# Exploring Changing-look Active Galactic Nuclei with the Sloan Digital Sky
Survey V:
First Year Results

Grisha Zeltyn School of Physics and Astronomy, Tel Aviv University, Tel Aviv
69978, Israel  Benny Trakhtenbrot School of Physics and Astronomy, Tel Aviv
University, Tel Aviv 69978, Israel  Michael Eracleous Department of Astronomy
& Astrophysics, The Pennsylvania State University, University Park, PA 16802,
USA  Institute for Gravitation and the Cosmos, The Pennsylvania State
University, University Park, PA 16802, USA  Qian Yang Center for Astrophysics,
Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Paul Green
Center for Astrophysics, Harvard & Smithsonian, 60 Garden Street, Cambridge,
MA 02138, USA  Scott F. Anderson Astronomy Department, University of
Washington, Box 351580, Seattle, WA 98195, USA  Stephanie LaMassa Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Roberto J. Assef Instituto de Estudios Astrofísicos, Facultad de Ingeniería y
Ciencias, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago,
Chile  Franz E. Bauer Instituto de Astrofísica, Pontificia Universidad
Católica de Chile, Casilla 306, Santiago 22, Chile  Millennium Institute of
Astrophysics (MAS), Nuncio Monse$\tilde{\rm n}$or Sótero Sanz 100,
Providencia, Santiago, Chile  Space Science Institute, 4750 Walnut Street,
Suite 205, Boulder, Colorado 80301, USA  W.N. Brandt Department of Astronomy &
Astrophysics, The Pennsylvania State University, University Park, PA 16802,
USA  Institute for Gravitation and the Cosmos, The Pennsylvania State
University, University Park, PA 16802, USA  Department of Physics, 104 Davey
Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
Megan C. Davis Department of Physics, 196A Auditorium Road, Unit 3046,
University of Connecticut, Storrs, CT 06269, USA  Sara E. Frederick Department
of Physics and Astronomy, Vanderbilt University, VU Station 1807, Nashville,
TN 37235, USA  Logan B. Fries Department of Physics, 196A Auditorium Road,
Unit 3046, University of Connecticut, Storrs, CT 06269, USA  Norman A. Grogin
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218,
USA  Muryel Guolo Department of Physics and Astronomy, Johns Hopkins
University, 3400 North Charles Street, Baltimore MD 21218, USA  Lorena
Hernández-García Millennium Institute of Astrophysics (MAS), Nuncio
Monse$\tilde{\rm n}$or Sótero Sanz 100, Providencia, Santiago, Chile  Anton M.
Koekemoer Space Telescope Science Institute, 3700 San Martin Drive, Baltimore,
MD 21218, USA  Mirko Krumpe  Leibniz-Institut für Astrophysik Potsdam (AIP),
An der Sternwarte 16, 14482 Potsdam, Germany  Xin Liu Department of Astronomy,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA  National
Center for Supercomputing Applications, University of Illinois at Urbana-
Champaign, Urbana, IL 61801, USA  Center for Artificial Intelligence
Innovation, University of Illinois at Urbana-Champaign, 1205 West Clark
Street, Urbana, IL 61801, USA  Mary Loli Martínez-Aldama Astronomy Department,
Universidad de Concepción, Casilla 160-C, Concepción 4030000, Chile  Claudio
Ricci Instituto de Estudios Astrofísicos, Facultad de Ingeniería y Ciencias,
Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing
100871, China  Donald P. Schneider Department of Astronomy & Astrophysics, The
Pennsylvania State University, University Park, PA 16802, USA  Institute for
Gravitation and the Cosmos, The Pennsylvania State University, University
Park, PA 16802, USA  Yue Shen Department of Astronomy, University of Illinois
at Urbana-Champaign, Urbana, IL 61801, USA  National Center for Supercomputing
Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801,
USA  Marzena Śniegowska School of Physics and Astronomy, Tel Aviv University,
Tel Aviv 69978, Israel  Matthew J. Temple Instituto de Estudios Astrofísicos,
Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército
Libertador 441, Santiago, Chile  Jonathan R. Trump Department of Physics, 196A
Auditorium Road, Unit 3046, University of Connecticut, Storrs, CT 06269, USA
Yongquan Xue CAS Key Laboratory for Research in Galaxies and Cosmology,
Department of Astronomy, University of Science and Technology of China, Hefei
230026, China  Joel R. Brownstein Department of Physics and Astronomy,
University of Utah, 115 S. 1400 East, Salt Lake City, UT 84112, USA  Tom
Dwelly Max-Planck-Institut für extraterrestrische Physik, Giessenbachstraße,
D-85748 Garching, Germany  Sean Morrison Department of Astronomy, University
of Illinois at Urbana-Champaign, Urbana, IL 61801, USA  Dmitry Bizyaev Apache
Point Observatory and New Mexico State University, P.O. Box 59, Sunspot, NM,
88349-0059, USA  Sternberg Astronomical Institute, Moscow State University,
Moscow  Kaike Pan Apache Point Observatory and New Mexico State University,
P.O. Box 59, Sunspot, NM, 88349-0059, USA  Juna A. Kollmeier The Observatories
of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena,
CA 91101, USA  Canadian Institute for Theoretical Astrophysics, 60 Saint
George Street, Toronto, ON M5S 3H8, Canada Grisha Zeltyn
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

“Changing-look” Active Galactic Nuclei (CL-AGNs) are challenging our basic
ideas about the physics of accretion flows and of circumnuclear gas around
supermassive black holes (SMBHs). Using first-year Sloan Digital Sky Survey V
(SDSS-V) repeated spectroscopy of nearly 29,000 previously-known AGNs,
combined with dedicated follow-up spectroscopic observations, and publicly
available optical light curves, we have identified 116 CL-AGNs where (at
least) one broad emission line has essentially (dis-)appeared, as well as 88
other extremely variable systems. Our CL-AGN sample, with 107 newly identified
cases, is among the largest reported to date, and includes $\sim 0.4\%$ of the
AGNs re-observed in the first year of SDSS-V operations. Among our CL-AGNs,
67% exhibit dimming while 33% exhibit brightening. Our data and sample probe
extreme AGN spectral variability on timescales of months to decades, including
some cases of recurring transitions on surprisingly short timescales
($\lesssim 2$ months in the rest frame). We find that CL events are
preferentially found in lower Eddington ratio ($f_{\mathrm{Edd}}$) systems:
Our CL-AGNs have a $f_{\mathrm{Edd}}$ distribution that significantly differs
from that of a redshift- and a carefully constructed, luminosity-matched
control sample ($p_{\rm KS}\lesssim 2\times 10^{-4}$; median
$f_{\mathrm{Edd}}\approx 0.025$ vs. $0.043$). This preference for low
$f_{\mathrm{Edd}}$ strengthens previous findings of higher CL-AGN incidence at
lower Eddington ratios, found in much smaller samples of spectroscopically
confirmed CL-AGNs. Finally, we show that the broad Mg ii emission line in our
CL-AGN sample tends to vary significantly less than the broad H$\beta$
emission line. Our large CL-AGN sample demonstrates the advantages and
challenges in using multi-epoch spectroscopy from large surveys to study
extreme AGN variability, SMBH fueling, and AGN physics.

Supermassive black holes (1663), Quasars (1319), Active galactic nuclei (16)

††journal: ApJ††facilities: Sloan (SDSS and BOSS), ING:Herschel (ISIS), LCOGT
(FTN: FLOYDS), ARC (KOSMOS), HET (LRS-2).††software: AstroPy (Astropy
Collaboration et al., 2018), Matplotlib (Hunter, 2007), NumPy (Harris et al.,
2020), SciPy (Virtanen et al., 2020), PyQSOFit (Guo et al., 2018; Shen et al.,
2019), floyds_pipeline, PypeIt (Prochaska et al., 2020),Panacea

##  1 Introduction

The multiwavelength radiation emerging from active galactic nuclei (AGNs) has
long been known to exhibit stochastic variability, of order of a few to tens
of percents, and on timescales ranging from days to years (e.g., Ulrich et
al., 1997; Vanden Berk et al., 2004; MacLeod et al., 2012, and references
therein). While the origin of this variability is not yet clearly understood,
there has been a recent advancement in observations of yet more extreme and
coherent-looking changes in AGNs, thanks to advances in time-domain astronomy.
Among those dramatic variability events, “Changing-look” AGNs (CL-AGNs) are
those systems that are observed to transition between phenomenologically
distinct spectral states, on rest-frame timescales as short as several months.
Specifically, CL-AGNs identified in the UV-optical regime show the
disappearance or appearance, or significant weakening or strengthening, of the
broad emission line and/or the blue quasar-like continuum emission components,
that are typically seen in unobscured AGNs (see, e.g., Ricci & Trakhtenbrot
2023 for a recent review of these events).

While the earliest such changing-look event was reported nearly 50 years ago
(Tohline & Osterbrock, 1976), many more systems have been discovered over the
past decade, starting from a few cases that expanded the study of CL-AGNs to
higher, quasar-like luminosities (i.e., $L>10^{45}\,{\rm erg\,s}^{-1}$; see,
e.g., LaMassa et al., 2015; Runnoe et al., 2016)111Throughout this work, we
use the term “quasars” to describe AGN-dominated sources which, at least at
some point, exhibit broad emission lines and a blue, power-law-like
continuum., followed by more sizable, spectroscopically-confirmed samples
(e.g., MacLeod et al., 2016, 2019; Yang et al., 2018; Green et al., 2022) and
higher redshift sources (Ross et al., 2018; Guo et al., 2020a).

In general, the physical mechanisms driving CL-AGN transitions remain
uncertain. Most studies which have focused either on detailed analysis of
individual objects (e.g., Denney et al., 2014; LaMassa et al., 2015; Husemann
et al., 2016; Ruan et al., 2016; Runnoe et al., 2016; Sheng et al., 2017;
Stern et al., 2018; Hutsemékers et al., 2019; Trakhtenbrot et al., 2019; Wang
et al., 2019; Hutsemékers et al., 2020; Ricci et al., 2020; Guolo et al.,
2021) or on larger samples (e.g., MacLeod et al., 2016; Yang et al., 2018;
Green et al., 2022; Temple et al., 2023) favor drastic changes to the
accretion flows that power the AGNs as the source of observed variability,
based mainly on evidence of the radiative response of various circumnuclear
gas regions to accretion-generated, UV photons (e.g., the torus response in
the IR regime), a lack of concurrent signatures of obscuration changes, and
other evidence from multi-wavelength observations (e.g., Ruan et al., 2019).
This explanation, however, poses a significant challenge to our understanding
of accretion physics, as the observed changes occur on timescales that are
much shorter than what is expected within the thin accretion disk paradigm
(see, e.g., Stern et al. 2018; Ricci & Trakhtenbrot 2023 for discussions of
the relevant timescales, but see also Shen 2021 for a counter-argument).

In contrast, evidence for variable obscuration being the driver of extreme
UV/optical spectral variations is much more limited. Such variations of the
disk and the broad line region (BLR) components have been inferred from the
variable optical spectra of a few Seyfert 1.8 and 1.9 galaxies (see Goodrich,
1995, and references therein). In addition, it has been proposed that some
variations in the profiles of broad emission lines can be explained by partial
obscuration of the BLR by outflowing dusty gas clumps (Gaskell & Harrington,
2018). Recently, Zeltyn et al. (2022) presented a CL-AGN which may be better
explained by a variable, and perhaps dynamic, obscuring medium. Whichever the
driving mechanisms of specific CL-AGN transitions may be, they allow us to
probe the structure of AGNs, and potentially test our current understanding of
their accretion flows, as well as the BLR, the dusty torus, and other gas
components that surround them.

Several models have been proposed to explain the extreme and fast transitions
observed in CL-AGNs. These include, e.g., rapid mass-accretion drops, enabled
by a larger-than-fiducial sound speed (Noda & Done, 2018), disk processes
happening on the thermal and/or cooling-front timescales, such as thermal disk
instabilities (Stern et al., 2018), tidal disruptions of stars onto pre-
existing AGN disks (Merloni et al., 2015; Chan et al., 2019), semi-periodic
effects that may explain recurring CL-AGNs (Sniegowska et al., 2020; Pan et
al., 2021), and other effects in disks that deviate from the standard thin-
disk paradigm (e.g., Dexter & Begelman, 2019; Jiang & Blaes, 2020). Some
models even suggest a direct link between the formation and destruction of the
BLR itself and variable accretion power, however, this is only relevant for
systems with very low accretion rates (see Elitzur et al., 2014, and
references therein).

Understanding these extreme phenomena requires studying large samples,
preferably drawn from surveys with systematic and stable targeting and cadence
strategies. While the searches for CL-AGNs and the emerging samples are indeed
growing, there are still many challenges to overcome. The criteria used for
finding CL-AGNs among large, multi-epoch spectroscopic datasets are
complicated and vary between studies. Importantly, most large CL-AGN samples
are based on repeated spectroscopy taken several years or even decades apart,
as dictated by the corresponding survey strategies (e.g., Yang et al., 2018;
Green et al., 2022; Guo et al., 2023). Such low-cadence surveys prohibit a
detailed analysis of the transitions themselves, and, in the absence of
contemporary light curves, yield only upper limits on the transition
timescales.

While years-to-decades transition timescales are already challenging for
accretion disk models (see, e.g., the discussion in Ricci & Trakhtenbrot,
2023), some CL-AGNs were found to transition on even shorter timescales
($\lesssim 1\,\rm{yr}$ rest-frame), as seen either directly in spectroscopy
(e.g., Trakhtenbrot et al., 2019; Ross et al., 2020; Zeltyn et al., 2022), or
implied indirectly from photometric monitoring (e.g., Gezari et al., 2017;
Yang et al., 2018; Frederick et al., 2019; Yan et al., 2019; Green et al.,
2022).

