR1, is, no
doubt, too small to definitively answer the question of the origin of radio
emission in RQ quasars. However, in the absence of information about small-
scale jets, an increase of the radio-detection fraction with C IV distance
might suggest that radio emission is produced by either winds or star
formation with positive feedback, while a decrease of the radio-detection
fraction with C IV distance would point to either a coronal origin or star
formation origin (with the AGN contributing negative feedback).

##  6 Star Formation Rates

As the predictions in Section 5 involve not just expectations for radio
emission but also for SFRs, we turn to a discussion of how radio and SFRs are
expected and observed to be related in these data. We estimate the level of
detectable star formation using the far-infrared radio correlation (Helou et
al., 1985; Yun et al., 2001; Calistro Rivera et al., 2017), which ties
together star formation rates, far-IR emission, and radio emission. For our
VLA observations, we find that a 3-$\sigma$ detection in an image with an rms
of 10$\mu$Jy should probe to SFRs of $\sim$400 $M_{\sun}\,{\rm yr}^{-1}$. In
estimating the SFR probed (purple points in Fig. 4), we are naïvely applying
the Yun et al. (2001) relationship to objects at a very different redshift to
that where the correlation was derived. Furthermore, according to Yun et al.
(2001), the relationship between SFR and 1.4-GHz radio applies for objects
fainter than $\log(L_{\rm 1.4GHz}[\mathrm{W\,Hz^{-1}}])\approx 24$. More
accurately, the relation applies to objects below the break of the luminosity
function (above which the AGN is thought to be contributing as much or more
than star formation to the radio emission). A break luminosity at $z\simeq
1.65$ of $\log(L_{\rm 1.4GHz}(z)[\mathrm{W\ Hz^{-1}}])\approx 24.45$
translates to a SFR of $\simeq 1650\,M_{\sun}\,{\rm yr}^{-1}$. Thus, radio
luminosities (or SFRs) in our sample that are higher should be taken as a
strong sign that something other than star formation is contributing to the
radio emission, and we indicate this in Figure 2 as where AGN-related
processes might be expected to dominate star formation.777Noting that an SFR
in excess of $\approx 1650\,M_{\sun}$yr-1 is high, but Rowan-Robinson et al.
(2018) have argued for the existence of “extreme” starbursts with SFRs in
excess of $5000\,M_{\sun}$yr-1.

We can also make specific predictions for radio emission from star formation
by taking advantage of far-infrared observations, with the assumption that
continuum emission at far-IR wavelengths is dominated by star formation rather
than the AGN. Specifically, Harris et al. (2016) performed stacking analysis
on a sample of 1000 SDSS quasars at $2<z<3$ that are largely undetected by
Herschel. These data were used to determine the SFR as a function of various
properties, under the assumption that the far-infrared emission ($\lambda_{\rm
rest}>80\mu{\rm m}$) is dominated by star formation and not the AGN itself;
see also Rosario et al. (2013). Harris et al. (2016) present results as a
function of the C IV EW and optical luminosity, which makes their
investigation an excellent source of comparison for our own work. They also
investigated the SFR as a function of C IV FWHM and the C IV line asymmetry;
we have not made use of either of these two results as it is not clear that
the FWHM of C IV has a clear relationship with black-hole mass (Coatman et
al., 2017) and the asymmetry parameter is different from the blueshifts
investigated herein. However, Maddox et al. (2017) perform a similar analysis
to that of Harris et al. (2016), measuring the C IV blueshifts (using the same
definition adopted herein) of a sample of 1185 quasars at $1.6<z<4.8$ with
2-$\sigma$ detections at $\lambda_{\rm obs}>250\mu$m (and a matched sample of
undetected sources). Therefore we can compare our work to independent
indicators of SFR as a function of both EW (Harris et al., 2016) and blueshift
(Maddox et al., 2017); i.e., as a function of C IV distance.

