# AT2022aedm and a new class of luminous, fast-cooling transients in
elliptical galaxies

M. Nicholl Astrophysics Research Centre, School of Mathematics and Physics,
Queens University Belfast, Belfast BT7 1NN, UK<EMAIL_ADDRESS>S.
Srivastav Astrophysics Research Centre, School of Mathematics and Physics,
Queens University Belfast, Belfast BT7 1NN, UK  M. D. Fulton  Astrophysics
Research Centre, School of Mathematics and Physics, Queens University Belfast,
Belfast BT7 1NN, UK  S. Gomez Space Telescope Science Institute (STScI), 3700
San Martin Drive, Baltimore, MD 21218, USA  M. E. Huber  Institute for
Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu HI 96822, USA
S. R. Oates  School of Physics and Astronomy, University of Birmingham,
Birmingham B15 2TT, UK  Institute for Gravitational Wave Astronomy, University
of Birmingham, Birmingham B15 2TT, UK  P. Ramsden School of Physics and
Astronomy, University of Birmingham, Birmingham B15 2TT, UK  Institute for
Gravitational Wave Astronomy, University of Birmingham, Birmingham B15 2TT, UK
L. Rhodes Department of Physics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford OX1 3RH, UK  S. J. Smartt Department of Physics,
University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
Astrophysics Research Centre, School of Mathematics and Physics, Queens
University Belfast, Belfast BT7 1NN, UK  K. W. Smith Astrophysics Research
Centre, School of Mathematics and Physics, Queens University Belfast, Belfast
BT7 1NN, UK  A. Aamer School of Physics and Astronomy, University of
Birmingham, Birmingham B15 2TT, UK  Institute for Gravitational Wave
Astronomy, University of Birmingham, Birmingham B15 2TT, UK  Astrophysics
Research Centre, School of Mathematics and Physics, Queens University Belfast,
Belfast BT7 1NN, UK  J. P. Anderson  European Southern Observatory, Alonso de
Córdova 3107, Casilla 19, Santiago, Chile  Millennium Institute of
Astrophysics MAS, Nuncio Monsenor Sotero Sanz 100, Off. 104, Providencia,
Santiago, Chile  F. E. Bauer Instituto de Astrofísica, Facultad de Física,
Pontificia Universidad Católica de Chile, Campus San Joaquín, Av. Vicuña
Mackenna 4860, Macul Santiago, Chile, 7820436  Centro de Astroingeniería,
Facultad de Física, Pontificia Universidad Católica de Chile, Campus San
Joaquín, Av. Vicuña Mackenna 4860, Macul Santiago, Chile, 7820436  Millennium
Institute of Astrophysics MAS, Nuncio Monsenor Sotero Sanz 100, Off. 104,
Providencia, Santiago, Chile  E. Berger  Center for Astrophysics $|$ Harvard &
Smithsonian, Cambridge, MA 02138, USA  T. de Boer  Institute for Astronomy,
University of Hawaii, 2680 Woodlawn Drive, Honolulu HI 96822, USA  K. C.
Chambers Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive,
Honolulu HI 96822, USA  P. Charalampopoulos Department of Physics and
Astronomy, University of Turku, Vesilinnantie 5, FI-20500, Finland  T.-W. Chen
Technische Universität München, TUM School of Natural Sciences, Physik-
Department, James-Franck-Straße 1, 85748 Garching, Germany  R. P. Fender
Department of Physics, University of Oxford, Denys Wilkinson Building, Keble
Road, Oxford OX1 3RH, UK  M. Fraser  School of Physics, O’Brien Centre for
Science North, University College Dublin, Belfield, Dublin 4, Ireland  H. Gao
Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu
HI 96822, USA  D. A. Green Astrophysics Group, Cavendish Laboratory, 19 J. J.
Thomson Avenue, Cambridge CB3 0HE  L. Galbany Institute of Space Sciences
(ICE-CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193 Barcelona, Spain
Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain  B.
P. Gompertz  School of Physics and Astronomy, University of Birmingham,
Birmingham B15 2TT, UK  Institute for Gravitational Wave Astronomy, University
of Birmingham, Birmingham B15 2TT, UK  M. Gromadzki Astronomical Observatory,
University of Warsaw, Al. Ujazdowskie 4, 00-478 Warszawa, Poland  C. P.
Gutiérrez Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona,
Spain  Institute of Space Sciences (ICE-CSIC), Campus UAB, Carrer de Can
Magrans, s/n, E-08193 Barcelona, Spain  D. A. Howell  Las Cumbres Observatory,
6740 Cortona Drive, Suite 102, Goleta, CA 93117-5575, USA  Department of
Physics, University of California, Santa Barbara, CA 93106-9530, USA  C.
Inserra Cardiff Hub for Astrophysics Research and Technology, School of
Physics & Astronomy, Cardiff University, Queens Buildings, The Parade,
Cardiff, CF24 3AA, UK  P. G. Jonker Department of Astrophysics/IMAPP, Radboud
University, PO Box 9010, 6500 GL Nijmegen, The Netherlands  SRON, Netherlands
Institute for Space Research, Niels Bohrweg 4, 2333 CA Leiden, the Netherlands
M. Kopsacheili  Institut d’Estudis Espacials de Catalunya (IEEC), E-08034
Barcelona, Spain  Institute of Space Sciences (ICE-CSIC), Campus UAB, Carrer
de Can Magrans, s/n, E-08193 Barcelona, Spain  T. B. Lowe  Institute for
Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu HI 96822, USA
E. A. Magnier Institute for Astronomy, University of Hawaii, 2680 Woodlawn
Drive, Honolulu HI 96822, USA  S. L. McGee School of Physics and Astronomy,
University of Birmingham, Birmingham B15 2TT, UK  Institute for Gravitational
Wave Astronomy, University of Birmingham, Birmingham B15 2TT, UK  T. Moore
Astrophysics Research Centre, School of Mathematics and Physics, Queens
University Belfast, Belfast BT7 1NN, UK  T. E. Müller-Bravo Institute of Space
Sciences (ICE-CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193
Barcelona, Spain  Institut d’Estudis Espacials de Catalunya (IEEC), E-08034
Barcelona, Spain  T. Pessi Instituto de Estudios Astrofísicos, Facultad de
Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército Libertador
441, Santiago, Chile  M. Pursiainen DTU Space, National Space Institute,
Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark  A.
Rest Space Telescope Science Institute, Baltimore, MD 21218, USA  Department
of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21218,
USA  E. J. Ridley  School of Physics and Astronomy, University of Birmingham,
Birmingham B15 2TT, UK  Institute for Gravitational Wave Astronomy, University
of Birmingham, Birmingham B15 2TT, UK  B. J. Shappee  Institute for Astronomy,
University of Hawaii, 2680 Woodlawn Drive, Honolulu HI 96822, USA  X. Sheng
Astrophysics Research Centre, School of Mathematics and Physics, Queens
University Belfast, Belfast BT7 1NN, UK  G. P. Smith School of Physics and
Astronomy, University of Birmingham, Birmingham B15 2TT, UK  M. A. Tucker
CCAPP Fellow  Department of Astronomy, The Ohio State University, 140 West
18th Avenue, Columbus, OH, USA  Department of Physics, The Ohio State
University, 191 West Woodruff Ave, Columbus, OH, USA  Center for Cosmology and
Astroparticle Physics, The Ohio State University, 191 West Woodruff Ave,
Columbus, OH, USA  J. Vinkó  Konkoly Observatory, CSFK, MTA Centre of
Excellence, Konkoly Thege M. út 15-17, Budapest, 1121, Hungary  ELTE Eötvös
Loránd University, Institute of Physics and Astronomy, Pázmány Péter sétány
1/A, Budapest, 1117 Hungary  Department of Experimental Physics, University of
Szeged, Dóm tér 9, Szeged, 6720, Hungary  Department of Astronomy, University
of Texas at Ausin, 2515 Speedway Stop C1400, Austin, TX, 78712-1205, USA  R.
J. Wainscoat  Institute for Astronomy, University of Hawaii, 2680 Woodlawn
Drive, Honolulu HI 96822, USA  P. Wiseman School of Physics and Astronomy,
University of Southampton, Southampton, SO17 1BJ, UK  D. R. Young Astrophysics
Research Centre, School of Mathematics and Physics, Queens University Belfast,
Belfast BT7 1NN, UK

###### Abstract

We present the discovery and extensive follow-up of a remarkable fast-evolving
optical transient, AT2022aedm, detected by the Asteroid Terrestrial impact
Last Alert Survey (ATLAS). AT2022aedm exhibited a rise time of $9\pm 1$ days
in the ATLAS $o$-band, reaching a luminous peak with $M_{g}\approx-22$ mag. It
faded by 2 magnitudes in $g$-band during the next 15 days. These timescales
are consistent with other rapidly evolving transients, though the luminosity
is extreme. Most surprisingly, the host galaxy is a massive elliptical with
negligible current star formation. X-ray and radio observations rule out a
relativistic AT2018cow-like explosion. A spectrum in the first few days after
explosion showed short-lived He II emission resembling young core-collapse
supernovae, but obvious broad supernova features never developed; later
spectra showed only a fast-cooling continuum and narrow, blue-shifted
absorption lines, possibly arising in a wind with $v\approx 2700$ km s-1. We
identify two further transients in the literature (Dougie in particular, as
well as AT2020bot) that share similarities in their luminosities, timescales,
colour evolution and largely featureless spectra, and propose that these may
constitute a new class of transients: luminous fast-coolers (LFCs). All three
events occurred in passive galaxies at offsets of $\sim 4-10$ kpc from the
nucleus, posing a challenge for progenitor models involving massive stars or
massive black holes. The light curves and spectra appear to be consistent with
shock breakout emission, though usually this mechanism is associated with
core-collapse supernovae. The encounter of a star with a stellar mass black
hole may provide a promising alternative explanation.

