# The Atacama Cosmology Telescope: Systematic Transient Search of 3-Day Maps

Yaqiong Li Department of Physics, Cornell University, Ithaca, NY 14853, USA
Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca,
NY 14853, USA  Emily Biermann Department of Physics and Astronomy, University
of Pittsburgh, Pittsburgh, PA, 15213, USA  Sigurd Naess Institute of
theoretical astrophycis, University of Oslo, Norway  Simone Aiola Center for
Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY
10010 USA  Rui An  Department of Physics and Astronomy, University of Southern
California, Los Angeles, CA 90089, USA  Nicholas Battaglia Department of
Astronomy, Cornell University, Ithaca, NY 14853, USA  Tanay Bhandarkar
Department of Physics and Astronomy, University of Pennsylvania, 209 S. 33rd
Street, Philadelphia, PA 19104, USA  Erminia Calabrese School of Physics and
Astronomy, Cardiff University, The Parade, Cardiff, Wales CF24 3AA, UK  Steve
K. Choi Department of Physics, Cornell University, Ithaca, NY 14853, USA
Department of Astronomy, Cornell University, Ithaca, NY 14853, USA  Kevin T.
Crowley Center for Astrophysics and Space Studies; University of California,
San Diego, SERF Building Room 333; 9500 Gilman Dr, La Jolla, CA 92093-4204,
USA  Mark Devlin Department of Physics and Astronomy, University of
Pennsylvania, 209 S. 33rd Street, Philadelphia, PA 19104, USA  Cody J. Duell
Department of Physics, Cornell University, Ithaca, NY 14853, USA  Shannon M.
Duff National Institute of Standards and Technology, Quantum Sensors Group,
325 Broadway, Boulder, CO 80305  Jo Dunkley Joseph Henry Laboratories of
Physics, Jadwin Hall, Princeton University, Princeton, NJ 08544, USA
Department of Astrophysical Sciences, Peyton Hall, Princeton University,
Princeton, NJ 08544 USA  Rolando Dünner Instituto de Astrofísica and Centro de
Astro-Ingeniería, Facultad de Física, Pontificia Universidad Católica de
Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago, Chile  Patricio A.
Gallardo Kavli Institute for Cosmological Physics, University of Chicago, 5640
S Ellis Ave., Chicago, IL 60637, USA  Yilun Guan David A. Dunlap Department of
Astronomy &Astrophysics, University of Toronto, 50 St. George St., Toronto ON
M5S 3H4, Canada  Carlos Hervías-Caimapo Instituto de Astrofísica and Centro de
Astro-Ingeniería, Facultad de Física, Pontificia Universidad Católica de
Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago, Chile  Adam D.
Hincks David A. Dunlap Department of Astronomy &Astrophysics, University of
Toronto, 50 St. George St., Toronto ON M5S 3H4, Canada  Specola Vaticana
(Vatican Observatory), V-00120 Vatican City State  Johannes Hubmayr National
Institute of Standards and Technology, Quantum Sensors Group, 325 Broadway,
Boulder, CO 80305  Kevin M. Huffenberger Department of Physics, Florida State
University, Tallahassee, FL 32306, USA  John P. Hughes Department of Physics
and Astronomy, Rutgers University, 136 Frelinghuysen Road, Piscataway, NJ
08854, USA  Arthur Kosowsky Department of Physics and Astronomy, University of
Pittsburgh, Pittsburgh, PA, 15213, USA  Thibaut Louis Université Paris-Saclay,
CNRS/IN2P3, IJCLab, 91405 Orsay, France  Maya Mallaby-Kay Department of
Astronomy and Astrophysics, University of Chicago, 5640 S Ellis Ave, Chicago,
IL 60637, USA  Jeff McMahon Department of Astronomy and Astrophysics,
University of Chicago, 5640 S Ellis Ave, Chicago, IL 60637, USA  Department of
Physics, University of Chicago, 5720 S Ellis Ave, Chicago, IL 60637, USA
Kavli Institute for Cosmological Physics, University of Chicago, 5640 S Ellis
Ave., Chicago, IL 60637, USA  Enrico Fermi Institute, University of Chicago,
5640 S Ellis Ave, Chicago, IL 60637, USA  Fermi National Accelerator
Laboratory, Batavia, IL 60637, USA  Federico Nati University of Milano-Bicocca
Michael D. Niemack Department of Physics, Cornell University, Ithaca, NY
14853, USA  Department of Astronomy, Cornell University, Ithaca, NY 14853, USA
John Orlowski-Scherer Department of Physics, McGill University, 3600
University Street, Montreal, QC, H3A 2T8, Canada  Lyman Page Joseph Henry
Laboratories of Physics, Jadwin Hall, Princeton University, Princeton, NJ
08544, USA  Maria Salatino Physics Department Stanford University 94305
Stanford CA, US  Kavli Institute for Astroparticle Physics and Cosmology
(KIPAC) 94305 Stanford CA, US  Cristóbal Sifón Instituto de Física, Pontificia
Universidad Católica de Valparaíso, Casilla 4059, Valparaíso, Chile  Suzanne
T. Staggs Joseph Henry Laboratories of Physics, Jadwin Hall, Princeton
University, Princeton, NJ 08544, USA  Cristian Vargas Instituto de Astrofísica
and Centro de Astro-Ingeniería, Facultad de Física, Pontificia Universidad
Católica de Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago, Chile
Eve M. Vavagiakis Department of Physics, Cornell University, Ithaca, NY 14853,
USA  Yuhan Wang Joseph Henry Laboratories of Physics, Jadwin Hall, Princeton
University, Princeton, NJ 08544, USA  Edward J. Wollack NASA Goddard Space
Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA

