# Improving the selection of changing-look AGNs through multi-wavelength
photometric variability

E. López-Navas1,2, P. Sánchez-Sáez3,4, P. Arévalo1,2, S. Bernal1, M. J.
Graham5, L. Hernández-García4,1, D. Homan6, M. Krumpe6, G. Lamer6, P. Lira7,
M.L. Martínez-Aldama1,2, A. Merloni8, S. Ríos7, M. Salvato8,9, D. Stern10 and
D. Tubín-Arenas6.
1Instituto de Física y Astronomía, Facultad de Ciencias, Universidad de
Valparaíso, Gran Bretaña 1111, Valparaíso, Chile
2Millennium Nucleus on Transversal Research and Technology to Explore
Supermassive Black Holes (TITANS)
3European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching
bei München, Germany
4Millennium Institute of Astrophysics (MAS), Nuncio Monseñor Sótero Sanz 100,
Providencia, Santiago, Chile
5California Institute of Technology, 1200 E. California Blvd, Pasadena, CA
91125, USA
6Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482
Potsdam, Germany
7Departamento Astronomía, Universidad de Chile, Casilla 36D, Santiago, Chile
8Max-Planck-Institut für extraterrestrische Physik, Giessenbachstr. 1, 85748
Garching, Germany
9Exzellenzcluster ORIGINS, Boltzmannstr. 2, 85748 Garching, Germany
10Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak
Grove Drive, Pasadena, CA 91109, USA E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present second epoch optical spectra for 30 changing-look (CL) candidates
found by searching for Type-1 optical variability in a sample of active
galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a
random-forest-based light curve classifier and spectroscopic follow-up,
confirming 50 per cent of candidates as turning-on CLs. In order to improve
this selection method and to better understand the nature of the not-confirmed
CL candidates, we perform a multi-wavelength variability analysis including
optical, mid-infrared (MIR) and X-ray data, and compare the results from the
confirmed and not-confirmed CLs identified in this work. We find that most of
the not-confirmed CLs are consistent with weak Type 1s dominated by host-
galaxy contributions, showing weaker optical and MIR variability. On the
contrary, the confirmed CLs present stronger optical fluctuations and
experience a long (from five to ten years) increase in their MIR fluxes and
the colour W1–W2 over time. In the 0.2–2.3 keV band, at least four out of 11
CLs with available SRG/eROSITA detections have increased their flux in
comparison with archival upper limits. These common features allow us to
select the most promising CLs from our list of candidates, leading to nine
sources with similar multi-wavelength photometric properties to our CL sample.
The use of machine learning algorithms with optical and MIR light curves will
be very useful to identify CLs in future large-scale surveys.

###### keywords:

galaxies: active – accretion, accretion discs – quasars: emission lines

††pubyear: 2023††pagerange: Improving the selection of changing-look AGNs
through multi-wavelength photometric variability–A

##  1 Introduction

For the past few years, a growing (>200) population of active galactic nuclei
(AGNs) with emerging or disappearing optical broad emission lines (BELs) has
been found, arousing great interest among the astrophysics community (see
review by Ricci & Trakhtenbrot, 2022). Most studies favour an accretion rate
change as the origin of such dramatic changes in unobscured AGNs, so these
sources are often called changing-state (CS) AGNs. Other mechanisms such as
variable absorption and tidal disruption events (TDEs) are also expected to
produce variations in the BELs, so the term changing-look (CL) is generally
used to refer to all AGNs that show such spectral transitions, regardless of
the physical mechanisms driving these changes. This term is borrowed from the
X-ray community, where a CL event is led by extreme variable X-ray absorption,
causing a switch between Compton-thin ($N_{\rm H}$ < 1024 cm-2) and Compton-
thick ($N_{\rm H}$ $\gtrsim$ 1024 cm-2) states in AGNs (e.g. Matt et al.,
2003).

The CL phenomenon is characterised by drastic changes in the optical BELs. The
BELs consist of permitted and semi-forbidden emission lines with typical line
widths FWHM $\geq$ 1000 km s-1, formed by high density gas clouds called the
broad line region (BLR) located close to the central engine (e.g. Netzer,
2015). Therefore, most of the effort to find CL AGNs has focused on systematic
searches of broad Balmer line variations (generally >3$\sigma$ flux change in
broad H $\beta$) in sources with multi-epoch spectroscopy (although other
lines such as Mg ii are also possible, see MacLeod et al., 2016; Ross et al.,
2018; Guo et al., 2019). In particular, some sizable samples have been found
comparing repeated spectra from different surveys such as the Sloan Digital
Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic
Telescope (LAMOST) (Yang et al., 2018; Green et al., 2022).

CL events are often accompanied by large photometric changes in the optical
and ultraviolet (UV) bands, and this has been used as a selection criteria to
find new CL AGNs (i.e., |$\Delta$g| > 1 mag, |$\Delta$r| > 0.5 mag in MacLeod
et al., 2016, 2019). However, the link between extreme spectroscopic and
photometric changes is uncertain, since just 10–15 per cent of photometrically
variable AGNs have been found to display CL behaviour (MacLeod et al., 2019).
This uncertainty can be affected by the time-scales involved in CL events,
which have been constrained just in a few sources (Trakhtenbrot et al., 2019),
and the fact that the CL behaviour has been found to occur repeatedly in some
sources (e.g. depending on the Eddington ratio, Guolo et al., 2021).

More recently, some studies have concentrated on the search for CL events
based on the physical expectations for an accretion-state change. In this
scenario, it is expected to see gradual changes in the optical and mid-
infrared (MIR) flux and colours associated with monotonically varying BEL
strengths and/or continuum changes, as the AGN goes to bright (AGN dominated)
or dim (host dominated) states (e.g., Sheng et al., 2017; Yang et al., 2018;
Lyu et al., 2022). In the optical, CS AGN candidates have been selected by
searching for anomalous variability (Sánchez-Sáez et al., 2021b) and bluer
optical colours in turning-on AGNs (Hon et al., 2022), where the latter method
shows a higher success rate for confirmed CL compared to other selection
techniques. In the MIR band, individual CL AGNs have been found by identifying
highly MIR-variable quasars in the Wide-field Infrared Survey Explorer (WISE)
and Near-Earth Object WISE Reactivation (NEOWISE) data stream (Stern et al.,
2018; Assef et al., 2018). In Graham et al. (2020), 111 CS quasars were found
by applying two different criteria: strongly enhanced optical variability over
some time-scales and a large absolute difference between the initial and final
state in the WISE light curve (i.e, $|\Delta\textit{W1}|>0.2$ or
$|\Delta\textit{W2}|>0.2$). That work led to a CS sample at higher luminosity
than previous CL AGNs in the literature.

Moreover, individual CL events have been associated with changes in soft-X-
ray/UV emission, responsible for photoionizing the BLR gas, as in the case of
Mrk 1018 (Cohen et al., 1986; McElroy et al., 2016; Noda & Done, 2018). More
extreme X-ray spectral and flux variability was found in the CL source 1ES
1927+654, which has been suggested to be caused by a TDE in an AGN
(Trakhtenbrot et al., 2019; Ricci et al., 2020, 2021) or a magnetic flux
inversion event (Laha et al., 2022). Recently, in a CL search of sources with
multi-epoch optical spectroscopy within the Swift-BAT AGN Spectroscopic Survey
(BASS), it was reported that five out of nine events with Swift-BAT data
available could be associated with significant flux changes in the 14–195 keV
hard X-ray band (Temple et al., 2023).

With the advent of deep, large sky-coverage monitoring surveys such as the
Zwicky Transient Facility (ZTF, Bellm et al., 2019) and the upcoming Legacy
Survey of Space and Time (LSST, Ivezić et al., 2019), the identification of CL
AGNs will be possible using machine-learning techniques. In López-Navas et al.
(2022, hereafter LN22), we present a method specifically looking for turn-on
events using a balanced random forest algorithm with the ZTF alert stream
(Sánchez-Sáez et al., 2021a), confirming CL behavior in four out of six
sources that we re-observed based on follow-up spectroscopy. Extending this
work further, we obtained a second epoch spectra of 30 additional CL
candidates, confirming $\sim$50 per cent as CLs. In this paper, we present the
new observations and perform a multi-wavelength (optical, MIR and X-ray)
variability analysis of the CL sources. This effort enables us to improve the
selection technique and reinforces the common features of these CL events.
Throughout this work, we assume a standard cosmological model with $H_{0}$ =
70 km s-1 Mpc-1, $\Omega_{m}$ = 0.3, and $\Omega_{\Lambda}$ = 0.7.

##  2 Selection of the sample

According to the classic view of AGNs, the optical variability in Type 2
sources is highly suppressed due to obscuration of the continuum coming from
the central source by the dusty torus (Antonucci, 1993). Based on this
consideration, looking for Type 1-like optical flux variability (coming from
the accretion disc) in spectrally-classified Type 2 AGNs (whose accretion disc
should be hidden) has led to the finding of turning-on CL sources. We note
that in these cases, the previous Type 2 classification is due to the absence
of significant BELs in their spectra, and not due to the identification of the
viewing angle of the system. This is the selection strategy we followed in
LN22 to find potential CL candidates. Here, we update the candidate list
reported in LN22 and clean it further.

Our initial sample consists of all the spectrally classified Type 2 AGNs
included in the Million Quasars Catalog (MILLIQUAS, Version 7.7, N and K
types, Flesch, 2021). We removed sources classified as Seyfert 1, Low
Ionization Nuclear Emission Region (LINER) or blazar in any other study
according to the SIMBAD Astronomical Database, and those included in the Type
1 AGN catalogues from Oh et al. (2015) and Liu et al. (2019). We also required
the sources to have a public ‘GALAXY AGN’ or ‘QSO AGN’ spectrum in SDSS DR17,
and discarded objects with a BROADLINE classification. This led to 20834 Type
2 AGNs. We also checked that none of these sources were included in the Roma-
BZCAT Multifrequency Catalogue of Blazars (Massaro et al., 2015).