Transient sources (1851) — Supernovae (1668) — Tidal disruption (1696)

††facilities: NTT, PS1, Liverpool:2m, LCOGT, MMT, Swift, AMI††software:
Astropy (Astropy Collaboration et al., 2013, 2018), Matplotlib (Hunter, 2007),
Numpy (Harris et al., 2020), SciPy (Virtanen et al., 2020), Astroquery
(Ginsburg et al., 2019), Astrometry.net (Lang et al., 2010), Astroalign
(Beroiz et al., 2020), Lacosmic (van Dokkum et al., 2012), Pyzogy (Guevel &
Hosseinzadeh, 2017), Photutils (Bradley et al., 2020), Superbol (Nicholl,
2018), Mosfit (Guillochon et al., 2018), Aladin (Bonnarel et al., 2000)

##  1 Introduction

Astrophysical transients are now found in their thousands by optical time-
domain surveys with wide-field robotic telescopes, such as the Asteroid
Terrestrial impact Last Alert System (ATLAS; Tonry et al., 2018), Panoramic
Survey Telescope and Rapid Response System (Pan-STARRS; Chambers et al.,
2016), Zwicky Transient Facility (ZTF; Bellm et al., 2019), and All-sky
Automated Search for Supernovae (ASAS-SN; Shappee et al., 2014). These are
unearthing a variety of new phenomena, and survey power is now set to increase
even further with the Rubin Observatory, the first wide-field survey on an
8m-class telescope (Ivezić et al., 2019).

Improvements in survey cadence allow us to probe populations of transients
that rise and fade on timescales of days, compared to the weeks-months of
typical supernovae (SNe). The majority of fast transients seem to arise from
stripped massive stars (Ho et al., 2023). These include the initial cooling
peaks of Type IIb SNe, as well as events strongly interacting with a dense
circumstellar medium (CSM) deficient in hydrogen (SNe Ibn; Pastorello et al.,
2007; Hosseinzadeh et al., 2017) and sometimes helium (SNe Icn; Fraser et al.,
2021; Gal-Yam et al., 2022; Perley et al., 2022).

A more mysterious population of fast transients has also been uncovered, with
blue colours and a wide range of peak luminosities up to $M<-20$ mag,
approaching superluminous supernovae (SLSNe; Quimby et al., 2011; Gal-Yam,
2019). Since their identification in Pan-STARRS by Drout et al. (2014), such
objects have been discovered in data from the Palomar Transient Factory and
the Supernova Legacy Survey (Arcavi et al., 2016), the Dark Energy Survey
(Pursiainen et al., 2018), ATLAS (Prentice et al., 2018), Kepler (Rest et al.,
2018), Hyper Suprime Cam (HSC; Tampo et al., 2020) and ZTF (Ho et al., 2023).
They have been termed Fast Blue Optical Transients (FBOTs) or Rapidly Evolving
Transients (RETs). Their association with star-forming galaxies suggests a
connection with massive stars (Wiseman et al., 2020), and their photometric
evolution appears consistent with shock breakout from a dense, extended
envelope (Drout et al., 2014; Pursiainen et al., 2018).

Several of the best observed RETs also show persistent high temperatures and
luminous X-ray and radio emission. The most famous example is AT2018cow
(Prentice et al., 2019; Margutti et al., 2019; Perley et al., 2019), but other
well-studied objects include CSS161010 (Coppejans et al., 2020), AT2018lug (Ho
et al., 2020), AT2020xnd (Perley et al., 2021; Ho et al., 2022; Bright et al.,
2022), and AT2020mrf (Yao et al., 2022). These ‘Cow-like’ events seem to be
energised by continuous injection from a central engine (Margutti et al.,
2019), though interaction with CSM can also contribute luminosity (Ho et al.,
2020). Tidal disruption events (TDEs) of stars by intermediate-mass black
holes (BHs) have been considered as an alternative model (Perley et al.,
2019), though accretion onto a stellar-mass BH following the collapse of a
massive star appears to be favoured by most authors (e.g Perley et al., 2021;
Coppejans et al., 2020; Gottlieb et al., 2022).

Here we present an extraordinary new rapid transient that points to a distinct
class of luminous, fast-cooling events. AT2022aedm, or ATLAS22bonw, was
discovered by ATLAS on 30 Dec 2022 (Tonry et al., 2022). Spectroscopy carried
out the following day by the Advanced Public ESO Spectroscopic Survey of
Transient Objects (ePESSTO+; Smartt et al., 2015) suggested a likely SN,
though of indeterminate spectral type (Gkini et al., 2022). A spectroscopic
host galaxy redshift $z=0.14343$ from the Sloan Digital Sky Survey (SDSS;
Albareti et al., 2017) indicated a peak absolute magnitude $M_{o}=-21.5$
mag111We assume a flat $\Lambda$CDM cosmology with $H_{0}=70$ km s-1 Mpc-1 and
$\Omega_{\Lambda}=0.7$, giving a luminosity distance $D_{L}=679$ Mpc, and a
Galactic extinction $E(B-V)=0.0428$ for this line-of-sight (Schlafly &
Finkbeiner, 2011), but the rising light curve was faster than any known SLSN.
A relatively featureless spectrum, and most surprisingly an elliptical host
galaxy, further added to the intrigue of this event, motivating extensive
follow-up observations.

Figure 1:  Follow-up of AT2022aedm. Left: multi-colour light curves from UV to
NIR. Arrows indicate upper limits. The inset shows the early ATLAS flux data
with a second-order fit (also shown on the multi-colour plot) and time above
half-maximum. Taking the shallowest and steepest rises allowed by the ATLAS
data changes the time of zero flux by $\lesssim 0.5$ days. Right:
spectroscopic follow-up. Spectra are labelled by telescope/instrument and time
since explosion in the rest frame.

##  2 Observations

###  2.1 Ground-based imaging

AT2022aedm was discovered in the ATLAS transient stream processed by the ATLAS
Transient Science Server (Smith et al., 2020). Calibrated ATLAS data in the
cyan ($c$) and orange ($o$) bands were obtained using the ATLAS forced
photometry service (Tonry et al., 2018; Smith et al., 2020; Shingles et al.,
2021). ATLAS typically obtains four exposures per night in a given band; we
combined each quad into a single average flux measurement to improve the
signal-to-noise ratio. After AT2022aedm had faded below $o\sim 19$ mag,
observations were binned over neighbouring nights to improve the signal-to-
noise ratio.

Follow-up photometry was obtained from Pan-STARRS in the $i,z,y$ bands, the
Las Cumbres observatory global telescope network (LCO, as part of the Global
Supernova Project) in the $B,g,V,r,i$ bands, the Liverpool Telescope (LT) in
the $u,g,r,i,z$ bands. Data from the European Southern Observatory New
Technology Telescope (NTT) were obtained using both EFOSC2 for the optical
$g,r,i,z$ bands and SOFI for the near-infrared (NIR) $J,H,K$ bands, as part of
ePESSTO+. Data were reduced (de-biased and flat-fielded) either automatically
by facility pipelines, or manually using the PESSTO pipeline (Smartt et al.,
2015) in the case of the NTT data.

###  2.2 Photometry sans frustration

Photometry was performed using a custom pipeline, ‘Photometry Sans
Frustration’ (or psf)222https://github.com/mnicholl/photometry-sans-
frustration. This is a fully python-based code, employing aperture and Point-
spread function (PSF) fitting photometry routines from astropy (Astropy
Collaboration et al., 2013, 2018) and photutils (Bradley et al., 2020). As
well as deriving the image background, PSF and zeropoint, the code
automatically downloads local star catalogs and reference images using
astroquery (Ginsburg et al., 2019), and provides options to solve the
coordinate system using astrometry.net (Lang et al., 2010), clean cosmic rays
using lacosmic (van Dokkum et al., 2012), align and stack images using
astroalign (Beroiz et al., 2020), and subtract transient-free reference images
of the field using pyzogy (Zackay et al., 2016; Guevel & Hosseinzadeh, 2017).

All NTT, LCO and LT images were cleaned and stacked within each night.
Calibration stars and template images were obtained in $g,r,i,z$ from Pan-
STARRS (Flewelling et al., 2020), in $u$ from the SDSS, and in $J,H,K$ from
the VISTA Kilo-degree Galaxy Survey (VIKINGS; Edge et al., 2013). LCO $B,V$
reference images were obtained after the transient faded. The full photometric
data set is shown in Figure 1.