###### Abstract

We conduct a systematic search for transients in three years of data
(2017–2019) from the Atacama Cosmology Telescope (ACT). ACT covers 40 percent
of the sky at three bands spanning from 77 GHz to 277 GHz. Analysis of 3-day
mean-subtracted sky maps, which were match-filtered for point sources, yielded
29 transients detections. Eight of these transients are due to known
asteroids, and three others were previously published. Four of these events
occur in areas of with poor noise models and thus we cannot be confident they
are real transients. We are left with 14 new transient events occurring at 11
unique locations. All of these events are associated with either rotationally
variable stars or cool stars. Ten events have flat or falling spectra
indicating radiation from synchrotron emission. One event has a rising
spectrum indicating a different engine for the flare.

Transient Sources(1851) — Stellar flares(1603) — Cosmic microwave background
radiation(322) — Asteroids(72)

##  1 Introduction

Transient astronomical sources comprise some of the most dramatic and extreme
astrophysical events, such as supernovae, binary star mergers, active galactic
nuclei (AGN), gamma ray bursts (GRB), and tidal distruption events (TDE).
Continuous monitoring of large sky regions to detect transients has been a
major motivation behind the upcoming Rubin Observatory (Ivezić et al., 2019)
and several other efforts at optical wavelengths, such as the Zwicky Transient
Facility (Masci et al., 2018) and ASAS-SN (Christy et al., 2022). Transient
astronomy has also received a large increase in attention with the first
gravitational wave sources detected by LIGO (Abbott et al., 2016), and
detection of sources in both gravitational waves and multiple electromagnetic
wave bands. Wide-field millimeter wavelength surveys have focused on mapping
the unchanging microwave background for cosmological applications, but
recently some have reached the combination of high angular resolution, wide
survey area and sensitivity required to start probing the time-variable
millimeter sky.

In recent years there have been a growing number of millimeter transient
detections. In 2016, the South Pole Telescope (SPT) reported one candidate
event observed during the 2012–2013 season at a $2.6\sigma$ significance with
a peak flux of $16.5\pm 2.4$ mJy in the 150 GHz band (Whitehorn et al., 2016).
This event was broadly consistent with a GRB orphan afterglow. Kuno et al.
(2004) reported a detection at 90 GHz of the afterglow of GRB 030329
($z=0.17$) with a flux around 65mJ. More than a decade later, Laskar et al.
(2019) detected a polarized reverse shock from GRB 190114C at a frequency of
97.5 GHz. Other types types of events are also seen in the millimeter. For
instance, Yuan et al. (2016) reported millimeter emission from the TDE IGR
J12580+0134. Ho et al. (2019) studied the unprecedented transient AT2018cow in
radio and submillimeter bands, finding its emission to be inconsistent with
synchrotron emission or Compton scattering of UVIOR photons, indicating a new
class of millimeter transients.