To select potential CL candidates, we searched for current Type 1 optical flux
variability in our Type 2 sample. In particular, we used the host- and core-
dominated AGN classifications given by the Automatic Learning for the Rapid
Classification of Events (ALeRCE, Förster et al., 2021) broker light curve
classifier (LCC, Sánchez-Sáez et al., 2021a). The ALeRCE broker is currently
processing the alert stream from the Zwicky Transient Facility (ZTF, Bellm,
2014; Bellm et al., 2019) to provide a fast classification of variable objects
by applying a balanced random forest algorithm. In particular, the LCC
computes a total of 174 features, including colours obtained from AllWISE and
ZTF photometry and variability features, for all objects that had generated at
least 6 alerts in either g or r band in the ZTF alert stream. Since each alert
is produced when a 5$\sigma$ variation in the template-subtracted image
occurs, only sufficiently variable objects are detected and classified by the
LCC. In the case of AGNs, only 10 per cent of known Type 1s with $r<20.5$ mag
exhibit variations reaching this threshold and produce alerts in ZTF.
Therefore, using this method we expect to select the 10 per cent most variable
CLs in the parent sample.

We performed a sky crossmatch within 1 arcsec between our Type 2 parent sample
and the sources classified primarily as AGN or QSO by the LCC (updated on 2022
November 30), which led to 71 matches. Ten sources had two different
identification names in ZTF, resulting in 61 CL candidates. Of these, $\sim$30
objects were identified as bad candidates from a visual examination of the
optical light curves and the SDSS spectra. In the first place, some sources
show BELs in their optical spectra and were apparently misclassified in the
catalogues. This generally occurs in the lower-luminosity and lower-black
hole-mass regimes, where the BELs fail to meet the FWHM $\geq$ 1000 km s-1
criterion (Liu et al., 2019), and also in intermediate Type 1.8/1.9 AGNs,
which show weak broad Balmer lines (e.g. Hernández-García et al., 2017).
Secondly, other sources appear misclassified by the ALeRCE LCC due to a small
number of data points or transient events in the reference image used to
construct the difference images (mostly supernovae, Sánchez-Sáez et al.,
2023).

We note that the ZTF alert light curves only contain alerts generated since
the ZTF started its operations in 2018, but the actual length of the alert
light curves depend on how variable the object is. We stress that the first
ZTF alert does not necessarily mark the time of the change of state,
considering that most known Type 1 AGNs have not triggered alerts or have
taken years after the start of ZTF to show their first alert. This method does
not give information about time the CL transition occurred other than that it
happened some time between the SDSS spectrum was taken 10–20 years ago and the
ALeRCE LCC classification as Type 1 AGN. In order to confirm the selected
candidates as CLs, we need to perform optical follow-up spectroscopy that
quantifies the changes in the BELs with respect to the SDSS spectrum.

In this work, we further investigate the variability and spectral properties
of the most promising CL candidates to improve the selection method and shed
light on the origin of these phenomena.

##  3 Spectroscopic follow-up

###  3.1 Data

We obtained second epoch spectra for 36 of our 61 CL candidates, allowing us
to confirm CL objects by quantifying BEL changes with respect to archival SDSS
spectra. These sources were selected via visual examination of the optical
light curves and archival spectra of the CL candidates. Six out of the 36
sources were reported in LN22. In this paper, we present spectral analysis for
the remaining 30 objects. The new optical spectra were taken during February
and April 2022 using either the Double Spectrograph (DBSP) on the Palomar
200-inch Hale Telescope (P200) or the Low Resolution Imaging Spectrometer
(LRIS) spectrograph on the Keck I telescope at the W. M. Keck Observatory, as
specified in the Appendix, Table 8. The spectra were obtained using a blue and
a red arm with the 600 and 316 lines/mm grating respectively, 1.5" or 2" slit
widths and 1x1 binning, and processed using standard procedures. All the
observed sources fall within the redshift range 0.04$\leq z\leq$ 0.22,
therefore covering the H $\alpha$ for all sources. In some of the cases, the H
$\beta$ and [O iii] emission lines were in the border between the blue (2500 –
5700 Å) and red (4800 – 10700 Å) useful regions, leading to uncertain fits.
Thus, we compared the broad H $\alpha$ and the H $\alpha$/[S ii] ratio between
epochs, instead of the broad H $\beta$ or the H $\alpha$/[O iii] ratio as
performed in other studies (e.g. in Graham et al., 2020).

###  3.2 Spectroscopic fitting

We fit the archival DR17 SDSS spectra and the second epoch spectra from the
Keck I and P200 telescopes using the Penalized Pixel-Fitting (pPXF) software
(Cappellari, 2017). To account for the stellar continuum component we used the
E-MILES library (Vazdekis et al., 2010), and to model the AGN emission we
added the following components:

  * •

A power low template for the accretion disc contribution of the form
$(\frac{\lambda}{\lambda_{N}})^{\alpha}$ where $\lambda$ is the wavelength,
$\lambda_{N}=5000$ Å is a normalization factor and $\alpha$ goes from $-3$ to
0 in steps of 0.1.

  * •

One kinematic component with both permitted and forbidden emission lines, with
free normalizations, to model the narrow lines.

  * •

One kinematic component with permitted emission lines, with free
normalizations, to model the possible BELs.

  * •

One component for possible outflows with velocity dispersion values from 400
to 1000 km s-1.

To fit the second epoch spectra we used the same stellar population templates
obtained from the SDSS fit and we left the normalization free during the
fitting process. We obtained errors for each spectrum by performing Monte
Carlo simulations using the best-fitting model and simulating random noise
generated from the standard deviation of the best-fitting residuals. Then we
fit the simulated spectra using the same procedure described for the SDSS and
second epoch spectra, providing an error on the model parameters.

Table 1 shows the results of the spectral fitting for broad H $\alpha$ and the
1$\sigma$ error from the simulations. We identify 13 confirmed CL AGNs with
>3$\sigma$ change in the EW of broad H $\alpha$ and >3$\sigma$ change in the H
$\alpha$/[S ii] ratio. We also identify as CL two sources with a >3$\sigma$
change in the H $\alpha$/[S ii] ratio, whose change can be confirmed via
visual inspection. In total, we find 15 CL sources (highlighted in bold, see
Table 1). Their optical spectra are shown in the Appendix, Fig. 14. Some of
the sources show bluer continuum emission and/or asymmetric and complex BEL
profiles (e.g., ZTF18acbzrll, ZTF19aavyjdn and ZTF20aaxwxgq) during the high
state, as found in previous CL works (Oknyansky et al., 2021). In one case,
ZTF19aafcyzr, the changes in the BELs according to the spectral fits are
significant but they are not obvious when looking at the difference spectrum,
so we speculate these changes could be driven by differences in the
spectroscopy (that is, different instruments and set-up) and not to physical
changes.

For the 36 observed sources from our CL candidates, we distinguish the 19
confirmed CL AGNs as the CL sample (including the four CLs reported in LN22)
and the 17 not confirmed CLs as the NOT CL sample (including two such sources
reported in LN22).