Another complementary approach for discovering CL-AGNs is to spectroscopically
monitor those AGNs that exhibit dramatic photometric variability (e.g.,
MacLeod et al., 2016, 2019; Rumbaugh et al., 2018; Graham et al., 2020;
Senarath et al., 2021; López-Navas et al., 2022, 2023). While this photometry-
driven approach benefits from large parent samples covered by wide-field,
high-cadence surveys, it might miss host-dominated CL-AGNs, where extreme
changes in the broad emission lines can occur without a significant continuum
change. In this case, temporally resolving the spectral transition is also
challenging.

An additional challenge for the identification of CL-AGN is that not all broad
emission lines appear to vary in the same manner. Specifically, the broad Mg
ii $\lambda 2798$ line varies by a lesser degree in extremely variable quasars
(EVQs) compared to the quasar continuum and does not exhibit the line
‘breathing’ that is characteristic of broad Balmer emission lines (e.g., Yang
et al., 2020, and references therein). These findings are consistent with the
low degree of Mg ii variability reported in some reverberation mapping (RM)
campaigns (e.g., Cackett et al., 2015; Sun et al., 2015; Homayouni et al.,
2020), and may be linked to the particular excitation mechanisms, optical
depths, and/or the BLR geometry of the Mg ii emission line, or to some other,
yet unknown BLR physics (e.g., Sun et al., 2015; Guo et al., 2020b).

In this work we present a large sample of CL-AGNs assembled from the first
year of the Sloan Digital Sky Survey V (SDSS-V; Kollmeier et al. 2017; Almeida
et al. 2023). Our work is part of a dedicated effort within SDSS-V to obtain
repeated spectroscopy of $>30,000$ previously-known SDSS quasars, some of
which will be revisited several times within SDSS-V. This program will allow
us to perform a systematic search for CL-AGNs and other dramatic variability
events, directly in spectroscopy, over a multitude of timescales.

This paper is organized as follows. Section 2 presents the main spectroscopic
and the ancillary photometric data used to identify CL-AGNs, as well as the
criteria used for selecting our final sample. Section 3 describes the
reference AGN sample used to compare with our CL-AGN sample, and the spectral
decomposition methods we use to analyze our CL-AGNs and to compare them with
the reference sample. Section 4 presents the population properties of our CL-
AGN sample as compared to the general AGN population, and discusses the
implications of our findings. Finally, we conclude with Section 5, where we
summarize our key results. Throughout this work, we adopt a flat $\Lambda$
cold dark matter cosmology with $H_{0}=70\,{\rm km\,s}^{-1}\,\rm{Mpc}^{-1}$
and $\Omega_{\rm{m}}=0.3$.

##  2 Data and Observations

Our sample and analysis of strongly variable AGNs are based on optical
spectroscopy obtained during the first year of SDSS-V of AGNs
spectroscopically observed during the previous four SDSS generations (SDSS
I-IV, DR16; Ahumada et al. 2020).

In this section, we describe the parent sample, the CL-AGN selection criteria,
as well as the follow-up spectroscopic and ancillary photometric data used to
corroborate our CL-AGN candidates. Table 1 summarizes this process and lists
the number of sources selected or excluded in each of the steps described
below.

Table 1: CL-AGN selection Notes | Number
---|---
Unique AGNs observed during first year of SDSS-V | 44,761
AGNs with archival SDSS/DR16 spectroscopy | 29,631
Non–BHM-RM targets | 28,873
Sources flagged by the automated selection criterion (Section 2.2, Equation 2) | 3,338
Passed visual inspection (Section 2.2) | 130
Passed sample refinement through spectral decomposition (Section 2.3) | 123
Additional candidates that pass criterion after spectral decomposition (Section 2.3) | +14
Passed sample refinement through spectroscopic follow-up (Section 2.4.1) | 134
Final core sample - passed sample refinement through photometric cross-match (Section 2.4.2) | 113
BHM-RM CL-AGNs (Section 2.6.1) | 3
Other extremely variable sources (Section 2.6.2) | 88

###  2.1 SDSS-V Repeated Spectroscopy

Our main dataset consists of medium-resolution ($R\sim 2000$) spectra obtained
through the Black Hole Mapper (BHM) program within the first year of
operations of SDSS-V, covering October 2020 through June 2021. These spectra
were acquired using the Baryon Oscillation Spectroscopic Survey (BOSS; Smee et
al., 2013) spectrograph, mounted on the Sloan Foundation 2.5 m telescope (Gunn
et al., 2006) at the Apache Point Observatory (APO), and utilizing plates to
position fibers on pre-selected science targets (as well as standard stars and
sky regions). The new, robotic Focal Plane System that constitutes a key
upgrade of SDSS-V (Pogge et al., 2020) compared with previous SDSS projects
was _not_ operational during the relevant observation period.

One of the key goals of the BHM program is to investigate spectral AGN
variability using repeated spectroscopy of previously-known broad-line AGNs
with archival SDSS spectroscopy (see Almeida et al. 2023 for a detailed
account of SDSS-V/BHM targeting). These core AGN targets with previous Sloan
spectra can be divided into two main subsets:

(1) All-Quasar Multi-Epoch Spectroscopy (AQMES) targets: about 20,000 $i\leq
19.1$ broad-line AGNs drawn from SDSS I-IV, that will be observed 2–12 times
throughout the entire duration of SDSS-V, with medium-to-low cadence (months
to years); and (2) reverberation-mapping targets (BHM-RM): about 1400
$i\lesssim 20$ broad-line AGNs that will be observed over 100 times throughout
the entire duration of SDSS-V with high cadence (down to $\sim 1-2$ days in
the observer frame). Additional sets of BHM sources that may also provide
repeated spectroscopy include targets that were observed as part of the
SPectroscopic IDentfication of eRosita Sources (SPIDERS; Clerc et al., 2016)
and the Chandra Source Catalog (CSC; Evans et al., 2010) programs, which
target X-ray point-like sources identified with the eROSITA and Chandra
telescopes, respectively.

During its first year of operations, the SDSS-V/BHM program obtained at least
one new spectrum for 44,761 unique AGNs. For 29,631 of these sources, there
was at least one archival SDSS spectrum available in SDSS/DR16 (Ahumada et
al., 2020). We note that 28,804 of these (97.2%) are part of the DR16 quasar
catalog compiled by Lyke et al. (2020, DR16Q hereafter). We further restrict
this sample of 29,631 sources by excluding 758 BHM-RM targets, for which the
large number of (high-cadence) spectra was found to challenge our automatic
CL-AGN search methodology (discussed further in Section 2.6.1). Excluding the
BHM-RM targets, we are left with 28,873 unique AGNs. We use this latter sample
of non-BHM-RM AGNs with repeated spectroscopy, covering $0<z<5.1$ (median:
$z=1.49$; see Figure 3), as the parent sample for our search for CL-AGNs and
other extremely variable AGNs.

The archival DR16 spectra we use were obtained following a variety of
selection criteria (targeting various types of galaxies) and with two
different instrumental setups (Smee et al., 2013). Specifically, the spectra
collected up to July 2008 (and published in DR7 of SDSS I-II; Abazajian et al.
2009) were obtained with the SDSS spectrographs and 3′′-diameter fibers, and
focused on a large, flux-limited sample of quasars, selected through a rather
homogeneous color-based criterion (Richards et al., 2002; Schneider et al.,
2010). All subsequent spectra were obtained with the BOSS spectrographs and
2′′-diameter fibers, and added a large number of quasars selected through a
wider variety of criteria, including probabilistic methods, variability, and
other sets (see Lyke et al., 2020). We discuss the implications of the
aperture differences for our work in Section 3.2. The various generations of
SDSS surveys also slightly differ in their wavelength coverage, with the red
end of the spectra changing from $\approx$9,200 Å for the SDSS spectrograph to
$\approx$10,300 Å in the BOSS configuration used for SDSS-IV and the first
year of SDSS-V. For completeness, we note that our work used products of
version v6.0.9 of the BOSS pipeline.

###  2.2 Searching for CL-AGN candidates

Given the large size of the parent SDSS-V sample, the first step in our search
employed automated and relatively simple methods. Our approach avoids detailed
spectral decomposition, which relies on a large set of assumptions (see
Section 3.2 below) that may not be appropriate for all observed, multi-epoch
spectra of all sources.

Specifically, we searched for extreme variations of broad line emission. The
broad emission lines considered in our search are (depending on the target’s
redshift): H$\alpha$, H$\beta$, Mg ii $\lambda 2798$, C iii] $\lambda 1909$,
and C iv $\lambda 1549$. The search was conducted in the following way. We
first shifted each spectrum to its rest-frame, and cleaned it by removing bad
pixels (marked with $\texttt{ivar}=0$) and pixels with particularly low
signal-to-noise ratios (S/N$<1$). Each spectrum was smoothed by applying a
rolling median with a window size of 9 pixels.222Each spectral pixel covers 69
km s-1. For each spectrum, the continuum level was measured near each of the
aforementioned broad emission lines based on adjacent relatively line-free
spectral bands. Specifically, we calculated the median flux density in two
such bands, located blueward and redward of the line in question (see Table 1
in Mejía-Restrepo et al., 2016), and linearly interpolated between the two
median flux densities. This simple linear continuum model was subtracted from
the total spectrum to obtain the emission line spectrum. To quantify the
emission line flux, we integrated the continuum-subtracted flux density over a
wavelength band corresponding to $\pm 4000\,{\rm km\,s}^{-1}$ around the
expected central wavelength of the emission line of interest. The rest-frame
wavelength ranges used for continuum placement and line flux integration are
listed in Table 2. This process allowed a measurement of the broad emission
line fluxes without the need to decompose the continuum emission (into host
and AGN) or to model the line profiles.

Table 2: Wavelength ranges used in the automated CL-AGN candidate search

Emission line | ContinuumaaWavelength bands used for placing the simple, linear continuum model. | Line fluxbbWavelength range used for the simple line flux integration.
---|---|---
| blue | red |
H$\alpha$ | (6150–6250) | (6950–7150) | (6477.1–6652.1)
H$\beta$ | (4670–4730) | (5080–5120) | (4797.8–4927.5)
Mg ii | (2650–2670) | (3030–3070) | (2761.4–2836.1)
C iii] | (1680–1720) | (1960–2020) | (1883.3–1934.2)
C iv | (1420–1470) | (1680–1720) | (1528.4–1569.7)

Note. — All wavelengths are given in vacuum, rest frame, and in units of Å.

The line flux variability for each source was estimated by comparing all the
pairs of line measurements available from our multi-epoch spectroscopy. To
evaluate the significance of the line variability we used the following
quantity:

$\rm C\left(\rm line\right)\equiv F_{2}/F_{1}-\Delta\left(F_{2}/F_{1}\right),$
(1)

where $F_{2}/F_{1}$ is the ratio between the (continuum-subtracted) line
fluxes for the two specific epochs, with $F_{2}$ defined to be the higher flux
of the two, and $\Delta\left(F_{2}/F_{1}\right)$ is the 1–$\sigma$ equivalent
uncertainty on the line flux ratio, propagated from the two error spectra.
333We note that the spectrophotometric calibration of the data is designed to
be accurate at the $\sim 5\%$ level (e.g., Shen et al., 2015; Almeida et al.,
2023).

The quantity defined in Equation 1 is designed to estimate the fractional flux
variability of each emission line while taking into account the uncertainties
in the flux measurements. We note that this quantity, which measures changes
in emission line fluxes based on simple integration of observed flux
densities, may often underestimate the real change in the line flux. This may
happen because the integrated flux encompasses not only the variable broad
emission component but also some essentially constant narrow emission line
flux. It is thus possible that a number of objects that did experience a
significant change in their broad emission line flux would be missed by our
search. One way to address this would involve applying our search criteria to
narrow-line subtracted spectra. However, this approach would necessitate a
full and reliable spectral decomposition for all epochs of the 28,873 AGNs in
our parent sample, a task that requires a non-trivial set of assumptions to be
used in the spectral decomposition procedure. This is beyond the scope of the
present work. We will return to this point in Section 2.3.

After calculating $\rm C\left(\rm line\right)$ for each emission line, for all
pairs of spectra for a given source, we define our initial CL-AGN candidates
to be any source for which the broad emission line flux changed by at least a
factor of two. That is, a CL-AGN candidate is a source for which the maximal
fractional emission line flux variability among all available pairs is greater
than two, that is:

$\max\left[\rm C\left(\rm line\right)\right]>2\,.$ (2)

This universal cut was designed to focus only on those systems that show
(line) flux variations far above the typical rest-frame UV-optical variability
seen in AGNs. For example, MacLeod et al. (2012) find that only $\sim 1\%$ of
quasars vary in flux by more than a factor of two over periods of 8-10 years
(in the observed frame). For comparison, the recent study by Guo et al. (2023)
adopts a criterion that corresponds to line variation by a factor of $\times
2.5$, however with a different treatment of uncertainties. We note that some
previous CL-AGN samples were selected based on the significance, but not
necessarily the amplitude, of their emission line variations (see discussions
in MacLeod et al. 2019 and Guo et al. 2023). In addition, we visually
inspected all the spectra obtained using a randomly-selected plate, including
well over 100 AGNs, and verified that our chosen automatic search criterion
did not overlook obvious CL-AGN candidates using our search criterion.

In total, our selection criteria flagged 3,338 candidates (among the 28,873
unique AGNs in our parent sample), all of which were then visually inspected
to identify CL-AGNs or other extreme variability events. Performing such a
visual inspection on a dataset as large as our parent sample was not
practical, which again demonstrates the utility of our automatic search. This
visual inspection focused on identifying calibration issues mistakenly flagged
by the automatic search procedure. We mainly inspected narrow emission lines,
whenever they were present, as these are not expected to vary significantly on
the relevant timescales (see, e.g., Peterson et al. 1982 but also Peterson et
al. 2013). Additionally, we examined the consistency between multiple SDSS-V
spectra of the same source, whenever more than one SDSS-V spectrum was
available (obtained within months). Furthermore, many sources were incorrectly
flagged as showing significant spectral variations due to incorrect estimation
of the continuum and/or broad line emission due to noise in one of the
epochs—a situation that is relatively easy to identify visually. Two examples
of CL-AGN candidates, flagged by our automated search but not passing visual
inspection, are shown in Figure 8 (in Appendix B).