There are also many examples of stellar flares seen in the millimeter. ACT
reported three bright transient sources coincident with stars (Naess et al.,
2021a) and SPT reported 15 additional transient events (Guns et al., 2021).
Most of the sources reported from SPT were consistent with stellar activity,
except for two of their events which may be extragalactic in origin.
Millimeter stellar flares have also been reported in other works such as Bower
et al. (2003), Brown & Brown (2006), Massi et al. (2006), Mairs et al. (2019)
and are most likely due to stellar magnetic field reconnection.

In this work, we complete a systematic search for transient events with three
years’ of ACT data using 3-day stacked maps. In Section 2, we introduce the
ACT survey and the maps used for this analysis. In Section 3, we describe how
the transient events are detected and how data cuts applied to the sample. In
Section 4, we present our findings and possible counterparts for each
transient. After all cuts are applied we are left with 29 events. Three of
these events are redetections from Naess et al. (2021a). Eight appear to be
from asteroids that were not masked in the 3-day maps and four occur in areas
with poor noise models and cannot be confirmed as real transients. This leaves
14 new transient detections at 11 unique positions on the sky. Most of the
events have clear stellar associations. In Section 5, we summarize the nature
of these counterparts.

##  2 Data

The Atacama Cosmology Telescope (ACT) was a 6 m off-axis Gregorian telescope
located in the Atacama Desert in Chile at an altitude of 5200 m (Fowler et
al., 2007). AdvACT was the third generation ACT instrument. It housed three
optics tubes with a total field of view (FOV) spanning
${\sim}1.5^{\circ}$(Thornton et al., 2016), each containing a set of lenses,
low-pass filters, and a single AdvACT detector array (Ho et al., 2017; Choi et
al., 2018). The ACT dataset considered here covers the three bandpasses f090
(77–112 GHz), f150 (124–172 GHz) and f220 (182–277 GHz) using the three
dichroic polarization-sensitive transition edge sensor (TES) bolometer arrays
PA4 (f150+f220), PA5 (f090+f150) and PA6 (f090+f150). During observations, the
telescope scanned the sky in azimuth at a fixed elevation at a scan speed of
${\sim}1.5$ deg per second. When the sky was rising, it took ${\sim}$6 minutes
for a point on the sky to be gradually swept across by the detectors in PA4
and PA5. Then, after $\sim$3 minutes this process would repeat for the
detectors in PA6. This order was reversed when the sky was setting. The
bolometer signals were stored in time-ordered data (TOD) files, each
containing a roughly 10 minute time series for all detectors in a single
array. Detailed data selection and calibration process is described in Aiola
et al. (2020).

We search for transient events using 3-day maps originally created to search
for Planet 9 (Naess et al., 2021b). The search covered 18000 deg2 of the sky
using ACT data taken from 2017 to 2019. Although these maps were not designed
for a systematic transient search, their short timescale is well suited for
this purpose. The maps are made by dividing the daytime and nighttime TODs
taken by each of the three detector array at each of the two frequency bands
into approximately 3-day long data chunks. Therefore, there are twelve maps
(day and night time data within three arrays with two bands) for each 3-day
period. The maps are made by calculating the maximum likelihood estimate for
the CMB temperature from TOD. The map is then matched filtered to look for
point sources, which gives an estimation of flux density and $S/N$ at each
pixel. Detailed methods are described in Naess et al. (2021b). For each 3-day
map, a mean sky subtraction at the corresponding frequency band is implemented
so only the time-varying signal remains. Regions around the planets and bright
asteroids are masked to avoid contamination from planet sidelobes and false
transient detection from known moving astronomical objects. Since the 3-day
maps were made as part of a search for Planet 9, some tradeoffs are made that
are suitable when performing large stacks of maps, but at times not ideal when
analyzing maps in isolation. In particular the maps’ matched filter numerator
was computed directly in time domain while the denominator was computed in
map-space. This approach is fast, but only accurate if the time-domain noise
model and map-space noise model are consistent. In practice this is an
approximation which introduces a bias in areas where the map hit-density
changes rapidly from pixel to pixel. This does not happen when performing
large stacks like in the Planet 9 search, but it is quite common when looking
for objects in the individual 3-day maps, resulting in many spurious
detections (see Section 3.1). Furthermore, the matched filtered 3-day maps are
stored at 1 arcminute resolution, which was an acceptable performance tradeoff
for the Planet 9 search (where this was a minor contribution to the overall
smoothing budget), but is suboptimal for blind transient detection. The ways
in which the position uncertainty introduced by the selected resolution are
handled is detailed in Section 4.1. We plan to resolve these deficiencies in a
future paper using maps tailored for transient detection.

