# TESS Shines Light on the Origin of the Ambiguous Nuclear Transient
ASASSN-18el

Jason T. Hinkle Institute for Astronomy, University of Hawai‘i, 2680 Woodlawn
Drive, Honolulu, HI 96822, USA  Christopher S. Kochanek Department of
Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH
43210, USA  Center for Cosmology and Astroparticle Physics, The Ohio State
University, 191 W. Woodruff Avenue, Columbus, OH 43210, USA  Benjamin J.
Shappee Institute for Astronomy, University of Hawai‘i, 2680 Woodlawn Drive,
Honolulu, HI 96822, USA  Patrick J. Vallely Department of Astronomy, The Ohio
State University, 140 West 18th Avenue, Columbus, OH 43210, USA  Katie
Auchettl School of Physics, The University of Melbourne, Parkville, VIC 3010,
Australia  ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions
(ASTRO 3D)  Department of Astronomy and Astrophysics, University of
California, Santa Cruz, CA 95064, USA  Michael Fausnaugh Department of Physics
and Kavli Institute for Astrophysics and Space Research, Massachusetts
Institute of Technology, Cambridge, MA 02139, USA  Thomas W.-S. Holoien NHFP
Einstein Fellow  The Observatories of the Carnegie Institution for Science,
813 Santa Barbara Street, Pasadena, CA 91101, USA  Helena P. Treiber Institute
for Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822,
USA  Department of Physics and Astronomy, Amherst College, C025 New Science
Center, 25 East Dr., Amherst, MA 01002-5000, USA  Anna V. Payne NASA
Fellowship Activity Fellow  Institute for Astronomy, University of Hawai‘i,
2680 Woodlawn Drive, Honolulu, HI 96822, USA  B. Scott Gaudi  Department of
Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH
43210, USA  Jose L. Prieto  Núcleo de Astronomía de la Facultad de Ingeniería
y Ciencias, Universidad Diego Portales, Av. Ejército 441, Santiago, Chile
Millennium Institute of Astrophysics, Santiago, Chile  Keivan G. Stassun
Department of Physics and Astronomy, Vanderbilt University, Nashville, TN
37235, USA  Todd A. Thompson Department of Astronomy, The Ohio State
University, 140 West 18th Avenue, Columbus, OH 43210, USA  Center for
Cosmology and Astroparticle Physics, The Ohio State University, 191 W.
Woodruff Avenue, Columbus, OH 43210, USA  John L. Tonry  Institute for
Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA
Steven Villanueva Jr. NPP Fellow  NASA Goddard Space Flight Center, Exoplanets
and Stellar Astrophysics Laboratory (Code 667), Greenbelt, MD 20771, USA

###### Abstract

We analyze high-cadence data from the Transiting Exoplanet Survey Satellite
(TESS) of the ambiguous nuclear transient (ANT) ASASSN-18el. The optical
changing-look phenomenon in ASASSN-18el has been argued to be due to either a
drastic change in the accretion rate of the existing active galactic nucleus
(AGN) or the result of a tidal disruption event (TDE). Throughout the TESS
observations, short-timescale stochastic variability is seen, consistent with
an AGN. We are able to fit the TESS light curve with a damped-random-walk
(DRW) model and recover a rest-frame variability amplitude of
$\hat{\sigma}=0.93\pm 0.02$ mJy and a rest-frame timescale of
$\tau_{DRW}=20^{+15}_{-6}$ days. We find that the estimated $\tau_{DRW}$ for
ASASSN-18el is broadly consistent with an apparent relationship between the
DRW timescale and central supermassive black hole mass. The large-amplitude
stochastic variability of ASASSN-18el, particularly during late stages of the
flare, suggests that the origin of this ANT is likely due to extreme AGN
activity rather than a TDE.

Accretion(14) — Active galactic nuclei(16) — Black hole physics (159) —
Supermassive black holes (1663) — Tidal disruption (1696)

##  1 Introduction

Active galactic nuclei (AGNs) are the actively accreting supermassive black
holes (SMBHs) found at the centers of $\sim 1-5$% of galaxies in the local
universe (e.g., Ho, 2008; Haggard et al., 2010; Lacerda et al., 2020). For
decades, it has been known that AGNs vary stochastically both photometrically
(e.g., Ulrich et al., 1997; Drake et al., 2009; MacLeod et al., 2012) and
spectroscopically (e.g., Bianchi et al., 2005). Due to the recent growth of
optical transient surveys, AGNs and quiescent galaxies alike have also been
found to undergo extreme flaring behavior. Such events include tidal
disruption events (TDEs; van Velzen et al., 2011; Gezari et al., 2012; Holoien
et al., 2014a, 2016a; Payne et al., 2021; Hinkle et al., 2021b), dramatic
changes in the accretion rates of known AGNs (e.g., Denney et al., 2014;
Shappee et al., 2014; Wyrzykowski et al., 2017; Trakhtenbrot et al., 2019a;
Frederick et al., 2019, 2021), and a growing class of ambiguous nuclear
transients (ANTs; Neustadt et al., 2020; Hinkle et al., 2021c; Holoien et al.,
2021).