Over a range of more than three orders of magnitude in optical luminosity,
Harris et al. (2016) find an increase in SFR from $\approx 100$ to $\approx
450\,M_{\sun}$yr-1 with increasing optical luminosity; see also Stanley et al.
(2017). For the narrow luminosity range of our sample, the expected systematic
increase in SFR is from $\approx 490$ to $\approx 620\,M_{\sun}$yr-1. The SFRs
determined by Harris et al. (2016) from binning by C IV EW span a larger SFR
range ($\approx 50$ to $\approx 500\,M_{\sun}$yr-1) with decreasing EW (from
$\approx$ 135Å to $\approx 25$ Å). Our sample spans a larger range of EWs than
the Harris et al. (2016) sample; the predicted SFRs range from $\approx
190\,M_{\sun}$yr-1 for the largest EW sources ($\approx$ 65Å) to one object
with very low EW (SDSS J1200+0005) having a predicted value of SFR $\approx
1690\,M_{\sun}$yr-1. While Maddox et al. (2017) do not perform the same sort
of stacking analysis, their comparison of FIR detections to non-detections
indicate that quasars with larger C IV blueshifts are systematically brighter
in the FIR. Thus objects with large C IV distance might be expected to have
higher SFRs.

Figure 4 shows the expected SFR based on the correlation between SFR and both
EW (orange) and optical luminosity (green) found by Harris et al.
(2016)—against the observed radio luminosity. With the caveat that these
calculations intrinsically assume that all quasars are similar to the mean
quasar, comparing the green and orange points (with SFRs predicted from non-
radio data) to the purple points (with SFRs predicted from the radio
luminosity using Yun et al. 2001) reveals that the most luminous radio sources
may have contributions to the radio in excess of that expected from star
formation. However, we can use the results of Rankine et al. (2021) to
understand what might be expected from analysis of a distribution of quasars
instead of considering mean quasar properties via stacking analysis. Based on
detections and non-detections from LoTSS, Rankine et al. (2021) performed a
Monte Carlo simulation that suggests that the radio observations are
consistent with a population of SFRs from 10s to 1000s of solar masses per
year that could potentially produce radio emission to levels as high as
$\log(L_{\rm 144MHz}[\mathrm{W\,Hz^{-1}}])\approx 26$, which is equivalent to
$\log(L_{\rm 1.4GHz}[\mathrm{W\,Hz^{-1}}])\approx 25.3$ for $\alpha=-0.7$. It
may be that Harris et al. (2016) do not find SFRs in the 1000s as a result of
the small area of sky sampled by Herschel in their study (i.e., a lack of
sensitivity to rare quasars with high SFRs), and thus that their stacking
approach causes us to underestimate the range of SFRs using their empirical
correlations. Therefore, even given the apparent discrepancy with the Yun et
al. (2001) SFR predictions starting at $\log(L_{\rm
1.4GHz}[\mathrm{W\,Hz^{-1}}])\approx 24$, it is quite possible that star
formation accounts for the radio emission up to $\log(L_{\rm
1.4GHz}[\mathrm{W\,Hz^{-1}}])\approx 24.5$–25.3.

Figure 4: SFRs of our 50 VLA targets predicted from the observed radio
luminosity using Yun et al. (2001) and expected based on the optical
luminosity [$M_{i}(z=2)$] and EW relations from Harris et al. (2016) as a
function of their observed radio luminosities (3-GHz flux densities measured
from the VLA are converted to 1.4 GHz assuming $\alpha=-0.7$). Marginal
distributions are shown on the top and side axes. The black vertical lines
mark the luminosity above which the AGN is expected to dominate the observed
radio emission (dashed) and our intended flux density limit with the VLA
(dotted; §3.1).

Figure 5 (left) scales the points in the C IV space of Figure 3 by the
expected SFR as predicted by Harris et al. (2016). Here we have averaged the
SFRs predicted from both the EW and optical luminosity. It is apparent that
the results of Harris et al. (2016) and Maddox et al. (2017) would predict a
trend of increasing radio luminosity (from star formation) with C IV distance.