##  3 Methodology

###  3.1 Initial Detection and Spurious Candidate Cuts

Figure 1: Process of the initial detection, with each plot showing a 0.3 deg
by 0.3 deg map. The first step is to make a mask (middle) on $S/N$ map (left)
selecting pixels that have $S/N>5$. The mask is then applied to the flux map
(right), and the candidate position, shown as the red cross mark, is evaluated
as the center of mass weighted by the flux values within the selected pixels.

Any pixel or group of pixels with $S/N>5$ in the 3-day sky-subtracted maps is
considered a transient candidate. For each set of 3-day S/N and flux density
maps, we first mask pixels with $S/N<5$, then apply this mask to the
corresponding flux density map (Figure 1). The positions of candidates are
evaluated to be the flux-weighted centroids. This initial detection finds
332,333 candidate events. If a detection is a real transient event and not
simply a noise fluctuation, we will likely detect it in more than one array.
Thus, we cross match each candidate between detector arrays with a matching
distance of 1.5 arcminutes, which is 1.5 times the resolution of the 3-day
maps. We only keep candidates that appear in at least two frequency and array
combinations. This cuts $76\%$ of the initial detections.

Even with this cut applied, there are still many spurious detections grouped
in clusters and along map edges. We perform three additional geometric cuts,
requiring a candidate to be cut in all detected frequency and array
combinations to exclude it from the analysis. The results of these cuts are
summarized in Table 1.111Note that a candidate might be cut by more than one
criteria We apply these cuts in tandem to the frequency and array requirement,
thus the following statistics include all candidates.

Figure 2: Left: A histogram of the distance to each detection’s nearest
neighbor with a binsize of 5 arcminutes. The peak close to zero indicates
there are clusters of spurious detections in many of the maps so we cut any
candidate with a nearest neighbor of 20 arcminutes or less. Center: A
histogram of each candidate’s distance from the nearest zero variance contour,
defined to be a variance of less than $1.5\times 10^{-5}K$, with a bin size of
3 pixels. There is a large peak of candidates near zero variance contours
which drops after 3 pixels. We mask out to 5 pixels, cutting $74\%$ of all
sources. Right: A histogram of each candidate’s distance from the map edge in
pixel units with a bin size of 3 pixels. As expected, there is an excess
number of candidates near the edge of the map as the map edges are noisy and
so appear variable when sampled every three days. At a mask size of 5 pixels,
we cut off the peak of candidates near the edge.

The first geometric cut is motivated by the observation that many of the 3-day
exhibit a stripy pattern of spurious detections along the scanning direction.
These false detections are caused by a systematic underestimation of the
variance when the coverage is uneven due to low hit counts. The stripy regions
are reliably identified by searching for detections with nearby neighbours
within the same 3-day day map. This approach is motivated by the lefthand plot
in Figure 2 which shows a histogram of each candidate’s nearest neighbour:
there is a clear excess of detections with neighbours within $\sim$1 deg,
which would not be expected for real detections since extragalactic transients
should be spatially uncorrelated. Based on this histogram, we cut any
candidates with a neighbor within 20 arcminutes as there is an overdensity of
detections with a closest neighbor within this range. This cut removes $60\%$
of all candidates.