In total, 130 AGNs passed visual inspection as viable candidates. The number
of flagged candidates, the number of candidates passing the visual inspection,
and the true positive rate (TPR) are listed in Table 3, which further breaks
down these numbers based on the emission line(s) used for selection. Notably,
the drastic reduction from the $>$3,300 initially flagged sources to the 130
viable candidates is driven by high-redshift sources, which are unfortunately
prone to several selection effects, which we further discuss in Section 4.1
(including our inability to rule out flagging due to flux calibration issues).
Indeed, our automatic search is rather efficient (TPR roughly between
$10-20\%$) in the lower-redshift regime, where H$\alpha$ and/or H$\beta$ are
accessible.

We note that, as the quantity we use as a selection criterion (Equations 1 and
2) employs flux ratios, it is meaningless if either one of the fluxes is
negative. In such cases we decided to ignore the specific measurement of the
particular line in the corresponding particular epoch, and did not consider it
when looking for (drastic) line variability. We note that considering objects
that otherwise did not pass our criterion, but where one of the emission line
flux measurements yielded a negative value as CL-AGN candidates, greatly
enlarged the number of flagged candidates (from 3,338 to 5,329). However, a
visual inspection of a subset of these newly flagged sources revealed that a
vast majority of them were flagged due to an incorrect estimation of the
emission line and/or continuum flux levels, typically driven by low $S/N$ data
in the relevant spectral regions. Therefore, the exclusion of such objects
from our search may lead us to miss only a few genuine CL-AGN, at a cost of
much higher search efficiency.

We acknowledge that our search for extreme spectral AGN variability may be
incomplete, as most SDSS-V spectra were not inspected visually, and some
variable AGNs might have been missed by our search. However, we stress that
our primary focus was to ensure the purity of our large sample, rather than
its completeness. This is further detailed in Section 2.4 below, where we
present our efforts to corroborate our candidates through follow-up
spectroscopy and photometric light curves.

Another, broader issue that affects our sample completeness is inherent to the
SDSS-V survey design. Since the core repeated spectroscopy programs within
SDSS-V focus on previously-known, luminous broad-line AGNs (i.e., quasars)
identified in archival SDSS data, any search for dramatic variability events
in (early) SDSS-V data is expected to favor the identification of dimming
events of these previously-known AGNs, rather than of their further
brightening. This bias is discussed in detail, and quantified, by Shen & Burke
(2021). We will return to this point in Sections 2.5 and 4.3.

We have also experimented with alternative search procedures and criteria,
specifically ones based on changes in the flux of the lines relative to those
in the (adjacent) continuum. Several studies have studied CL-AGNs samples
selected through this kind of approach (e.g., the criterion defined in MacLeod
et al. 2019, which is also employed in Green et al. 2022, and Guo et al.
2023). Specifically, we looked into using the equivalent widths (EWs) of the
broad emission lines, rather than their integrated absolute fluxes, as an
alternative selection path. In principle, one would expect that searching for
large EW variations would be robust to calibration issues. However, we found
such methods to be less suitable for our needs, for several reasons. First,
they would flag cases where the continuum changed while the broad line roughly
retained its flux. Although such events could be considered as showing extreme
spectral variability, these variations are different from the CL-AGNs we chose
to focus on in the present work (i.e. systems with dramatic broad emission
line variations). Second, given the possible driving mechanisms of CL-AGNs,
one may expect the broad emission lines and the AGN continuum to vary
(roughly) in unison, either due to the emission lines being reprocessed seed
ionizing continuum radiation444Note that the ionizing continuum itself is far
beyond the observed spectral range., or due to a variable obscurer attenuating
both emission components. In such situations, the EW can remain roughly
constant even in genuine CL-AGNs.

Table 3: Automated CL-AGN candidate search

|  | Before refinement | After refinement | Core sample
---|---|---|---|---
Emission line | # Flagged | # Candidates | TPRaaTrue Positive Rate–i.e., the percentage of candidates confirmed through visual inspection, out of all the sources flagged based on the corresponding line. | # Candidates | TPRbbTrue Positive Rate–i.e., the percentage of candidates confirmed through visual inspection and spectral refinement, out of all the sources flagged based on the corresponding line. | # CL-AGNs
H$\alpha$ | 173 | 38 | 22% | 37 | 21% | 38
H$\beta$ | 637 | 81 | 13% | 74 | 12% | 79
Mg ii | 1228 | 44 | 3.6% | 43 | 3.5% | 43
C iii] | 1065 | 2 | 0.19% | 2 | 0.19% | 1
C iv | 1086 | 3 | 0.28% | 3 | 0.28% | 2
All lines | 3338 | 130 | 3.9% | 123 | 3.7% | 113

Note. — Some sources were flagged by multiple lines and thus result in a total
count of flagged sources and candidates per emission line that exceeds the
total counts.

###  2.3 Sample Refinement Through Spectral Decomposition

To ensure the robustness of the broad emission line measurements for our CL-
AGN sample, we performed a full spectral decomposition for the 130 CL-AGN
candidates, using the dedicated and widely-used PyQSOFit package (Guo et al.,
2018). We discuss this procedure in detail in Section 3.2, and here only
briefly mention the key steps related to the sample refinement. Specifically,
for each CL-AGN candidate where this is applicable, we re-scaled the brighter
spectra to yield the [O iii]$\,\lambda 5007$ narrow line flux observed in the
dimmer relevant spectrum. This is motivated by the expectation that narrow
emission lines do not vary significantly over the timescales probed by our
data. We then subtracted the best-fitting (re-scaled) narrow line profiles
from all epochs of the CL-AGN candidate. Subsequently, we reevaluated each CL-
AGN candidate using the re-scaled, narrow-line subtracted multi-epoch spectra.
This step led to the removal of seven candidates, for which these refined
spectra no longer satisfied our CL selection criterion555That is, the re-
scaled spectra resulted in $\rm C({\rm line})\leq 2$., leaving a total of 123
candidates.

As previously discussed in Section 2.2, our automated search, which was
applied to the observed SDSS spectra, might have missed some CL-AGNs due the
inclusion of non-variable narrow line emission in our line flux estimators. We
thus used the detailed spectral decomposition procedure mentioned above (and
discussed in Section 3.2) in an attempt to identify additional, robust CL-
AGNs. These objects, which initially did not meet our criterion, were
(sporadically) identified during our experimentation with various search
methods and were confirmed as viable CL-AGN candidates through visual
inspection, and—importantly—met the criterion only when applied to the narrow-
line subtracted and renormalized spectra. This process identified 14
additional robust CL-AGN candidates, increasing our sample to 137 candidates.

###  2.4 Ancillary Observations and Data

With an initial sample of CL-AGN candidates in hand, we acquired several sets
of additional, ancillary observations and publicly available data to further
refine the sample, and to gain further insights regarding the nature of the
variable emission from our candidates. This included additional optical
spectroscopy, optical light curves, and mid-IR light curves, as detailed
below.

####  2.4.1 Follow-up Spectroscopy

In addition to the SDSS-V spectroscopy, we performed an extensive
spectroscopic effort to corroborate the nature of the identified CL-AGN
candidates, using various facilities. This follow-up effort is important for
two reasons: (1) to rule out false-positive candidates caused by calibration
issues in the new SDSS-V spectra, which may be mistaken for real flux changes
in our automated search and visual inspection (particularly for spectra
lacking narrow emission lines); and (2) to follow up on variable sources to
see whether they exhibit further dramatic spectral variability (see Zeltyn et
al., 2022, for an example of such a case).

Additional optical spectroscopy was obtained with the 2 m Faulkes Telescope
North (FTN) and South (FTS) facilities, which are part of the Las Cumbres
Observatory network (LCOGT; Brown et al., 2013); the Low-Resolution
Spectrograph 2 (LRS2; Chonis et al., 2016) on the 10 m Hobby–Eberly Telescope
(HET; Hill et al. 2021) at McDonald Observatory; and the Double Spectrograph
(DBSP; Oke & Gunn, 1982) on the 5.1 m Hale Telescope at the Palomar
Observatory. Spectra were obtained through either long slits with widths of
either 2′′ (FTN and FTS) or 1.5′′ (Hale), or with dynamic, seeing-matched
apertures of $1.7-2.5$ ′′(HET), and calibrated using standard stars observed
during the corresponding nights. Table 7 in Appendix A lists the targets
observed through this effort, as well as the spectral setups used in these
observations. The spectra were reduced using standard procedures and the well-
tested pipelines of the corresponding instruments (see Appendix A).

In total we obtained 109 spectra for 74 unique sources of interest, including
52 spectra for 33 unique sources from our sample of 137 CL-AGN candidates,
with no particular prioritization. Among the latter set of 33 CL-AGN
candidates, in 22 cases the additional spectra confirmed the spectral changes
seen in the first year SDSS-V spectra, while three sources were identified as
false candidates. For the remaining eight CL-AGN candidates, the follow-up
spectroscopy could neither confirm nor refute their CL nature. The three
refuted candidates are all $z>1.5$ sources, where the SDSS-V spectroscopy
indicated a significant dimming of both continuum and broad emission lines,
but the follow-up spectroscopy is highly consistent with the archival SDSS
spectra. Given the high redshifts of the source, we couldn’t use the [O iii]
line to account for such effects, which are most likely driven by flux
calibration issues. An example of how follow-up spectroscopy helped to
identify one of these three false CL-AGN candidates is presented in Figure 8
(in Appendix B).

Consequently, after these spectroscopic follow-up efforts, we retained 134 of
our CL-AGN candidates for further analysis. Interestingly, the follow-up
spectra of six targets displayed various degrees of variability compared with
the (rather recent) SDSS-V spectra, with some targets showing a continuation
of the trends discovered in SDSS-V (i.e., the AGN continued to dim or
brighten), while others showed a reversal of the SDSS-V trend (e.g., a
brightening AGN began to fade). The number of candidates observed, confirmed,
and refuted with each observational facility are listed in Table 4.

Table 4: Follow-up observations and photometric cross-match of CL-AGN candidates Facility | AllaaSpectroscopic follow-up observations conducted for all types of extreme variability candidates flagged within the scope of our work. | CL-AGN candidatesbbSpectroscopic follow-up observations and photometric light curve data for CL-AGN candidates that satisfied our automatic CL selection criterion and passed visual inspection. |
---|---|---|---
| observed | observed | confirmedccThe unique number of CL-AGN candidates that the ancillary data (spectroscopy or light curves) confirmed their CL nature, refuted it, or did not provide conclusive evidence. | refutedccThe unique number of CL-AGN candidates that the ancillary data (spectroscopy or light curves) confirmed their CL nature, refuted it, or did not provide conclusive evidence. | inconclusiveccThe unique number of CL-AGN candidates that the ancillary data (spectroscopy or light curves) confirmed their CL nature, refuted it, or did not provide conclusive evidence. | further
|  |  |  |  |  | variability
LCO | 54 | 24 | 14 | 3 | 7 | 5
HET | 25 | 15 | 10 | 0 | 5 | 3
Palomar | 11 | 2 | 2 | 0 | 0 | 0
Total spec.ddThe total numbers of unique sources with spectroscopic follow-up observations and/or with photometric cross-matches. | 74 | 33 | 22 | 3 | 8 | 6
ZTF | - | 116 | 7 | 10 | 99 | 4
ATLAS | - | 128 | 13 | 11 | 104 | 3
CRTS | - | 98 | 4 | 6 | 88 | -
PS1 | - | 115 | 5 | 6 | 104 | -
Total phot.ddThe total numbers of unique sources with spectroscopic follow-up observations and/or with photometric cross-matches. | - | 133 | 19 | 21 | 93 | 4

####  2.4.2 Optical photometric light curves

To further corroborate and examine our candidates, we used publicly-available
optical photometric light curves obtained through the Zwicky Transient
Facility (ZTF; Masci et al., 2019; IRSA, 2022), Asteroid Terrestrial-impact
Last Alert System (ATLAS; Tonry et al., 2018), Catalina Real-time Transient
Survey (CRTS; Drake et al., 2009), and Pan-STARRS1 (PS1; Kaiser et al., 2010)
datasets and compared these with synthetic photometry derived from the
candidates’ (SDSS) spectra. Specifically, we generated ZTF and ATLAS light
curves using the corresponding forced photometry services and the DR16
coordinates of our AGNs666Instructions for producing ZTF and ATLAS light
curves can be found at https://irsa.ipac.caltech.edu/data/ZTF/docs/ and
https://fallingstar-data.com/forcedphot/, respectively. which employ forced
PSF-fit photometry on images to create the light curves (see, e.g., Tonry et
al., 2018; Smith et al., 2020; Masci et al., 2023, for more details on the
forced-photometry procedures). The CRTS and PS1 light curves were obtained by
cross-matching our sources with the publicly available catalogs, allowing for
a search radius of 1′′and 2′′, respectively.

Performing such photometric cross-matches was particularly useful for
identifying flux calibration issues in SDSS-V spectra, i.e., cases where the
synthetic photometry derived from these spectra showed large discrepancies
compared with (pseudo-)concurrent photometry (i.e., $\Delta m\gtrsim 1$ mag).