Figure 5: Left: C IV plot with points scaled by SFR predicted from the average
of the optical luminosity [$M_{i}(z=2)$] and EW relations from Harris et al.
(2016). Right: C IV plot with points scaled by the ratio of observed radio
luminosity to that predicted by the Harris et al. (2016) and Yun et al. (2001)
relationships. Point sizes larger than that of the non-detections indicate
more radio emission than predicted from SF; the detected quasars are generally
brighter in the radio than expected from SF. The color scheme of both panels
follows that of Figure 3. Dashed lines (which are perpendicular to the best-
fit curve in scaled space) divide the parameter space into regions of low
(upper left), moderate (lower left), and high (lower right) C IV distance (see
§4).

On the other hand, there is also reason to think that the SFRs estimated by
the Harris et al. (2016) analysis are instead over-estimates. As noted by
Harris et al. (2016), their assumption of an SFR origin for the entirety of
the FIR emission ignores the possible kpc-scale dust distribution in AGNs.
Nevertheless, they conclude that AGNs are likely to contribute significantly
only to the shortest-wavelength Herschel bandpass. A possible reason that this
modeling may be incomplete is the assumption of a single SED to describe all
AGN, whereas it is well known that AGNs can have significantly different SEDs
(at least at 10–20$\mu$m) as a function of optical luminosity (Krawczyk et
al., 2013).

Wang et al. (2013) and Temple et al. (2021) find a significant correlation
between C IV blueshift and hot dust, attributing that correlation to a greater
ability to view the hot dust component in quasars with strong winds. If that
hot dust component is correlated with a cooler dust component on larger
scales, then the assumption inherent to the Harris et al. (2016) argument may
not be true. In such a case, it is possible that the FIR emission is a
stronger indicator of AGN-related processes than SFR-related processes; see
also Symeonidis (2017).

While Symeonidis et al. (2016) find no significant difference between low- and
high-luminosity AGNs in the FIR, we note that the objects used in that
investigation, while luminous for their redshift, are much less luminous than
the samples investigated in Harris et al. (2016) and herein. Thus, the
Symeonidis et al. (2016) results do not necessarily contradict the finding of
higher FIR emission in more luminous quasars.

The predicted trend of SFR with C IV distance is such that, if the FIR is
indeed due to star formation, then quasar winds are either correlated with the
same physics that regulates star formation or are providing positive feedback
since those objects with evidence of strong winds are correlated with higher
rates of star formation. If, on the other hand, the results of Temple et al.
(2021) and Symeonidis (2017) lead to the conclusion that the FIR emission is
AGN dominated, then the predictions for radio emission from star formation
derived by Harris et al. (2016) must be considered upper limits (and the
prediction of increasing SFR with C IV distance is then uncertain).

##  7 Discussion

Having made predictions for radio emission and SFRs as a function of C IV
distance, what do we find? We explore this question in terms of the radio
detection fraction, the radio luminosity, and the radio luminosity relative to
that expected from SFR—as a function of C IV distance (Section 7.1). With our
basic results in mind (no simple linear correlation between radio luminosity
or detection fraction with C IV distance and radio detections in excess of
that expected from star formation at low C IV distance), we discuss recent
work from the literature (Section 7.2) in an attempt to frame an explanation
for our findings (Section 7.3).

###  7.1 Results

The non-linear trend in radio detection fraction with C IV distance seen in
Section 4 does not itself reveal the origin of radio emission in RQ quasars,
but it does strongly suggest that there is more than one source of radio
emission in RQ quasars, as the observations show a more complex relationship
with C IV distance than predicted from star formation. In terms of our
predictions from Section 5, the initial decline in radio detection fraction
could come from negative feedback on star formation, the corona, or decreasing
presence of weak jets. The subsequent rise in radio-detection fraction could
come from positive feedback on star formation or shock-related emission due to
winds. More work is needed to explore trends in the radio-loud fraction (e.g.,
as explored by Jiang et al. 2007, White et al. 2007, Kratzer & Richards 2015
and Rankine et al. 2021) with C IV distance to determine if it also follows a
non-linear distribution.