Second, we see a similar stripy pattern along zero variance contours. During
the map making process, some approximations result in streaks of spurious
detections with vanishing variance and therefore a high $S/N$. In the center
plot in Figure 2 we plot a histogram of the candidates’ distances from a zero
variance region and cut any candidate within 5 pixels of these features (about
$74\%$ of all candidates).

Lastly, we cut any candidate near a map edge. Map edges are especially noisy
and appear to be variable when sampled every three days. In the right-hand
plot in Figure 2 we plot a histogram of the pixel distance of each candidate
from the edges of the map. We see a spike in candidates within five pixels
from the map edge and so we mask this region. This cuts applies to $17\%$ of
all candidates.

After these cuts are applied an internal cross match is performed to find
repeating events. Candidates with positions within one arcminute of each other
are considered the same object. This leaves us with 667 independent
candidates.

| Remaining Candidates | Fraction Cut | Fraction of map masked
---|---|---|---
frequency and array cut | 78,367 | 0.76 | –
neighbor cut | 133,119 | 0.60 | 0.005
ZVC cut | 88,936 | 0.74 | 0.030
edge cut | 278,044 | 0.17 | 0.010
all cuts | 5,020 | 0.94 | 0.045
Table 1: This table summarizes the three geometric data cuts applied to each
3-day map after the requirement that a candidate appear in at least two
frequency or array combinations is applied. The first column quotes the number
of candidates remaining after that cut, the second gives the fraction of all
candidates that do not pass these cuts, and the last column gives the fraction
of the total number of pixels masked by each cut. Note that these cuts are
done in tandem and are independent from each other. The neighbor cut removes
candidates with neighbors in the same map within 20 arcminutes, the zero
variance contour (ZVC) cut masks candidates within five pixels of zero
variance contours, and the edge cut masks candidates within five pixels of the
edge.

###  3.2 Candidate Verification

For each of the 667 candidates, we calculate the mean flux by applying a
matched filter to the mean sky map using data from 2017 to 2021, and mask
candidates with mean flux $>$ 50 mJy or $<-50$ mJy. The high-mean-flux
candidates are likely variable point sources such as AGN or dusty star forming
galaxies (DSFG). These high-mean-flux candidates are already detected in our
standard point source catalogs, and there are dedicated studies in preparation
on the light curves of these candidates. In this paper we will concentrate on
candidates that are not detectable in the mean sky maps. The candidates with
negative mean fluxes are located close to bright point sources. The flux
density around this region is negative due to the effect of the matched filter
that acts as high pass filter, and will increase when the flux of the
corresponding point source decreases, resulting in a false detection. There
are 82 candidates left after applying the cut on mean flux. We then make light
curves to confirm these detections, requiring a $S/N>$ 3.5 in at least two
arrays or in one array at both frequency bands. These light curves are made
from forced photometry, which gives a per-detector flux and flux error, and
then evaluating a weighted mean flux as the array-wise result at each
frequency band for each scan. This analysis further cuts 41 candidates.

For the rest of the candidates, we made $2^{\circ}$ by $2^{\circ}$ intensity
maps (shown in Figure 7) from TODs covering a three day period (or shorter if
the scanning cadence is especially high) centered on each event. These maps,
referred as “thumbnail maps,” have 0.25-arcminute resolution and are centered
at the candidate positions. We visually inspected these maps both before and
after matched filtering222This is pure map-space matched filtering, which
should be more accurate than the mixed TOD-level/map-space matched filtering
used in the main 3-day maps used for the initial search, but still depend on
an accurate noise model. and remove candidates with with an extended or
irregular shape, since we expect all our transients to be point sources. This
issue may be caused by poor data quality causing arcminute scale fluctuations.
For a few of the remaining candidates, we observed that the raw and filtered
maps appear discrepant with each other, e.g. a supposedly strong detection
having no visible counterpart in the maps before filtering; or there was an
overdensity of $>3\sigma$ peaks near the event in the filtered maps, which
should not happen if the noise model is correct. We do not discard these
events, but classify them as low quality candidates. All in all, we are left
with 29 transient detections. Eight of these events are due to asteroids (see
section 4.1), three were previously detected, and four are low quality
candidates.