###  2.3 Swift observations

We obtained ultraviolet (UV) and X-ray data using target of opportunity
observations with the UV-Optical Telescope (UVOT; Roming et al. 2005) and
X-ray Telescope (XRT; Burrows et al. 2005) on-board the Neil Gehrels Swift
Observatory (Swift; Gehrels et al. 2004). UVOT imaging was carried out in the
$uvw2$, $uvm2$ and $uvw1$ filters. The light curves were measured using a
$5^{\prime\prime}$ aperture. Count rates were obtained using the Swift
uvotsource tools and converted to magnitudes (in the AB system) using the UVOT
photometric zero points (Breeveld et al., 2011). No host subtraction was
performed in the UV bands, as host contamination is negligible at these
wavelengths (this is confirmed by the later UVOT visits that result in only
non-detections).

We processed the XRT data using the online analysis tools provided by the UK
Swift Science Data Centre (Evans et al., 2007, 2009). AT2022aedm is not
detected in the combined $15.3$ ks exposure, with a limiting count rate
$<8.53\times 10^{-4}$ s-1. Assuming a power-law spectrum with $\Gamma=2$
(Coppejans et al., 2020), and a Galactic hydrogen column density towards
AT2022aedm of $4.2\times 10^{20}$ cm-2 (HI4PI Collaboration et al., 2016),
this corresponds to an unabsorbed 0.3-10 keV luminosity $L_{X}<3.8\times
10^{42}$ erg s-1. This upper limit is deeper than the observed X-ray
luminosities $\sim 10^{43-44}$ erg s-1 in AT2018cow (Margutti et al., 2019),
AT2020xnd (Ho et al., 2022) and AT2022tsd (Matthews et al., 2023).

###  2.4 Radio observations

We observed AT2022aedm with the Arcminute Microkelvin Imager - Large Array
(AMI–LA; Zwart et al., 2008; Hickish et al., 2018) over 4 epochs, beginning 20
days after discovery. AMI–LA is an eight-dish interferometer based in
Cambridge, UK. Each dish is 12.8 m in diameter enabling an angular resolution
of $\sim 30"$. The facility observes at a central frequency of 15.5 GHz with a
bandwidth of 5 GHz.

Data taken with AMI–LA were reduced using a custom pipeline reduce_dc. We used
3C286 and J1119$+$0410 as the primary and secondary calibrators, respectively,
to perform amplitude and phase calibration. The pipeline also flags the data
for radio-frequency interference, effects of poor weather and antenna
shadowing. The data were then exported in uvfits format ready for imaging.

Further flagging and imaging were conducted in the Common Astronomy Software
Applications (casa) (Version 4.7.0 McMullin et al., 2007; CASA Team et al.,
2022) using the tasks tfcrop, rflag and clean. AT2022aedm is not detected in
any of the final images, with 3$\sigma$ upper limits of
$F_{\nu}<[41,210,156,96]\,\mu$Jy at 20, 66, 107 and 110 days after the first
optical detection. These correspond to limits on the spectral luminosity of
$L_{\nu}<2.3\times 10^{28}-1.2\times 10^{29}$ erg s-1 Hz-1. For comparison,
Cow-like RETs typically exhibit radio emission at the level of $L_{\nu}\gtrsim
10^{29}$ erg s-1 Hz-1 (at $\sim 10$ GHz) on timescales of months (Coppejans et
al., 2020; Ho et al., 2020).

Figure 2:  Photometric comparison of AT2022aedm and other fast transients. For
visual clarity, we plot the uncertainties only for AT2022aedm. Left: absolute
rest-frame light curves. Right: $g-r$ colour evolution.

###  2.5 Spectroscopy

We obtained optical spectra of AT2022aedm using EFOSC2 on the NTT (through
ePESSTO+), the LCO 2-m telescopes, the SuperNova Integral Field Spectrograph
(SNIFS; Lantz et al., 2004; Tucker et al., 2022) on the University of Hawaii
2.2-m telescope, and Binospec on the 6.5-m MMT (Fabricant et al., 2019).
Spectroscopy from ePESSTO+ commenced on 31 Dec 2022 (within one day after the
object was flagged by ATLAS) and continued until 20 Feb 2023, by which time
the spectrum was indistinguishable from a pre-explosion host galaxy spectrum
from SDSS.

Standard reductions of these data, including de-biasing, flat-fielding,
spectral extraction, flux and wavelength calibration, were performed using
instrument-specific pipelines. The reduced spectra are plotted in Figure 1 and
labelled with the instrument and phase with respect to our estimated explosion
date. We assume that host galaxy extinction is negligible, supported by the
lack of Na ID absorption in these spectra (Poznanski et al., 2012), and the
early blue colours in our spectra and photometry. All data will be made
publicly available via WISeREP (Yaron & Gal-Yam, 2012).

##  3 Analysis

###  3.1 Light curve

The rising light curve of AT2022aedm is well constrained by the early ATLAS
$o$-band detections. The discovery point on MJD 59941.1 at $o=19.51$ mag is a
factor $\simeq 5$ below the peak $o$-band flux. Fitting a second-order
polynomial to the early flux light curve (Figure 1) indicates the explosion
occurred on MJD $59940.0\pm 0.5$ ($\approx 1$ day before the first detection),
reaching $o$-band peak on MJD $59950.6\pm 0.5$. We take these as the dates of
explosion and peak throughout. We note however that the last non-detection is
15 days before detection, so a slightly earlier explosion date cannot be
entirely ruled out if the early light curve shape is more complex. The rest-
frame rise-time from half the peak flux is $t_{r,1/2}=6.6$ days, much shorter
than the SLSNe that reach comparable peak luminosities but with
$t_{r,1/2}=10-40$ days (Nicholl et al., 2015; De Cia et al., 2018; Lunnan et
al., 2018).

The fading timescale of AT2022aedm is also much quicker than most other
luminous transients. The $g$-band light curve fades by 2 magnitudes in the 15
days after peak, and by 18 days has declined to 10% of peak $g$-band flux. In
$o$-band, where we also have the rise, the full-width at half-maximum (i.e.
the total time spent within 50% of peak flux) is $t_{1/2}=19\pm 1$ days. These
timescales are well within the distributions measured for RETs discovered in
DES (Pursiainen et al., 2018). Although the measured $t_{1/2}$ for AT2022aedm
is longer than the $t_{1/2}<12$ days defining RETS in PS1 (Drout et al., 2014)
and ZTF (Ho et al., 2023), this is attributable to using different photometric
filters: we measured $t_{1/2}$ in $o$, where the rate of fading in AT2022aedm
is $\approx 55\%$ slower than in the $g$-band. Correcting by this factor gives
an estimated $g$-band $t_{1/2}$ of $\approx 12$ days.

Figure 3:  Results from fitting the multi-band photometry with superbol. Left:
bolometric and pseudo-bolometric (optical) light curves compared to AT2018cow,
AT2020bot and Dougie. We also show a representative SN Ia (Nugent et al.,
2011) and a SLSN sample (Nicholl et al., 2015). For visual clarity, we plot
the uncertainties only for AT2022aedm. Right: Temperature and radius evolution
from blackbody fits, together with best-fitting power-laws. The orange lines
are not fit to the first two data points, for which no UV or NIR data are
available.

The extinction-corrected $g$-band peak luminosity of AT2022aedm,
$M_{g}=-22.04\pm 0.05$, makes it one of the brightest RETs discovered to date.
It outshines all but one event (DES16E1bir) in the combined PS1+DES+ZTF
sample. The closest spectroscopically-classified RETs in terms of luminosity
are the Cow-like RETs, typically reaching $\approx-21$ mag (Ho et al., 2023).
To highlight the exceptional luminosity of AT2022aedm, we show a combined
$g$\- and $c$-band rest-frame light curve in Figure 2, compared to
representative examples of different types of fast-fading transients.
AT2022aedm is broader and brighter than AT2018cow, but fades faster than the
fastest SLSN, SN2018bgv (Chen et al., 2023). It is much more luminous than a
typical RET (Drout et al., 2014), the fastest TDE, AT2020neh (Angus et al.,
2022), the fastest broad-lined SNe Ic, such as iPTF16asu (Whitesides et al.,
2017) (see also SN2018gep and SN2018fcg; Pritchard et al. 2021; Gomez et al.
2022), or any fast interacting transients of Types IIn (Ofek et al., 2010),
Ibn (Hosseinzadeh et al., 2017) or Icn (Fraser et al., 2021; Perley et al.,
2022).

Figure 2 also shows the $g-r$ (or $B-V$) colour evolution of AT2022aedm
compared to the same sample of objects (where multiple bands are available).
From an initial $g-r=-0.37$ at 10 days after explosion, AT2022aedm
dramatically reddens by 1.8 magnitudes in colour index over the next 35 rest-
frame days. Cow-like RETs, TDEs, and interacting transients generally show a
more gradual or flat colour evolution. The colour change in AT2022aedm is more
consistent with events with expanding, cooling photospheres; it lies
intermediate between iPTF16asu and SN2018bgv. PS1-10bjp shows a similar colour
evolution over the first 10 days.