Unlike the trend in radio-detection fraction, we do not observe any clear
trends with C IV distance in terms of radio luminosity. However, it is
instructive to explore how the radio luminosity relative to that expected from
star formation (Fig. 5, left) changes as a function of C IV distance. In the
right panel of Figure 5 we instead plot sources according to the ratio of
their measured radio emission (for detections) as compared to their predicted
radio emission (from the left panel), where points larger than the non-
detections (red) are indicative of radio emission in excess of star formation.
While the Harris et al. (2016) predictions only capture the median SFR, there
is a systematic trend that may nevertheless be robust. Specfically, small C IV
distance quasars have radio emission in excess of that predicted from star
formation, whereas quasars at large C IV distance (thought to have higher
$L/L_{\rm Edd}$) are more consistent with SFR predictions of radio emission.

If the origin of radio emission is primarily related to the AGN, then one
might expect the radio luminosity to scale simply with the optical/UV
luminosity. We test that prediction by comparing our data to that of Kimball
et al. (2011b) and Condon et al. (2013) in Figure 6, finding that our sources
are about 1.1 dex more luminous in the optical/UV and 1 dex more luminous in
the radio. Of course, it is possible that the observed higher radio luminosity
with higher optical luminosity could also be associated with higher star
formation (Stanley et al., 2017). The radio non-detections, despite having
much higher optical/UV luminosity, have upper limits on their radio
luminosities that are not inconsistent with those from the lower-redshift
sample from Kimball et al. (2011b) and Condon et al. (2013).

Figure 6: Radio vs optical luminosity (both in W Hz-1). Green triangles and
filled green histograms represent the full low-redshift ($0.2<z<0.3$) 6-GHz
sample of 179 quasars from Kimball et al. (2011b) converted to 1.4 GHz
assuming $\alpha=-0.7$. Condon et al. (2013) uses NVSS forced photometry to
extend this sample to $z<0.45$; sources with positive flux densities from this
sample (965/1313) are shown by the green contours/small data points and open
green histograms. Magenta points and contours represent the forced photometry
of our parent sample of 8403 quasars (§3.4), limited to $z<1.75$ and sources
with positive NVSS flux densities, which brings the total source count to
1498. Our 3-GHz VLA data of 50 quasars converted to 1.4 GHz assuming
$\alpha=-0.7$ are shown in red, blue, and orange; the downward arrows on the
non-detections emphasize that their radio luminosities are plotted as
3-$\sigma_{\mathrm{rms}}$ upper limits. The marginal distributions are given
by the histograms on the top and right axes.

###  7.2 Literature Review

Kimball et al. (2011b) analyzed a sample of 179 SDSS quasars with $0.2<z<0.3$
and more luminous than $M_{i}=-23$. They concluded that the radio luminosity
function is consistent with two independent origins for radio emission in
quasars, which they attributed to starburst-level SF processes at the faint
end and AGN processes at the bright end. The transition region is roughly
$\log L_{\rm 6GHz}=29.5$–$30.5\,{\rm erg\,s^{-1}}$, which corresponds to $\log
L_{\rm 1.4GHz}=23.34$–$24.34\,{\rm W\,Hz}^{-1}$, assuming linear evolution of
the break luminosity with redshift. Figure 6 demonstrates that the objects in
our sample are much more luminous than the lower-redshift sample of Kimball et
al. (2011b) in the optical/UV and also the radio.

Condon et al. (2013) extended the work of Kimball et al. (2011b) by building a
larger low redshift sample (1313 quasars at $0.2<z<0.45$) and adding a high
redshift sample (2471 quasars at $1.8<z<2.5$), which is more similar to the
sample investigated herein. They found that, analogous to the low-redshift
sample, there is sign of multiple radio origins among NVSS sources: radio
emission in the RL tail is dominated by AGNs, but the radio luminosity
distribution must have a bump at low flux densities, which is consistent with
a star formation origin dominating the radio emission for the fainter sources.
They argued that the level of radio emission in their high-$z$ sample suggests
SFRs as high as $\dot{M}\sim 500M_{\odot}\,{\rm yr}^{-1}$.