Inspecting the photometric data led to a removal of 21 candidates from our
sample, retaining 113. Indeed, all the refuted sources are at relatively high
redshift ($z>1.2$), where we could not use [O iii] to assess the robustness of
their selection as CL-AGN candidates. For 19 of the remaining candidates, the
photometry was consistent with the spectral variations seen in the SDSS-V
data, which could allow to further tighten the constraints on the timescales
of extreme spectral variability. Moreover, the light curves of four targets
showed prolonged variability after the SDSS-V spectra were taken, with some
targets continuing the trends identified between the archival and the new
SDSS-V spectra (i.e., the target keeps brightening or dimming), while others
showed a reversal of the SDSS-V trend (i.e., the spectra suggested dimming,
but the light curve showed rebrightening).

Table 4 summarizes the number of candidates observed, confirmed, and refuted
by each facility. Figure 9 (in Appendix B) shows two examples of how optical
light curves were used to refute or retain CL-AGN candidates.

####  2.4.3 WISE infrared light curves

We used publicly available data from the Wide-field Infrared Survey Explorer
(WISE; Wright et al., 2010) to create IR light curves for our candidates in
the W1 ($\sim$3.4 $\mu$m) and W2 ($\sim$4.6 $\mu$m) bands, with a cadence of
about 6 months.777WISE light curves were extracted using the Gator catalog
list at https://irsa.ipac.caltech.edu/applications/Gator/ Specifically, we
searched for counterparts within radii of 2′′ and 3′′, for the AllWISE
(covering 2010-2011; WISE Team 2020a) and the NEOWISE reactivation mission
(NEOWISE-R, covering 2013 to the present; WISE Team 2020b) mission datasets,
respectively (Mainzer et al., 2011, 2014).888We have decided to be more
permissive with NEOWISE-R matches, as NEOWISE-R sources are not pre-matched
between frames. We obtained matches for all 113 of our candidates in the
NEOWISE-R dataset. In the AllWISE dataset, we found matches for 109 out of 113
candidates.

These data were collected in order to examine the mechanism behind the
changing-look transitions in our sample, as the IR emission covered by WISE is
not expected to be significantly affected by variable obscuration. In
contrast, dramatic variations to the accretion flow are expected to be echoed
by the IR emission, as the latter is thought to be driven by reprocessing the
former on timescales of weeks to months (e.g., Minezaki et al., 2019; Ricci &
Trakhtenbrot, 2023, and references therein).

We show two examples of WISE light curves in Figure 10 (in Appendix C).

###  2.5 Final Core Sample

Our final core sample consists of 113 robust CL-AGN candidates, listed in
Table 5. Spectra of several examples of CL-AGN from our final sample are
presented in Figure 2. We discuss some of these and other noteworthy cases in
Appendix D, and show the spectra of _all_ the systems in our final sample in
Figure 11 there (available online).

Of the 113 CL-AGNs in our final core sample, 38 were selected based on drastic
changes in their broad H$\alpha$ line emission, 79 based on H$\beta$, 43 based
on Mg ii, two based on C iv, and one based on C iii] (see Table 3). There is
significant and complex overlap between these subsets, as demonstrated in the
line-pair-wise Venn diagrams shown in Figure 1. Of the various overlapping
subsets, we particularly note that: (1) of the two C iv-selected CL-AGNs, one
is also C iii]-selected; (2) 30 of the 38 (79%) H$\beta$-selected CL-AGNs for
which the H$\alpha$ line is accessible are also H$\alpha$-selected CL-AGNs;
and (3) 22 of the 37 (59%) H$\beta$-selected CL-AGNs for which the Mg ii line
is accessible are also Mg ii-selected CL-AGNs.

Figure 1: Venn diagrams showing the overlap between CL AGNs in our final core
sample that were selected based on the extreme variability of different
emission lines. The panels show the subsets of objects that have spectral
coverage of both H$\alpha$ and H$\beta$ (left); both H$\beta$ and Mg ii
(center); or H$\alpha$, H$\beta$, and Mg ii (right).

Our CL-AGNs span a redshift range of $0.06<z<2.4$, and the SDSS-V spectroscopy
was capable of probing extreme variability on rest-frame timescales in the
range of 1 year $\lesssim\Delta t_{\rm rest}\lesssim$ 19 years. In some cases,
our subsequent follow-up spectroscopy revealed dramatic variability on yet
shorter timescales (as short as $\Delta t_{\rm rest}\approx 2$ months; see
Zeltyn et al. 2022). Of these 113 systems, 67% (76 systems) show dimming in
their recent SDSS-V spectroscopy compared to previous data, while 33% (37
systems) show brightening.

The non-unity ratio of brightening-to-dimming events we find (i.e., 2:1), is
expected given the nature of our sample, which focuses on revisiting
previously-known broad-line AGNs. Specifically, Shen & Burke (2021) quantified
the expected bias in flux-limited quasar variability studies. They find that
if all AGNs intrinsically vary in a symmetrical manner, the ratio of
significant brightening-to-dimming events is indeed expected to be $\sim 2:1$
for an AQMES-like survey (i.e., $i\lesssim 19$ sources revisited after $\sim
10-20$ years).

Of the 113 CL-AGN in our final core sample, 87 appear in the DR16Q catalog
(Lyke et al., 2020). We stress that the remaining 26 CL-AGNs do have optical
spectra in the SDSS DR16 dataset, but were not included in DR16Q. These 26
sources were re-observed in SDSS-V as part of the SPIDERS and/or CSC targets.

Our final sample of 113 CL-AGN includes 104 newly discovered CL-AGNs. The
remaining nine objects were already noted in prior studies, including: five
CL-AGNs that are part of the sample presented in MacLeod et al. (2019), three
in Green et al. (2022), two in MacLeod et al. (2016), two in Guo et al.
(2023), and one in each of Ruan et al. (2016) and Yang et al. (2018).999There
is some overlap between the samples reported in all these studies. In
addition, our sample includes the archetypal CL quasar studied in detail by
LaMassa et al. (2015, J0159+0033), and another intriguing CL-AGN which was
published earlier by the SDSS-V collaboration (J1628+4329; Zeltyn et al.,
2022). The relevant references for these nine previously-known CL-AGN are also
included in Table 5.

Our first-year SDSS-V based sample of 113 spectroscopically identified CL-AGNs
greatly increases the number of such sources, and is among the two largest
spectroscopically-selected CL-AGN samples reported to date (particularly when
considering the 88 additional extreme spectral variability events we discuss
in Section 2.6.2). For comparison, a study looking for CL-AGNs in SDSS-IV
found 19 CL-AGNs, of which 15 were newly discovered Green et al. (2022).
Another SDSS-IV-based study, focusing on high-redshift CL-AGNs, identified 23
systems at $z>1.5$ (Guo et al., 2020a). Other large sample studies carried out
by MacLeod et al. (2019) and Yang et al. (2018) discovered 17 and 21 new CL-
AGNs, respectively. Most recently, Guo et al. (2023) published a sample of 130
CL-AGN identified in the early data collected with The Dark Energy
Spectroscopic Instrument (DESI) spectroscopic survey (DESI Collaboration,
2023), which re-observed over 82,000 sources that already had spectra in
SDSS/DR16. While DESI has clear advantages over SDSS-V in terms of the total
number of AGNs with repeated spectroscopy, of sensitivity and spectral
resolution, it does not (currently) plan to further revisit these sources, for
example to study recurring and/or short-timescale spectral transitions—which
are part of the design drivers of SDSS-V (see Section 2.1 above).
Specifically, the AQMES-Medium component of SDSS-V is designed to obtain up to
10 new spectra of $\sim 2000$ quasars over the entire duration of SDSS-V (see
Almeida et al., 2023), which will allow to study in detail, and indeed
temporarily resolve, changing-look transitions and other extreme variability
events.

Figure 2: Three CL-AGN candidates observed in SDSS-V. In all panels, the
colors of the spectra mark the observing epoch, moving from early, SDSS-I-IV
spectroscopy (blue-cyan) to the recent SDSS-V spectroscopy (red-orange-
yellow), and our follow-up observations (with LCOGT; black, purple, magenta).
All the spectra were boxcar-smoothed over 7 pixels. Top – J075934.95+322143.3
($z=0.26888$): The archival 2002 spectrum classifies this source as a Type 1.9
quasar, while the more recent 2021 SDSS-V and 2022 follow-up LCOGT spectra
show the appearance of the broad H$\beta$ and a quasar-like continuum. Middle
– J090550.30+003948.1 ($z=0.354041$): The archival SDSS spectra show a typical
quasar, while the more recent 2021 SDSS-V spectrum shows the disappearance of
the broad H$\beta$ and the quasar-like continuum, and a significant weakening
of the broad H$\alpha$. Bottom – J012946.72+150457.3 ($z=0.364851$): The
archival SDSS spectra reveal a dimming between 2000–2019. Recent (2020–2021)
SDSS-V spectra show the continuation of the dimming, with the broad H$\beta$
almost completely disappearing. Additional LCO spectra taken in late-2021 and
early-2022 (black, purple, magenta) confirm the SDSS-V dimming.

###  2.6 Additional Extremely Variable Sources

In addition to our core sample of 113 CL-AGNs, we have identified two subsets
of extremely variable sources during our work: three CL-AGN candidates drawn
from BHM-RM, and 88 sources showing extremely variable but persistent broad
emission lines; these subsets are described below. We stress that none of
these sources is included in our detailed analyses (described in Sections 3
and 4), which focus solely on the 113 candidates in our core CL-AGN sample.

####  2.6.1 Additional CL-AGNs from BHM-RM

Our CL-AGN candidate selection criterion (Section 2.2) initially yielded an
exceptionally large number of candidates among BHM-RM targets, due to the
pairwise nature of our search and the large number of (high cadence) pairs of
spectra for BHM-RM targets. To make our automatic search efficient, we decided
to exclude BHM-RM targets from our primary, complete sample.

However, during our initial experimentation with various search methods, we
identified three additional, robust CL-AGN candidates based on the spectra
collected within the BHM-RM effort. The multi-epoch spectra and light curves
of these three sources make them irrefutable CL-AGNs (also by the criterion in
Equation 2), which could be of particular interest for follow-up studies,
given the continued spectroscopic monitoring within SDSS-V/BHM-RM. We
therefore chose to list them in Table 5 (with appropriate demarcation). The
multi-epoch spectra of these three BHM-RM CL-AGNs are also part of Figure 11
(available online).

We stress that our search for CL-AGNs within BHM-RM data is incomplete, and
that this dataset could indeed include many more CL-AGNs. A more nuanced
search should be conducted as part of a future analysis of SDSS-V data.

####  2.6.2 Extremely Variable Quasars

In the course of our work, we identified 88 additional sources that satisfied
our automatic selection criterion (Equation 2), and were confirmed by visual
inspection as presenting genuine, dramatic changes in their broad line
emission. However, unlike in our final, core CL-AGN sample, in these sources
all the observable broad lines remained prominent and robustly detected, in
all epochs, even in their dimmer states.101010Including in our follow-up
spectroscopy, when available. Thus, we deemed these extremely variable quasars
(EVQs) to be qualitatively different from our working definition of CL-
AGNs—i.e., systems where at least one of the broad lines essentially
(dis-)appears.

There are two important, and potentially interlinked, caveats regarding the
identification of these extremely variable sources. First, the apparent
distinction between them and the “proper” CL-AGNs we retain in our core sample
may be driven by our visual inspection process, which in some cases may miss
the presence of broad emission lines concealed in low $S/N$, dim-state
spectra. Moreover, a high fraction of starlight in the fiber, whether due to a
lower AGN luminosity (c.f. the host galaxy) and/or aperture and distance
effects, can make the identification of broad emission lines harder in some
systems. Second, the physical distinction may also be artificial, as we cannot
rule out that all types of (extreme) spectral variability are driven by the
same physical mechanisms, particularly by dramatic changes to the accretion-
driven (ionizing) continuum radiation. It is indeed possible that some of the
dimming events among these 88 EVQs will lose their broad emission lines, and
thus become proper CL-AGNs, some time in the (near) future. We hope to be able
to monitor them within SDSS-V and/or using dedicated observations with other
telescopes.

Notwithstanding these caveats, these 88 sources are noteworthy examples of
extreme spectral variations, and are part of the more general class of
extremely variable or flaring AGNs (see, e.g., Lawrence et al., 2016; Graham
et al., 2017; Rumbaugh et al., 2018; Assef et al., 2018). We tabulate all 88
such sources in Table 5 and present two examples in Figure 8 (in Appendix B).
The full, statistical analysis of these EVQs is beyond the scope of the
present paper.