Two unclassified fast transients show a comparable colour evolution, in
combination with a peak absolute magnitude brighter than $-20$ mag. One is
AT2020bot, the only RET in the ZTF sample that did not fit into any of the
stripped-envelope, interacting or Cow-like sub-populations (Ho et al., 2023).
It is fainter than AT2022aedm, with a faster rise and redder average colour. A
stronger similarity is exhibited by ‘Dougie’ (Vinkó et al., 2015), a
mysterious transient discovered by ROTSE in 2009. This event peaked at $-22.5$
mag after a fast rise of $\approx 10$ days. The early light curve shape is
very similar to AT2022aedm, though the decline may flatten after 30-40 days.
However, this flattening could also be due to a host contribution in its UVOT
photometry. While the light curve could be plausibly interpreted as a super-
Eddington TDE (Vinkó et al., 2015), its position offset from the host nucleus,
and lack of distinct spectroscopic features, make this classification far from
certain.

###  3.2 Bolometric light curve

To measure the overall energetics of AT2022aedm, we integrate our multi-band
photometry using superbol (Nicholl, 2018). We construct a pseudo-bolometric
light curve using the excellent $g,r,i,z$ and $o$-band coverage, and estimate
the full bolometric light curve by fitting blackbody functions to these bands
and to our UV and NIR photometry where available. These light curves are shown
in Figure 3.

Figure 4:  Spectroscopic analysis of AT2022aedm. Left: line identification in
the highest signal-to-noise ratio spectra. No unambiguous broad features are
identified, but we clearly detect early emission lines of H$\alpha$ and He II,
and at later times blueshifted absorption lines of H and He I. Right:
comparison of AT2022aedm with other fast transients. Dougie and AT2020bot also
show a lack of obvious SN features and occurred in similar environments to
AT2022aedm.

The peak pseudo-bolometric luminosity is typical of SLSNe, reaching
$L_{griz}=10^{43.8}$ erg s-1, but the light curve rise and decline rates fall
well outside the SLSN distribution. The decay rate is comparable to AT2018cow,
though the rise is longer than the $<2$ days exhibited by that event. The
estimated full bolometric luminosity of AT2022aedm at peak is exceptionally
high, reaching $\approx 10^{45}$ erg s-1. This is due to a high temperature
$T\gtrsim 30,000$ K (shown in the top right panel), suggested by the very blue
$g-r$ and $r-i$ colours in the first LCO images. The temperature exhibits a
monotonic decline with a scaling of roughly $T\propto 1/t$. The blackbody
radius increases throughout our observations, suggestive of an expanding
photosphere that remains optically thick. The estimated bolometric light
curves of Dougie and AT2020bot, also constructed using superbol, peak at
similar luminosities to AT2022aedm.

###  3.3 Spectra

The spectroscopic evolution of AT2022aedm is shown in Figure 1. The cooling
observed in the photometry is also evident in its spectra. The first NTT
spectrum 3.5 days after explosion shows a strong blue continuum, which weakens
and disappears by day 27. By day 48, the spectrum is indistinguishable from an
archival SDSS spectrum of the host galaxy.

The spectra mostly lack obvious broad emission, absorption or P Cygni lines
typically seen in SNe. The spectra on days 3 and 14 (both from NTT) and 20
(from MMT) with the best signal-to-noise ratios are examined in more detail in
Figure 4. Weak broad features may exist at around 4000-5000 Å after day 20,
though these could also be caused by contamination from the host galaxy, which
is around 2 magnitudes brighter than AT2022aedm at this phase. We show this
explicitly by adding an arbitrary $20,000$ K blackbody to the host, finding
that this reasonably reproduces the overall shape of the day 20 spectrum.

Figure 4 does show several _narrow_ spectral lines of a clearly transient
nature. The day 3 spectrum shows a sharply peaked emission line consistent
with He II $\lambda 4686$, possibly with a broader base. This line is often
observed in very young SNe (Gal-Yam et al., 2014; Khazov et al., 2016; Bruch
et al., 2021) and in TDEs (Gezari et al., 2012; Arcavi et al., 2014), due to
the high radiation temperatures capable of ionising helium. We also detect a
weak narrow H$\alpha$ emission line. Other Balmer lines are not visible at
this signal-to-noise. He II is not detected in any later spectra, likely
because the temperature has fallen too low to maintain helium ionisation.

Figure 5:  Top left: Fit to the AT2022aedm host galaxy SED using Prospector.
Top right: Host galaxy colour image. Bottom row: The elliptical host galaxies
and offsets of two other luminous fast-cooling transients with unusual spectra
(Vinkó et al., 2015; Ho et al., 2023). Images are from Pan-STARRS and centered
at the transient positions.

The spectra on days 14 and 20 show narrow absorption, rather than emission,
from hydrogen and neutral helium. The first three transitions of the Balmer
series are clearly visible, but blue-shifted from their rest wavelengths by
$\approx 2700$ km s-1. The high-resolution MMT/Binospec data on day 20 also
clearly show He I $\lambda 5875$ absorption, blue-shifted by $\approx 2500$ km
s-1. These lines are not visible in the host spectrum, confirming their
association with the transient.

Figure 4 also includes a comparison between the early spectra of AT2022aedm
and other fast transients. The initial He II and H$\alpha$ emission is
reminiscent of some SNe Ibn (Hosseinzadeh et al., 2017), as well as the fast-
evolving TDE AT2020neh (Angus et al., 2022). The earliest spectrum is also a
reasonable match for young SNe IIn, including the shock-breakout candidate
PT09uj (Ofek et al., 2010). However, AT2022aedm never develops the strong
emission lines typically seen in these classes at later times. The largely
featureless spectrum even at 15 days is a poor match for any fast evolving SN
Ic, SLSN, or SN Icn.

Cow-like RETs exhibit quite featureless spectra at peak, but the prototype
AT2018cow showed increasingly clear H and He emission lines as it evolved,
opposite to the case in AT2022aedm. Interestingly, the spectra of Dougie
remained featureless for $\gtrsim 30$ days (Vinkó et al., 2015), and seemed to
cool in a manner similar to AT2022aedm. AT2020bot lacks a full spectroscopic
time series, making a detailed comparison difficult. It shows an unusual
spectrum at maximum light. Ho et al. (2023) note the presence of possible
broad features, weak compared to typical SN lines and without an obvious
identification. In section 3.1 we observed that these two events also shared
some key photometric properties with AT2022aedm.

###  3.4 Host galaxy

The host galaxy of AT2022aedm is LEDA 1245338 (or SDSS J111927.73+030632.7).
This is a bright, red galaxy with $M_{r}=-22.8$ mag; Figure 5 illustrates this
with a colour image obtained from Pan-STARRS. An SDSS spectrum is also
available (shown in Figure 4). Both the SDSS spectral fitting and Galaxy Zoo
morphological analysis classify LEDA 1245338 as an elliptical galaxy.

The SDSS data release also includes automated analysis of the SDSS spectrum
with the Portsmouth pipeline (using the method of Maraston et al. 2009). The
spectral fitting measures a total stellar mass of $\approx 10^{11.5}$ M⊙, and
a star-formation rate (SFR) consistent with zero. They find a mean age of the
stellar population of 4.8 Gyr. Given that SED modelling is highly sensitive to
the assumed functional form of the star-formation history (Carnall et al.,
2019; Leja et al., 2019), we run our own analysis on the host photometry, over
a wider wavelength range, using prospector (Johnson et al., 2021). In
particular, we use the prospector-$\alpha$ model employing a non-parametric
star-formation history, with 6 equal-mass star-forming bins of flexible width
(see Leja et al., 2017, for details). We include archival host photometry from
SDSS, the 2 Micron All-Sky Survey (2MASS; Skrutskie et al., 2006), and the
Wide-field Infrared Survey Explorer (WISE; Wright et al., 2010). The fit is
shown in Figure 5. We measure $M_{*}=10^{11.45}$ M⊙, consistent with SDSS
results, and a specific SFR in the last 50 Myr of $\log({\rm
sSFR/yr}^{-1})=-11.69$.

The SDSS analyses and our prospector results confirm that the host of
AT2022aedm is a massive, ‘red and dead’ galaxy. This is surprising: recent
work by Irani et al. (2022) shows that less than 1% of core-collapse
explosions occur in such environments. Moreover, this galaxy is especially
unlike the hosts of most transients with comparable luminosity. SLSNe occur
almost exclusively in low-mass galaxies with $<10^{10}$ M⊙ (Lunnan et al.,
2014; Leloudas et al., 2015; Perley et al., 2016; Chen et al., 2017; Schulze
et al., 2018), and their average specific star-formation rate is three orders
of magnitude greater than our measurement for AT2022aedm. Bright RETs are also
found in relatively low-mass, star-forming host galaxies: the sSFRs of the RET
hosts in PS1 and DES span $-10\lesssim\log({\rm sSFR/yr}^{-1})\lesssim-8$,
with only three (out of 73) fitting best to a passive galaxy model, and only
two having a stellar mass greater than $10^{11}$ M⊙. Wiseman et al. (2020)
conducted a systematic analysis of RET hosts in DES, finding evidence for
star-formation in all of the 49 galaxies for which redshifts were available,
and the five RETs from HSC were also found in star-forming galaxies (Tampo et
al., 2020).