On the other hand, White et al. (2017) analyzed a sample intermediate in
redshift between the two Condon et al. (2013) samples, specifically 70
$z\approx 1$ Herschel AGN. As with Harris et al. (2016) they used FIR emission
as a tracer of star formation—assuming that AGNs contribute little at the
longest FIR wavelengths. Using two SFR correlations they found that 92% of RQ
quasars have radio emission that is accretion dominated and that 80% of the
emission is due to accretion.

Mullaney et al. (2013), Zakamska & Greene (2014), Zakamska et al. (2016), and
Hwang et al. (2018) similarly concluded that radio emission in RQQs is
dominated by the AGN, but argued that the emission is related to shocks from
uncollimated, subrelativistic winds interacting with the inter-stellar medium.
Papers addressing such an origin from the theoretical perspective include
Ciotti et al. (2010), Jiang et al. (2010a), and Nims et al. (2015). Nims et
al. (2015) argued that it should be possible to distinguish between jets and
winds by the spatial extent of emission and radio spectral index (where they
suggest $\alpha\approx-1$ for winds). However, it is harder to then
distinguish between AGN and star formation—without looking to other
wavelengths for help.

Zakamska & Greene (2014) found that the [O III] velocity width of outflows is
correlated with radio luminosity in RQ quasars and that the width is
additionally correlated with mid-IR luminosity, also suggesting an AGN-
intrinsic origin (because the mid-IR is dominated by the AGN). Zakamska &
Greene (2014), however, instead looked to shocks from quasar-driven winds as
suggested by Stocke et al. (1992), similar to how SNe produce radio emission.
They concluded that it would be hard to distinguish jet emission from wind
emission based on morphology of the ionized gas or the radio spectral index,
but there must be a threshold for wind driving on the order of bolometric
luminosity, $L_{\rm Bol}=3\times 10^{45}{\rm erg\,s}^{-1}$. The median $\log
L_{\rm Bol}$ for our targets is 46.3 in those same units—consistent with the
design of the observing program to target quasars likely to show evidence of
accretion disk winds.

To further connect the Zakamska & Greene (2014) result with our own, we note
that Coatman et al. (2019) showed that [O III] blueshift and EW are correlated
and anti-correlated, respectively, with C IV blueshift. Thus, if winds
contribute significantly to radio emission in RQ quasars, we might expect a
bias towards more/higher radio emission at high blueshift (large C IV
distance). While there are indications of winds in the form of BALQSOs across
the full C IV parameter space (Rankine et al., 2020), if radio emission is
coming from shocks due to winds, then we might expect quasars with the
strongest troughs (largest balniticy indices; Weymann et al. 1991) to produce
more radio emission. We note, however, that the winds highlighted by Zakamska
& Greene (2014) are associated with strong [O III], whereas the strongest
BALQSOs tend to have have weak [O III] (Turnshek et al., 1997). Thus it may be
that [O III] is no longer useful as a tracer of winds for the very objects
where it might be most interesting (i.e., those with the strongest winds;
compare Figure 8 in Coatman et al. 2019 and the right column of Figure 16 in
Rankine et al. 2020). One explanation is that the narrow line region (NLR) may
be “running out of gas” at high luminosity (Netzer, 1990; Hainline et al.,
2014). Moreover, only with IR spectroscopy is [O III] available at $z\sim 2$
redshifts—spanning the peak of SF and AGN activity. Thus investigation of
winds using C IV allows for construction of larger samples. While the winds
traced by [O III] and C IV presumably are at very different distances from the
central engine, the NLR wind traced by [O III] in the results of Coatman et
al. (2019) suggests that the NLR may be connected to winds driven by the
central engine, which ultimately influences galaxy scales.

Zakamska et al. (2016) analyzed two $z<1$ samples: type-2 objects from Reyes
et al. (2008) and type-1 objects from Shi et al. (2007). While they agree with
Kimball et al. (2011b) and Condon et al. (2013) that their results indicate
that a second contribution beyond jets is needed, they concluded that the SF
is insufficient to explain radio by an order of magnitude. As a result,
Zakamska & Greene (2014) and Zakamska et al. (2016) make the case for radio
emission resulting from shocks due to uncollimated, sub-relativistic winds
impacting the ISM of the host galaxy.