Broadly, nuclear transients share a set of observed characteristics including
bright ultraviolet (UV) emission (e.g., Holoien et al., 2016a; Neustadt et
al., 2020; Hinkle et al., 2021c), strong emission lines (Holoien et al.,
2014a; Trakhtenbrot et al., 2019a; Leloudas et al., 2019), slow and often
smooth photometric evolution (Frederick et al., 2021; Hinkle et al., 2020; van
Velzen et al., 2021), and, for some events, X-ray emission (Holoien et al.,
2016a; Neustadt et al., 2020; Hinkle et al., 2021b). These different classes
of nuclear transients often have distinct properties in their X-ray continua,
color evolution, and prescence of light curve rebrightening episodes (e.g.,
Frederick et al., 2021). However, in the case of ANTs, the lack of expected
features or the presence of unexpected features can make a definitive
classification difficult (Neustadt et al., 2020; Frederick et al., 2021;
Hinkle et al., 2021c; Malyali et al., 2021; Holoien et al., 2021).

One example of an ANT is ASASSN-18el (AT2018zf111https://www.wis-
tns.org/object/2018zf; Nicholls et al., 2018; Trakhtenbrot et al., 2019b).
ASASSN-18el was discovered by the All-Sky Automated Survey for Supernovae
(ASAS-SN; Shappee et al., 2014; Kochanek et al., 2017) on 2018 March 3 in a
known AGN, 1ES 1927+654 (Giacconi et al., 1979). Archival spectra were that of
a “true” Type 2 AGN, with narrow lines and no broad lines even in polarized
light (Boller et al., 2003; Tran et al., 2011). Pre-discovery imaging from the
Asteroid Terrestrial-impact Last Alert System (ATLAS; Tonry et al., 2018a)
indicates that the source became active as early as 2017 December 23
(Trakhtenbrot et al., 2019b).

Spectroscopic follow-up of the flare found that the AGN had broad emission
lines, suggesting that ASASSN-18el was an optical changing-look AGN (CL-AGN,
e.g., Shappee et al., 2014; Denney et al., 2014; LaMassa et al., 2015),
changing from a Seyfert 2 to a Seyfert 1 over the course of several months.
During the optical flare, the X-ray flux dropped by several orders of
magnitude (Trakhtenbrot et al., 2019b; Ricci et al., 2021; Laha et al., 2022)
and the dominant X-ray component softened from a hard power-law typical of an
AGN (e.g., Ricci et al., 2017) to a more TDE-like blackbody spectrum (e.g.,
Auchettl et al., 2017). Based on the X-ray evolution and smooth UV/optical
flare, Ricci et al. (2020) speculated that ASASSN-18el was a TDE. Nonetheless,
the overall properties of ASASSN-18el make a definitive distinction between an
AGN flare and TDE difficult.

In this paper, we analyze photometry of ASASSN-18el from the Transiting
Exoplanet Survey Satellite (TESS; Ricker et al., 2015), additional late-time
optical light curves, and late-time X-ray light curves from the Neutron Star
Interior and Composition Explorer (NICER; Gendreau et al., 2012) to understand
the emission of ASASSN-18el in the growing context of nuclear flares. This
paper is organized as follows. In Section 2 we detail the data used in this
work. In Section 3 we present our analysis and then discuss our results in
Section 4. Finally, we summarize our findings in Section 5. Throughout the
paper, we assume a cosmology of $H_{0}$ = 69.6 km s-1 Mpc-1,
$\Omega_{M}=0.29$, and $\Omega_{\Lambda}=0.71$ (Wright, 2006; Bennett et al.,
2014). The source redshift of z = 0.01905 implies a luminosity distance of
83.3 Mpc and the Galactic foreground extinction in the direction of
ASASSN-18el is $A_{V}=0.236$ mag (Schlafly & Finkbeiner, 2011). Trakhtenbrot
et al. (2019b) measured a virial mass of $M_{BH}=1.9\times 10^{7}$M⊙ using
spectra from their follow-up campaign, which we adopt in this manuscript. As
this is a single epoch measurement, we will assume an uncertainty of 0.3 dex
(e.g., Guo et al., 2020).

##  2 Data

Data near peak emission and up to roughly 400 days after were presented
originally in Trakhtenbrot et al. (2019b), with additional X-ray data shown in
Ricci et al. (2021) and X-ray/UV data shown in Laha et al. (2022). Here we re-
reduce ground-based survey data on ASASSN-18el from ASAS-SN and ATLAS, now
extending roughly 1300 days after peak. We additionally use TESS, Swift,
NICER, and ground-based follow-up data. In this section we describe the
procedures used to obtain and reduce these data.

Figure 1: Optical and UV light curves of ASASSN-18el, showing ASAS-SN ($gV$),
ATLAS ($o$), Swift ($UBV+UV$), and other ground-based ($BVrRiI$) photometry.
The photometry shown here spans from roughly 200 days prior to peak optical
emission in the ATLAS $o$-band to roughly 1250 days after in observer-frame
days. The vertical red bands indicate time periods with TESS coverage. The
vertical black line indicates the time of the ATLAS $o$-band peak. Horizontal
error bars indicate the date range of observations stacked to obtain deeper
limits and higher S/N detections, although for some data they are smaller than
the symbols. Downward-facing triangles indicate 3$\sigma$ upper limits. All
data are corrected for Galactic extinction and are shown in the AB system.