ZTF ID RA DEC $z$ MJD fiberid plate EW H $\alpha$ SDSS EW H $\alpha$ new H
$\alpha$/[S ii] SDSS H $\alpha$/[S ii] new $\deg$ $\deg$ Å Å ZTF18aaiescp
207.21292 57.646792 0.13 52668 198 1158 30$\pm$2 14$\pm$1 1.75$\pm$0.12
1.14$\pm$0.07 ZTF18aaiwdzt 199.48361 49.258651 0.09 52759 390 1282 22$\pm$2
24$\pm$2 0.7$\pm$0.07 0.69$\pm$0.04 ZTF18aajywbu 205.45327 37.013091 0.20
53858 513 2101 23$\pm$4 94$\pm$2 2.4$\pm$0.4 9.01$\pm$0.23 ZTF18aaqftos
180.95505 60.888181 0.07 52405 420 954 23$\pm$3 116$\pm$3 1.81$\pm$0.21
25.09$\pm$0.74 ZTF18aaqjyon 180.42264 38.47264 0.06 53473 358 2108 6$\pm$2
0$\pm$0 0.56$\pm$0.18 0.0$\pm$0.25 ZTF21aaqlazo/ZTF18aasudup 170.03619
34.312731 0.04 53713 346 2100 15$\pm$1 43$\pm$1 1.79$\pm$0.11 6.43$\pm$0.2
ZTF18aavxbec* 243.08151 46.495172 0.13 52370 408 814 17$\pm$2 16$\pm$0
1.47$\pm$0.18 3.14$\pm$0.1 ZTF18aawoghx 156.10498 37.650863 0.10 52998 254
1428 9$\pm$2 34$\pm$1 0.91$\pm$0.2 3.95$\pm$0.17 ZTF18aawwcaa 128.10120
35.859979 0.14 52668 459 1197 26$\pm$2 73$\pm$2 5.49$\pm$0.77 16.74$\pm$1.06
ZTF19aabyvtv/ZTF18aayyapb 197.87773 31.866893 0.07 53819 632 2029 17$\pm$1
6$\pm$3 17.57$\pm$3.38 8.09$\pm$9.03 ZTF18acbzrll 124.82294 30.32660 0.10
52619 94 931 7$\pm$2 90$\pm$3 1.29$\pm$0.43 20.13$\pm$0.88
ZTF18acgvmzb/ZTF18aclfugf 148.96896 35.965616 0.04 52998 251 1596 8$\pm$1
8$\pm$1 0.52$\pm$0.03 0.73$\pm$0.04 ZTF18achdyst 157.39925 24.777606 0.11
53734 545 2349 26$\pm$1 15$\pm$1 5.72$\pm$0.02 1.37$\pm$0.1
ZTF18acusqpt/ZTF18adppkkj 177.91898 12.036714 0.07 53142 116 1609 18$\pm$1
17$\pm$2 3.27$\pm$0.32 6.07$\pm$0.65 ZTF19aafcyzr 125.71008 15.673859 0.12
53713 517 2272 11$\pm$1 30$\pm$1 1.2$\pm$0.17 3.98$\pm$0.24 ZTF19aaixgoj
146.80538 12.205624 0.12 53053 575 1742 14$\pm$1 13$\pm$1 2.65$\pm$0.29
2.61$\pm$0.24 ZTF19aaoyjoh 180.18956 14.967685 0.11 53463 158 1763 10$\pm$5
59$\pm$1 1.25$\pm$0.37 10.44$\pm$0.31 ZTF19aapehvs 199.74519 57.501847 0.10
52759 338 1320 32$\pm$2 18$\pm$1 3.02$\pm$0.27 2.34$\pm$0.17 ZTF19aavqrjg
181.24583 15.58718 0.22 53467 447 1764 51$\pm$3 67$\pm$9 35.65$\pm$2.0
10.61$\pm$10.55 ZTF19aavyjdn 202.39941 $-$1.509453 0.08 52377 524 910 14$\pm$4
77$\pm$18 2.94$\pm$0.63 31.1$\pm$6.41 ZTF20aaeutuz 164.81581 12.483378 0.15
55956 726 5357 30$\pm$1 34$\pm$1 2.83$\pm$0.11 5.31$\pm$0.2 ZTF20aagwxlk
153.42829 55.432205 0.15 52407 179 946 31$\pm$2 27$\pm$8 3.69$\pm$0.29
1.14$\pm$0.64 ZTF20aagyaug 172.41282 36.883602 0.20 55673 426 4648 41$\pm$3
96$\pm$2 1.35$\pm$0.09 4.17$\pm$0.11 ZTF20aakreaa 181.52333 42.169888 0.10
53120 149 1448 17$\pm$1 20$\pm$10 4.85$\pm$0.92 1.03$\pm$2.34 ZTF20aaorxzv
132.39052 3.68048 0.08 52224 606 564 18$\pm$1 16$\pm$16 1.63$\pm$0.16
1.04$\pm$0.31 ZTF21abwoxbv/ZTF20aaxwxgq 234.63610 46.126392 0.20 52781 559
1332 11$\pm$3 68$\pm$10 2.47$\pm$0.74 56.73$\pm$9.03 ZTF20abcvgpb* 238.24977
21.046358 0.17 53557 220 2171 49$\pm$4 44$\pm$1 2.98$\pm$2.3 11.77$\pm$0.69
ZTF20abgnlgv 232.52715 7.172269 0.13 54208 322 1820 41$\pm$2 35$\pm$1
3.98$\pm$0.38 4.8$\pm$0.17 ZTF21aafkiyq 174.93478 $-$1.727439 0.11 52294 614
327 18$\pm$2 54$\pm$1 1.79$\pm$0.23 4.42$\pm$0.17 ZTF21abcsvbr 184.64829
18.771713 0.22 54477 299 2611 19$\pm$4 44$\pm$1 1.37$\pm$0.25 5.35$\pm$0.29
ZTF19abixawb 2.56204 0.13912 0.10 51793 516 388 8${}^{+3}_{-1}$
47.0${}^{+10}_{-0.3}$ ZTF20abshfkf 18.29924 1.59516 0.24 57282 83 7859
7${}^{+20}_{-4}$ 133$\pm$2 ZTF18accdhxv 118.93488 19.39342 0.11 53315 306 1922
21${}^{+9}_{-1}$ 90${}^{+3}_{-20}$ ZTF19aalxuyo 123.16978 7.25791 0.08 53083
294 1757 2.2${}^{+5}_{-0.4}$ 54${}^{+1}_{-3}$ ZTF19aaxdiui 325.19182 9.27550
0.40 55475 234 4093 – – ZTF18abtizze 327.73225 -1.11506 0.09 53172 138 1031
3${}^{+21}_{-1}$ 13${}^{+1}_{-4}$

Table 1: Sample of the CL candidates observed and results of the spectral
fitting. $z$ denotes redshift from the DR17 SDSS data base. MJD, fiberid and
plate denote the date and observational information from the SDSS spectra. EW
denotes the equivalent width of the BELs. Sources identified as CL are shown
in bold. Asterisks denote CL sources showing a >3$\sigma$ change in the H
$\alpha$/[S ii] ratio, but with no significant changes in EW H $\alpha$. Some
sources have two different ZTF IDs, which are separated by a slash. For
completeness, we include the information for the six sources from LN22 at the
end of the table

##  4 Results

###  4.1 Improvement of the selection method: ALeRCE features for the alert
light curves

All the CL candidates considered in this work have generated at least 6 ZTF
alerts in either g or r band and have been classified by the AleRCE LCC. An
alert is generated when a 5$\sigma$ variation in the template-subtracted image
occurs. In this section, we analyse the variability of the sample to determine
whether the CL phenomenon is related to any physical parameter or if we can
make other improvements to the selection method and thereby to the CL
candidate list.

The LCC uses a total of 174 features, most of them computed solely with the
public ZTF g and r data. The complete set of features is described in the
ALeRCE website111http://alerce.science/features/, and can be requested using
the ALeRCE python client. In this work, we separate the features that dominate
the classifier (both the ‘Top’ level and the ‘Stochastic’ level of the LCC) as
reported in Sánchez-Sáez et al. (2021a) and the secondary, not-ranked
features. For comparison, we obtained the features for the known AGNs that
were used to train the LCC, which includes the Weak Type 1 sources from Oh et
al. (2015) and the host-dominated AGNs (class ‘A’) from MILLIQUAS, totalling
4612 sources.

####  4.1.1 Top-ranked variability and colour features

Most of the features that dominate the LCC consist of ZTF and AllWISE colours
and variability features related to the amplitude and time-scale of the
variability and to a decrease/increase of the luminosity. In order to evaluate
the difference in distribution of these features between the CL and NOT CL
samples we applied the Kolmogorov–Smirnov (KS) test to all their ranked-
features. In Table 2 we present the features that dominate the LCC and have a
p-value <0.05, that is, where we can reject the null hypothesis that the two
distributions (from the CL and the NOT CL samples) are identical.

Name Filter Description Top ranked variability and colour features used in the
LCC SF$\\_$ML$\\_$amplitude g rms magnitude difference of the structure
function computed over a one year time-scale MHPS$\\_$low g Variance
associated with the 100 days time-scale (’low’ frequency) SPM$\\_$tau$\\_$rise
g Initial rise time-scale from the supernova parametric model
GP$\\_$DRW$\\_$sigma g Amplitude of the variability at short time-scales, from
the damp random walk (DRW) model GP$\\_$DRW$\\_$tau g Relaxation time from the
DRW model IAR$\\_$phi g Level of autocorrelation from an irregular
autoregressive (IAR) model positive$\\_$fraction g,r Number of detections in
the difference images that are brighter than the template
delta$\\_$mag$\\_$fid g Difference between the maximum and minimum observed
magnitudes in a given band r–W3 colour computed using the ZTF mean r magnitude
and the AllWISE W3 filter g$\\_$r$\\_$mean$\\_$corr ZTF g–r colour using the
mean magnitudes of each band g$\\_$r$\\_$max ZTF g–r colour using the
brightest magnitudes of each band Other features used in the LCC n$\\_$pos g,r
Number of positive detections in the alert light curve n$\\_$neg g,r Number of
negative detections in the alert light curve iqr g Difference between the 3rd
and the 1st quartile of the light curve MHPS$\\_$ratio g Ratio between the
variances at 100 days and 10 days time-scales for a given band, applying a
Mexican hat filter Variability features for the forced-photometry light curves
SPM$\\_$A r Amplitude from the supernova parametric model LinearTrend g,r
Slope of a linear fit to the light curve ExcessVar g Intrinsic variability
amplitude Meanvariance g Ratio of the standard deviation to the mean magnitude
Std g Standard deviation of the light curve Amplitude g Half of the difference
between the median of the maximum 5 per cent and of the minimum 5 per cent
magnitudes SPM$\\_$tau$\\_$rise g, r See above MHPS$\\_$low r See above
MHPS$\\_$ratio r See above GP$\\_$DRW$\\_$sigma g,r See above
SF$\\_$ML$\\_$amplitude g,r See above

Table 2: Features that show different distributions for the CL and the NOT CL
samples. Features recovered for both the alert light curves and the forced-
photometry light curves are highlighted in bold.

We note that the DRW parameters determination is generally biased for light
curve lengths shorter than 10 times the true $\tau$ value (Kozłowski, 2017;
Sánchez et al., 2017), which is the case of our ZTF data. Therefore, the DRW
parameters obtained in this work should be considered just as variability
features and not as physically correct estimations.

In Fig. 1 we show the distribution of some of the variability features
computed in the g band that present different distributions between the CL and
NOT CL sources. In particular, the DRW relaxation time for the NOT CL objects
peaks at the minimum data sampling ($\sim$ 1 d), and spreads up to >1000 d,
larger than the maximum light curve length. This indicates a DRW model is
unable to properly model the optical variability for some NOT CLs, and thus it
is unlike Type-1 AGN. For the CLs however, the DRW relaxation time peaks at
10–100 days as expected for Type 1 AGNs. In terms of the amplitude of the
variability, from the GP$\\_$DRW$\\_$sigma distributions we see that some NOT
CL objects reach much smaller values (log10(GP$\\_$DRW$\\_$sigma) $<-6$),
which again indicates they have most likely been misclassified as Type 1 AGN
by the LCC. Interestingly, the amplitude of the variations for all our objects
peaks at a smaller value than the distribution for the AGN training set,
suggesting their variability could be diluted by the host galaxy contribution.
On the other hand, the autocorrelation of the light curves, given by the
IAR$\\_$phi parameter, reaches smaller values for the NOT CL sample than for
the CL sample. These features could be used to further clean the CL candidate
list.

Apart from the variability features, the classifier is also dominated by ZTF
and AllWISE colours and the morphological properties of the images. In
general, the optical colours for both samples look similar to each other but
show a redder tendency than the AGN training set distribution, as shown in
Fig. 2(a).