Hwang et al. (2018) found that a special population of quasars (extremely red
quasars [ERQs] at $2<z<4$) have moderate-luminosity, compact radio sources
with steep spectra. They argued that the excess emission is more likely to be
due to uncollimated winds than star formation or coronal emission. Such
outflows can be from radiation line-driven winds, radiation pressure on dust,
or magnetic fields— all of which can produce synchrotron from shocks (Jiang et
al., 2010a; Nims et al., 2015). Hwang et al. (2018) concluded that the radio
can be from winds if just 1% of the bolometric luminosity is converted to
kinetic energy.

On the other hand, Jarvis et al. (2019) argued that evidence for a wind origin
of radio emission based on [O III] may be biased by the lack of high-
resolution radio data as they find evidence for jets in their sub-arcsecond
resolution images. However, the luminosity of their sources is below the
threshold for winds as determined by Zakamska & Greene (2014) and probed by
both our targets and the Hwang et al. (2018) sources. Nevertheless, other
evidence for weak/frustrated jets exists (e.g., Blundell & Beasley, 1998;
Ulvestad et al., 2005; Herrera Ruiz et al., 2016; Nyland et al., 2020), but it
is unclear to what extent such systems operate at the redshifts and
luminosities investigated herein (Condon et al., 2013).

###  7.3 Multiple Sources of Radio Emission in RQ Quasars?

Even at the depth probed by our observations, less than 50% of quasars are
detected in the radio, making it difficult to identify clear trends with C IV
distance that can be used to test the hypotheses that we have outlined (where
the stochasticity of the detections may itself be an important clue to the
origin of radio emission in RQ quasars). However, one of the reasons for the
lack of a clear trend may be that RQ quasars have multiple sources of radio
emission and it may be that those origins have opposite trends in terms of C
IV distance.

Taken at face value, the results of Harris et al. (2016) and Figure 5 suggest
that quasars with small to moderate C IV distance have a significant source of
radio emission in excess of that from star formation, which becomes less
significant for low-EW (higher $L/L_{\rm Edd}$) sources. Such a source could
be the corona (see Section 5). For example, in the picture of Raginski & Laor
(2016), radio emission from the X-ray corona could correlate with X-ray
luminosity. The X-ray luminosity decreases with increasing C IV distance as a
result of trends with overall luminosity (and the well-known $L_{\rm
uv}-\alpha_{\rm ox}$ relationship) and cooling of the corona by the accretion
disk. Such a trend could thus reproduce the decrease in radio detection
fraction with C IV distance at small to moderate C IV distance.

This radio excess and the decreasing radio detection fraction with C IV
distance are inconsistent with the predictions of Harris et al. (2016) and a
star formation origin of this radio emission in RQ quasars. These empirical
results are also unlikely to be due to winds (given that the shape of the
optical-to-X-ray SED changes across C IV space in a way that one would predict
weaker winds at high C IV EW; e.g., Giustini & Proga 2019).

As the prevalence of radio-loud sources decreases with C IV distance (Stocke
et al., 1992; Richards et al., 2011; Kratzer & Richards, 2015; Rankine et al.,
2021), we suggest that the presence of jets decreases with C IV distance; see
also Timlin et al. (2021). If frustrated/weak/small-scale or newly active jets
(e.g., Ulvestad et al., 2005; Nyland et al., 2020) will evolve into RL
sources, such jets may follow the same trend as RL sources and could account
for the excess of radio emission over that expected from star formation at
small C IV distances. That would also be consistent with radio detection
fraction going down from the lowest to moderate C IV blueshifts.

Going from moderate to high C IV distance we instead see a trend of increasing
radio detection fraction, consistent with the results of Rankine et al.
(2021). Given the anti-correlation with the radio-loud fraction and with X-ray
emission, it would seem that the source of this trend is unlikely to be weak
jets or coronal emission (Laor et al., 2019). Instead it is more consistent
with star formation or shocks from winds. Considering the well-known Baldwin
(1977) effect and the anti-correlations between C IV EW and luminosity, the
simplest conclusion may be that the trend of increasing radio detection
fraction with C IV distance simply reflects more star formation in higher
luminosity sources (e.g., Stanley et al. 2017, and indirectly illustrated in
the left-hand panel of Figure 5). The right-hand panel of Figure 5 is indeed
consistent with that hypothesis as the point sizes indicate that the expected
radio emission is broadly consistent with that expected from star formation at
large C IV distance.