| pa4f220 | pa4f150 | pa5f150 | pa6f150 | pa5f090 | pa6f090 |  |  | pa4f220 | pa4f150 | pa5f150 | pa6f150 | pa5f090 | pa6f090
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
1 |  |  |  |  |  |  |  | 8 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
3 |  |  |  |  |  |  |  | 7 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
4a |  |  |  |  |  |  |  | 11 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
4b |  |  |  |  |  |  |  | 12a |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
5a |  |  |  |  |  |  |  | 12b |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
5b |  |  |  |  |  |  |  | 14 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
6 |  |  |  |  |  |  |  | 15 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
2 |  |  |  |  |  |  |  | 9 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
10 |  |  |  |  |  |  |  | 13 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
Figure 3: 10$\arcmin$x10$\arcmin$ 3-day thumbnail maps for each transient. The
upper row is the intensity map with $\pm$5000 $\mu$K color range. The bottom
row is the $S/N$ map after applying a matched filter, with $\pm$5 color range.
Due to the conjugate gradient iteration used to solve the maximum-likelihood
map-making equation only being run for 10 steps, these maps are effectively
mildly highpass filtered. The affected scales have negligible weight in the
matched filter. Events 2, 9, 10, 13 at the bottom of the table are the four
events that are difficult to determine if they are real transients.

##  4 Results

###  4.1 Characterization

Two groups of our transient events appeared to be close in time and space to
each other (see Table 2). Using the IAU Minor Planet Center NEOChecker
webtool,333See https://minorplanetcenter.net/cgi-bin/checkneo.cgi we found
three events to be coincident with asteroid 10 Hygiea and five events to be
coincident with asteroid 511 Davida. In Figure 4, we plot the paths of these
asteroids with our events overlayed. All eight events appear consistent in
space and time with the asteroid observations which were not masked in our
maps. For future blind transient searches we must be prepared for the
possibility of detections from moving objects such as asteroids. This will
become especially important for real-time follow-up observations. A paper
dedicated to studying asteroids in ACT data is in preparation.444Orlowski-
Scherer et al., in prep.

Name | RA $\arcdeg$ | Dec $\arcdeg$ | Pos. | Peak Flux (mJy) | Mean Flux (mJy) | Peak | $\alpha$
---|---|---|---|---|---|---|---
Acc.$\arcsec$ | f220 | f150 | f090 | f220 | f150 | f090 | Time (UTC)
Hygiea(a) | 285.1166 | $-$23.9962 | 14 | 334 | 199 | 104 | 1.4 | 1.3 | 0.8 | 2017-05-21 | 1.4
$\pm$71 |  $\pm$21 |  $\pm$20 |  $\pm$4.0 |  $\pm$1.5 |  $\pm$2.6 | 04:27 |  $\pm$0.3
Hygiea(b) | 274.0251 | $-$23.4008 | 5 | 369 | 218 | 92 | 19.1 | 3.0 | 0.5 | 2017-07-24 | 1.5
$\pm$42 |  $\pm$18 |  $\pm$20 |  $\pm$6.7 |  $\pm$2.6 |  $\pm$4.6 | 05:50 |  $\pm$0.2
Hygiea(c) | 273.7942 | $-$22.6426 | 10 | 351 | 144 | 65 | 18.5 | 8.5 |  $-$0.2 | 2017-09-09 | 2.1
$\pm$52 |  $\pm$20 |  $\pm$19 |  $\pm$7.3 |  $\pm$2.8 |  $\pm$5.0 | 02:54 |  $\pm$0.4
Davida(a) | 117.7733 | 16.5879 | 8 | 206 | 114 | 53 | 4.0 | 1.9 |  $-$1.9 | 2019-10-24 | 1.6
$\pm$34 |  $\pm$15 |  $\pm$14 |  $\pm$2.1 |  $\pm$0.8 |  $\pm$1.4 | 09:01 |  $\pm$0.3
Davida(b) | 122.6793 | 18.2278 | 5 | 277 | 155 | 57 |  $-$0.8 | 0.2 | 4.0 | 2019-11-30 | 1.8
$\pm$32 |  $\pm$13 |  $\pm$12 |  $\pm$ 2.1 |  $\pm$0.8 |  $\pm$1.4 | 07:12 |  $\pm$0.2
Davida(c) | 122.6946 | 18.3167 | 8 | 192 | 118 | 57 |  $-$2.2 | 0.3 | 3.1 | 2019-12-01 | 1.6
$\pm$39 |  $\pm$16 |  $\pm$13 |  $\pm$2.0 |  $\pm$0.8 |  $\pm$1.3 | 08:57 |  $\pm$0.4
Davida(d) | 122.4329 | 19.3231 | 2 | 343 | 162 | 86 | 5.1 | 1.1 | 3.3 | 2019-12-11 | 1.7
$\pm$27 |  $\pm$14 |  $\pm$10 |  $\pm$2.0 |  $\pm$0.7 |  $\pm$1.3 | 06:45 |  $\pm$0.2
Davida(e) | 122.0468 | 19.9151 | 6 | 313 | 174 | 62 |  $-$5.1 | 0.8 | 1.8 | 2019-12-16 | 1.8
$\pm$44 |  $\pm$16 |  $\pm$15 |  $\pm$1.9 |  $\pm$0.8 |  $\pm$1.4 | 05:10 |  $\pm$0.3
Table 2: Eight events associated with asteroids.