In Fig. 2(b) we present the 2010–2011 AllWISE W1–W2 versus the W2–W3 colours
for the CL and NOT CL objects, in comparison to the Type 2 parent sample and
the AGN training set. Most of our objects have fairly similar AllWISE colours,
but the distribution of the W1–W2 colour peaks at a lower value than the AGN
training set and closer to the Type 2 distribution, which implies that the MIR
colours are generally dominated by the stellar populations. We note that the
AllWISE observations were taken between 2010 and 2011, so these features are
not indicative of their current MIR colour. We further investigate the
behaviour of the MIR colours in Section 4.2.4, including contemporaneous WISE
observations.

(a) (a)

(b) (b)

Figure 1: Distributions of the alert light curves top-ranked variability
features for the CL and NOT CL samples and the AGN LCC training set: (a)
relaxation time and amplitude of the variability on short time-scales,
obtained from the damp random walk (DRW) model; and (b) level of
autocorrelation from an irregular autoregressive (IAR) model. These features
show distinct distributions between the CL and NOT CL samples and could be
used to select the most promising CL candidates.

(a) (a)

(b) (b)

Figure 2: Alert light curves top-ranked colour features for the CL and NOT CL
samples and the AGN LCC training set: (a) g–r colour obtained with ZTF data
and (b) AllWISE MIR colours in comparison with the Type 2 parent sample. Both
the CL and NOT CL samples show optical and MIR colours more host-galaxy
dominated than the AGN LCC training set. Figure 3: Number of positive
detections in the alert light curves for the CL and NOT CL samples and the AGN
LCC training set, where the CL sample tends to increase their optical flux.

####  4.1.2 Not-ranked features

The top-ranked features are the most important features that the LCC uses for
the classification of variable sources. However, there are secondary features
that could potentially allow us to evaluate whether a source is a good CL
candidate or not. In Table 2 we present other secondary features showing
distinct distributions between the CL and NOT CL samples. A particular example
is the number of positive detections in the alert light curves (n$\\_$pos, see
Fig. 3), which reaches higher values for the CL sample, meaning that CL
objects tend to increase more their flux with respect to the template image
(as expected for turning-on events).

###  4.2 Characteristics of the CL vs NOT CL AGNs

####  4.2.1 BPT diagnostics

To investigate the emission-line properties of the samples, we calculated the
BPT (Baldwin et al., 1981) diagnostics from the archival SDSS spectra. We used
the classification system defined by Kewley et al. (2006) for different
ionisation mechanisms utilizing the three BPT diagnostic criteria ([N ii], [S
ii], and [O i]) and found all sources are consistent with a Seyfert
classification. The KS-test leads to large p-values >0.4 for the three cases,
showing that these samples are indistinguishable in terms of the emission
lines properties from their old (pre-CL) optical spectra. Similar results were
found by analysing second epoch spectra. As an example, the [S ii] BPT diagram
for the CL and NOT CL samples is plotted in Fig. 4.

Figure 4: BPT diagram for the CL and NOT CL samples obtained from their
archival SDSS spectra. The solid and dashed lines show the classification
scheme from Kewley et al. (2001, 2006). The comparison indicates these samples
are indistinguishable in terms of the BPT diagnostic criteria.

####  4.2.2 Eddington ratio estimates

We estimated the black hole masses ($M_{\rm BH}$) and continuum luminosity at
5100 Å ($L_{5100}$) using the full width at half maximum (FWHM) and luminosity
of broad H $\alpha$ as outlined in Reines et al. (2013), using the values
obtained from the new spectra. Then, we computed the Eddington ratios for the
old and new spectra $\lambda_{\rm\rm Edd}=L_{\rm bol}/L_{\rm Edd}$, where
$L_{\rm Edd}=1.5\times 10^{38}(M_{\rm BH}/$M${}_{\odot})$ erg s-1 is the
Eddington luminosity and $L_{\rm bol}$ is the bolometric luminosity defined as
$L_{\rm bol}=40\cdot(L_{5100}/10^{42})^{-0.2}$ erg s-1 according to Netzer
(2019). In Fig. 5 we present $\lambda_{\rm\rm Edd}$ for both samples computed
for the old ($\lambda_{\rm\rm Edd1}$) and new spectra ($\lambda_{\rm\rm
Edd2}$), and the difference of accretion rate ($\Delta\lambda_{\rm\rm Edd}$).
We find that the old accretion rate is similar for both samples, but in the
second epoch spectra the distribution shifts towards higher values for the
CLs. These are the expected results for turning-on AGNs, since both the method
to compute $\lambda_{\rm\rm Edd}$ and the criteria to confirm the sources as
CL (e.g. >3$\sigma$ change in the EW of broad H $\alpha$) use the properties
of broad H $\alpha$.

Figure 5: Eddington ratios for the CL and NOT CL samples obtained from the
archival SDSS spectrum (left panel) and current spectra (middle panel), and
their difference ( $\Delta\lambda_{\rm\rm Edd}$ = $\lambda_{\rm\rm
Edd2}-\lambda_{\rm\rm Edd1}$, right panel). CL objects have increased their
Eddington ratios and are now accreting at 1–5 per cent $L_{\rm Edd}$.

####  4.2.3 ALeRCE features for the forced-photometry light curves

To compare with the alert light curves, we also analysed variability in the
full forced-photometry light curves, which are produced based on all ZTF
difference images available. We requested the most updated forced photometry
(up to 2022 September 26) of the entire sample from the ZTF forced-photometry
service and generated the cleaned light curves according to the
recommendations outlined in Masci et al. (2019). A python library to extract
variability features in astronomical light curves is publicly
available222https://github.com/alercebroker/lc_classifier. The forced-
photometry light curves have a mean of 376 (453) data points in the g (r)
filter, in comparison with the 30 (27) detections from the alert light curves.
As a result, the variability features can be better constrained and we find
more top-ranked features that show different distributions according to the
KS-test between the CL and NOT CL samples, as shown in Table 2. We recover
many variability features related to the amplitude of the variations and the
deviations from the mean (e.g. ExcessVar, Meanvariance or Std), which are
missing in the comparison of features from the alert light curves. Fig. 6
shows the amplitude and the standard deviation in the g filter for the forced-
photometry light curves for both samples. The CL objects present generally
higher values for both features, indicating that their variability is more
similar to the expected Type 1 behaviour than the NOT CL sample.

Figure 6: Amplitude and standard deviation in the g filter for the CL and NOT
CL forced-photometry light curves. CL objects present higher values for both
features, indicating a stronger optical variability.

####  4.2.4 Mid-infrared variability

Some of the features that the LCC uses to classify variable objects are
computed with AllWISE data, which are indicative of the state of the sources
ten years ago. To investigate whether the CL and NOT CL samples show distinct
MIR behaviour we downloaded all the AllWISE multi-epoch and NEOWISE-R single
exposure (L1b) photometric data spanning from 2010 to 2021 and averaged every
six months. For each source, we obtained the following variability features
for the W1 and W2 bands: the maximum magnitude and colour variations
($\Delta$W1, $\Delta$W2, $\Delta$W1–W2), the colour from the last epoch
(W1–W2f), the intrinsic variability ($\sigma_{m1}$ and $\sigma_{m2}$) computed
as in Lyu et al. (2022) and the slopes of a linear trend fit to the MIR
magnitude light curves (a1 and a2) and to the W1–W2 colour (a12). Table 3
shows the comparison between the median values with the 1$\sigma$ errors for
the CL and NOT CL samples. All the features except for the maximum colour
variation $\Delta$W1–W2 show distinct distributions according to the KS-test,
with CL sources having a stronger variability. Moreover, the results from the
linear fits indicate that the CLs have become brighter in both bands and have
higher W1–W2 values (see Fig. 7 and 14), whereas for the NOT CL the
distributions peak closer to zero resulting in no net increase or decrease in
brightness or colour.

Feature CL NOT CL $\langle\Delta$W1$\rangle$ (mag)* 0.5${}^{+0.3}_{-0.1}$ 0.4
$\pm$0.2 $\langle\Delta$W2$\rangle$ (mag)* 0.7$\pm$0.3 0.5${}^{+0.1}_{-0.3}$
$\langle\Delta$W1–W2 $\rangle$(mag) 0.3${}^{+0.2}_{-0.1}$
0.3${}^{+0.2}_{-0.1}$ $\langle$W1–W2${}_{f}\rangle$(mag)*
0.5${}^{+0.2}_{-0.1}$ 0.4${}^{+0.1}_{-0.2}$ $\langle\sigma_{m1}\rangle$ *
0.15$\pm$0.05 0.09$\pm$0.07 $\langle\sigma_{m2}\rangle$*
0.18${}^{+0.09}_{-0.05}$ 0.14${}^{+0.04}_{-0.09}$
$\langle$a${}_{1}\rangle\cdot 10^{-5}$ (mag-1)* $-4^{+4}_{-9}$
0.5${}^{+4}_{-5}$ $\langle$a${}_{2}\rangle\cdot 10^{-5}$ (mag-1)*
${}^{+9}_{-13}$ 2${}^{+4}_{-8}$ $\langle$a${}_{12}\rangle\cdot
10^{-5}$(mag-1)* 2${}^{+3}_{-4}$ 0${}^{+3}_{-2}$

Table 3: Mid-infrared variability features. Asterisks indicate the features
that show distinct distributions between the samples according to the KS-test
(p-value<0.05). The errors correspond to the 1$\sigma$ deviation from the
median.  Figure 7: Distributions of the linear slope of the W1 10-years-long
light curve (a1) and the last epoch colour (W1–W2f) for the CL and NOT CL
samples. The lower a1 values for the CL sample indicates the sources are
getting brighter in the MIR waveband, while the slopes for the NOT CL
distribute around zero, indicating that as a sample they are neither
brightening nor dimming. The higher last epoch colour (W1–W2f) for the CLs is
expected for AGN-dominated galaxies (W1–W2 >0.5).

####  4.2.5 X-ray variability

In order to obtain the X-ray fluxes of our sources, we have used the
individual eROSITA (extended ROentgen Survey with an Imaging Telescope Array,
Predehl et al., 2021) All-Sky Surveys (eRASS1 to eRASS5). The data were
processed with the eROSITA Standard Analysis Software System (eSASS, Brunner
et al. 2022). We used the newest available pipeline processing version c020
which is an updated version of the software used for the first eROSITA Data
Release (Merloni et al. 2023, in prep).