Notably, two other fast transients from our photometric and spectroscopic
comparisons also occurred in elliptical galaxies: Dougie and AT2020bot (Vinkó
et al., 2015; Ho et al., 2023). The Pan-STARRS images of their hosts are shown
alongside AT2022aedm in Figure 5. We also note that one SN Ibn, PS1-12sk,
exploded in a bright elliptical, prompting Hosseinzadeh et al. (2019) to
suggest that not all SNe Ibn result from massive stars.

##  4 Discussion

###  4.1 A new class of transient?

AT2022aedm is a puzzling event with a very unusual set of properties:

  * •

a high peak luminosity in the optical, with $M_{g}\approx-22$ mag

  * •

no luminous radio or X-ray emission

  * •

fast rise and decline rates, fading $\sim 1$ mag per week in the $g$-band

  * •

rapid cooling from $\sim 30,000$ K to $\sim 4,000$ K in a few weeks following
peak

  * •

a spectrum dominated by a smooth continuum with no high equivalent width
absorption or emission lines at any phase

  * •

a massive host galaxy comprised of an old stellar population, with no evidence
for current star formation.

This combination is not consistent with any known class of transients. An
extensive search of the literature reveals two other objects which share the
key properties: $M<-20$ mag, rise time $<10$ days, fast decline and colour
evolution, and weak spectroscopic features at all times. Together, these
events indicate a new class of fast transients with high optical luminosities
and fast cooling after peak. Dougie (Vinkó et al., 2015) in particular shows a
strong photometric and spectroscopic similarity, while AT2022bot (Ho et al.,
2023) may represent an even faster-evolving member of this class. All three
occurred off-centre within passive host galaxies, indicating that – uniquely
among RETs – these new ‘Luminous Fast Coolers’ (LFCs) do not require young
stellar populations.

###  4.2 Rates

We estimate the volumetric rate of these events using the ATLAS Survey
Simulator (McBrien, 2021; Srivastav et al., 2022). Interpolated absolute light
curves of AT2022aedm in $c$ and $o$ bands are inserted into the simulation at
10,000 random times, positions and redshifts (up to some $z_{\rm max}$), and
compared to the cadence, footprint and depth of the true ATLAS survey to
determine the number of $5\sigma$ detections of each injected event.

We define a discovery as those objects detected in more than $n_{\rm det}$
images (or $\approx n_{\rm det}/4$ nights, since ATLAS typically obtains a
quad of exposures per night at a given pointing). We then calculate the
fraction classified of all real transients brighter in apparent magnitude than
the injected transient at $z=z_{\rm max}$ and having at least the same number
of ATLAS detections, and take this as our spectroscopic completeness. The rate
is then $R=N_{\rm events}/(f_{\rm disc}f_{\rm spec}VT)$, where $T=2.5$ yr is
the duration and $V$ the volume (within $z=z_{\rm max}$) of the mock survey,
$f_{\rm disc}$ and $f_{\rm spec}$ are the fractions discovered and classified,
and $N_{\rm events}=1$ is the number of observed AT2022aedm-like transients in
ATLAS.

We set $z_{\rm max}=0.2$ ($V=1.9$ Gpc3), covering the redshift range within
which LFCs have been discovered, and set $n_{\rm det}=20$. The latter is
motivated by the modal number of detections for real ATLAS transients, and
with observations on 5 nights should also enable identification of the fast
light curve shape. Varying $n_{\rm det}$ leads to less than a factor two
variation in our derived rate, due to a trade-off between $f_{\rm disc}$ and
$f_{\rm spec}$: stricter requirements lead to a smaller discovered sample but
with a higher spectroscopic completeness. For these parameters, our survey
simulation returns $f_{\rm disc}=0.35$ and $f_{\rm spec}=0.58$, giving
$R\approx 1$ Gpc-3 yr-1.

We caution that this estimate applies to the brightest LFCs such as AT2022aedm
and Dougie, and that fainter and faster events such as AT2020bot are likely
more common volumetrically but harder to detect. Nevertheless, our derived
rate indicates that these events are very rare, $\sim 10^{-5}$ of the core-
collapse SN rate. This rate is lower than the SLSN rate of a few $\times 10$
Gpc-3 yr-1 (Frohmaier et al., 2021), but may be consistent with the rate of
Cow-like events, estimated as $0.3-420$ Gpc-3 yr-1 (Ho et al., 2023).

###  4.3 Physical scenarios for LFCs

####  4.3.1 Tidal disruption events

Vinkó et al. (2015) favoured a TDE as the origin of Dougie. A model with a
relatively low-mass BH of $\sim 10^{5}$ M⊙ provided a good match to the fast
evolving light curve. Although the host galaxy luminosity was more consistent
with a central BH mass of $\sim 10^{7}$ M⊙, Dougie’s location $\sim 4$ kpc
from the nucleus could indicate a disruption around a wandering intermediate-
mass BH.

This scenario has difficulties accounting for AT2022aedm. We are unable to
find an acceptable fit using the TDE model in mosfit333This is an updated
version of the same model used to fit Dougie; see Guillochon et al. (2014).
(Guillochon et al., 2018; Mockler et al., 2019), where models cannot reproduce
the steep decline from peak or the fast colour evolution. The shallower decay
of Dougie at late times may include some host contribution, causing a
flattening that mimics the power-law decay of TDE models.

Other circumstantial problems arise in trying to explain LFCs as TDEs.
AT2022aedm and AT2020bot have even larger offsets from the nuclei of their
hosts. In particular, AT2020bot shows an offset of $\gtrsim 10$ kpc, in the
outskirts of the galaxy where the stellar density is low. This would make a
TDE very unlikely (though at available imaging depths we cannot rule out a
globular cluster). TDE models would also need to explain why these offset
events show such a strong evolution in colour compared to TDEs in their host
nuclei, and are so much brighter than other TDEs with fast evolution
(Blagorodnova et al., 2017; Nicholl et al., 2020; Charalampopoulos et al.,
2022; Angus et al., 2022).

####  4.3.2 Nickel powering and white dwarf explosions

Most SNe are heated by the decay of radioactive nickel (56Ni) to cobalt and
then iron, but this mechanism can be excluded for many of the known RETs (e.g.
Drout et al., 2014; Pursiainen et al., 2018; Perley et al., 2021). The problem
is that fast light curves require low ejecta masses ($M_{\rm ej}$), while
bright peak luminosities require large nickel masses ($M_{\rm Ni}$). Drout et
al. (2014) showed that to produce a peak luminosity of $\sim 10^{44}$ erg and
a rise time $<10$ days, a nickel-powered model would need an ejecta velocity
$>0.1c$. We can probably rule out a very relativistic explosion in the case of
AT2022aedm, due to our radio non-detections, though cannot exclude a mildly
relativistic expansion. More problematic is the requirement for $M_{\rm
ej}\sim M_{\rm Ni}\sim 1$M⊙. This would produce a spectrum dominated by iron-
group absorption, very different to the blue and largely featureless spectra
of LFCs at peak.

Despite the difficulties with a pure nickel-powered model, white dwarf
progenitor models (i.e. variants of SNe Ia) would still be appealing to
explain transients in old stellar populations. A SN Ia interacting with a
dense CSM could produce a peak luminosity well in excess of typical SNe Ia
without requiring $M_{\rm Ni}\sim M_{\rm ej}$. However, our NTT observations
at 70–80 days after explosion indicate that AT2022aedm had already faded below
the luminosity of a SN Ia at the same epoch, making a hidden SN Ia unlikely.
Moreover, known interacting SNe Ia produce spectra with strong, broad hydrogen
emission and broad metal P Cygni lines (Silverman et al., 2013), very unlike
the spectra of LFCs.

####  4.3.3 Magnetar birth

Rapidly rotating nascent magnetars are suspected to play a role in many
luminous and/or rapid transients, such as SLSNe, gamma-ray bursts and fast
radio bursts. Central engines are also thought to be required in the Cow-like
RETs. Powering short-timescale, luminous events like LFCs would require a
combination of rapid rotation to provide a large energy reservoir, a strong
magnetic field to extract this energy quickly, and a low ejecta mass (e.g.
Kasen & Bildsten, 2010). We find that although the mosfit magnetar model
(Nicholl et al., 2017) is able to adequately fit the decline phase of
AT2022aedm, it struggles to simultaneously match the fast rise, even with an
ejecta mass $\lesssim 1$ M⊙.

The environments of known LFCs are also a major problem for this model. SLSNe
and long GRBs are thought to prefer low-metallicity, star-forming galaxies
because these are favourable for rapidly-rotating core collapse. The massive,
elliptical hosts of LFCs are decidedly unfavourable. Less than 1% of core-
collapse SNe occur in elliptical galaxies (Irani et al., 2022), and only $\sim
10\%$ of core-collapses form magnetars (Kouveliotou et al., 1998). It is
therefore unlikely that three unusual magnetar-forming explosions would all be
found in elliptical galaxies. Moreover, the blue-shifted narrow H and He lines
in the later spectra of AT2022aedm may indicate a dense wind pre-explosion,
which would strip angular momentum and inhibit magnetar formation.