###  2.1 ASAS-SN Data

The ASAS-SN transient survey consists of five sites located at Haleakalā
Observatory, McDonald Observatory, the South African Astrophysical Observatory
(SAAO), and two at Cerro Tololo Inter-American Observatory (CTIO), all hosted
by the Las Cumbres Observatory (Brown et al., 2013). Each site hosts four
telescopes on a common mount, and the telescopes are 14-cm aperture Nikon
telephoto lenses with 8$\farcs$0 pixels. However, at the time of the discovery
of ASASSN-18el, ASAS-SN operated with two units taking $V$-band images and
three units taking $g$-band images. After the seasonal break following the
transient peak emission, ASAS-SN had fully switched to $g$-band imaging and
all of the later time ASAS-SN data are in the $g$-band.

The images were reduced using the automated ASAS-SN pipeline (Shappee et al.,
2014), which uses the ISIS image subtraction package (Alard & Lupton, 1998;
Alard, 2000). We then used the IRAF apphot package with a 2-pixel radius
(approximately 16$\farcs$0) aperture to perform aperture photometry on each
subtracted image, to produce a differential light curve. We calibrated our
photometry using the AAVSO Photometric All-Sky Survey (Henden et al., 2015).
To obtain good detections or limits throughout the transient evolution, we
stacked the ASAS-SN epochs. We used 10-day bins prior to and during the flare
for both $V$\- and $g$-band data, and 50-day bins for the $g$-band data after
the seasonal break to sample the decline. We excluded ASAS-SN data more than
1000 days after peak optical emission as contamination from the residuals of a
nearby star became similar in amplitude to the transient.

###  2.2 ATLAS Data

At the time of discovery, ATLAS consisted of two 0.5-m f/2 Wright Schmidt
telescopes located on Haleakalā and at the Mauna Loa Observatory. The ATLAS
telescopes typically obtain four 30-second exposures of 200–250 fields per
night, covering roughly a quarter of the sky visible from Hawai’i each night
(Smith et al., 2020). Depending on the Moon phase, ATLAS uses either the
‘cyan’ ($c$) filter from 420–650 nm or the ‘orange’ ($o$) filter covering the
560–820 nm range (Tonry et al., 2018a). As there was little $c$-band coverage
of the flare, particularly in the late phases where we focus our study, we
have excluded it from our analysis.

The ATLAS images are processed by a pipeline that includes flat-field,
astrometric, and photometric calibrations. We performed forced photometry on
the subtracted ATLAS images of ASASSN-18el as described in Tonry et al.
(2018a) to create a differential light curve. Due to the bright host galaxy
and nearby stars there are many epochs with artifacts remaining in the
subtracted images, as indicated by the high reduced $\chi^{2}$ of the PSF
fits. We therefore excluded images with reduced $\chi^{2}>10$. Similar to our
ASAS-SN light curves, we combined individual epochs to increase the S/N. Prior
to the flare, we combined the data in 10-day bins, during the flare we
combined the four intra-night observations, and after the seasonal break we
stacked in 30-day bins.

###  2.3 Additional Photometry

We also obtained $BVrRiI$ data from several ground-based observatories. These
include the Las Cumbres Observatory Global Telescope (LCOGT) network (Brown et
al., 2013), the 0.5-m DEdicated MONitor of EXotransits and Transients
(DEMONEXT; Villanueva et al., 2016, 2018), the 0.5-m Iowa Robotic Telescope,
and the Post Observatory.

We reduced these data using typical procedures including flat-field
corrections and then obtained the astrometry for each image using
astrometry.net (Barron et al., 2008; Lang et al., 2010). For these ground-
based data we used apphot to measure 5$\farcs$0 aperture magnitudes of the
host plus transient emission, and subtracted the 5$\farcs$0 host flux computed
by Hinkle et al. (2021a). We used refcat (Tonry et al., 2018b) magnitudes of
stars in the field and the corrections of Lupton (2005) for calibration. We
stacked all of this ground-based photometry in 1 day bins as the source was
bright and there were several facilities taking images concurrently.

We also use the Swift $UBV+UV$ photometry of ASASSN-18el presented in Hinkle
et al. (2021a). Some of these data were originally presented in Trakhtenbrot
et al. (2019b), but were re-reduced in Hinkle et al. (2021a) to account for
corrected calibration files for the Swift UV filters. Figure 1 shows the light
curves of ASASSN-18el from ASAS-SN, ATLAS, Swift and ground-based
observatories in AB magnitudes and corrected for Galactic foreground
extinction.

###  2.4 TESS Observations

The host galaxy of ASASSN-18el lies close to the North Ecliptic Pole and the
TESS northern continuous viewing zone. It was observed in each sector of Cycle
2 except for Sector 24. The source was very close to the edge of the chip in
Sector 14, and therefore we do not include this sector. This allowed us to get
a high-cadence and high-precision light curve beginning roughly 500 days after
the peak optical emission. The source was also observed again in Sectors 40
and 41 of Cycle 4.

The differential light curves are generated from the TESS full frame images
(FFIs) following the procedures of Vallely et al. (2019, 2021). Because the
TESS PSF has non-trival structure and the camera orientations rotate between
sectors, we constructed independent reference images for each sector. We
selected the first 100 FFIs of good quality obtained in each sector, excluding
images with above average sky background levels or PSF widths. We also
excluded FFIs that were affected by an instrument anomaly, showed significant
backgrounds due to scattered light, had data quality flags, or a compromised
spacecraft pointing. We converted the measured fluxes into TESS-band
magnitudes using an instrumental zero point of 20.44 electrons per second in
the FFIs (TESS Instrument Handbook; Vanderspek et al., 2018).