The counterparts are determined using the same procedure adopted in the
eROSITA/eFEDS field (Salvato et al., 2022), but applied to Legacy Survey
DR10333https://www.legacysurvey.org/dr10/ and Gaia DR3 separately. After the
identification of the CL candidates with the counterparts, we obtained the
0.2–2.3 keV flux from the corresponding eROSITA catalog (see Brunner et al.
2022 for a description of the eROSITA catalog processing).

From our list of 61 CL candidates, there are 28 sources within the eROSITA-DE
footprint (Galactic longitude 179.9442 < $l$ < 359.9442 deg): 11 CLs, seven
NOT CLs and ten CL candidates without a second epoch optical spectrum. From
the CLs, ten sources have at least one detection within the five different
eRASS, and one (ZTF18aawoghx) has only upper limits. The upper limits are
calculated based on X-ray photometry on the eROSITA standard pipeline data
products (science image, background image, and exposure time) following the
Bayesian approach described by Kraft et al. (1991). For details about eROSITA
upper limits, see Tubín-Arenas et al. (2023, in prep). We consider a circular
aperture with a radius given by a PSF encircled energy fraction of EEF = 0.75
($\sim 30\arcsec$) and a single-sided 3$\sigma$ confidence level. From the NOT
CLs, four sources have been detected in at least one eRASS. The remaining
three (ZTF18acgvmzb/ZTF18aclfugf, ZTF19aaixgoj, and ZTF20aaorxzv) have only
upper limits. Interestingly, six CL sources show an X-ray flux increase
between eROSITA scans by factors of 2 to 15 times. For two sources
(ZTF19aavyjdn and ZTF21abcsvbr) the difference between the maximum and the
minimum values is similar to the error of the minimum value. For the remaining
four sources (ZTF18accdhxv, ZTF19aalxuyo, ZTF21aafkiyq and ZTF21aaqlazo) the
difference is at least eight times the error (see Fig. 8).

We also checked archival X-ray fluxes from other missions to compare to the
eROSITA fluxes. All the 11 CLs and 7 NOT CLs in the eROSITA-DE footprint have
at least one X-ray upper limit from either the XMM-Newton Slew (Saxton et al.,
2008) or ROSAT Survey (Boller et al., 2016). However, due to the low
sensitivity of the data, most of the archival upper limits fall above the
current eROSITA measurements. This hinders us from finding the possible
changes, with the notable exception of four of the CLs that show a significant
($\gtrapprox 1.5\sigma$) increase in the eROSITA 2021 flux with respect to the
archival 1990–1993 ROSAT 1$\sigma$ upper limits or fluxes, which are shown in
Table 4. In the table, we converted the observed ROSAT fluxes to the 0.2–2.3
keV band for a direct comparison, using an absorbed power law model with
photon index $\Gamma=2$ and column density $N_{\rm H}=3\cdot 10^{20}$ cm-2.
These sources are also the CLs that experience a significant X-ray increase
during the eROSITA monitoring as shown in Fig. 8.

ZTF ID |  ROSAT flux | eROSITA flux
---|---|---
|  $\cdot$ 10-13 erg s-1 cm-2 |  $\cdot$ 10-13 erg s-1 cm-2
ZTF18accdhxv | <2.853 | 5.0 $\pm$ 0.8
ZTF19aalxuyo | <3.279 | 4.6 $\pm$ 0.8
ZTF21aafkiyq | <1.049 | 7.7 $\pm$ 0.9
ZTF21aaqlazo | 0.51 $\pm$ 0.06 | 11.1 $\pm$ 0.1
Table 4: 0.2–2.3 keV X-ray fluxes for four CL sources that show an increase
between the archival 1990–1993 ROSAT 1$\sigma$ upper limits or fluxes and the
2021 eROSITA data.

Figure 8: ZTF forced-photometry light curves and contemporaneous eROSITA
fluxes in the 0.2–2.3 keV band. The triangle in the last plot indicates an
upper limit. These sources experience an increase in their X-ray flux during
the eROSITA monitoring.

To compare the eROSITA fluxes between the CL and NOT CL sources, we selected
the last X-ray detection within the five eRASS, or the last eROSITA upper
limit for the sources without detections. We also computed the ratio between
the X-ray flux and the optical flux in the g band, obtained from the ZTF
forced photometry light curves. To avoid spurious results coming from
variability, we chose pairs of contemporaneous fluxes, i.e., that were taken
within the same days or week in the X-ray and optical bands. The results are
plotted in Fig. 9, which shows the CL sources are generally brighter in the
X-ray band than the NOT CL sources, both in absolute terms and relative to
their g band fluxes. The KS-test indicates the X-ray flux distribution is
significantly distinct between the CL and NOT CL samples (p-value $<0.05$),
both considering just detections (p-value $=0.01$) and considering detections
and upper limits (p-value $=0.01$). However, the X-ray to optical ratio
distributions are not significantly distinct according to the KS-test, either
considering just detections (p-value $=0.08$) or considering detections and
upper limits (p-value $=0.10$). Therefore, although the X-ray to optical
ratios tend to be higher for the CLs than for the NOT CLs, the difference is
not statistically significant for the sources considered in this work, and
more extended samples are needed to improve the statistics in terms of the
X-ray behaviour for CLs.

As a final step we also checked the harder, 2.3–5 keV eROSITA fluxes. Most of
the sources have just upper limits in this band, thus we cannot draw further
conclusions about the X-ray spectral shape.

Figure 9: Distributions of the 0.2–2.3 keV eROSITA fluxes (left) and ratios
between the 0.2–2.3 keV flux and the g-band flux from the ZTF forced
photometry light curves, taken within the same day or week (right). UP
indicates the eROSITA upper limits.

##  5 The origin of the NOT CL sources

The previous section showed that the CL and NOT CL samples are significantly
different in terms of their optical, MIR and X-ray flux variability
properties, with the CLs showing stronger optical and MIR variability with a
tendency to increase their MIR flux and the colour W1–W2 over time. In order
to understand the nature of the NOT CL sources we visually inspected the ZTF
forced photometry and alert light curves and the reference images used by ZTF
to compute the difference images. In Table 5 we present the characteristics of
the variability in individual NOT CL objects, indicating the most probable
cause of their variations. As a result, we find two sources that have been
most likely misclassified by the LCC due to a small number of alerts
(ZTF20aakreaa and ZTF20aaorxzv) and another two sources are possibly false
detections due to a bad template image subtraction (ZTF18acgvmzb/ZTF18aclfugf
and ZTF18acusqpt/ZTF18adppkkj). Three sources apparently show a transient
event (that is, a flare-type variation in an otherwise flat curve) in the
optical light curves: two of them resemble SN events (ZTF18aaiwdzt and
ZTF18aaqjyon) and one shows a sharp rise in the optical, followed by an MIR
echo, which we speculate could be due to a TDE. The occurrence of TDEs in
turning-on AGNs is theorised to be more likely than in other galaxies, due to
the possibility of ‘Starfall’ (McKernan et al., 2022). This TDE candidate in a
Type 2 AGN (shown in Fig. 10) could potentially be happening in a turning-on
AGN whose BELs are still too weak to be detected, and merits further study
which is beyond the scope of this paper. This discovery highlights the
possibility of finding TDE candidates in AGNs, in order to compare their rate
of occurrence to TDEs in quiescent galaxies.

Figure 10: Optical ZTF forced-photometry light curves and evolution of the
WISE MIR fluxes and W1 – W2 colour for a TDE candidate belonging to the NOT CL
sample. Note the different time-scales of the ZTF and WISE data: the optical
monitoring starts at the end of the MIR light curves.

Notably, the remaining ten sources show small-amplitude, stochastic optical
variations, characteristic of Type 1 AGNs. This is also consistent with their
optical spectra, which show weak broad H $\alpha$ emission lines, indicative
of weak Type 1 AGNs. Interestingly, eight of these ten objects show a decrease
in their optical flux along with a decrease in their MIR flux and the colour
W1–W2, which suggests they are now transitioning to a dimmer state. Fig. 11
shows the optical and MIR light curves of two clear examples of this
behaviour, ZTF19aapehvs and ZTF20aagwxlk. Generally, the MIR colours from
these NOT CL weak Type 1s are galaxy dominated (i.e., W1–W2 < 0.5), which
suggests their weaker variability and BELs are not due to an orientation
effect, but to an intrinsically lower AGN luminosity diluted by the emission
of the host galaxy. The remaining two sources, ZTF19aavqrjg and ZTF20abgnlgv,
also show Type-1 like variability and stronger broad H $\alpha$ emission (EW H
$\alpha$ SDSS > 40 Å), indicative of Type 1 AGNs.

Figure 11: Optical ZTF forced photometry light curves and evolution of the
WISE MIR fluxes and W1 – W2 colour for two NOT CLs sources. Note the different
time-scales of the ZTF and WISE data: the optical monitoring starts at the end
of the MIR light curves, where there is a dimming in the MIR emission. This
evolution suggests the sources are now transitioning to a dimmer state.