Figure 4: Left: The three transient events consistent with Hygiea
observations. Right: The five transient events consistent with Davida
observations. All of the plotted positions and observation times are
consistent with the asteroids’ paths. The position errors are on the order of
0.1 arcminutes.

The properties of the remaining 21 transient events are shown in Table 3.
Three single detections labeled as “N” were previously published in Naess et
al. (2021a). Among the newly detected events, 12 are single events and three
repeat twice, with time intervals ranging from one month to one year. The low
(1 arcminute) resolution of the 3-day maps potentially increases the
candidates’ position error, so we re-estimate the candidate positions using a
method similar to the one used in the initial detection on the higher-
resolution thumbnail maps described in Section 3.2. We first subtract the
corresponding f220/f150/f090 mean sky map and apply a matched filter. The
final position for each candidate is the inverse-variance-weighted average
among individual positions, using the ratio between half maximum of the beam
and the appropriate $S/N$ as weights. The position accuracy is evaluated by
the square root of the variance of weighted mean. As mentioned in Section 3.2,
four out of the 12 single events are difficult to determine if they are real
transients due to unusual noise patterns on the intensity maps. We present
these events at the bottom of the table without assigning a formal name.

Ind | Name | RA ∘ | Dec ∘ | Pos. | Peak Flux (mJy) | Mean Flux (mJy) | Time | $\alpha$
---|---|---|---|---|---|---|---|---
(ACT-T) |  Acc.$\arcsec$ | f220 | f150 | f090 | f220 | f150 | f090 | Peak (UTC) | Rise | Fall
1 | J060702 | 91.7603 | 17.6993 | 22 | 36 | 78 | 64 | $-1.2$ | $-2.3$ | $-4.6$ | 2017-09-05 | $<43\,$min | $<25\,$h | 0.0
| +174157 |  $\pm$35 |  $\pm$13 |  $\pm$14 |  $\pm$2.3 |  $\pm$0.9 |  $\pm$1.6 | 05:35 |  $\pm$0.5
3 | J224500 | 341.2510 | $-$33.2589 | 14 | 192 | 253 | 255 | 5.5 | 4.4 | 8.8 | 2017-10-08 | $<1\,$d | $>23\,$min |  $-$0.6
| -331532 |  $\pm$41 |  $\pm$20 |  $\pm$12 |  $\pm$2.3 |  $\pm$0.8 |  $\pm$1.4 | 18:17 |  $\pm$0.2