###  2.5 NICER Observations

Throughout the evolution of ASASSN-18el, X-ray observations were obtained
using NICER (Gendreau et al., 2012), an external payload on the International
Space Station. NICER observed ASASSN-18el a total of 431 times between 2018
May 22.8 and 2021 February 17.7 (ObsIDs: 1200190101$-$1200190287,
2200190201$-$2200190370, and 3200190301$-$3200190451; PI: Kara).

The NICER data were reduced using NICERDAS version 6a and HEASOFT version
6.26.1. We applied standard filtering criteria in nicerl2. These criteria
include222See
https://heasarc.gsfc.nasa.gov/docs/nicer/data_analysis/nicer_analysis_guide.html
or (Bogdanov et al., 2019) for more details.: a NICER pointing of ANG_DIST
$<$0.015 degrees from the source location and exclusion of events during a
South Atlantic Anomaly passage or when Earth was 30∘ (40∘) above the dark
(bright) limb. We also removed events which were flagged as overshoot or
undershoot events (EVENT_FLAGS=bxxxx00), and used the “trumpet filter” to
remove events with a PI_RATIO $>$ 1.1+120/PI (Bogdanov et al., 2019).

To extract count rates for each epoch, we used XSELECT, and the standard
ancillary response (ARF; nixtiaveonaxis20180601v002.arf) and response matrix
(RMF; nixtiref20170601v001.rmf) files from the NICER CALDB. We assumed a
photon index of 1.7 and the Galactic column density of $6.4\times 10^{20}$
cm-2 (Ricci et al., 2020) to convert the count rates to fluxes. Data within
$\sim$500 days of peak optical emission were presented in Ricci et al. (2021).
Here we extend the NICER data coverage until nearly 1100 days after peak to
trace the X-ray luminosity evolution at late times.

##  3 Analysis

###  3.1 Optical Light Curve

In addition to the pre-flare and near-peak data presented in Trakhtenbrot et
al. (2019b), the light curves shown in Figure 1 now extend several years after
the peak emission of the transient. It is clear that at the beginning of the
TESS observations of ASASSN-18el, the transient is still detectable in both
the stacked ASAS-SN $g$-band and the ATLAS $o$-band data.

Figure 2: Top panel: ATLAS $o$-band light curve (orange diamonds) and the
best-fitting power-law (exponential-decay) model in blue (red). The light gray
shaded region shows the time period included in the fits. Bottom panel: The
residuals of the models, shown in the same color as the fits themselves.

To obtain a time of peak optical light, we fit a parabola to the ATLAS
$o$-band light curve as it is the most complete on both sides of the peak.
Since the cadence is low, we fit the parabola over a wide date range of dates
between MJD = 58116.2 and MJD = 58292.5. We generated 10,000 realizations of
the ATLAS light curve over this date range with each flux perturbed by its
uncertainty assuming Gaussian errors. We then fit a parabola to each of the
perturbed light curves and took the median value as the peak of the
distribution and used the 16th and 84th percentiles as the uncertainties. This
leads to a time of peak $o$-band emission of MJD $=58210\pm 2$ with a peak
flux of $1.9\pm 0.1$ mJy. Several studies of TDE flares have found significant
offsets between the peak times in different optical bands (Holoien et al.,
2019, 2020; Hinkle et al., 2021b) although the cadence of ASAS-SN data and the
lack of UV data near peak preclude such comparisons.

Figure 3: TESS data stacked in 2-hour bins (black points) with the mean DRW
light curve shown in red. Here, no correction for Galactic extinction has been
applied. The shading represents the RMS of the DRW light curves. The range of
allowed light curves broadens rapidly when there is no data in a given time
interval. The vertical gray bars mark the beginning of new sectors. In this
model, linear offsets are computed between the sectors such that the global
mean flux is zero. The inter-sector calibration has larger errors than the
point-to-point precision in the TESS light curve (shown as the error bar in
the upper right) but these uncertainties are included in our analysis and do
not affect our results.

One distinguishing feature of TDE light curves is that they are commonly
described by a power-law decline in flux. In particular, when the mass per
unit binding energy is constant, the decline slope is $t^{-5/3}$ (e.g., Evans
& Kochanek, 1989). To examine the decline slope of ASASSN-18el in the context
of TDEs, we fit the ATLAS $o$-band flux as

$f=f_{10}\bigg{(}\frac{t-t_{0}}{10\text{ days}}\bigg{)}^{\alpha}+h$ (1)

to estimate the time $t_{0}$, the flux 10 days later $f_{10}$, the flux offset
$h$, and the power-law index $\alpha$. We fit the ATLAS data from 10 days
after peak to MJD = 58817.5. An MCMC fit yields best-fit parameters of
$t_{0}\text{(MJD)}=58101^{+17}_{-14},\ f_{10}=28^{+12}_{-10}\ \text{mJy},\
h=-0.10^{+0.06}_{-0.09}\ \text{mJy},\ \text{and}\
\alpha=-1.05^{+0.15}_{-0.12}$ and this model is shown in Figure 2. This
decline is consistent with the UV decline slope of $\alpha=-0.91\pm 0.04$
measured by Laha et al. (2022). This decline slope is significantly flatter
than expected for TDEs (e.g., Rees, 1988; Phinney, 1989), consistent with the
qualitative comparison shown in Trakhtenbrot et al. (2019b).