ZTF ID Optical flux MIR flux MIR colour 0.2–2.3 keV flux Most likely cause
variability trend W1–W2 ($\cdot$ 10-14 erg s-1 cms-2) ZTF18aaiescp yes, decr.
decr. 0.5–0.3 $/$ Weak Type 1 ZTF18aaiwdzt transient decr. 1–0.7 $/$ Transient
event in the optical light curve ZTF18aaqjyon transient – < 0.3 $/$ Transient
event in the optical light curve ZTF19aabyvtv/ZTF18aayyapb yes, decr. decr.
decr. <0.4 $/$ Weak Type 1 ZTF18acgvmzb/ZTF18aclfugf no decr. decr. < 0.5 <
9.9 Bogus, bad image subtraction ZTF18achdyst yes, decr. decr. decr. < 0.5 8
$\pm$ 3 Weak Type 1 ZTF18acusqpt/ZTF18adppkkj no var. < 0.3 8 $\pm$ 3 Bogus,
bad image subtraction ZTF19aaixgoj yes, decr. decr. decr. < 0.5 <11 Weak Type
1 ZTF19aapehvs yes, decr. decr. decr. < 0.5 $/$ Weak Type 1 ZTF19aavqrjg yes –
$\sim$0.45 6 $\pm$ 3 Type 1 (BELs) ZTF20aaeutuz yes, decr. decr. decr. < 0.5
13 $\pm$ 5 Weak Type 1 ZTF20aagwxlk yes, decr. decr. decr. 0.8–0.4 $/$ Weak
Type 1 ZTF20aakreaa no var. 0.2–0.6 $/$ Misclassified, small number of alerts
ZTF20aaorxzv no – $\sim$ 0.2 < 9.5 Misclassified, small number of alerts
ZTF20abgnlgv yes var. 0.4–0.6 $/$ Type 1 (BELs) ZTF18abtizze * yes var. 0.4–1
$/$ Possible TDE ZTF19aaxdiui * yes, decr. decr. decr. 0.6–0.2 $/$ Weak Type 1

Table 5: Characterization of the NOT CL sources analysed in this work. decr.
denotes a decreasing trend in the optical forced-photometry or MIR light
curve. var. denotes a variable trend. Dashes denote the light curve is fairly
flat. Slashes denote no available eROSITA-DE data. Asteriks denote the two NOT
CLs reported in LN22.

##  6 Discussion

###  6.1 Improvement of the CL selection method through ALeRCE

One of the main aspects of the selection of CL candidates through ALeRCE
relies on the correct classification of their alert light curve variability by
the LCC. As mentioned in Section 5, there are seven out of 17 NOT CLs that
have been misclassified by the LCC due to a bad subtraction of the ZTF images
used to compute the alerts, a small number of data points and/or transient
events in the alert light curves. On the other hand, we also found that ten
($\sim$60 per cent) NOT CLs have been correctly classified by the LCC as Type
1 AGN and are spectroscopically consistent with being weak Type 1 AGNs whose
optical and MIR properties are dominated by the host galaxy contribution.

It is possible to increase the completeness of the CL candidate list as well
as the purity by applying two additional criteria on the ALeRCE features
simultaneously. The best combinations in the g band, with a loss of 0–5 per
cent of CLs and removal of 50 per cent of NOT CLs, were found to be all the
possible pairs between GP$\\_$DRW$\\_$sigma, GP$\\_$DRW$\\_$tau and
delta$\\_$mag$\\_$fid, and the combinations of iqr with MHPS$\\_$low or
delta$\\_$mag$\\_$fid. We note that our samples are too small to judge the
universality of this additional cleaning with bootstrap methods. However,
similar cleaning can be devised in the future for ZTF forced photometry and/or
the classifications of the ZTF data release light curves (Sánchez-Sáez et al.,
2023), which would help to improve the selection of CL candidates.

Feature Limiting value $\%$ cleaning positive$\\_$fraction 0 56
SF$\\_$ML$\\_$amplitude $-$0.2 56 SPM$\\_$tau$\\_$rise 44 50
GP$\\_$DRW$\\_$sigma 4$\cdot 10^{-5}$ 56 GP$\\_$DRW$\\_$tau 9 56 IAR$\\_$phi
0.97 44 MHPS$\\_$low 0.012 63 delta$\\_$mag$\\_$fid 0.17 56 r–W3 6.7 6
g$\\_$r$\\_$mean$\\_$corr 0.60 18 g$\\_$r$\\_$max $-$0.10 12 n$\\_$pos 0 56
iqr 19.7 63

Table 6: Variability and colour features in the g band that can be used to
clean the CL candidate list. The limiting lower values correspond to the 16th
percentile obtained for the CL sample. The third column indicates the
percentage of NOT CL sources that can be discarded by limiting each feature to
its limiting value.

###  6.2 Characterizing the turning-on CL properties

Apart from the ALeRCE LCC features, we have analysed other multi-wavelength
properties of the sample to investigate what can increase the likelihood of
finding a turning-on transition.

####  6.2.1 BPT diagrams

While most of the reported CLs in the literature are Seyferts, some of them
have been found to lie on the borderline between a LINER and Seyfert
classification, with extreme order-of-magnitude changes in continuum and
emission-line flux compared to less dramatic CLs occurring in Seyferts
(Frederick et al., 2019).

Here, we tested whether the CL transitions could be related to the emission-
line properties of the sources by computing the line-ratio diagnostic diagrams
involving the line ratios [O iii]/H $\beta$, [N ii]/H $\alpha$, [O i]/H
$\alpha$, and [S ii]/H $\alpha$ (Baldwin et al., 1981; Kewley et al., 2006).
By selection, all our CL candidates are consistent with a Seyfert
classification, and the BPT criteria indicate that the line ratios from both
their archival and new optical spectra distribute similarly, regardless of
whether they experience a CL transition or not.

####  6.2.2 Forced photometry variability

Due to a higher number of data points, analyzing the complete light curves
(instead of the alert curves) gives us a much more reliable determination of
the variability of the sources. In general, we find a stronger variability
typical of luminous Type 1 AGNs in the CL sample (during or post transition),
which indicates that most of the candidates with non-variable BELs (i.e., the
NOT CL sample) belong to a different population. Recently, ZTF light-curve
variability analysis has been found to be a powerful tool to seek new CL
candidates. In López-Navas et al. (2023), by analysing ZTF forced photometry
light curves of $\sim$ 15000 Type 2 AGNs, we were able to distinguish between
weak Type 1 and Type 2 sources and select CL candidates that have also been
found through ALeRCE in the work presented here. Sánchez-Sáez et al. (2021b)
used light curves from ZTF data release 5 (ZTF DR5) instead, and applied deep
learning techniques to find anomalous behaviour in AGNs, leading to the
identification of 75 promising CS candidates.

####  6.2.3 MIR variability

The study of the photometric variability in the MIR band can help us
understand the physical mechanisms that may be involved in CL events. This
emission is believed to come from dust thermally heated by the AGN, which
emits relatively unimpeded by dust extinction. Indeed, simple W1–W2 (i.e.,
$[3.4]-[4.6]\mu$m) colour cuts with WISE data are able to reliably
differentiate AGNs from stars and galaxies (e.g. W1–W2 $\geq$ 0.8, Stern et
al., 2012). Along these lines, an important feature in our analysis is a12,
which indicates the linear trend of the W1–W2 colour. Our CL sample shows a
general upward trend during the last $\sim$11 years, indicating a redder
colour in the MIR band. This behavior is consistent with a turning-on
transition, where the sources change from galaxy-like (i.e., W1–W2 <0.5) to
AGN-like (i.e., W1–W2 > 0.8) MIR colour, as it has been found in previous CL
studies (Sheng et al., 2017; Yang et al., 2018; Sheng et al., 2020; Lyu et
al., 2022). This phenomenon can be explained by an increase of the brightness
of the AGN, which illuminates the torus and produces a stronger contribution
of hot dust in the W1 and W2 bands, with a larger effect on the latter. This
scenario is supported by the observed W1 and W2 fluxes in our CL sample, which
also show an upward trend given by the slopes a1 and a2.

To estimate the variability of the CLs in comparison with the NOT CL sample,
we computed the parameters $\langle\sigma_{m1}\rangle$ and
$\langle\sigma_{m2}\rangle$ (indicative of the intrinsic variability) and the
maximum variations of W1 and W2 ($\langle\Delta$W1$\rangle$ and
$\langle\Delta$W2 $\rangle$) as in Lyu et al. (2022). Those authors present a
study of the variability of the W1 and W2 light curves of a population of CLs
in comparison with low-luminosity AGN and QSO samples. They also use the
AllWISE multi-epoch photometry and NEOWISE single exposure (L1b) source table
data, though we average the data every six months. Our results, presented in
Table 3, are consistent with their conclusions. On the one hand, we find that
the CL population has a higher $\langle\sigma_{m1}\rangle$ and
$\langle\sigma_{m2}\rangle$ variability than the NOT CL population. On the
other hand, the CL sample exhibits maximum variations of W1 and W2
($\langle\Delta$W1$\rangle$ and $\langle\Delta$W2$\rangle$) greater than 0.3
mag. In the scenario of variable obscuration, Yang et al. (2018) estimate that
a variation in the W1 band due to dust extinction would yield a factor of
$\sim$21 change in the g band magnitude, assuming the extinction curve in the
optical and MIR and considering micrometer-sized grains (Wang et al., 2015). A
variation of 0.3 mag in the W1 band would lead to $\sim$6.3 mag difference in
the g band, which is not consistent with the observed properties of CLs.
Therefore, the variability and general increase in the MIR flux and the colour
W1–W2 of the CL sample, over the last 11 years, are most likely due to an
intrinsic brightening of the AGN and cannot be solely explained by the motion
of absorbing clouds within our line of sight.

####  6.2.4 X-rays

The CL sample shows higher X-ray fluxes and higher X-ray to g-band flux ratios
than the NOT CL sample in the recent eROSITA scans. We note that the AGN
continuum in the optical spectra of these sources is sub-dominant or
undetectable under the starlight contribution, so a significant fraction of
the g-band flux can be associated with the host galaxy. For a given host-
galaxy flux, the CL sources are X-ray brighter, as expected if their AGNs are
more dominant than in the NOT CL sample.

There is a relatively small number of CL sources with contemporaneous X-ray
data in the literature, so it is still unclear whether these events are always
accompanied by a clear change in the X-ray band. We note that we are talking
about optical CLs, not X-ray CLs where the changes are due to obscuration. In
the well studied case of the CL Mrk 1018, a strong soft X-ray excess was found
when the source was in the bright state, which dropped by much more than the
hard X-rays when the source transitioned to the dimmest state (McElroy et al.,
2016; Husemann et al., 2016). In this case, the changes in the BELs were
associated with an increase/decrease of ionizing photons from the soft X-ray
excess (Noda & Done, 2018). Additionally, Temple et al. (2023) found that five
out of nine CL events with Swift-BAT data available could be associated with
significant X-ray changes in the hard 14–195 keV band. The hard band is less
affected by obscuration and thus rules out a varying column density as the
origin of the flux changes. In this work, we find further evidence that at
least some of the CL transitions could be associated with significant X-ray
flux changes. In particular, four out of the 11 CLs with eROSITA data have
experienced an X-ray flux rise with respect to archival ROSAT data taken 30
years earlier. These sources also show variable flux within the eROSITA
2019–2022 light curves, with the X-ray flux being four, eight and up to 15
times higher than the minimum value (see Fig. 8). However, due to the small
number of data points and the intrinsic X-ray variability in unobscured AGNs,
we are unable to discard the possibility that these changes are purely
stochastic and not related to the CL event.