Table 5: Robust CL-AGNs and EVQs identified in this work

Name | $z$ | Epochs | Class | Emission line change parameter aaThe line variation parameter, as defined by Equation 1. All the sources in this table have (at least) one emission line with $C({\rm line})>2$ (Equation 2). For CL-AGNs from our core sample (“Class”: CL-AGN), these values are calculated using the decomposed, narrow-line subtracted spectra. For EVQs and RM CL-AGNs (“Class”: EVQ and CL-AGN RM, respectively), these values are calculated directly from the observed spectra. | Ancillary data | NoteseeAdditional information regarding each source. “new”: object was not mentioned in previous works (to the best of our knowledge). Otherwise, we mark the previous studies that noted some of the CL-AGNs in our sample. “L15”: object noted by LaMassa et al. (2015), M16: MacLeod et al. (2016), R16: Ruan et al. (2016), Y18: Yang et al. (2018), H19: Hutsemékers et al. (2019), M19: MacLeod et al. (2019), G20: Graham et al. (2020), G22: Green et al. (2022), Z22: Zeltyn et al. (2022), G23: Guo et al. (2023).
---|---|---|---|---|---|---
|  | (MJD) |  | C($\rm{H}\alpha$) | C(H$\beta$) | C(Mg ii) | C(C iii]) | C(C iv) | Spec. bbFollow-up spectroscopy flag. “0”: no follow-up spectroscopy taken, “1”: follow-up spectroscopy is inconclusive, “2”: follow-up spectroscopy confirms the SDSS-V spectroscopy. | Opt. phot. ccOptical photometry flag. “1”: the available photometric data are irrelevant or inconclusive for our work, “2”: photometric data confirm concurrent spectral changes. | WISE ddWISE-based, IR photometry flag. “0”: no relevant WISE data, “1”: trend in WISE light curve does not match concurrent spectral changes, “2”: trend in WISE light curve matches concurrent spectral changes. |
J000719.90$+$253128.6 | 0.559321 | 56543, 59217 | EVQ | - | 2.1 | 1.3 | - | - | 0 | 1 | 2 | new
J000750.51$+$005815.2 | 0.345113 | 52903, 59202 | CL-AGN | - | 2.9 | - | - | - | 0 | 1 | 0 | new
J001103.49$+$010032.6 | 0.485892 | 55478, 59202 | CL-AGN | - | -2 | 2.8 | - | - | 0 | 1 | 1 | new
J001133.95$-$000001.1 | 0.44707 | 52518, 59202 | EVQ | - | 1.7 | 2.2 | - | - | 0 | 1 | 0 | new
J001333.96$+$014506.8 | 0.881555 | 55511, 59202 | CL-AGN | - | - | 2.3 | - | - | 0 | 1 | 0 | new
J002956.65$+$010537.2 | 0.373015 | 55447, 59167 | EVQ | 2 | 2.2 | - | - | - | 0 | 1 | 1 | new
J003242.74$+$003110.9 | 0.360592 | 51782, 59167 | EVQ | - | 2.3 | - | - | - | 0 | 2 | 2 | new
J003841.53$+$000226.7 | 0.649649 | 55182, 59187 | CL-AGN | - | 4.1 | 1.4 | - | - | 0 | 1 | 2 | new
J003849.27$-$011722.4 | 0.500226 | 55827, 59187 | EVQ | - | 1.3 | 2.1 | - | - | 0 | 1 | 2 | new
J004459.09$-$010629.3 | 0.228215 | 52531, 59187 | CL-AGN | 3.2 | 5.1 | - | - | - | 2 | 1 | 2 | new

Note. — Table 5 is published in its entirety in the machine-readable format. A
portion is shown here for guidance regarding its form and content.

##  3 Comparison Samples and Key Measurements

###  3.1 The Control Sample(s)

To contextualize our sample of CL-AGN and compare it to the broader population
of (SDSS-observed) broad-line AGNs, we relied on the catalog of spectral
measurements for the SDSS DR16Q catalog (Lyke et al., 2020), as measured and
compiled by Wu & Shen (2022, WS22 hereafter). Those spectral measurements were
(also) obtained using the PyQSOFit spectral fitting package (Guo et al., 2018,
see more details below). Only 87 of the 113 CL-AGNs in our final sample appear
in the WS22 catalog. The remaining 26 sources are not part of DR16Q, which is
the “parent” catalog for the WS22 work (as noted in Section 2.5).

In Section 4.2 we compare our sample of CL-AGN to this control sample of
quasars, in terms of key properties such as AGN luminosity, BH mass, and
accretion rate ($L_{\rm bol}$, $M_{\rm BH}$, and $f_{\mathrm{Edd}}$,
respectively; see Section 3.3 below for how these quantities are derived).
These comparisons necessitate the construction of control sub-samples that are
subsets of the large WS22 control sample, and that are _matched_ to our CL-
AGNs sample in terms of redshift and certain other properties.

The matched control samples are constructed as follows:

  1. i.

Each control sample is meant to match our CL-AGNs in terms of redshift and one
other property: $L_{\rm bol}$, $M_{\rm BH}$, or $f_{\mathrm{Edd}}$, as
measured in (earlier) SDSS-I-IV spectroscopy.

  2. ii.

For each such pair of properties, we first split the two-dimensional space
covered by our CL-AGN sample into $10\times 10$ bins. Importantly, we map the
fraction of CL-AGNs among our sample that reside in these bins, i.e., the two-
dimensional fractional distribution of our CL-AGNs.

  3. iii.

For each non-empty bin in our CL-AGN two-dimensional (fractional)
distribution, we select those quasars from the WS22 catalog that correspond to
the same bin in redshift and the other relevant property.

  4. iv.

We then randomly down-select only a subset of the bin-matched WS22 quasars so
that the resulting two-dimensional fractional distribution of the matched
control sample will be identical to that of our CL-AGN sample. I.e., if only
4% of the CL-AGNs in our core sample reside in a certain $(z,L_{\rm bol})$
bin, the WS22 quasars in all bins are down-selected such that 4% of them will
reside in that specific bin.

We stress that, due to the random nature of the down-selection process in the
last step above, each property-matched control sample is pseudo-random,
unique, and not entirely reproducible. Thus, in our statistical analyses that
rely on these control samples, we repeat the process of control sample
construction 1000 times and base our insights on the collective emergent
statistical results.

###  3.2 Spectral Measurements of CL-AGNs

Throughout this work, we have used three different sets of spectral
measurements for our CL-AGN sample, as outlined below. Each set of
measurements has certain merits and limitations, making it useful for various
aspects of our analysis, which focuses on continuum and (broad) emission line
properties (luminosities, line widths) as well as a few key derived AGN/SMBH
properties (BH mass and accretion rate).

  1. 1.

Wu & Shen (2022, WS22) catalog values:
We take the subset of our CL-AGN sample whose earlier SDSS spectral epoch
(i.e., pre-SDSS-V) appears in the WS22 catalog. As noted above, this is
applicable for 87 out of our 113 CL-AGN candidates, for which we simply adopt
the measurements tabulated in WS22. This approach ensures the consistency
between the measurements for the CL-AGN and comparison samples measurements,
but is obviously limited to only a subset ($\sim$77%) of our entire CL-AGN
core sample.

This approach was used for Figure 3 and for the analysis shown in Figures 4
and 5.

  2. 2.

WS22-like decomposition:
We decompose the spectra of all our CL-AGN candidates following the
methodology outlined in WS22, using PyQSOFit (Guo et al., 2018). A detailed
account of the fitting procedure can be found in WS22 and references therein.
In short, the fitting procedure employed in WS22 de-reddens the spectra using
Milky-Way dust maps (Schlegel et al., 1998), and models the continuum emission
using a combination of a power law, a third-order polynomial (introduced to
account for peculiar continuum shapes, e.g. due to reddening), and Fe ii UV
and optical empirical templates (Boroson & Green, 1992; Vestergaard & Wilkes,
2001). Each of the emission lines in the continuum-subtracted spectra are then
fitted with combinations of Gaussians, as detailed in WS22. The uncertainties
in the continuum and line parameters were obtained through a Monte Carlo
refitting approach, using 25 realizations for each spectrum, relying on the
corresponding error spectra.

This approach allows us to take advantage of the entire sample of CL-AGN
candidates (113 in total) and enhance the statistical significance of our
results, while maintaining high consistency with the WS22 catalog values. We
verified that the properties obtained from our decomposition are highly
consistent with those derived by WS22, in terms of both accuracy and
precision. We also verified that the usage of our spectral decomposition
(rather than the WS22 catalog values) does not affect the primary findings
presented through the rest of this work (see discussion in Section 4.2).

This approach was used for Figure 12 (in Appendix E) and the corresponding
analysis, and for deriving the values tabulated in Table 6.

  3. 3.

Two-epoch decomposition:
For comparisons between multi-epoch spectra of the same source (i.e., for line
variability measurements), as well as to determine the representative flux
ratio value for each source (as defined in Equation 1), we conducted detailed
spectral decompositions that are better suited to our needs. As these
measurements focus only on the core CL-AGN sample, the (strict) requirement to
be fully consistent with WS22 is lifted.

For each CL-AGN in our sample, we chose two spectra to represent the ‘dim’ and
‘bright’ AGN states. Many of our candidates displayed complex temporal
behavior that was captured in multiple spectra, such as gradual transitions or
alternating changes between dimmer and brighter states; however, for the sake
of simplicity, we only used two spectra to represent each CL transition.
Specifically, for each object, we used an archival SDSS spectrum (pre-SDSS-V)
and a more recent SDSS-V spectrum, focusing only on those pairs of spectra
that satisfy our CL-AGN selection criterion (Equation 2), we chose the
brightest and dimmest spectra available within these distinct survey-defined
datasets. Whenever several spectra of comparable brightness level were
available, we chose the latest spectrum from the SDSS archive and the earliest
from new SDSS-V observations. This choice is motivated by our desire to probe,
or constrain, the shortest possible CL transitions between archival SDSS and
new SDSS-V spectroscopy. It is possible that yet faster transitions will be
identified within SDSS-V, which would be extremely interesting given how
poorly understood these phenomena are and the challenges they pose to AGN
models. We leave searching for transitions occurring within SDSS-V
observations to a future work.

Our decomposition also relied on the PyQSOFit package, however with a somewhat
different setup compared with that used in WS22. The continuum was decomposed
using a power law, a polynomial, and empirical UV and optical iron emission
templates (Boroson & Green, 1992; Vestergaard & Wilkes, 2001). We modeled each
broad emission line using two Gaussian components, while the narrow lines (of
H$\beta$, [O iii], H$\alpha$, [N ii], and $\left[{\rm S}\,\textsc{ii}\right]$)
were modeled with one Gaussian, each. We further tied the widths and shifts of
all narrow lines in each spectral complex, and forced the ratio between the [O
iii]$\,\lambda\lambda 4959,5007$, [N ii]$\,\lambda\lambda 6549,6585$, and [S
ii]$\,\lambda\lambda 6718,6732$ doublets to be 1:3, 1:3, and 1:1
(respectively).111111For comparison, WS22 used three Gaussians for the
H$\alpha$, H$\beta$, and C iv broad lines; included an additional broadened
component for each of the two [O iii] lines, and did not force these lines to
have any specific intensity ratio.

As we are mainly interested in emission line flux measurements, we did not
include any host decomposition in our modeling. We visually verified that this
does not negatively affect the quality of the continuum (shape) fits of our
spectra. This choice also means that we cannot account for stellar absorption
features underlying the (broad) Balmer lines–a common issue in nearly all
studies of broad-line AGNs (see e.g. Oh et al. 2015 for an exception).

In order to minimize the effects of calibration issues on our measurements,
whenever possible and deemed justified (see below), we matched the narrow [O
iii] emission flux measurements in the two epochs, as follows. For each CL-
AGN, we first fitted the two epochs separately with both broad and narrow
emission lines. Next, we renormalized the bright-state spectrum using the
ratio between the narrow [O iii] line fluxes in the bright and dim states, so
that the narrow line emission is consistent between the two epochs. The [O
iii]-based normalization factors we applied are in the range $0.72-1.87$, with
a median value of $1.03$. Furthermore, to maximize the consistency between the
(broad) emission line measurements of the two epochs, we subtracted the narrow
emission line profiles deduced from the dim-state spectrum from the (scaled)
bright-state spectrum, so that the latter remains essentially free of narrow
line emission. Finally, the scaled, narrow-line subtracted bright-state
spectrum was fitted through another run of PyQSOFit, but now without any
narrow line components. This procedure enhances the quality of the broad line
profile fitting for the bright-state spectra, particularly for the rather
complex and blended H$\alpha$ profiles.

For 50 of our CL-AGN spectra, the two epochs were obtained using different
fiber sizes (i.e., 3′′-diameter fibers for the SDSS spectrographs and
2′′-diameter fibers for the BOSS spectrographs, for the earlier and later
epochs, respectively). For such sources, a normalization based on matching
narrow emission line fluxes may yield inconsistent results, since more narrow-
line emission from the (extended) NLR may enter the larger apertures used for
the earlier epochs. For these sources, and only when the narrow emission line
flux difference between the epochs could (qualitatively and, roughly,
quantitatively) be explained by the difference in fiber sizes, we did not
perform any renormalization, but rather carried out our final spectral
measurements on the original (pipeline produced) spectra. For yet other
candidates, visual inspection of the decomposed spectra revealed that the
normalization procedure produced unphysical results, such as a mismatch
between host components in the two epochs (i.e., a drastic difference between
the appearances of host stellar absorption features). In such cases, which can
be perhaps explained by seeing differences between the two epochs, we also
chose to not perform any renormalization and instead relied on the original
spectra.

The spectral measurements resulting from this approach were used for Figures 6
and 7, and the corresponding analysis.

###  3.3 Calculation of AGN Properties

The various spectral decomposition and measurement approaches allow us to
derive estimates of bolometric luminosities ($L_{\rm bol}$), black hole masses
($M_{\rm BH}$), and Eddington ratios ($f_{\mathrm{Edd}}$). The only purpose of
these derived quantities for the present work is to compare our CL-AGNs to the
general quasar population (i.e., to the DR16Q-based WS22 catalog). As these
extensive catalogs relied on SDSS-I-IV spectra for their spectral
measurements, we also chose to use the spectral measurements of the earlier,
SDSS-I-IV spectra of our CL-AGNs in what follows. We acknowledge that all
these derived quantities are based on a set of assumptions, and carry large
systematic uncertainties. Our derived quantities should thus be treated with
the same caution as do other compilations of $L_{\rm bol}$, $M_{\rm BH}$ and
$f_{\mathrm{Edd}}$ for broad-line AGNs with rest-frame UV-optical spectroscopy
(see, e.g., Shen, 2013; Mejía-Restrepo et al., 2022, and references therein).

Following the methods of WS22, we estimate the bolometric luminosity, $L_{\rm
bol}$, using the measured monochromatic continuum luminosities, $\lambda
L_{\lambda}$, at three specific wavelengths: 1350, 3000, and 5100 Å
($L_{1350}$, $L_{3000}$, and $L_{5100}$, respectively). The corresponding
bolometric corrections are 3.81, 5.15, and 9.26, respectively (Richards et
al., 2006). We prioritize using the estimate based on $L_{3000}$, as it is
less affected by host contamination compared to $L_{5100}$ and less affected
by reddening than $L_{1350}$. For sources where $L_{3000}$ is unavailable, we
used whichever of the other two continuum measurements that was available (no
single source had both $L_{1350}$ and $L_{5100}$ measurements available). We
acknowledge that estimating the AGN continuum emission using $L_{3000}$ may
also be inaccurate. Specifically, contamination from hot (young) stars and/or
from (blended) iron transitions within the relevant spectral band may be
mistaken for AGN continuum. We note, however, that $L_{3000}$ is the preferred
estimate in WS22, from which we draw our parent sample.