####  4.3.4 Shock breakout

Interaction with CSM is another mechanism thought to be responsible for many
luminous or unusual transients, including (SL)SNe IIn, and fast events like
SNe Ibn/Icn. Models for luminous SNe IIn generally invoke a massive CSM that
releases energy slowly via post-shock diffusion (Smith & McCray, 2007). In the
case of rapidly evolving transients, the more relevant models are shock
breakout from an extended CSM or wind, which have been investigated by
Chevalier & Irwin (2011) and Ginzburg & Balberg (2012). This model provided a
reasonable explanation for the rapid SN IIn, PTF09uj (Ofek et al., 2010), and
other RETs (Drout et al., 2014). The early He II emission in AT2022aedm is
often seen during the shock breakout phase in normal Type II SNe (Gal-Yam et
al., 2014; Bruch et al., 2022).

We apply the equations of Chevalier & Irwin (2011), following the
prescriptions from Margutti et al. (2014), to estimate the ejecta and wind
masses required in AT2022aedm. We set the input parameters based on our
superbol results: peak time $t_{\rm peak}=8$ days, total radiated energy
$E_{\rm rad}=1.1\times 10^{51}$ erg, and breakout radius $R_{\rm bo}\approx
2\times 10^{15}$ cm. The equations are degenerate in $M_{\rm ej}/E^{2}$, where
$E$ is the kinetic energy of the explosion. We find $M_{\rm
ej}=0.02(E/10^{51}{\rm erg})^{2}$ M⊙, with a wind density parameter444Defined
as $D_{*}\equiv\rho r^{2}/(5\times 10^{16}\,{\rm g\,cm}^{-1})$, where $\rho$
is the wind density at radius $r$. $D_{*}=0.27$. For a wind velocity of 2700
km s-1, based on the blue-shifted absorption lines in the spectrum, this
corresponds to a pre-explosion mass-loss rate $\dot{M}=0.7$ M⊙ yr-1.

Vinkó et al. (2015) noted that Dougie could also be explained by a reasonable
shock-powered model, with an estimated $\approx 8\times 10^{50}$ erg deposited
in $\approx 2.6$ M⊙ of CSM, but disfavoured this model based on the lack of
shock-excited lines in the spectrum. AT2022aedm shows that emission lines can
be very weak and short-lived in these events, perhaps making a CSM
interpretation of Dougie more palatable. Nevertheless, the parameters we infer
for AT2022aedm (if shock breakout is the dominant power source) are difficult
to associate with any specific progenitor. For a standard $10^{51}$ erg
explosion, the low ejecta mass would be indicative of an ultra-stripped SN,
yet the wind is H- and He-rich, and the unsustainable high mass-loss rate
would require that it is lost in the years immediately before explosion. For a
very energetic explosion with $10^{52}$ erg, the implied ejecta mass is a more
reasonable $\sim 2$ M⊙. But this large energy would likely require a massive
progenitor, increasing the tension with the passive host galaxies of the LFCs.

###  4.4 Stellar mass compact mergers

The old stellar populations hosting AT2022aedm and other LFCs are more
compatible with a compact object origin, rather than massive stars. However,
we encountered inconsistencies interpreting these objects as white dwarf
explosions or TDEs from massive BHs. Fortunately, recent years have seen rapid
progress in understanding the diversity of transients resulting from mergers
involving neutron stars (NS) and stellar mass BHs, and we compare to such
models here.

Kilonovae, transients powered by the decay of heavy elements ejected from NS
mergers, have been discovered in targeted follow-up of gravitational waves
(Abbott et al., 2017; Margutti & Chornock, 2021) and gamma-ray bursts (Tanvir
et al., 2013; Berger et al., 2013; Rastinejad et al., 2022). However, LFCs are
much brighter and bluer than any plausible kilonovae, in which low ejecta
masses $<0.1$ M⊙ and large opacities conspire to produce faint, red transients
visible for only a few days in the optical. While no definitive detections
exist, kilonovae from NS-BH mergers are expected to be even fainter and redder
than those from binary NSs (Gompertz et al., 2023). AT2018kzr was a so-far
unique event suggested to be the merger of a NS with a white dwarf (McBrien et
al., 2019; Gillanders et al., 2020). It was somewhat brighter and longer lived
than known kilonovae, peaking at $M\approx-18$ mag. However, it still faded
much faster than LFCs (Figure 2) and showed broad metal absorption lines
resembling SNe Ic, inconsistent with our events (Figure 4).

Lyutikov & Toonen (2019) proposed that Cow-like RETs can arise from accretion-
induced collapse following the merger of a carbon-oxygen white dwarf with an
oxygen-neon-magnesium white dwarf. This model produces a magnetar in
combination with a low ejecta mass. If one of the white dwarfs retained a
surface hydrogen layer (type DA), a residual pre-collapse wind can help to
explain the observed lines in the spectrum of AT2022aedm. This model may be a
plausible progenitor channel for LFCs. However, it also predicts Cow-like non-
thermal emission, which is ruled out in the case of AT2022aedm. This channel
also has a short delay time, such that the host galaxies are likely to still
be star-forming, in tension with the elliptical hosts of our objects.

Finally, mergers involving a main sequence or evolved star with a stellar mass
BH have also been suggested as progenitors of Cow-like events. Given the older
host environments in LFCs, mergers with Wolf-Rayet stars (Metzger, 2022) are
probably disfavoured in this case. Kremer et al. (2021, 2023) presented models
for tidal disruptions of main sequence stars by stellar mass BHs in dense
clusters. Some of their wind-reprocessed models exhibit optical rise times and
post-peak temperatures similar to LFCs. However, while bolometric luminosities
can reach $\sim 10^{44}$ erg s-1, most of this energy is emitted in the UV and
X-ray regime, and none of the models reach the peak optical magnitudes of our
objects.

Noting the early similarity of AT2022aedm to some TDE spectra, we encourage a
broader exploration of the parameter space for stellar-mass TDEs. Another
important test of TDE models (stellar or intermediate mass) will be
identifying globular clusters at the positions of LFCs. The James Webb Space
Telescope (JWST) can reach the peak of the globular cluster luminosity
function ($\approx-7.5$ mag; Harris & Racine 1979) in 10 ks with NIRCam for an
event within 200 Mpc. At the distance of AT2022aedm, it is possible to achieve
the same constraint in $\approx 100$ ks. We note however that the angular
extent of a typical globular cluster at this distance is comparable to the
NIRCam pixel scale, making identification challenging.

##  5 Conclusions

We have presented a detailed analysis of AT2022aedm, a very unusual, rapidly
evolving transient in a massive elliptical galaxy. It has a rise time $<10$
days, a luminous peak $M_{g}=-22$ mag, and a fast decline of 2 mag in the
subsequent 15 days. The optical colours evolve quickly to the red during the
decline phase, while the spectrum remains devoid of identifiable broad
features throughout. We do however detect narrow emission lines of H$\alpha$
and He II during the first few days, reminiscent of shock cooling in young
SNe, and narrow blue-shifted absorption lines at later times.

We identify two previous transients, both with uncertain classifications, that
share the key properties of bright peaks, fast declines, strong colour
evolution and spectra without high-equivalent width emission or absorption
lines. This suggests that AT2022aedm represents a well-observed example of a
previously unrecognised class of luminous, fast-cooling events that we term
LFCs. All three events occurred in passive galaxies, with offsets of $\sim
4-10$ kpc.

Their unique combination of properties poses challenges for any physical
scenario. The passive environments disfavour a massive star origin, while the
light curves and spectra are inconsistent with thermonuclear SNe interacting
with a dense medium. Mergers of compact object binaries are unable to
reproduce the peak luminosity. Tidal disruption of a star by an intermediate
mass BH, as suggested for Dougie (Vinkó et al., 2015), may struggle to produce
the fast colour evolution. Instead we find that these events may be broadly
consistent with an extreme shock breakout, though from an as-yet unknown
progenitor. Alternatively, models of TDEs from stellar-mass BHs show promise,
though current models emit too much of their luminosity in the UV and X-rays.
Refinements of stellar-mass TDE models, and searches for dense local
environments at the sites of LFCs, should help to confirm or rule out this
scenario.

We thank Anna Ho for sharing the data on AT2020bot. We also thank Joe Bright,
Paul Scott and David Titterington for help with the AMI-LA observations. MN,
AA and XS are supported by the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant
agreement No. 948381) and by UK Space Agency Grant No. ST/Y000692/1. PR is
supported by STFC Grant 2742655. SJS acknowledges funding from STFC Grant
ST/X006506/1 and ST/T000198/1. TP acknowledges the support by ANID through the
Beca Doctorado Nacional 202221222222. JV is supported by NKFIH-OTKA grant
K-142534 from the National Research, Development and Innovation Office,
Hungary. T-W.C thanks the Max Planck Institute for Astrophysics for hosting
her as a guest researcher. MP is supported by a research grant (19054) from
VILLUM FONDEN. PC acknowledges support via an Academy of Finland grant
(340613; P.I. R. Kotak). PW acknowledges support from the Science and
Technology Facilities Council (STFC) grant ST/R000506/1. MF is supported by a
Royal Society - Science Foundation Ireland University Research Fellowship. MK
is partially supported by the program Unidad de Excelencia María de Maeztu
CEX2020-001058-M. LG, CPG, MK and TEMB acknowledge support from Unidad de
Excelencia María de Maeztu CEX2020-001058-M, from Centro Superior de
Investigaciones Científicas (CSIC) under the PIE project 20215AT016, and from
the Spanish Ministerio de Ciencia e Innovación (MCIN) and the Agencia Estatal
de Investigación (AEI) 10.13039/501100011033 under the PID2020-115253GA-I00
HOSTFLOWS project. L.G. also acknowledges support from the European Social
Fund (ESF) “Investing in your future” under the 2019 Ramón y Cajal program
RYC2019-027683-I. CPG also acknowledges financial support from the Secretary
of Universities and Research (Government of Catalonia) and by the Horizon 2020
Research and Innovation Programme of the European Union under the Marie
Sklodowska-Curie and the Beatriu de Pinós 2021 BP 00168 programme. TEMB also
acknowledges financial support from the 2021 Juan de la Cierva program
FJC2021-047124-I. GPS acknowledges support from The Royal Society, the
Leverhulme Trust, and the Science and Technology Facilities Council (grant
numbers ST/N021702/1 and ST/S006141/1). FEB acknowledges support from ANID-
Chile BASAL CATA ACE210002 and FB210003, FONDECYT Regular 1200495, and
Millennium Science Initiative Program – ICN12_009.