The root mean squared (RMS) residuals from the power-law fit are 0.11 mJy
after subtracting the median ATLAS flux uncertainty (e.g., Vaughan et al.,
2003). For the remaining calculations of the RMS scatter, we subtract the
median flux uncertainty to correct for the contribution of statistical noise
to the observed variability.

We next fit the decline of ASASSN-18el using an exponential-decay model

$f=ae^{-(t-t_{peak})/\tau_{decay}}+c$ (2)

similar to what has been done for some TDEs (e.g., Holoien et al., 2014b,
2016a, 2016b; Payne et al., 2021). Here we set $t_{peak}$ of MJD = 58210 from
our fit to the ATLAS $o$-band light curve and fit the ATLAS data over the same
time period to find best-fit parameters of $a=1.69^{+0.07}_{-0.06}\
\text{mJy},\ \tau_{decay}=147^{+11}_{-10}\ \text{days},\ \text{and}\ c=0.22\pm
0.02\ \text{mJy}$. This model is shown in Figure 2 and it also provides a
reasonable description of the data, with RMS residuals of 0.15 mJy. The best-
fit $\tau_{decay}\sim 147$ days is roughly three times longer than found for
typical TDEs (Holoien et al., 2014b, 2016a, 2016b).

We next compare the RMS measurements obtained from the decline fits to the RMS
scatter in the archival ATLAS light curve. Only considering data taken before
the beginning of the rise on MJD $\simeq$ 58110, we find an RMS scatter of
0.02 mJy in the ATLAS o-band data, suggesting that the transient is
significantly more variable than the host galaxy was in quiescence.

###  3.2 TESS Light Curve

Figure 4: Contours of log($\hat{\sigma}$) and log($\tau_{DRW}$), shown at 1,
2, and 3$\sigma$ from the best-fit value shown as a black cross. The values of
$\hat{\sigma}$ and $\tau$ have been corrected into the rest frame using the
host redshift of z = 0.01905 and $\hat{\sigma}$ has been corrected for
Galactic extinction.

Figure 5: Power spectral density (left panel) and structure function (right
panel) of the 2-hour binned TESS light curve. The PSD is computed using
scipy.signal.periodogram. The red lines represent the PSD and SF binned in 100
logarithmic bins and the red shading indicates the 1$\sigma$ scatter over that
bin width. The gray band around the SF is the uncertainty at that lag. The
blue lines are the estimated $\tau_{DRW}$, with the shading representing the
uncertainty. As expected, the best-fit $\tau_{DRW}$ roughly coincides with the
break in the PSD from a flat white noise to the higher frequency power-law
slope. The purple lines are a power-law fits, with the PSD fit giving
$\alpha=-2.11$ and the SF fit yielding $\gamma=0.54$, both consistent with the
expectations of a DRW model. The blue arrows represent the regions over which
the power-law fits are done.

With our TESS light curve, we are able to study the variability properties of
ASASSN-18el in great detail. In order to understand this variability in the
context of AGN, we used a damped random walk (DRW) model (e.g., Kelly et al.,
2009; Kozłowski et al., 2010; MacLeod et al., 2010; Zu et al., 2011; MacLeod
et al., 2012; Zu et al., 2013) to fit the variability seen in Figure 3.
Fundamentally, a DRW variability model assumes that the power spectral density
(PSD) of an AGN can be described by a flat spectrum (white noise) at low
frequencies, with a smooth transition to a $f^{-2}$ power-law (red noise) at
higher frequencies. The parameters of the model are the characteristic
timescale $\tau_{DRW}$ and the amplitude $\hat{\sigma}=\sigma(\frac{2\times
365.25\textrm{ d}}{\tau_{DRW}})^{1/2}$.

Following Kozłowski et al. (2010), we fit the observed TESS light curve from
Sectors 15-26 allowing for an independent flux offset for each sector (these
are simply additional linear parameters in the model). This provides a well-
defined statistical means of including the non-adjacent data in Sectors 25-26,
drives the mean flux to zero, and includes all the uncertainties from these
flux offsets into the estimates of $\hat{\sigma}$ and $\tau_{DRW}$. The
magnitude of the median offset is 57 $\mu$Jy with a dispersion of 83 $\mu$Jy.
We binned our TESS light curve in 2-hour bins so that the light curve has a
reasonable signal-to-noise ratio per binned epoch. Throughout the remainder of
this manuscript, we use these flux offsets to calibrate the fluxes for each
individual Cycle 2 sector relative to the first TESS sector. However, we note
that the inter-sector calibration likely has larger errors than the point-to-
point precision in the TESS light curve, and long timescale trends should be
viewed with caution.

The contours for variability timescale and amplitude are shown in Figure 4,
with the parameters corrected for source redshift and Galactic extinction. At
the 1$\sigma$ level, the contours are well-behaved, but the upper bound on
$\tau_{DRW}$ increases rapidly at higher confidence. The characteristic
timescale of $\tau_{DRW}=20^{+15}_{-6}$ days is similar to those found for
other AGNs with similar mass SMBHs (e.g., Kelly et al., 2009; Burke et al.,
2021). The variability amplitude of $\hat{\sigma}=0.93\pm 0.02$ mJy,
corresponding to $\sigma\sim 0.13$ mJy for the above $\tau_{DRW}$, is large,
immediately suggestive of AGN-like variability.