We estimate $M_{\rm BH}$ using single-epoch, virial BH mass prescriptions (see
Mejía-Restrepo et al., 2022, and references therein). Contrary to WS22, who
used only the broad H$\beta$, Mg ii, and C iv emission lines to estimate BH
masses, we also utilized the broad $\rm{H}\alpha$ emission line. This choice
is explained in detail in what follows. To minimize potential systematic
errors due to host contamination, we adopted a hierarchical approach. Whenever
possible, we used mass estimates based on the luminosity and width of the
broad component of the $\rm{H}\alpha$ emission line, following the
prescription of Greene & Ho (2005) but re-scaling it by $\times 4/3$ (or
$+0.125$ dex; see Mejía-Restrepo et al. 2022 for details). As this estimate
does not rely on continuum measurements, it is the least affected by host
contamination. Whenever $\rm{H}\alpha$ was not accessible, we relied on width
of the broad Mg ii line and $L_{3000}$ (again: due to host contamination and
reddening concerns). When neither the broad $\rm{H}\alpha$ nor the broad Mg ii
lines were available, we deferred to prescriptions that use either the broad
H$\beta$ line and $L_{5100}$, or the broad C iv line and $L_{1350}$, depending
on the source redshift.

We estimate $f_{\mathrm{Edd}}$ following

$f_{\mathrm{Edd}}\equiv\frac{L_{\rm bol}}{L_{\rm Edd}}=\frac{L_{\rm
bol}}{1.5\times 10^{38}\times(M_{\rm BH}/M_{\odot})\,{\rm erg\,s}^{-1}}\,,$
(3)

which is appropriate for solar-metalicity gas.

Throughout our analysis (described below), we have utilized two distinct sets
of the quantities $L_{\rm bol}$, $M_{\rm BH}$, and $f_{\mathrm{Edd}}$. First,
we employed the aforementioned mass prescriptions to calculate $M_{\rm BH}$
and $f_{\mathrm{Edd}}$ for all the quasars in the WS22 catalog. This
calculation relied on the spectral measurements in that catalog (luminosity
and line widths), rather than using the $M_{\rm BH}$ values provided in the
catalog. Second, we also calculated these quantities based on our own
“WS22-like” spectral decomposition of the earliest SDSS I-IV epochs of our
candidates (see Section 3.2 for details). As these values are not part of any
public catalog, we have compiled them along with the relevant spectral
measurements (luminosity and line widths) in Table 6. Also, as noted in
Section 3.2, we verified that the spectral measurements and derived properties
stemming from our “WS22-like” decomposition are highly consistent with the
ones available through the WS22 catalog, with differences much smaller than
0.1 dex.

Table 6: Spectral measurements of CL-AGNs

Name | $z$ | Spec. | $\log L$ | FWHM | $\log M_{\rm BH}$ | $\log L_{\rm bol}$ | $\log f_{\mathrm{Edd}}$
---|---|---|---|---|---|---|---
|  | complex | $\left({\rm erg\,s}^{-1}\right)$ | $\left({\rm km\,s}^{-1}\right)$ | $\left(M_{\odot}\right)$ | $\left({\rm erg\,s}^{-1}\right)$ |
J000750.51$+$005815.2 | 0.345113 | H$\alpha$ | 42.3 | 7203 | 8.37 | 44 | -2.56
J001103.49$+$010032.6 | 0.485892 | H$\alpha$ | 42.4 | 3774 | 7.82 | 44.7 | -1.34
J001333.96$+$014506.8 | 0.881555 | Mg ii | 44.2 | 3747 | 8.2 | 44.9 | -1.43
J003841.53$+$000226.7 | 0.649649 | Mg ii | 44.7 | 5690 | 8.83 | 45.4 | -1.62
J004459.09$-$010629.3 | 0.228215 | H$\alpha$ | 42.3 | 5228 | 8.07 | 44.8 | -1.43
J004616.68$+$000249.1 | 0.4531 | Mg ii | 43.3 | 4177 | 7.74 | 44.1 | -1.87
J011536.11$+$003352.4 | 0.364029 | H$\alpha$ | 42.4 | 7371 | 8.44 | 44.4 | -2.26
J011851.07$-$010417.3 | 0.573171 | Mg ii | 44.1 | 4515 | 8.28 | 44.8 | -1.64
J012025.45$+$142727.9 | 0.263752 | H$\alpha$ | 41.9 | 5850 | 7.97 | 43.8 | -2.32
J012256.19$-$000252.6 | 0.340502 | H$\alpha$ | 42.2 | 4633 | 7.9 | 44.4 | -1.7

Note. — Table 6 is published in its entirety in the machine-readable format. A
portion is shown here for guidance regarding its form and content.

##  4 Analysis & Results

In what follows, we examine various properties of the core CL-AGN sample, as
well as some (multi-epoch) emission line measurements. We stress that the
analyses described hereafter focus only on the core sample of 113 CL-AGN
(Section 2.5), and do _not_ include the 3 additional CL-AGN identified within
the BHM-RM data (Section 2.6.1).

###  4.1 CL-AGN Sample Properties

To ensure consistency with the fitting procedures in WS22, we initially
examine only the subset of our core CL-AGN sample that is present in the DR16Q
catalog (87 out of 113 CL-AGNs), and use the catalog values from WS22 for both
our CL-AGN sub-sample and the WS22 comparison sample. This means that we are
using only measurements based on the earlier (archival) spectra for the
following analysis.

Figure 3 compares the redshift-luminosity distribution of three different
samples: the entire DR16Q catalog, the subset of the DR16 quasar catalog that
has been re-observed during the first year of SDSS-V operations (not including
BHM-RM targets), and our CL-AGN sample (which was drawn from the first-year
SDSS-V data). As can be seen in the figure, CL-AGNs occupy a lower redshift
and luminosity range compared to all known SDSS/DR16 quasars, even when
accounting for the partial coverage of SDSS-V repeated spectroscopy.
Specifically, 94% of the subset of CL-AGN that are present in the WS22 catalog
reside at $z\lesssim 0.9$, compared with 14% of the entire WS22 quasar catalog
and 19% of the quasars re-observed in the first year of SDSS-V operations.

This difference may be explained by the longer rest-frame timescales that are
probed for low-redshift targets, and hence the higher probability of detecting
a significant spectral change at low-$z$. An additional explanation may lie in
the known anti-correlation between AGN luminosity and variability amplitude in
general (e.g., Vanden Berk et al., 2004; Wilhite et al., 2008; Kozłowski et
al., 2010; MacLeod et al., 2010; Meusinger & Weiss, 2013; Morganson et al.,
2014; Simm et al., 2016; Caplar et al., 2017) and extreme variability in
particular (e.g., Rumbaugh et al., 2018), as well as the occurrence of CL-AGN
(e.g., MacLeod et al., 2019; Green et al., 2022; Temple et al., 2023).

Furthermore, part of the difference between the distributions could be
attributed to our method of searching for CL-AGNs, which primarily relied on
detecting variability in specific emission lines. Beyond $z\sim 1.1$, the
$\rm{H}\beta$ line is shifted out of the BOSS spectra. At higher redshifts,
our CL-AGN search algorithm relies instead primarily on the broad Mg ii line.
Several studies reported that this line displays less variability than the
broad Balmer lines (e.g., Cackett et al., 2015; Sun et al., 2015; Homayouni et
al., 2020; Yang et al., 2020, and references therein). In addition, beyond
$z\approx 1$, the BOSS spectra lack the prominent narrow [O iii]$\,\lambda
5007$ emission line, which was used to robustly identify significant changes
in the broad H$\beta$ line (i.e., as a flux-calibration anchor). We were not
able to employ such an approach at yet higher redshifts, thus leading to the
apparent bias of our sample towards $z\lesssim 1$ sources.

Figure 3: The distributions of SDSS DR16 quasars (black contours; Wu & Shen,
2022), SDSS DR16 quasars that have been reobserved in the first year of SDSS-V
and that also appear in the WS22 quasar catalog (blue contours), and SDSS-V
CL-AGNs that appear in the WS22 catalog (red contours), in the luminosity-
redshift plane. For each distribution, the enclosed regions correspond to the
68%, 95%, and 99.7% percentiles. The dashed vertical line marks the redshift
where the broad $\rm{H}\beta$ line moves out of the SDSS-V/BOSS spectral
coverage. Figure 4: Top row: the CL-AGN occurrence rate as a function of
$L_{\rm bol}$ (left), $M_{\rm BH}$ (middle), and $f_{\mathrm{Edd}}$ (right),
for $0<z\leq 0.5$ (red) and $0.5<z\leq 1$ (blue), for the 82 CL-AGN at $z\leq
1$ in our core sample that also appear in the WS22 catalog. The rates were
calculated in 0.5 dex wide bins, and the number of CL-AGNs in each such bin is
indicated next to it. To illustrate some of the trends in these panels, we
also show (as dashed lines) best log-linear fits to the data, for the cases
where there is a statistically significant correlation (i.e., a Spearman
correlation test that resulted in $p_{\rm S}<0.01$). Bottom row: same as top
panels, but for the 13 AQMES CL-AGN at $z\leq 1$.

In Figure 4 we examine the occurrence rates of CL-AGNs, $f_{\rm CL}$, and its
dependence on redshift, luminosity, BH mass, and Eddington ratio (all derived,
again, from the earlier SDSS spectra). For this analysis, we only consider the
82 CL-AGNs at $z\leq 1$ in our core sample that also appear in the WS22
catalog, and further split them to those located at $0<z\leq 0.5$ and
$0.5<z\leq 1$ (43 and 39 sources, respectively).

The top row of panels of Figure 4 presents the occurrence rates of our (subset
of) CL-AGNs, calculated by dividing the number of detected CL-AGNs by the
total number of WS22 quasars with repeated spectroscopy in SDSS-V, in each of
the two redshift ranges, and for each bin of AGN bolometric luminosity, black
hole mass, and Eddington ratio. We recall that the DR16Q quasars, and thus the
WS22 quasars, constitute a heterogeneous superset constructed with various
selection criteria (and related biases). In each panel, and for each of the
redshift ranges, we split the corresponding sample of CL-AGNs to evenly spaced
bins of width 0.5 dex, such that each bin would contain at least one CL-AGN
(i.e., the bins cover the range of properties observed in our CL-AGN sample).
As can be seen in Figure 4, $f_{\rm CL}$ increases with decreasing $L_{\rm
bol}$ and $f_{\mathrm{Edd}}$, across all redshifts. For example, for $0<z\leq
0.5$, the CL-AGN occurrence rate increases from $f_{\rm CL}\sim 1\%$ for
$\log(L_{\rm bol}/{\rm erg\,s}^{-1})\approx 45.4$ to $\sim 9\%$ for
$\log(L_{\rm bol}/{\rm erg\,s}^{-1})\approx 43.9$. Similarly, the occurrence
rate increases from $f_{\rm CL}\sim 1\%$ for $\log
f_{\mathrm{Edd}}\approx-0.4$ to $\sim 16\%$ for $\log
f_{\mathrm{Edd}}\approx-2.5$. Conversely, we find an apparently flat
dependence of $f_{\rm CL}$ on $M_{\rm BH}$ ($p_{\rm S}=0.62$). For the higher
redshift bin of $0.5<z\leq 1$ the trends are qualitatively similar.

The bottom row of panels of Figure 4 presents the occurrence rates of CL-AGNs
discovered within the AQMES program. As mentioned in Section 2.1, AQMES
provides the largest and most homogeneously-selected sample of AGNs with
repeated spectroscopy within SDSS-V, with a systematically programmed cadence
of repeated observations. While the focus on AQMES-observed AGNs allows us to
provide rates based on a clear selection criteria, it naturally comes at the
cost of a smaller sample (14 CL-AGNs). Here, $f_{\rm CL}$ is calculated by
dividing the number of detected AQMES CL-AGNs by the total number of AQMES
targets revisited during the first year of SDSS-V operations, within the
corresponding bin (in $L_{\rm bol}$, $M_{\rm BH}$, $f_{\mathrm{Edd}}$) and
redshift range. The results for the AQMES subsample are qualitatively similar
to the results for the entire sample: $f_{\rm CL}$ decreases with increasing
luminosity and Eddington ratio, and does not depend on $M_{\rm BH}$. These
AQMES-only panels are currently sparsely populated, but we envision that they
will provide the most statistically sound results as SDSS-V progresses, given
the clean target selection criteria, and the high purity, of the AQMES
program.

It is important to emphasize that, as the calculation of $f_{\rm CL}$ relies
on a straightforward division of the number of CL-AGNs by the total number of
AGNs, it does not properly account for the observational bias stemming from
the relation between luminosity and redshift, inherent biases in the CL-AGN
search, and degeneracies between the different BH properties (Equation 3).
Therefore, the trends seen in Figure 4 are only suggestive of a link between
fundamental AGN properties and the CL phenomena, and should be regarded with
caution. Instead, they serve as predictions for the occurrence rates of CL-
AGNs in an SDSS-V-like survey based on $L_{\rm bol}$, $M_{\rm BH}$, and
$f_{\mathrm{Edd}}$. Additionally, as our search methods may have missed some
CL-AGNs (i.e., our search is pure according to our definition of CL-AGNs, but
incomplete), these rates should be viewed as lower limits.