The CL AGN that has been most extensively observed in X-ray monitoring is 1ES
1927+654. This source was found to show dramatic changes in its X-ray spectral
shape, showing the disappearance and re-appearance of the X-ray corona after
the CL event (Trakhtenbrot et al., 2019; Ricci et al., 2020, 2021). The
authors suggest that this particular phenomenon was caused by a TDE in an AGN.
This strengthens the importance of multi-wavelength campaigns studying CLs,
which enable constraints on the evolution of the AGN components during the
transitions.

###  6.3 Promising CL candidates selected through multi-wavelength flux
variability

The multi-wavelength photometric analysis performed in this work provides a
powerful tool to identify turning-on CL events, allowing us to select the most
promising CL events from the candidate list we have not re-observed. In Table
7 we show the sources from our CL candidates that are currently varying in the
optical according to the refined ZTF feature criteria (all the possible pairs
of best combinations mentioned in Section 6.1), and that are experiencing a
MIR flux and colour W1–W2 increase over time. For the sources with eROSITA-DE
data, their X-ray fluxes were below the archival limits, so we did not use
X-ray data as a selection criterion. In Fig. 15 we present the optical and MIR
light curves for these sources. Some of them were not selected to be re-
observed because they already showed obvious BELs in their archival optical
spectra, such as ZTF18aaodaie and ZTF21abmjobi. However, according to their
increase in the MIR fluxes and colour, it is likely that these sources now
present stronger BELs and are therefore worth re-observing. We strongly
encourage follow-up spectroscopy to confirm the CL behaviour in the promising
candidates presented here, which will improve the statistics and refine
further the selection criteria of CL candidates. We note that the cleaning
criteria in the optical and the evolution of the MIR flux and color used to
select the most promising candidates are fairly independent for the redshifts
considered in this work, from $z=0$ to $z=0.4$.

Name Ra Dec z MJD fiberid plate Comments ZTF18aalmtum 136.365522 17.928647
0.40 56246 330 5769 Increasing MIR fluxes and W1–W2. ZTF18aaodaie/ZTF21ackxdiy
191.572165 28.342741 0.10 54205 442 2238 Increasing MIR fluxes and W1–W2.
ZTF18aaoudgg 221.975974 28.556697 0.16 53764 134 2141 Increasing MIR fluxes
and W1–W2. ZTF18acakoya/ZTF18abtndvk 43.709715 -2.797898 0.12 56978 181 7823
Increasing MIR fluxes and W1–W2. ZTF19adcfhxp 143.823381 34.320597 0.45 56336
727 5805 Increasing MIR fluxes and W1–W2. ZTF20acpcfgt 339.995984 0.860685
0.38 52201 620 674 Increasing MIR and optical fluxes and W1–W2. ZTF21abjprnu
230.292534 20.544009 0.14 54328 421 2159 Increasing MIR and optical fluxes.
Decreasing W1–W2, but still W1–W2 > 0.7 ZTF21abdvcpz 335.843667 0.771973 0.22
52140 454 375 Increasing MIR fluxes and W1–W2. ZTF21abmjobi 358.363382
0.123463 0.17 52523 520 684 Increasing MIR and optical fluxes and W1–W2.

Table 7: Potential CL sources from our list of CL candidates identified
through their current optical variability and the criteria mentioned in the
comments.

###  6.4 Key questions about the CL phenomena

####  6.4.1 Frequency and time-scales

CL events have been found to occur on time-scales as short as one or two
months (Trakhtenbrot et al., 2019; Zeltyn et al., 2022). However, most
research to date uses archival spectral and/or photometric data to look for CL
transitions, making it difficult to obtain a proper estimate of the typical CL
time-scales and thus understand the drivers of such extreme variations (Stern
et al., 2018). The time difference between the first and last spectral epochs
for our CL sample lies between 10 and 20 years (in the observed frame), which
must be taken as an upper limit on the time-scale for these transitions. Given
their low incidence rate (discussed below) it is difficult to find a large
amount of CL events as they take place. However, much larger and/or
intensively sampled spectroscopic campaigns, such as SDSS-V (Kollmeier et al.,
2017) and 4MOST (de Jong et al., 2019) will provide better constraints on the
typical CL time-scales, potentially finding shorter (weeks and even days) CL
events.

Another key question we can address with our results is the occurrence rate of
turning-on transitions, following the discussion in LN22. There are 30,333
AGNs classified as Type 2 (N and K types in MILLIQUAS, cleaned for weak Type
1s and LINERS) that can be detected by ZTF (Dec $>-28^{\cdot}$ and $r<21$
mag). From them, 178 have alerts and are classified as AGN or QSO by the LCC.
The 50 per cent confirmation rate found in this work implies that 0.3 per cent
of transitions can be detected by this method over an average timespan of 15
years. Taking into account that just 10 per cent of Type 1 AGNs are variable
enough to generate alerts in ZTF, we estimate a lower limit of 3 per cent of
turning-on events every 15 years (i.e., 0.2 per cent per year). These values
are consistent with the results from Hon et al. (2022), who finds a minimum
turn-on CL AGN rate of 3 per cent every 15 years, and from Temple et al.
(2023), who reports a CL rate of 0.7–6.2 per cent on 10–25 year time-scales
(including both turning-on and turning-off events). As a difference with
respect to the present work, LN22 selected the most promising candidates via
visual inspection of the light curves and archival spectra, reducing the
number of candidates and therefore the ratio between CL candidates to the
number of objects in the parent sample. Therefore, even though the
confirmation rate was higher than in this manuscript, they obtained a lower
estimate of the total rate of change (0.12 per cent per year). In the present
work we did not further restrict the candidate list to the most promising
candidates so the fraction of the parent sample that was considered as a
candidate is larger. Even if our success rate is slightly smaller, the product
of both factors leads to a somewhat larger total rate of change. We note that
in both cases we are estimating lower limits to the rate of change.

####  6.4.2 Physical origin

In principle, several mechanisms could lead to variations in the BELs,
including variable obscuration and changes in the accretion rate. Here, we
find that the MIR emission, which is dominated by the dust response (i.e.,
from the torus) to the UV–optical variations of the central engine, tends to
increase in the CL sources. Since the MIR emitting region is too large to be
obscured/unobscured on time-scales of <20 years, and the emission is much less
affected by dust obscuration, we infer that the changes in the MIR waveband
and by extension the CL transitions are due to changes in the accretion rate.

To explain the changes in the accretion flow at these time-scales, the
proposed scenarios include instabilities in the accretion disc and major disc
perturbations such as those caused by TDEs. In general, the light curves of
our CL sources do not follow the power law decay t-5/3 from the peak
brightness as traditionally expected for TDEs (Rees, 1990). On the other hand,
the values of $\lambda_{\rm\rm Edd}$ obtained for the CL sample in their
bright state are similar to the results for CL AGNs and QSOs reported in
recent works ($-2\lessapprox$ log$\lambda_{\rm\rm Edd}\lessapprox-1$, MacLeod
et al., 2019; Frederick et al., 2019; Graham et al., 2020; Temple et al.,
2023), and is consistent with attributing the changes in accretion rate
occurring preferentially in lower activity systems. Interestingly, our sources
have an Eddington ratio (post-CL) in the range 1–5 per cent $L_{\rm Edd}$,
which is in agreement with the expectations from a hydrogen ionization disc
instability (Noda & Done, 2018; Ruan et al., 2019; Śniegowska et al., 2023).
In this scenario, by analogy to the spectral transitions in black hole X-ray
binaries, the changes in the structure of the inner accretion disc occur
around a critical value of $\lambda_{\rm\rm Edd}\sim$ 0.02, which is in
agreement with the most recent CL studies (Graham et al., 2020; Guolo et al.,
2021; Temple et al., 2023). Furthermore, our CL sources show a redder-when-
brighter tendency in the MIR, with AGN-like MIR colours (i.e. W1–W2 > 0.5)
when they enter the bright state, which also has been found in other CL
sources (Yang et al., 2018; Lyu et al., 2022) and supports the accretion state
transition.

In this scenario, either the BELs appear due to the increase of ionizing
photons that excite the BLR (LaMassa et al., 2015), or the BLR itself re-
appears according to the expectations in disc-wind BLR models (Nicastro, 2000;
Elitzur & Ho, 2009). These models predict an evolutionary sequence for the BLR
depending on the accretion rate, leading to different intermediate type
spectral transitions and the BLR disappearance at very low luminosities (Lbol
$\lessapprox 5\cdot 10^{39}M^{2/3}_{7}$ erg s-1, where $M_{7}=M/10^{7}$M⊙,
Elitzur & Ho, 2009; Elitzur et al., 2014). According to the Eddington ratio
estimates, none of our sources fell close to these limits, which suggests the
BLR already existed in these sources but was too weakly ionised to produce
detectable broad lines. On the other hand, the residual spectra in Fig. 14
show that the continuum of the CLs currently looks either flat or blue. This
‘bluer when brighter’ effect, although clearly real in quasars, could at least
partially be due to differences in the relative contribution of the host
galaxy as these lower-luminosity AGNs change their intrinsic flux.