We tested how our decisions on connecting individual TESS sectors together
affected our results, finding that regardless of how we match adjoining
sectors, the recovered $\tau_{DRW}$ values are consistent within $1\sigma$
uncertainties. The variability amplitude $\sigma$ is much more sensitive to
choices on sector matching, but we are able to directly compute the RMS
scatter for each TESS sector, circumventing the DRW modeling and sector-
matching entirely. The median RMS scatter per sector is 24 $\mu$Jy, which is
8% of the median TESS flux over the same range.

Figure 5 shows the PSD and structure function (SF) of the TESS light curve of
ASASSN-18el between Sectors 15 and 23, avoiding the sector-long gaps in the
TESS coverage. The PSD was computed using scipy.signal.periodogram and a flat
top window function to increase amplitude accuracy. As expected from previous
studies of AGN variability (e.g., Mushotzky et al., 2011; Kozłowski, 2017),
the PSD is flat at low frequencies, with a declining power-law at higher
frequencies. Similarly, the SF is roughly flat at long lags with a declining
slope towards shorter lags. At the highest frequencies (shortest lags), the
PSD (SF) flattens again as the signal becomes dominated by photometric noise.
There is also a dip in the PSD at $\sim 0.04$ day-1, likely due to the 27 day
length of the TESS sector and the linear offsets applied between the sectors.
If we fit the PSD above the measured value of $\tau_{DRW}$ (in frequency) with
a single power-law plus a photometric noise term, we find a power-law index of
$\alpha=-2.11\pm 0.16$. Likewise, if we fit the SF below the measured value of
$\tau_{DRW}$ (in time) with the same model we find a power-law index of
$\gamma=0.54\pm 0.02$.

The canonical power-law slope for a DRW model is $\alpha=-2$ (e.g., Zu et al.,
2011, 2013) for the PSD and $\gamma=0.5$ (e.g., Kozłowski, 2016) for the SF.
Studies of AGNs using high-quality Kepler light curves (Mushotzky et al.,
2011; Smith et al., 2018) have shown a range of power-law slopes between
$\alpha=-1.7$ and $\alpha=-3.3$, although Moreno et al. (2021) showed that
Kepler systematics can mimic AGN variability. Therefore, we find that the
power-law slopes for the PSD and SF of ASASSN-18el are fully consistent with
other optical AGNs and with the theoretical assumptions made when fitting the
TESS light curve with a DRW model.

###  3.3 Optical/X-ray Correlation

Next we searched for a correlation between the TESS optical and NICER X-ray
light curves, shown in Figure 6. The X-ray emission is roughly an order of
magnitude higher than the optical emission at the same phase, unlike many
other ANTs (Neustadt et al., 2020; Hinkle et al., 2021c). Qualitatively, both
light curves show a plateau over the time period from 500 to 700 days after
the peak emission, where they overlap. After this plateau, both the NICER and
TESS light curves begin to decline. This is likely due to a change in the
overall accretion rate onto the central SMBH.

We tried to measure a temporal lag between the NICER and TESS light curves. If
the optical variability was a result of the reprocessing of the far more
luminous X-rays, we would expect to see a correlation between light curve
features in the X-ray and the optical, with the optical lagging behind the
X-ray. Assuming this is due to light travel time, this lag would give an
approximate physical size of the accretion disk between the X-ray emitting
corona and the cooler portions of the disk from which the emission seen in the
TESS bandpass originate. Unfortunately, neither the straightforward cross-
correlation function code PyCCF (Sun et al., 2018) nor the more sophisticated
DRW modeling code javelin (Zu et al., 2011, 2013) model produced a useful
result. Additionally, we tested if binning the TESS data and/or smoothing the
light curves to remove long-term trends affected the lag fits, but neither was
able to return an acceptable result.

Figure 6: NICER (black circles) and TESS (dark red pentagons) light curves of
ASASSN-18el. The NICER luminosity is integrated over the 0.3-10 keV bandpass
and the TESS luminosity is $\lambda$Lλ at the pivot wavelength of the TESS
bandpass. It is clear that the X-ray component of the emission is more than an
order of magnitude more luminous than the optical component during the time of
overlapping data.

###  3.4 Very Late-time Data

Figure 7: TESS data stacked in 2 hr bins (red points) spanning from the
initial Sector 15 light curve to the very late-time Sector 41 light curve. The
vertical gray bars mark the beginning of new sectors and the error bar in the
upper right indicates the median magnitude of the sector-to-sector offsets.

After TESS returned to the Northern hemisphere in Cycle 4, ASASSN-18el was
observed again. We show the full TESS light curve as of the end of 2021,
including Sectors 15-23, 25-26, and 40-41 in Figure 7. It is immediately clear
that the rapid high-amplitude variability has faded by the very-late time
sectors. To quantify the change in variability amplitude between the early and
late sectors, we measured the RMS scatter in the first sector and compared it
to the last sector. For Sector 15, the RMS scatter around the median flux is
46 $\mu$Jy. Roughly 2 years later in Sector 41, the RMS scatter had dropped by
a factor of almost 5 to 9 $\mu$Jy. ASASSN-18el will be observed in 13
continuous TESS sectors in Cycles 4 and 5 (Sectors 47 - 59), allowing for
additional late-time monitoring of the source variability well after the
transient has faded.