ATLAS is primarily funded through NASA grants NN12AR55G, 80NSSC18K0284, and
80NSSC18K1575. The ATLAS science products are provided by the University of
Hawaii, QUB, STScI, SAAO and Millennium Institute of Astrophysics in Chile.
The Pan-STARRS telescopes are supported by NASA Grants NNX12AR65G and
NNX14AM74G. Based on observations collected at the European Organisation for
Astronomical Research in the Southern Hemisphere, Chile, as part of ePESSTO+
(the advanced Public ESO Spectroscopic Survey for Transient Objects Survey).
ePESSTO + observations were obtained under ESO programme ID 108.220C
(PI:Inserra). This work makes use of data from the Las Cumbres Observatory
global network of telescopes. The LCO group is supported by NSF grants
AST-1911151 and AST-1911225. The Liverpool Telescope is operated on the island
of La Palma by Liverpool John Moores University in the Spanish Observatorio
del Roque de los Muchachos of the Instituto de Astrofisica de Canarias with
financial support from the UK Science and Technology Facilities Council. We
thank the staff of the Mullard Radio Astronomy Observatory for their
assistance in the maintenance and operation of AMI, which is supported by the
Universities of Cambridge and Oxford. We also acknowledge support from the
European Research Council under grant ERC-2012-StG-307215 LODESTONE.

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# Acoustic emission from magnetic flux tubes in the solar network

G Vigeesh1 and S S Hasan2 1 Department of Astronomy, New Mexico State
University, Las Cruces, NM, U.S.A.  2 Indian Institute of Astrophysics,
Koramangala, Bangalore, India<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

We present the results of three-dimensional numerical simulations to
investigate the excitation of waves in the magnetic network of the Sun due to
footpoint motions of a magnetic flux tube. We consider motions that typically
mimic granular buffeting and vortex flows and implement them as driving
motions at the base of the flux tube. The driving motions generates various
MHD modes within the flux tube and acoustic waves in the ambient medium. The
response of the upper atmosphere to the underlying photospheric motion and the
role of the flux tube in channeling the waves is investigated. We compute the
acoustic energy flux in the various wave modes across different boundary
layers defined by the plasma and magnetic field parameters and examine the
observational implications for chromospheric and coronal heating.

##  1 Introduction

Observations of the solar surface in various filtergrams show a distribution
of bright points, which are generally associated with an underlying
concentrated magnetic field located in intergranular lanes [1]. Magnetic flux
accumulates here after being swept in by granular flow forming strong vertical
flux tubes at the junction of granules. The continually changing solar
photosphere perturbs these magnetic structures resulting in the generation of
magnetohydrodynamic (MHD) waves that propagate within the flux tube and also
into the ambient plasma. The waves are excited by impulses imparted by
granules. Eventually, the flux tubes are advected towards regions of stronger
downdrafts present at the intersections of two or more granules where
convectively driven vortex flows occur. The footpoints of the magnetic field
structures are dragged in the vortex resulting in, presumably, a different
source of wave excitation in the flux tube [2]. The conditions that prevail in
the lower atmosphere impacts the upper layers, primarily through magnetic
fields. Estimating the energy supplied by various photospheric disturbance
will help us to determine the fraction of the overall heating of the upper
atmosphere by wave sources. In order to examine energy transport to the outer
layers of the quiet solar atmosphere mediated by magnetic fields [3, 4, 5, 6],
we need to clearly understand how the magnetic flux tube reacts to different
types of perturbations. Despite being idealized representations of the actual
processes, studies have shown that various wave-driven energy transport
mechanisms are possible in a magnetized atmosphere [7, 8, 9, 10, 11, 12].
Further investigations could reveal how these processes contribute to the
overall energy budget and shed light on the significance of wave related
sources. The purpose of this paper is to study two such scenarios with the aim
of evaluating them in terms of their contribution to the heating of
chromosphere and corona.

In this paper, we investigate the excitation of waves in magnetic flux tubes
extending vertically through the solar atmosphere. This work is an extension
of our previous study [12] that dealt with 3D modeling of MHD wave propagation
in a magnetic flux tube embedded in a stratified atmosphere. We showed that
there are possibly more than one mechanism of wave production effective which
can lead to temporal variations of the emission that occur at chromospheric
heights above these elements. In this paper our focus is to investigate the
generations of acoustic waves excited by these perturbations and estimate the
acoustic energy flux at different levels in the flux-tube. We calculate and
compare the acoustic emission from various boundaries in the flux-tube and
also from different $\beta$ (the ratio of gas to magnetic pressure) surfaces
as a result of the excitation.

##  2 Equilibrium Model and Boundary Conditions

Details of the equilibrium model that we consider in this study are given in
[12]. Briefly, we consider an intense (kG field strength at the base) axially
symmetric magnetic flux tube in a stratified solar model atmosphere that is in
magnetostatic equilibrium. The temperature in the model increases with height
to include the chromospheric temperature rise. The footpoint of the flux tube
is located in the solar photosphere. The mathematical construction of the flux
tube is described in the Appendix of [12] and the equilibrium properties of
the flux tube are given in Table 1.

Table 1:  The equilibrium model parameters on the axis of the flux tube and ambient medium (values shown within brackets). Height | T | $\rho$ | P | cS | vA | B | $\beta$
---|---|---|---|---|---|---|---
| (K) | (kg m-3) | (N m-2) | (km s-1) | (km s-1) | (G) |
$z$=1 Mm | 7263 | 3.4 $\times$ 10-8 | 1.6 | 8.8 | 77.7 | 161 | 64
(7195) | (1.0 $\times$ 10-7) | (4.8) | (8.7) | (39.2) | (141) | (16)
$z$=0 Mm | 4768 | 1.3 $\times$ 10-4 | 4.2 $\times$ 103 | 7.1 | 10.9 | 1435 | 1.9
(4766) | (4.0 $\times$ 10-4) | (1.2 $\times$ 104) | (7.1) | (0.003) | (0.77) | (1.9 $\times$ 10-7)
|  |  |  |  |  |  |

The plasma-$\beta=1$ surface essentially outlines the flux tube boundary in
the lower part of the tube. In these layers, $\beta<1$ inside the flux tube as
shown in Fig 1.

Figure 1:  Two-dimensional representation of the flux-tube model. The solid
lines mark the magnetic field lines on a $x$-$z$ plane at $y=0$ Mm. The
$\beta=1$ () and $\beta=0.1$ () contours are also shown.

Wave excitation is carried out by implementing velocity drivers at the bottom
boundary. We use a horizontal driver to mimic the granular buffeting motion
and a torsional driver to mimic the effect of vortex-like motion. The driving
motions at the bottom boundary are specified using the following velocity
drivers.

$\displaystyle V_{x}(x,y,0,t)=\left\\{\begin{array}[]{l l}\displaystyle
V_{0}\sin(2\pi t/P)&\mbox{for}\quad 0\leq t\leq P/2\,,\\\\[4.30554pt]
\displaystyle 0&\mbox{for}\quad 0>t>P/2\,.\end{array}\right.{\rm(Horizontal)}$
(3) $\displaystyle V_{\phi}(x,y,0,t)=\left\\{\begin{array}[]{l
l}\displaystyle-V_{0}\tanh\left(\frac{2\pi r}{\delta
r}\right)\sin\left(\frac{2\pi t}{P}\right)&\quad 0\leq t\leq
P/2,\\\\[4.30554pt] \displaystyle
0&\quad\phantom{0>}t>P/2.\end{array}\right.{\rm(Torsional)}$ (6)

These driving motions generate slow (predominantly acoustic) and fast
(predominantly magnetic) waves in the model along with the intermediate Alfvén
wave which we do not consider in this study.

##  3 Numerical Simulation

The three-dimensional numerical simulations were carried out using the
Sheffield Advanced Code [13]. The code uses a modified version of the set of
MHD equations to deal with a strongly stratified magnetized medium. In this
study, we solve the full set of ideal magnetohydrodynamic equations in three
dimensions to study the propagation of waves in the computational domain. The
domain is a 1 Mm $\times$ 1 Mm $\times$ 1 Mm cube discretized on 100 $\times$
100 $\times$ 100 grid points. We use transmitting boundary conditions at the
top and side boundaries to allow the waves to propagate out of the simulation
box.