###  4.2 What AGN Properties are Intrinsically Related with the CL Phenomenon?

The AGN luminosity, BH mass and Eddington ratio are closely related through
$f_{\mathrm{Edd}}\propto L_{\rm bol}/M_{\rm BH}$. To examine their relevance
to our CL-AGNs, while accounting for potential degeneracies, we constructed
three control samples drawn from the WS22 catalog. All three control samples
are first matched to our CL-AGN sample in terms of redshift distribution, to
counteract any redshift-related selection biases. Importantly, each of the
control samples is further matched to our CL-AGN sample in the distribution of
one of the properties we examine ($L_{\rm bol}$, $M_{\rm BH}$, or
$f_{\mathrm{Edd}}$), so we can compare the distributions of the other two
properties between the control and CL-AGN samples (for a detailed description
of the matched control samples construction procedure see Section 3.1).
Moreover, to ensure that our control samples and CL-AGNs are indeed drawn from
the same parent sample, the following analysis focuses on the subset of 87 CL-
AGNs that are found in the DR16Q and WS22 catalogs. We further verified that
our conclusions do not depend on this choice to focus on this subset of 87 CL-
AGNs, by performing the same analysis also on the entire core sample of 113
CL-AGNs. To obtain the spectral measurements needed for this latter analysis
of the entire CL-AGN sample, and the corresponding derived AGN properties, we
used the WS22-like spectral decomposition of the earlier (DR16) spectra of our
CL-AGNs, which allowed us to take advantage of our entire sample of 113 CL-
AGNs, and thus to increase the statistical significance of our results.

$L_{\rm bol}\,\&\,z$ matched
$M_{\rm BH}\,\&\,z$ matched
$f_{\mathrm{Edd}}\,\&\,z$ matched

Figure 5: Distributions of $L_{\rm bol}$, $M_{\rm BH}$, and $f_{\mathrm{Edd}}$
for our core sample of 113 CL-AGNs (red lines), for the subset of 87 CL-AGNs
that appear in the WS22 catalog (red shaded bars), and for the matched control
samples drawn from the WS22 catalog (blue shaded bars). Each row of panels
shows control samples that are matched based on redshift and either $L_{\rm
bol}$, $M_{\rm BH}$, or $f_{\mathrm{Edd}}$ (top, middle, and bottom panels,
respectively). Each panel includes the median $p$-value derived from KS-tests
between the WS22 subset of CL-AGNs and 1000 realizations of the corresponding
control sample.

Figure 5 displays the distributions of $L_{\rm bol}$, $M_{\rm BH}$, and
$f_{\mathrm{Edd}}$ for CL-AGN from our sample and the corresponding control
samples (red and blue shaded bars, respectively), where the control samples
are drawn from the WS22 catalog and are matched to the subset of 87 CL-AGNs
that appear that catalog based on a combination of redshift and either $L_{\rm
bol}$, $M_{\rm BH}$, or $f_{\mathrm{Edd}}$ (top, middle, and bottom panels,
respectively). For each of the properties examined, we compare the
distributions for our CL-AGN sample and the corresponding control sample using
the Kolmogorov-Smirnov (KS) test. Appendix E and Figure 12 present a
complementary analysis conducted for our entire core sample of 113 CL-AGNs,
based on our own “WS22-like” spectral decomposition.

Given that the control samples are constructed through a semi-random process
(i.e., the control samples are tailored to match our CL-AGN sample in various
properties but are otherwise drawn randomly from the WS22 quasar catalog; See
Section 3.1), the histograms in Figure 5 (and in Figure 12) are unique to each
realization of the control sample. Therefore, in order to obtain robust
results for the statistical tests discussed below we repeated the construction
of the control samples, and the statistical tests that compare them to our CL-
AGNs, 1000 times. All the $p$-values mentioned below are the median values of
the resulting distributions.121212We verified that the distributions of the
$p$-values are unimodal and well-centered (approximately) around these
representative values.

As can be clearly seen in the top panels of Figure 5, there is a statistically
significant difference in the distributions of both $M_{\rm BH}$ and
$f_{\mathrm{Edd}}$ for the $L_{\rm bol}$-matched samples ($p_{\rm KS}=2\times
10^{-4}$ and $9\times 10^{-3}$, respectively). For the $M_{\rm BH}$-matched
samples (middle panels), there is a statistically significant difference in
the distributions of both $L_{\rm bol}$ and $f_{\mathrm{Edd}}$ ($p_{\rm
KS}=3\times 10^{-5}$ and $9\times 10^{-8}$, respectively). The significantly
lower $p_{\rm KS}$ of the latter comparison suggests that the
$f_{\mathrm{Edd}}$ distributions are different to a greater degree than do the
$L_{\rm bol}$ ones. Most importantly, for the $f_{\mathrm{Edd}}$-matched
samples, there is no statistically significant difference in the distributions
of both $M_{\rm BH}$ and $L_{\rm bol}$ ($p_{\rm KS}>0.01$).

Replacing the subset of 87 CL-AGNs that are part of the WS22 catalog with the
full sample of 113 CL-AGNs yields generally consistent results, with some
changes in the $p_{\rm KS}$ values and—importantly—with a more significant
difference between the ($L_{\rm bol}$-matched) $f_{\mathrm{Edd}}$
distributions than between the ($f_{\mathrm{Edd}}$-matched) $L_{\rm bol}$
distributions (see Appendix E and Figure 12). However, unlike for the WS22
subset, the entire CL-AGN sample appears to significantly differ in $L_{\rm
bol}$ compared with the control sample, when matching for $f_{\mathrm{Edd}}$
($p_{\rm KS}=3\times 10^{-4}$). More generally, we find that the differences
between all distributions for the entire CL-AGN sample are more significant
that what is found for the WS22 subset of CL-AGNs. We suspect that these
differences are the direct result of the fact that the 26 CL-AGNs in our
sample that are not part of WS22 catalog were not part of the (parent) DR16Q
catalog, and are therefore “diluting” the sample with sources that cannot, and
should not, be compared with the control sample we use for our analysis.

Our analysis suggests that, among the properties we investigate here, it is
most likely the Eddington ratio that differentiates our CL-AGN from the
general quasar population and thus that the Eddington ratio—rather than BH
mass or AGN luminosity—may have an intrinsic relation to the CL phenomenon.
This conclusion is supported by the fact that the most statistically
significant difference between the CL-AGN sample and the various control
samples we have assembled is in the $f_{\mathrm{Edd}}$ distribution. We thus
postulate that any trends between CL-AGNs and either BH mass or AGN
luminosities are likely caused by their intrinsic links to the Eddington
ratio.

To further quantify the difference we find between the $f_{\mathrm{Edd}}$
distributions of our CL-AGNs and of the control sample, we note that for the
$L_{\rm bol}$ matched sample, the median $f_{\mathrm{Edd}}$ for the CL-AGN
WS22 subset is $(f_{\mathrm{Edd}})_{\rm med}=0.025$, while for the
corresponding control sample it is higher, at $(f_{\mathrm{Edd}})_{\rm
med}=0.043$. When considering the entire core sample of 113 CL-AGNs, and the
corresponding control sample, the medians are $(f_{\mathrm{Edd}})_{\rm
med}=0.030$ and $0.050$, respectively. This difference is in qualitative (and
roughly quantitative) agreement with what was found in other studies of CL-AGN
(e.g., Figure 8 in Rumbaugh et al. 2018, Figure 6 in MacLeod et al. 2019,
Figure 13 in Green et al. 2022; see also Temple et al. 2023). The two
$f_{\mathrm{Edd}}$ distributions have significant overlap, with median
absolute deviations (MADs) of 0.27 and 0.36 dex, for the WS22 subset of CL-
AGNs and the control sample, respectively. However, the difference between
them goes beyond the aforementioned KS tests: for example, while $\approx$25%
of the control sample lies at $f_{\mathrm{Edd}}>0.1$, only $\approx$10% of our
CL-AGNs are found in that range.131313The MAD values and percentiles we quote
reflect the subset of 87 CL-AGNs that are in the WS22 catalog, and the
corresponding control sample. For the latter, the quoted quantities are
themselves the median values obtained from the 1000 realizations of the
control samples.

These results have several important implications. First, the lack of a sharp
drop in the mass distribution of CL-AGNs around the value of
$10^{8}\,M_{\odot}$, as seen in tidal disruption event (TDE) rates (e.g., van
Velzen, 2018; Yao et al., 2023), disfavors TDEs as the main driving mechanism
of CL-AGNs, contrary to suggestions in some studies (see Merloni et al. 2015
for a specific case and detailed discussion). The drop in TDE rates for
$M_{\rm BH}\gtrsim 10^{8}\,M_{\odot}$ is commonly interpreted in the context
of basic TDE physics; a $1\,M_{\odot}$ main-sequence star can be disrupted
outside of the BH horizon (and produce an observable TDE) only if $M_{\rm
BH}<10^{8}\,M_{\odot}$ (for a non-spinning BH), or $M_{\rm BH}<7\times
10^{8}\,M_{\odot}$ (for a maximally-spinning BH; see Gezari 2021 and Rossi et
al. 2021 for recent reviews). Among our sample of CL-AGNs, 57 ($\sim$50%) and
12 ($\sim$10%) sources exceed these two limiting BH masses (respectively).

Another implication relates to the possible link between the extreme
variability observed in CL-AGNs and the more commonly seen stochastic optical
variability seen in the general AGN population (see Vanden Berk et al., 2004,
and references therein). One possibility is that the extreme and apparently
coherent changes observed in CL-AGN (and EV-AGNs in general) are simply the
rarest cases at the tail of the stochastic AGN variability distribution.
Testing this idea directly using (currently available) SDSS-V data is well
beyond the scope of the present work, however we note that the (final) SDSS-V
dataset would be ideal for this kind of analysis. The source of typical AGN
variability remains uncertain, and may be attributed to accretion disk
instabilities, Poisson processes, and/or gravitational microlensing.
Irrespective of the underlying mechanism, essentially all relevant studies
have found the variability amplitude to be anti-correlated with luminosity
(e.g., Vanden Berk et al., 2004; MacLeod et al., 2010; Simm et al., 2016;
Caplar et al., 2017; Arévalo et al., 2023). Some of these studies have also
investigated a possible link between variability amplitude and Eddington
ratio, finding conflicting results (e.g., Simm et al., 2016; Caplar et al.,
2017; Arévalo et al., 2023). Our work, however, demonstrates that when
controlling for the Eddington ratio, CL-AGNs do not have a significantly lower
luminosity as compared to the AGN population.

###  4.3 Comparing the Variability of Different Emission Lines

Figure 6: The distributions of line flux changes between the SDSS-V and DR16
epochs for the H$\alpha$ (top), H$\beta$ (middle), and Mg ii (bottom) broad
emission lines, and the corresponding rest-frame time differences, for our CL-
AGN sample. Objects that were selected through the relevant emission line in
each panel are shown as filled circles, while objects that were selected
through one of the other emission lines are represented by empty circles.
Displayed to the right of each panel is the corresponding CDF of the emission
line flux ratio for all the CL-AGNs in each panel.

We now examine how various broad emission lines have changed in our CL-AGNs,
as generally not all lines are expected to vary by the same amount between any
two spectral epochs. This analysis relies on our two-epoch spectral
decomposition approach, which provides spectral measurements for the ‘dim’ and
‘bright’ states of each of the CL-AGNs in our sample (see Section 3.2).

The three panels in Figure 6 present the distributions of line flux changes
between the SDSS-V and DR16 epochs for the H$\alpha$, H$\beta$, and Mg ii
broad emission lines, and the corresponding (rest-frame) time differences, for
our CL-AGN sample. Each panel displays the objects that were selected through
changes in the corresponding emission line (filled circles; See Section 2.2)
and those that were selected through one of the other emission lines (empty
circles). To the right of each panel, we show the cumulative distribution
functions (CDF) of the emission line flux ratios for all the CL-AGN in that
panel. As can be seen, the median $\Delta\log F\left(\rm line\right)$ in all
panels are negative, indicating that a majority of our candidates are dimming
or “turn-off” events. This outcome aligns with our expectations, given that
our data primarily rely on repeated spectroscopy of sources that were
previously identified as broad-line AGNs in archival SDSS data (see Section
2.5).

The three panels in Figure 7 compare the ratios of various emission line
fluxes, between the SDSS-V and earlier (DR16) epochs for our sample of CL-
AGN.141414Since our final core sample includes only three CL-AGNs where C iv
is observed, and two C iv-selected CL-AGNs, we chose to exclude this line from
this analysis. The dashed lines represent the hypothetical scenario in which
the broad emission lines would change at the same rate (i.e., a 1:1 relation),
while the solid lines denote the best linear fits to the data in each
panel.151515The linear fits were obtained through a standard orthogonal
distance regression. For the fits shown in Figure 7, we have set the intercept
to zero. This choice minimizes the bias introduced by our CL-AGN sample having
more dimming events compared to brightening ones (67% and 33% of the CL-AGN
core sample, respectively), which is reflected in the way the first and third
quadrants in Figure 7 are populated. Moreover, a non-zero intercept would
suggest that some CL-AGNs occupy peculiar regions in the two-line changes
phase space where one emission line strengthens while the other one weakens,
including in an apparently self-inconsistent way, and irregardless of whether
the CL-AGN is dimming or brightening. Such a scenario seems physically
implausible. As can be seen in all panels of Figure 7, almost all objects
exhibit highly correlated line variability, in the sense that various emission
lines either brighten or dim simultaneously.