However, we caution that that is not the only interpretation of the agreement
between predicted and observed radio emission at large C IV distance. If the
IR emission that is crucial to the construction of the SFR estimates in Harris
et al. (2016) is contaminated by emission from an AGN,888As might be suggested
by the correlation seen between blueshift and hot dust by Wang et al. 2013 and
Temple et al. 2021—which cannot be due to direct star-formation processes, but
could be due to a wind-modulated connection between the hot dust at small
distances from the BH and cold dust at large distances. then the FIR
predictions for radio emission break down. The apparent agreement between
predicted and observed radio emission at large C IV distance could instead be
indicative of radio emission dominated by shocks from winds. That is,
consistency with predictions from star formation does not necessarily confirm
a star formation origin for radio emission in RQ quasars at large C IV
distance. Indeed, such a trend in C IV space is exactly the behaviour we might
have expected from a component of radio emission with a wind-shock origin. As
such, it is not clear that trends in C IV alone could be used to distinguish
between a star formation origin and a wind origin of radio emission. More
observational and theoretical work is needed to determine if the predictions
themselves at large C IV distance could be influenced by accretion disk winds.
However, such an observation is most consistent with either a direct wind
origin for radio emission, winds contributing positive feedback to star
formation, or the radio and star formation being driven by the same underlying
physical parameter/conditions. A wind-related origin could explain both the
increasing radio-detection fraction (but decreasing radio-loud fraction) with
C IV distance and the stochastic nature of radio detection in an otherwise
homogeneous sample. Thus we conclude that a wind-related shock origin for
radio emission in RQ quasars merits further investigation.

Laor et al. (2019) make the case that the origin of radio emission in RL and
RQ quasars is different as a result of different trends in those two
populations. Our work is consistent with that interpretation and further
suggests that, even within the RQ quasar population, there may be multiple
sources of radio emission and that the C IV distance is a useful diagnostic
tool for exploring that hypothesis. Thus, we must consider the possibility
that radio emission in quasars simply does have multiple origins: jets, star
formation, coronae, and winds (e.g., Kimball, 2018), where it may be the case
that those processes are anti-correlated in a way that hinders answering the
question of the origin of radio emission in RQ quasars. This picture is
consistent with the results of Timlin et al. (2021), who find that only RQ
quasars that are flat-spectrum and have high radio luminosity exhibit excess
X-ray emission from jets. That conclusion is equivalent to saying that only
such RQ quasars have their radio emission dominated by jets. As they find that
these sources have large He II EWs, they also have small C IV distance.

##  8 Conclusions

We targeted 50 color-selected individual SDSS quasars at $z\simeq 1.65$ for
VLA radio observations probing the origin of radio emission in otherwise radio
quiet quasars. We coupled these data with 388 quasars covered by LoTSS DR1
that were drawn from the same parent sample of color-selected quasars. Such
luminous, high-redshift quasars provide an important complement to
investigations of radio emission in RQ quasars based on evidence for winds
from [O III] emission as the C IV emission line provides a unique diagnostic
of winds that are likely to have an origin in radiation line driving. To aid
in this analysis, we define a C IV “distance” that maps both C IV EW and
blueshift onto a single non-linear parameter and explore the radio properties
of RQ quasars as a function of that distance (large EW having small distance
and large blueshift having large distance).