The simulation starts with a localized, time dependent perturbation at the
bottom boundary resulting in the excitation of various kinds of MHD modes with
different strength. The gas pressure perturbations drive slow magneto-acoustic
wave (SMAW) within the $\beta<$1 region and fast magneto-acoustic wave (FMAW)
in the ambient medium where $\beta>$1\. The magneto-acoustic wave propagation
can be seen in the velocity and temperature perturbation in the medium as
shown in Fig 2. We notice that the horizontal excitation generates strong SMAW
within the flux tube as can be seen in the temperature fluctuations. Since the
SMAW propagates along field lines, we also see strong velocity parallel to the
field lines. The torsional excitation on the other hand generates SMAW that
are weaker than those generated by the horizontal driver.

Figure 2:  Top: Temperature perturbations at t=20 s,60 s, and 100 s at
different heights as a result of a transversal uni-directional excitation.
Bottom: Temperature fluctuations as a result of torsional excitation. The
projected velocity vectors on the $x$-$y$ plane are shown at each height. The
thick blue curve depicts the $\beta$=1 region and the blue dashed curve shows
the $\beta$=0.1 region.

In the previous study [12], we estimated the energy transport by MHD waves for
the two driving cases. The energy fluxes were calculated on a representative
field line and we showed that there is a strong acoustic flux associated with
SMAW in the case of the horizontal excitation, which is two orders of
magnitude more than that for the case of torsional excitation. However, these
studies were restricted to a single field line and hence it was not possible
to assess the response of the whole flux tube to the different perturbations
that were considered. In this paper, we extend the previous study by looking
at the flux tube as a whole and estimating the total acoustic emission from
various physical surfaces relevant to the model. Due to the nature of the
drivers, the analysis on a single field line depends wholly on the location of
the footpoint of the chosen field line with respect to the driving motion.
Especially, in the case of a horizontal motion, where the strongest
perturbations is localized to a small region on either side of the $y=0$
plane. Also, since the velocity driver acts in region where $\beta<1$ (close
to the axis) as well as $\beta>1$ (outer regions), a field line located in
either of these region sees a different mode of magneto-acoustic wave.
Considering an ensemble of field lines will give a better idea about the
reaction of the flux tube as a whole to the excitation at the base.

##  4 Results

###  4.1 Longitudinal & Lateral emission

Apart from acting as a conduit for MHD waves to the overlying layers, the
perturbed flux tube also transfers energy to the ambient medium in the form of
acoustic waves. It is interesting to look at acoustic energy flux leaking out
of the flux tube boundary and how it depends on the nature of the excitation
that the flux tube undergoes. Since we consider here a thick flux tube, a
strict definition of the flux tube boundary is ambiguous. However, for the
purpose of this study, we choose the surfaces of equal magnetic potential or
the magnetic isosurface as representative levels in the flux tube for purposes
of calculating the fluxes. The cross-section of the magnetic isosurface at any
given height is a circle centered at the axis, due to the cylindrical symmetry
of the initial model. This allows us to define a circle at a radial distance
$r$ of the flux tube on any horizontal plane. We calculate the acoustic flux
on three magnetic isosurfaces crossing radial distance of $r=0.2~{}$Mm,
$0.3~{}$Mm and $0.4~{}$Mm from the axis of the flux tube at $z=1~{}$Mm. The
magnetic field lines that define this isosurface can be identified from the
equilibrium model and they remain the same throughout the simulation, allowing
us to calculate physical quantities on this surface as it evolves. The
velocity vector at any point on this surface can be decomposed into three
orthogonal components, viz. parallel (${v_{s}}$), normal (${v_{n}}$) and
azimuthal (${v_{\phi}}$) component. The calculation of these component for a
single field line is described in [12]. Using these velocity components for a
collection of field lines defining various isosurfaces, we calculate the
parallel and normal acoustic fluxes according to,

$F_{\rm s,n}=\Delta p{v_{\rm s,n}}$ (7)

where $\Delta p$ is the gas pressure perturbation from the equilibrium.
Acoustic fluxes are calculated on field lines corresponding to three magnetic
isosurfaces. In Figure 3, the left panel shows the time averaged parallel
acoustic fluxes ($F_{\rm s}$) on field lines associated with different
isosurfaces as a function of height. The horizontal excitation results in
relatively stronger acoustic emission compared to torsional excitation in the
lower part of the flux tube. But, the averaged fluxes tend to be similar as we
go higher up in the atmosphere, since in the horizontal excitation case, only
a small fraction of field lines on either side of the tube partake in
transporting the longitudinal fluxes. The right panel of Fig. 3 shows the time
averaged acoustic flux directed normal to the flux-tube boundaries, which is
significantly lower than the fluxes directed along the field lines in the
tube.

Figure 3: Left: Time-averaged longitudinal acoustic flux on the three
isosurfaces for the horizontal (Solid lines) and torsional(dashed lines)
excitation cases. Right Normal acoustic flux on the three isosurfaces.

###  4.2 $\beta$-surface emission

The surface of equipartition ($\beta$=1 layer) between thermal and magnetic
energy density influences the MHD modes by acting as region of wave
transmission and conversion. As a preliminary step towards understanding this
region more closely, we look at the acoustic energy flux crossing normal to
the surfaces of constant plasma $\beta$ surface ($\beta$ isosurfaces). In this
study, we mainly focus on the $\beta=1$ isosurface, since this is where the
different MHD modes undergo strong coupling which eventually leads to the
generation of Alfvén waves in the upper chromosphere. The whole process
proceeds in two stages. Initially, the magneto-acoustic modes encounter the
equipartition zone as they propagate to the upper layers of the atmosphere.
The energy in the modes get redistributed partially by transmission and
partially by conversion to other modes. The transmission and conversion
depends on the properties of the wave and the background magnetic field across
this zone of influence [14]. In the second stage, the mode converted magnetic
wave (FMAW) propagates up to a certain height after which it gets partially
reflected down and partially gets converted to Alfvén waves due to steep
gradients in the Alfvén speed [15]. A better understanding of the energy
distribution during the initial stage where mode coupling occurs across the
$\beta=1$ surface is nessasary to further evaluate the energy available for
Alfvén wave. To this end, we look at the acoustic flux directed normal to the
surface of $\beta=1$ and compare it with that from a $\beta=0.1$ isosorface
which lies well above in the atmosphere. It should be noted that in the lower
part of the flux tube, the $\beta=1$ surface normal points towards the axis of
the flux-tube.

Figure 4 shows the spatially averaged acoustic flux directed normal to the
surfaces of constant $\beta$ for $\beta$=1 and 0.1 as a function of time. We
clearly see that the acoustic emission from the $\beta$=1 surface is stronger
compared to the emission on the $\beta$=0.1 surface for both horizontal and
torsional excitations. The $\beta$=1 surface responds more efficiently to a
horizontal uni-directional motion at the foot-point by emitting more acoustic
flux normal to the surface. Our analysis is limited by the fact that we
consider a strong flux tube where the $\beta=1$ surface dips below the bottom
boundary near the axis of the flux tube. To have a better understanding about
this layer, we need to look at flux tubes models with $\beta=1$ surfaces at
different levels in the atmosphere.

Figure 4: Time evolution of the normal acoustic flux on $\beta$=1 (solid line)
and $\beta$=0.1 (dotted) surface for horizontal (red line) and torsional
(blue) excitation of a magnetic flux tube

##  5 Summary & Conclusions

Three dimensional numerical simulation of wave propagation in a magnetic flux
tube embedded in a solar atmosphere were carried out. We investigated two
types of excitation mechanism, viz. transversal and torsional, that are
characteristic processes by which waves can be generated in the real solar
atmosphere. We calculated the acoustic energy components at various levels in
the flux tube as well as on the surfaces defined by constant plasma $\beta$ of
the flux tube due to the driving footpoint motions. We observe that the
acoustic power is predominantly directed vertically upward along the flux tube
in both cases. The lateral acoustic emission from the boundary of the flux
tube in both cases of horizontal and torsional excitations is much lower. The
magnetic flux tube acts as an efficient conduit for acoustic waves, with
negligible acoustic leakage from the tube boundary. Most of the acoustic
energy produced due to photospheric disturbances are efficiently transported
and are made available to higher layers regardless of the source of the
disturbance. As far as the comparison between the two excitation scenarios in
terms of acoustic energy transport to upper layers is concerned, we have not
been able to conclusively point out which of the two mechanisms is more
efficient. This is partly due to the axisymmetric equilibrium model that we
have used and the idealistic driving mechanisms that we consider in the study.
Nevertheless, our analysis supports that the magnetic field mediates the
coupling between various photospheric disturbances and the upper layers.
However meagre their contribution to the overall energy output is, these
scenarios must be considered when evaluating the available energy sources for
the heating of chromosphere and corona. The surface of equipartition between
magnetic and thermal energy density ($\beta=1$) is a strong source of acoustic
emission in both excitation scenarios and would be the focus of future
investigations.

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