While the overall light curve shape is not particularly important for
understanding the short-term variability expected of AGNs, we used stacked
late-time ATLAS data to measure the $o$-band flux concurrent with active TESS
sectors to allow us to create a long-term TESS light curve. We matched the
first TESS sector to the concurrent ATLAS $o$-band flux and applied the linear
offsets between sectors computed in the DRW modeling to calibrate the flux of
the remaining Cycle 2 TESS sectors. For the Cycle 4 TESS light curves, we
added the concurrent ATLAS $o$-band flux to the differential TESS light curve.
As our primary aim in Figure 7 is to show the consistent strong short-term
variability of ASASSN-18el over a 2 year time span, details on sector to
sector matching are largely inconsequential.

##  4 Discussion

Two plausible explanations have been put forward to explain the changing-look
phenomenon observed for the ANT ASASSN-18el. The first is related to
instabilities in the existing AGN disk, either as a result of a change in the
accretion flow (Trakhtenbrot et al., 2019b) or something more exotic like a
magnetic inversion in the disk (Scepi et al., 2021; Laha et al., 2022). The
second is increased accretion due to a TDE, which disrupted the existing X-ray
corona while powering a large UV/optical flare (Ricci et al., 2020).

The variability seen in the TESS light curve is typical of known AGNs and
consistent with the DRW model frequently used to describe AGN variability.
Both the PSD and SF of the TESS light curve are typical of AGNs. Therefore,
independent of any modeling of the TESS light curve and choices on sector
matching, this short-term stochastic variability suggests an AGN origin for
ASASSN-18el.

Nevertheless, with a measurement of $\tau_{DRW}$, we can compare to known
relationships, such as the one between $\tau_{DRW}$ and the central SMBH mass,
shown in Figure 8. As the black hole mass increases, so does the
characteristic timescale, resulting from the scaling of accretion disk
parameters (e.g., Kelly et al., 2009; Zu et al., 2013). In particular, Burke
et al. (2021) suggest that the characteristic variability timescale is linked
to the thermal timescale for the UV-emitting region of the accretion disk,
even when that $\tau_{DRW}$ is measured with optical rather than UV
photometry. ASASSN-18el lies near the trend between $\tau_{DRW}$ and SMBH
mass. In Figure 8 we have shown both the original best-fit line and our fit to
the data from Kelly et al. (2009) using the MCMC procedure of Kelly (2007) to
better illustrate the range of allowed slopes for this relationship given many
sources with large uncertainties.

Similarly, we can compare the pre-flare constraints from ASAS-SN and ATLAS on
variability to those obtained with TESS. Prior to the flare, there are only
hints of weak activity, with sparse detections in the ASAS-SN $V$-band
photometry. This is consistent with the archival Seyfert 2 classification of
the AGN 1ES 1927+654. The lack of strong variability prior to the flare in
contrast with short-timescale variability seen during the flare suggests that
ASASSN-18el is an example of an increase in the accretion rate onto the AGN
rather than a TDE. This scenario is also consistent with the late-time
decrease in variability, as the AGN begins to return to its quiescent state.

It is important to note that there are some caveats to the analysis of AGN
light curves using a DRW model. The primary concern is the length of the light
curve relative to the recovered timescale. Recent simulations suggest that for
robust recovery of DRW parameters, the length of the light curve should be at
least ten times the $\tau_{DRW}$ (e.g., Kozłowski, 2017, 2021), with shorter
light curves biased towards shorter timescales. For ASASSN-18el the length of
the TESS light curve is roughly 16 times longer than the recovered
$\tau_{DRW}$. Figure 4 of Kozłowski (2021) suggests that there is only a $\sim
0.1$ dex uncertainty in $\tau_{DRW}$ at this length. Even if the true
$\tau_{DRW}$ for ASASSN-18el was underestimated by a few tenths of a dex, it
would remain close to the trend shown in Fig. 8. Finally, as noted in Section
3.2, results related to the long-timescale trends in the TESS light curve
should be viewed with caution because of uncertainties and systematic issues
associated with the inter-sector calibrations.

Finally, with respect to the possibility that the changing-look phenomenon was
caused by a TDE, it is difficult to explain the observed variability in the
context of TDE emission. To date only a small number of TDEs have exhibited
any stochastic variability (e.g., Holoien et al., 2019), and TDE light curves
typically do not exhibit re-brightening episodes like the ones seen here in
ATLAS and ASAS-SN photometry. Late-time studies of TDEs have suggested a shift
in the emission mechanism from one tied to the fallback of mass onto the SMBH
to emission from the accretion disk itself (Balbus & Mummery, 2018; van Velzen
et al., 2019; Jonker et al., 2020). A natural assumption is that, similar to
AGN disks, this may lead to enhanced variability from instabilities in disk.
Nonetheless, when examining late-time data on the TDEs ASASSN-14ae and
ASASSN-14li (van Velzen et al., 2019; Hinkle et al., 2021a), we find that
there is no significant variability on several day timescales even long after
disk emission is thought to become the dominant component for these sources
(van Velzen et al., 2019). This suggests that a TDE transitioning to a disk-
dominated state is unlikely to show the very large variability that we see
from ASASSN-18el at similarly late times. In addition, the long overall
timescale of the event is not consistent with known TDEs (e.g., Holoien et
al., 2014b, 2016a, 2016b; Hinkle et al., 2020). These suggest that a TDE is
likely not the source of the transient emission for ASASSN-18el.