We find that only 22 of our targets are detected at radio luminosities in
excess of $\log L_{1.4GHz}/[{\rm W\,Hz}^{-1}]$ = 23.71, which corresponds to a
star formation rate of $\approx 300\,M_{\sun}\,{\rm yr}^{-1}$ using the method
of Yun et al. (2001). Similarly only 123 of the 388 LoTSS souces are detected.
For quasars with small C IV distance, the detected quasars have radio emission
intermediate between that expected from predictions of median star formation
and that typically seen in sources with large-scale jets, possibly suggesting
coronal emission or weak/small-scale jets. There is evidence for a higher
radio detection fraction at either extrema of the C IV distance, possibly
suggesting multiple origins of radio emission in RQ quasars. Until new surveys
come on line, more pointed observations are needed on more quasars to deeper
radio limits (and higher resolution) in order to resolve the question of the
origin of radio emission in these sources and determine the level to which
winds, coronae or weak jets may compete with star formation processes. That
said, the non-detections in our sample have radio emission that is consistent
with that predicted from star formation, but only if SFR predictions based on
far-IR emission are not biased by AGN-heated dust on galactic scales, which
could imply greater contribution from winds.

Future observational work suggested by this investigation would be to
determine the C IV distances of weak/young jet sources such as those from
Nyland et al. (2020). Similarly, additional deep, high-resolution observations
are needed to test for compact jets. It would further be useful to explore not
just the radio-detection fraction of the parent sample, but the radio-loud
fraction as well, since past work has shown those properties to be anti-
correlated (Kratzer & Richards, 2015; Rankine et al., 2021). However, the tiny
fraction of quasars that are RL mean that this requires survey data. Finally,
following Laor et al. (2019), an analysis of spectral indices (or more
generally radio SEDs) of the sample (e.g., using VLASS data) would reveal if
there are any trends in terms of optically thick vs. optically thin radio
emission as a function of C IV distance that could be used to understand the
origin of the radio emission in RQ quasars.

We thank Nadia Zakamska for fruitful conversations and Josh Marvil of NRAO for
assistance with correction of VLA gain compression. We thank the referee for
suggestions that significantly improved the paper. ALR acknowledges funding
via the award of a Science and Technology Facilities Council (STFC) Ph.D.
studentship. PCH acknowledges funding from STFC via the Institute of
Astronomy, Cambridge, Consolidated Grant. The National Radio Astronomy
Observatory is a facility of the National Science Foundation operated under
cooperative agreement by Associated Universities, Inc. Funding for the SDSS
and SDSS-II was provided by the Alfred P. Sloan Foundation, the Participating
Institutions, the National Science Foundation, the U.S. Department of Energy,
the National Aeronautics and Space Administration, the Japanese
Monbukagakusho, the Max Planck Society, and the Higher Education Funding
Council for England. The SDSS Web Site is http://www.sdss.org/. The SDSS is
managed by the Astrophysical Research Consortium for the Participating
Institutions. The Participating Institutions were the American Museum of
Natural History, Astrophysical Institute Potsdam, University of Basel,
University of Cambridge, Case Western Reserve University, University of
Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the
Japan Participation Group, Johns Hopkins University, the Joint Institute for
Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and
Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences
(LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for
Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico
State University, Ohio State University, University of Pittsburgh, University
of Portsmouth, Princeton University, the United States Naval Observatory, and
the University of Washington. Funding for SDSS-III has been provided by the
Alfred P. Sloan Foundation, the Participating Institutions, the National
Science Foundation, and the U.S. Department of Energy Office of Science. The
SDSS-III web site is http://www.sdss3.org/. This work made use of AstroPy
(Astropy Collaboration et al., 2013; Price-Whelan et al., 2018), Matplotlib
(Hunter, 2007), NumPy (Harris et al., 2020), SciPy (Virtanen et al., 2020),
and Pandas (pandas development team, 2020).

##  Appendix A ICA Reconstructions

Figure showing the ICA reconstruction of each of the 50 SDSS quasars in our
sample that were observed by the VLA. The C IV EW and blueshifts are derived
from these reconstructions rather than from the original spectra.

Figure 7: Spectra and the reconstructions from which C IV-emission parameters
are measured. The legend labels the abbreviated SDSSJ name of each spectrum’s
corresponding target and further specifies which of our three ICA
reconstructions were applied to each target: the 10-component “Standard” or
“Low” EW fit, or the 7-component “High” EW; see Rankine et al. (2020). BALQSOs
are indicated based on classifications in Shen et al. (2011).

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