##  7 Summary and conclusions

This paper is the continuation of work introduced in LN22, where we present a
method to search for turning-on CL candidates. The selection method consists
of searching for current Type-1 AGN variability in a sample of spectrally
classified Type 2 AGNs, using classifications given by a random-forest based
light curve classifier, the ALeRCE LCC (Sánchez-Sáez et al., 2021a). In order
to refine the selection method, we obtained second epoch spectra for 36 of our
61 CL candidates, six of which were reported in LN22, which allows the
confirmation of the CL objects by quantifying the change of the BELs in
comparison with the archival SDSS spectra taken 15–20 years earlier. As a
result, we find 19 ($\sim$ 50 per cent) turning-on CL confirmations (the CL
sample), and 17 sources without significant changes in their BELs (the NOT CL
sample).

We have analysed the multi-wavelength properties of the CL sample in
comparison with the NOT CL sample to investigate what would increase the
likelihood of finding a turning-on transition and understand its origin.
Firstly, we performed a variability analysis of the alert light curves from
ZTF, finding several variability features that are distinct between the
samples and that can be applied to select the most promising CL sources. We
also find that the turning-on transitions are characterised by an increase in
the WISE-MIR brightness and the MIR (W1–W2) colour, where the stronger H
$\alpha$ emission corresponds to an AGN-dominated MIR colour (W1–W2 > 0.5
mag). The current Eddington ratio estimations for the CLs are lower than the
overall Type 1 population, falling between one and five per cent $L_{\rm
Edd}$. In the X-ray band, we find that the CLs tend to be flux brighter than
the candidates that have not transitioned, and for four CLs we observe a
significant flux increase during the 2019–2022 X-ray monitoring. These results
are in agreement with previous CL/CS works, and with the expectations from an
accretion-state transition as the origin of these phenomena.

We also analyse the nature of the NOT CL sources according to their optical
and MIR variability. For seven out of the 17 objects, the Type 2 sources were
misclassified by the LCC due to a bad subtraction of the images, a small
number of data points and/or transient events in the light curves such as SNe
and one case of a likely TDE. Interestingly, we also find that ten sources are
consistent with a Type 1 classification, where the optical and MIR emission is
dominated by the host galaxy. This translates into lower amplitude variations
in the optical and MIR wavebands, weaker BELs and galaxy-dominated MIR colours
(i.e., W1–W2 <0.5). Incidentally, we also find that seven of the NOT CL
sources are currently decreasing their optical and MIR fluxes, suggesting they
are currently transitioning to a dimmer state. The multi-wavelength
differences between the CL and NOT CL sources allow us to select the most
promising CL candidates from our list without spectroscopic follow-up, leading
to nine sources that are worth re-observing.

The use of machine learning algorithms on complete optical light curves from
the ZTF or the upcoming LSST can be combined with MIR data to unequivocally
identify CLs, improve the statistics and ultimately understand the underlying
physics of these phenomena.

## Acknowledgements

ELN and SB acknowledge support from Agencia Nacional de Investigación y
Desarrollo (ANID) / Programa de Becas/ Doctorado Nacional 21200718 and
21212344. ELN acknowledges the California Institute of Technology and the
European Southern Observatory for their hospitality. PA, ELN, MLMA and PL
acknowledge financial support from Millenium Nucleus NCN$19\\_058$ (TITANs).
PA acknowledges financial support from the Max Planck Society through a
Partener Group with the between MPA and the University of Valparaíso. LHG
acknowledges funds by ANID – Millennium Science Initiative Program –
ICN12$\\_$009 awarded to the Millennium Institute of Astrophysics (MAS). PL
acknowledges partial support from FONDECYT through grant Nº 1201748. PSS
acknowledges funds by ANID grant FONDECYT Postdoctorado Nº 3200250. MJG
acknowledges partial support from the NSF grant AST-2108402. DT acknowledges
support by DLR grant FKZ 50 OR 2203. DH acknowledges support from DLR grant
FKZ 50 OR 2003. MK acknowledges support from DFG grant number KR3338/4-1.
Based on observations collected at the Samuel Oschin Telescope 48-inch and the
60-inch Telescope at the Palomar Observatory as part of the Zwicky Transient
Facility project. The ZTF forced-photometry service was funded under the
Heising-Simons Foundation grant 12540303 (PI: Graham). This publication makes
use of data products from the Wide-field Infrared Survey Explorer, which is a
joint project of the University of California, Los Angeles, and the Jet
Propulsion Laboratory/California Institute of Technology, funded by the
National Aeronautics and Space Administration. This publication also makes use
of data products from NEOWISE, which is a project of the Jet Propulsion
Laboratory/California Institute of Technology, funded by the Planetary Science
Division of the National Aeronautics and Space Administration. This work is
based on data from eROSITA, the soft X-ray instrument aboard SRG, a joint
Russian-German science mission supported by the Russian Space Agency
(Roskosmos), in the interests of the Russian Academy of Sciences represented
by its Space Research Institute (IKI), and the Deutsches Zentrum für Luft- und
Raumfahrt (DLR). The SRG spacecraft was built by Lavochkin Association (NPOL)
and its subcontractors, and is operated by NPOL with support from the Max
Planck Institute for Extraterrestrial Physics (MPE). The development and
construction of the eROSITA X-ray instrument was led by MPE, with
contributions from the Dr. Karl Remeis Observatory Bamberg & ECAP (FAU
Erlangen-Nuernberg), the University of Hamburg Observatory, the Leibniz
Institute for Astrophysics Potsdam (AIP), and the Institute for Astronomy and
Astrophysics of the University of Tübingen, with the support of DLR and the
Max Planck Society. The Argelander Institute for Astronomy of the University
of Bonn and the Ludwig Maximilians Universität Munich also participated in the
science preparation for eROSITA.

The eROSITA data shown here were processed using the eSASS/NRTA software
system developed by the German eROSITA consortium.

## Data Availability

The SDSS data underlying this article were accessed from SDSS DR17
(http://skyserver.sdss.org/dr17). The second epoch spectra data will be shared
on reasonable request to the corresponding author. The MIR data are publicly
available at https://irsa.ipac.caltech.edu/Missions/wise.html. The alert ZTF
light curves, together with the LCC classifications, can be downloaded at
https://alerce.online. The forced-photometry ligth curves can be requested via
the ZTF photometry service. The eROSITA data underlying this article were
provided by the eROSITA-DE collaboration by permission, and will be shared on
request to the corresponding author with permission of the eROSITA-DE
collaboration. eRASS-1 will be public from Fall 2023.

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##  Appendix A Light curves and spectra from the CL sample

Table 8 indicates the dates and instruments used for the second epoch spectra
of all the CL candidates observed in this work. Fig. 14 shows the ZTF optical
and WISE-MIR light curves (left) and optical spectra (right) for the 15 CL
sources identified in this paper. The optical spectra are scaled to the flux
of [S ii] in the earliest spectra and smoothed with a 10 Å box filter. The
lower plots in the right side show the difference between the new and the old
spectra. In some cases, the new spectra were taken with a blue and a red arms,
leading to very noisy regions that have been deleted. The other four CL
sources considered in this work are reported in López-Navas et al. (2022). All
the SDSS spectra analysed in this work were taken before the beginning of the
WISE-MIR light curves, except for the case of ZTF20aagyaug (MJD SDSS=55673).
The second epoch spectra were taken between MJD 59600 and 59700, at the end of
the optical and MID-IR light curves. Fig. 15 shows the ZTF optical and WISE-
MIR light curves for the most promising CL candidates selected according to
their optical and MIR photometric variability.

ZTF ID RA DEC MJD Instrument $\deg$ $\deg$ ZTF18aaiescp 207.21292 57.646792
59603 DBSP(P200) ZTF18aaiwdzt 199.48361 49.258651 59603 DBSP(P200)
ZTF18aajywbu 205.45327 37.013091 59604 LRIS(Keck I) ZTF18aaqftos 180.95505
60.888181 59696 DBSP(P200) ZTF18aaqjyon 180.42264 38.47264 59696 DBSP(P200)
ZTF18aasudup 170.03619 34.312731 59603 DBSP(P200) ZTF18aavxbec 243.08151
46.495172 59696 DBSP(P200) ZTF18aawoghx 156.10498 37.650863 59603 DBSP(P200)
ZTF18aawwcaa 128.10120 35.859979 59603 DBSP(P200) ZTF19aabyvtv 197.87773
31.866893 59603 DBSP(P200) ZTF18acbzrll 124.82294 30.32660 59696 DBSP(P200)
ZTF18acgvmzb 148.96896 35.965616 59616 DBSP(P200) ZTF18achdyst 157.39925
24.777606 59616 DBSP(P200) ZTF18acusqpt 177.91898 12.036714 59603 DBSP(P200)
ZTF19aafcyzr 125.71008 15.673859 59616 DBSP(P200) ZTF19aaixgoj 146.80538
12.205624 59603 DBSP(P200) ZTF19aaoyjoh 180.18956 14.967685 59603 DBSP(P200)
ZTF19aapehvs 199.74519 57.501847 59696 DBSP(P200) ZTF19aavqrjg 181.24583
15.58718 59603 DBSP(P200) ZTF19aavyjdn 202.39941 $-$1.509453 59603 DBSP(P200)
ZTF20aaeutuz 164.81581 12.483378 59696 DBSP(P200) ZTF20aagwxlk 153.42829
55.432205 59696 DBSP(P200) ZTF20aagyaug 172.41282 36.883602 59603 DBSP(P200)
ZTF20aakreaa 181.52333 42.169888 59603 DBSP(P200) ZTF20aaorxzv 132.39052
3.68048 59604 LRIS(Keck I) ZTF20aaxwxgq 234.63610 46.126392 59696 DBSP(P200)
ZTF20abcvgpb 238.24977 21.046358 59696 DBSP(P200) ZTF20abgnlgv 232.52715
7.172269 59696 DBSP(P200) ZTF21aafkiyq 174.93478 $-$1.727439 59603 DBSP(P200)
ZTF21abcsvbr 184.64829 18.771713 59603 DBSP(P200)

Table 8: Dates and instruments of the second epoch spectra for the CL
candidates observed in this work. Sources identified as CL are shown in bold.

Figure 14: ZTF optical and WISE-MIR light curves (left) and optical spectra
(right) for the CL sources.

Figure 15: ZTF optical and WISE-MIR light curves from the most promising CL
candidates.