Figure 8: The rest-frame DRW timescale ($\tau_{DRW}$) as compared to the SMBH
mass for a given AGN. The sample of Kelly et al. (2009) is shown in light
blue, with their best-fit line shown with the light blue solid line. The solid
gray line is our fit of the Kelly et al. (2009) sample using the methods of
Kelly (2007). The dashed gray lines are plus/minus one sigma from the best-fit
line. Both NGC 4395 (Burke et al., 2020) and ASASSN-18el are excluded from
this fit.

##  5 Summary

Through our analysis of late-time optical photometry, NICER X-ray data, and a
high-cadence TESS light curve of ASASSN-18el we find the following:

  * •

Optical transient emission from ASASSN-18el remains detectable above pre-flare
levels for roughly 700 days after the peak optical emission with late-time re-
brightening episodes continuing until at least 1250 days after peak.

  * •

The initial decline of ASASSN-18el as measured from the ATLAS $o$-band light
curve is significantly flatter than the canonical t-5/3 power-law for TDEs.
Additionally, when the decline is fit by an exponential-decay model, the
timescale of the decline is roughly three times longer than observed for
typical TDEs.

  * •

The high-S/N and high-cadence TESS light curve within 1000 days of peak
optical emission shows strong variability on timescales less than a single
TESS sector, immediately suggestive of strong AGN activity.

  * •

The TESS light curve’s large variability amplitude and characteristic
timescale are consistent with light curves of AGNs with similar SMBH masses,
supporting the claim that the observed transient emission at late times is
fully consistent with a dramatic AGN flare. However, there are large
uncertainties and potential systematic errors related to the long-timescale
trends in the TESS light curve.

  * •

The X-ray and optical evolution is distinct, with large dips in X-ray
luminosity at early and late times despite a relatively smooth optical light
curve. However, during the period of overlap between TESS and NICER data, a
multi-wavelength plateau phase is observed. Nonetheless, we were unable to
recover a temporal lag between the X-ray and TESS bandpasses.

  * •

The very late-time TESS data (more than 1000 days after peak emission)
continues to show stochastic variability, but at a lower amplitude than
earlier sectors.

From our analysis of these late-time data, and in particular the exquisite
TESS data, we find that the flare in ASASSN-18el is likely the result of a
large AGN flare. Many of the optical changing-look AGNs to date lack the
wealth of data presented here. However, the few that do, such as NGC 2617
(Shappee et al., 2014) and ASASSN-18el may suggest that such state changes are
not always long term but rather may be the result of large flares.

The use of TESS and its unparalleled combination of photometric precision,
cadence, and sky coverage is a promising avenue for studying the physical
origin of ANTs. TESS observations of an ANT at any phase can give detailed
insight into the variability properties of a galaxy. At the very least, a
sector of TESS data should be able to uncover the presence of AGN-like
variability in ANT host galaxies, well below what can be probed from the
ground. This gives promise that we can begin to understand the range of
nuclear behaviors and accretion processes through the disambiguation of ANTs.

## Acknowledgements

We thank Alexa Anderson and Willem Hoogendam for helpful comments on the
manuscript.

We thank Las Cumbres Observatory and its staff for their continued support of
ASAS-SN. ASAS-SN is funded in part by the Gordon and Betty Moore Foundation
through grants GBMF5490 and GBMF10501 to the Ohio State University, and also
funded in part by the Alfred P. Sloan Foundation grant G-2021-14192.
Development of ASAS-SN has been supported by NSF grant AST-0908816, the Mt.
Cuba Astronomical Foundation, the Center for Cosmology and AstroParticle
Physics at the Ohio State University, the Chinese Academy of Sciences South
America Center for Astronomy (CAS- SACA), the Villum Foundation, and George
Skestos.

JTH was supported by NASA grant 80NSSC21K0136. BJS and CSK are supported by
NSF grant AST-1907570/AST-1908952. BJS is also supported by NSF grants
AST-1920392 and AST-1911074. CSK is supported by NSF grant AST-181440. Support
for JLP is provided in part by FONDECYT through the grant 1191038 and by the
Ministry of Economy, Development, and Tourism’s Millennium Science Initiative
through grant IC120009, awarded to The Millennium Institute of Astrophysics,
MAS. TAT is supported in part by NASA grant 80NSSC20K0531.

Parts of this research were supported by the Australian Research Council
Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D),
through project number CE170100013.

B.S.G. was supported by Thomas Jefferson Chair for Discovery and Space
Exploration at the Ohio State University.

DEMONEXT was supported by National Science Foundation CAREER Grant AST-1056524
and the Vanderbilt Initiative in Data-intensive Astrophysics (VIDA).

This work is based on observations made by ASAS-SN and ATLAS. The authors wish
to recognize and acknowledge the very significant cultural role and reverence
that the summit of Haleakalā has always had within the indigenous Hawaiian
community. We are most fortunate to have the opportunity to conduct
observations from this mountain.

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