The left panel, which presents the flux variability of H$\alpha$ vs. that of
H$\beta$, shows the tightest correlation among the three pairs of lines we
examine, which is expected given that both lines originate from the same
species (and gas). All but one of the 46 CL-AGNs included in that panel (98%)
are found in the 1st and 3rd quadrants, i.e. both lines brighten or dim
synchronously (a Spearman correlation test yields $\rho_{\rm S}=0.79$). There
is, however, a significant deviation from the 1:1 relation, as the best linear
fit suggests that H$\beta$ exhibits greater variation than H$\alpha$. We first
note that 32 out of the 46 CL-AGN in that panel ($\approx$70%) have
$|\Delta\log({\rm H}\beta)|>|\Delta\log({\rm H}\alpha)|$. Moreover, the best-
fit slope we obtain for the correlation of $\Delta\log({\rm H}\beta)$ vs.
$\Delta\log({\rm H}\alpha)$ is $1.29\pm 0.05$, which corresponds to an
$\approx 6\sigma$ deviation from the 1:1 relation.

The trend of more variability in H$\beta$ than in H$\alpha$ is consistent with
what is expected from a variable obscuration scenario, in which shorter
wavelengths would be affected more than longer ones (see, e.g., Ramos Almeida
& Ricci, 2017; Zeltyn et al., 2022, and references therein). We discuss this
possibility further in Section 4.4 below.

Figure 7: A comparison of the flux ratios between the SDSS-V and DR16 epochs
for different emission lines within our CL-AGN sample: H$\alpha$ vs H$\beta$
(left), H$\alpha$ vs Mg ii (middle), and H$\beta$ vs Mg ii (right). The dashed
lines represent the hypothetical scenario in which the broad emission lines
would change at the same rate (i.e., a 1:1 relation), while the solid lines
denote the best linear fits to the data in each panel, where the intercept is
manually set to zero during the fitting procedure. The orange regions
surrounding the solid lines indicate the associated uncertainty for each fit.

The middle panel, which presents the flux variability of H$\alpha$ vs. that of
Mg ii, is sparsely populated, as the (redshift-dependent) spectral coverage of
most of our candidates does not allow simultaneous measurement of these two
emission lines. Consequently, the uncertainties on the best fitting linear
relation are substantial, and prohibit us from drawing any insights.

The right panel presents the flux variability of H$\beta$ vs. Mg ii, where the
two lines also appear to vary in a qualitatively synchronous manner, with all
but one of the 51 CL-AGNs in this panel (98%) located in the 1st and 3rd
quadrantsv($\rho_{\rm S}=0.70$). There is again a significant deviation from a
purely linear (1:1) relation, with H$\beta$ typically exhibiting significantly
more variation than Mg ii. Of the 51 CL-AGNs in this panel, 37 (73%) have
$|\Delta\log({\rm H}\beta)|>|\Delta\log({\rm Mg}\,\textsc{ii})|$. The best-
fitting slope for these data is determined to be $0.54\pm 0.05$ (a $\approx
10\sigma$ deviation from the 1:1 relation).

This result is consistent with previous works that showed that the Mg ii
emission line is less variable compared to the Balmer emission lines (e.g.,
Cackett et al., 2015; Sun et al., 2015; Homayouni et al., 2020). As discussed
in these works (and references therein), there are several possible factors
contributing to this observed trend, including the specific excitation
mechanisms, optical depth, and the BLR geometry of the Mg ii emission line,
or—alternatively—some other, not-yet-understood aspect of BLR physics (e.g.
Sun et al., 2015). In addition, this trend, unlike the H$\beta$ vs. H$\alpha$
trend seen in the left panel of Figure 7, is inconsistent with variable
obscuration driving (most of) the CL-AGN events in our sample, as in this
scenario we would expect the bluer Mg ii line to show higher variability than
H$\beta$ (assuming they are emitted from roughly the same region within the
BLR; see, e.g., Homayouni et al. 2020). It is important to note, however, that
our data display considerable scatter around the best fit line, with some
objects showing perfectly correlated (1:1) variability, while others objects
show greater variability in Mg ii than in H$\beta$.

In any case, the limited variability in Mg ii relative to H$\beta$ (right
panel of Figure 7) underscores the limitations facing any search for CL-AGNs
at $1\lesssim z\lesssim 2$ (including our own). Investigating CL-AGNs in this
redshift region is further complicated by the inability to use (strong) narrow
emission lines to overcome potential spectral calibration issues, which
presents a considerable challenge when searching for CL-AGNs at high $z$ in
general.

###  4.4 Accretion vs Obscuration

To date, most studies of CL-AGNs identified through (rest-frame) UV-optical
spectroscopy have favored the hypothesis that fluctuations in the ionizing
continuum, caused by changes to the accretion flow, are the (primary) driver
of the CL-AGN phenomena, as opposed to attributing them to variable
obscuration, which is indeed the favorable explanation for most of the CL-AGNs
identified in the X-ray regime (see, e.g., Ricci & Trakhtenbrot, 2023, and
references therein). This view has led to the usage of the term “changing-
state” AGN to describe the former type of CL-AGN events. One of the key lines
of evidence in favor of this explanation is the coincident and correlated IR
variability observed in several CL-AGNs (e.g., Sheng et al., 2017; Yang et
al., 2018; Stern et al., 2018; Wang et al., 2019). Within the widely accepted
picture of AGN structure, the mid-IR emission is not expected to be
substantially influenced by variable obscuration, however it is expected to
respond to (dramatic) UV variability from the central engine. Mid-IR light
curves could thus serve as a relatively efficient, if incomplete, way to
separate changing accretion from changing obscuration events in AGNs.

To examine the mechanism behind the CL-AGNs in our sample, we acquired WISE
light curves for all of our candidates. Out of the total 113 candidates, 46
exhibited variability in the WISE light curve that was consistent with the
observed optical changes, whereby a significant IR brightening (dimming)
occurred within the period between the archival SDSS and the new, brighter
(dimmer) SDSS-V spectra, while 14 other CL-AGNs displayed no such concurrent
IR variations. The example WISE light curves in Figure 10 (in Appendix C)
demonstrate these two scenarios. For the remaining 53 candidates, the light
curves were either inconclusive or had missing data points during the optical
transition.

This simple analysis suggests that the majority of CL-AGN are likely linked to
variations in the accretion flow, in agreement with the prevalent
interpretation in the literature, although there may also be a considerable
fraction of CL-AGN driven by variable obscuration. We note in particular that
one of the CL-AGNs in our sample (J1628+4329) indeed shows substantial
evidence for an obscuration-driven, transient CL event, as discussed in detail
in Zeltyn et al. (2022). The absence of observed IR variability in some of our
CL-AGNs, however, does not necessarily point to variable obscuration being the
source of variability for these sources. To directly determine the source of
variability for (each of) the sources identified following the approach
presented here (and in similar studies), we would ideally need to conduct a
coordinated multi-wavelength campaign, including X-ray observations (e.g.,
LaMassa et al., 2015; Ruan et al., 2019; Temple et al., 2023; Yang et al.,
2023). This effort is a challenging one due to the extensive data volume of
SDSS-V. Alternatively, a careful and detailed spectral analysis may hint at
the source of variability (e.g., Ruan et al., 2014; Zeltyn et al., 2022); such
an endeavor, however, is beyond the scope of this work.

##  5 Conclusions

This study used the data accumulated during the first year of SDSS-V
spectroscopic observations to identify AGNs that experienced dramatic
variations to their broad emission line strength, and to investigate their
nature. The main outcomes of this study are:

  1. 1.

We have constructed a large, high-purity sample totaling 116 CL-AGNs (113 in
our core sample and three from BHM-RM), and 88 other dramatic emission line
variations, drawn from a survey that is (partially) designed to identify such
events directly through wide-field, repeated spectroscopy. See Sections 2.5
and 2.6, Table 5, and Figure 11.

  2. 2.

In our experience, the selection of CL-AGNs and other (dramatic) spectral
variations is far from straightforward, given the need to quantify dimmed
spectra with limited $S/N$, and other limitations. Additional spectroscopic
observations and cross-match to (pseudo-)concurrent photometric data are
important. See Sections 2.4.1 and 2.4.2, and Figures 8 and 9 in Appendix B.

  3. 3.

Our core sample of 113 CL-AGNs covers the redshift range of $0.06<z<2.4$. The
SDSS-V spectroscopy of this core sample probes spectral transition timescales
of 1 year $\lesssim\Delta t_{\rm rest}\lesssim$ 19 years, with yet faster
transitions revealed by our additional spectroscopy (as short as $\Delta
t_{\rm rest}\approx 2$ months; see Zeltyn et al. 2022). Of the 113 systems in
our core sample, 67% (76 systems) show dimming in their recent SDSS-V
spectroscopy compared to previous data, while 33% (37 systems) show
brightening. See Sections 4.1 and 4.3, and Figures 3 and 6.

  4. 4.

Roughly $0.4\%$ of the quasars re-observed in the first year of SDSS-V
operations, and $1.25\%$ among those at $z<1$, showed CL-AGN-like behavior
over a timescale of $<20$ years. We stress that this is a lower limit on the
intrinsic occurrence rate of such phenomena, as SDSS-V observations are
designed to focus on previously-known quasars and miss inactive galaxies in
which quasar-like features have recently appeared. See Section 4.1 and Figure
4.

  5. 5.

Our sample of CL-AGNs has lower Eddington ratios compared with a ($z$ and
$L_{\rm bol}$\- or $M_{\rm BH}$-matched) control sample. The corresponding
median values for the subset of our CL-AGNs where this comparison is most
robust are $f_{\mathrm{Edd}}\approx 0.025$ vs. $0.043$ (i.e., a factor of
$>1.7$ lower). This finding is in agreement with previous studies, which were
based on much smaller spectroscopic samples. See Section 4.2 and Figure 5, and
Figure 12 in Appendix E.

  6. 6.

The available WISE IR data suggest that most—but not all—of the sources in our
sample are driven by changes to the radiation that emerges from their
accretion flows. This is based on the identification of IR flux changes that
(qualitatively) agree with those seen in optical spectroscopy (or lack
thereof). A more detailed investigation of this issue would require detailed
analysis of the optical spectra and of responsive, multi-wavelength follow-up
observations. See Section 4.4 and Figure 10.

We stress that the present study is the first sample study of CL-AGNs (and
related phenomena) based on SDSS-V data. We envision several near-future
pathways for expanding the study of such systems. The on-going SDSS-V/BHM
program provides a growing number of repeated spectroscopy of (previously
known) AGNs, thus enabling us to further enlarge the sample of CL and
extremely variable AGNs and to better understand how such systems are related
to more subtle and typical AGN variability. Obtaining responsive, multi-
wavelength targeted observations, particularly for CL-AGNs identified in the
higher-cadence SDSS-V observations, would allow us to constrain the transition
timescales and to further test ideas regarding the mechanisms that drive these
phenomena (i.e. accretion vs. obscuration). Finally, studying the host
galaxies of CL-AGNs through dedicated observations (e.g., understanding the
stellar populations in host-dominated spectra of recently dimmed sources)
could not only provide more insights regarding CL-AGNs, but indeed be utilized
to study the AGN-host connection in general.

The SDSS-V survey design and progress guarantee that yet larger and richer
samples of extremely variable AGN, and specifically CL-AGN, will become
available in the near future for in-depth analyses.

We thank Jamie Burke, Joseph Farah, Daichi Hiramatsu, Estefania Padilla
Gonzalez, Craig Pellegrino, Megan Newsome, and Giacomo Terreran, for their
assistance with the acquisition and reduction of LCOGT data. We acknowledge
support from: the European Research Council (ERC) under the European Union’s
Horizon 2020 research and innovation program (grant agreement number 950533;
G.Z., B.T., M.Ś); the Israel Science Foundation (grant number 1849/19; G.Z.,
B.T., M.Ś); FONDECYT Regular #123171 (R.J.A.), #1200495 (F.E.B.), and #1230345
(C.R.); ANID BASAL project FB210003 (R.J.A., F.E.B., C.R.); ANID Millennium
Science Initiative ICN12_009 (F.E.B., L.H.G.); DLR grant FKZ 50 OR 2307
(M.K.); NSF grants AST-1715579, AST-2009947 (Y.S.), and AST-2206499 (X.L.);
FONDECYT Postdoctorado #3220516 (M.J.T.); FONDECYT Iniciación #11241477
(L.H.G.); NSFC grants 12025303, 12393814, and 11890693 (Y.Q.X.); Millenium
Nucleus NCN19-058 TITANs (M.L.M.-A.); NSF grant AST-2106990 and the Penn State
Eberly Endowment (W.N.B). Funding for the Sloan Digital Sky Survey V has been
provided by the Alfred P. Sloan Foundation, the Heising-Simons Foundation, the
National Science Foundation, and the Participating Institutions. SDSS
acknowledges support and resources from the Center for High-Performance
Computing at the University of Utah. The SDSS web site is www.sdss.org. SDSS
is managed by the Astrophysical Research Consortium for the Participating
Institutions of the SDSS Collaboration, including the Carnegie Institution for
Science, Chilean National Time Allocation Committee (CNTAC) ratified
researchers, the Gotham Participation Group, Harvard University, Heidelberg
University, The Johns Hopkins University, L’Ecole polytechnique fédérale de
Lausanne (EPFL), Leibniz-Institut für Astrophysik Potsdam (AIP), Max-Planck-
Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für
Extraterrestrische Physik (MPE), Nanjing University, National Astronomical
Observatories of China (NAOC), New Mexico State University, The Ohio State
University, Pennsylvania State University, Smithsonian Astrophysical
Observatory, Space Telescope Science Institute (STScI), the Stellar
Astrophysics Participation Group, Universidad Nacional Autónoma de México,
University of Arizona, University of Colorado Boulder, University of Illinois
at Urbana-Champaign, University of Toronto, University of Utah, University of
Virginia, Yale University, and Yunnan University. The ZTF forced-photometry
service was funded under the Heising-Simons Foundation grant No. 12540303 (PI:
Graham). The Hobby–Eberly Telescope (HET) is a joint project of the University
of Texas at Austin, the Pennsylvania State University, Ludwig-Maximillians-
Universitaet Muenchen, and Georg-August Universitaet Goettingen. The HET is
named in honor of its principal benefactors, William P. Hobby and Robert E.
Eberly.

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