4a |  | 343.2597 | 16.8408 | 8 | 1036 | 1154 | 801 |  |  |  | 2018-09-10 |  | $<2\,$d | 0.5
J225302 |  $\pm$122 |  $\pm$52 |  $\pm$34 | 4.7 | 7.8 | 18.7 | 21:55 |  $\pm$0.1
4b | +165027 |  $-$15 | 141 | 148 |  $\pm$3.2 |  $\pm$1.1 |  $\pm$2.0 | 2019-06-06 | $<2\,$d | $<3\,$d |  $-$0.6
|  |  $\pm$58 |  $\pm$20 |  $\pm$18 |  |  |  | 04:18 |  $\pm$0.4
5a |  | 292.1328 | $-$35.1327 | 3 | 2496 | 2383 | 1525 |  |  |  | 2018-10-04 | $<1\,$d | $<20\,$d | 0.9
J192831 |  $\pm$177 |  $\pm$40 |  $\pm$27 | 4.7 | 11.7 | 37.2 | 02:22 |  $\pm$0.05
5b | -350757 | 545 | 645 | 490 |  $\pm$3.7 |  $\pm$1.4 |  $\pm$2.7 | 2019-08-10 | $<3\,$d | $>1\,$d | 0.4
|  |  $\pm$69 |  $\pm$23 |  $\pm$20 |  |  |  | 23:10 |  $\pm$0.1
6 | J190222 | 285.5938 | $-$5.6028 | 10 | 926 | 717 | 350 | 12.7 | 5.3 | 7.7 | 2018-10-20 | $<1\,$d | $<1\,$d | 1.2
| -53610 |  $\pm$87 |  $\pm$34 |  $\pm$29 |  $\pm$5.6 |  $\pm$2.1 |  $\pm$3.9 | 20:32 |  $\pm$0.1
7 | J085813 | 134.5579 | 19.7630 | 4 | 103 | 197 | 269 | 3.0 | 0.1 | 2.4 | 2018-11-15 | $<5\,$d | $<22\,$h |  $-$0.8
| +194546 |  $\pm$31 |  $\pm$9 |  $\pm$9 |  $\pm$1.9 |  $\pm$0.8 |  $\pm$1.5 | 05:18 |  $\pm$0.1
8 | J142555 | 216.4831 | 14.2020 | 8 | 446 | 563 | 562 |  $-$0.5 | 1.7 | 5.6 | 2018-11-21 | $<20\,$h | $>23\,$h |  $-$0.1
| +141207 |  $\pm$61 |  $\pm$17 |  $\pm$14 |  $\pm$1.9 |  $\pm$0.6 |  $\pm$1.1 | 08:07 |  $\pm$0.1
11 | J060757 | 91.9890 | $-$54.4408 | 20 | 108 | 113 | 113 | -3.6 | 0.8 | 2.6 | 2019-08-09 | $<24\,$h | $<18\,$h | 0.0
| -542626 |  $\pm$65 |  $\pm$21 |  $\pm$15 |  $\pm$2.6 |  $\pm$1.0 |  $\pm$2.1 | 12:18 |  $\pm$1.6
12a |  | 54.1961 | 0.5865 | 5 | 108 | 302 | 448 |  |  |  | 2019-08-10 | $<30\,$h | $>23\,$h |  $-$1.1
J033647 |  $\pm$66 |  $\pm$26 |  $\pm$25 | 11.7 | 11.9 | 30.1 | 13:35 |  $\pm$0.2
12b | +03511 | 356 | 487 | 580 |  $\pm$4.0 |  $\pm$1.5 |  $\pm$2.8 | 2019-09-28 | $>2\,$d | $<2\,$d |  $-$0.5
|  |  $\pm$67 |  $\pm$23 |  $\pm$21 |  |  |  | 10:23 |  $\pm$0.1
14 | J125045 | 192.6887 | 11.5607 | 14 | 76 | 202 | 218 |  $-$1.8 | 1.8 | 5.4 | 2019-09-17 | $<1\,$d | $<49\,$d |  $-$0.3
| +113338 |  $\pm$114 |  $\pm$29 |  $\pm$21 |  $\pm$1.8 |  $\pm$0.6 |  $\pm$1.2 | 11:34 |  $\pm$0.4
15 | J180723 | 271.8483 | 19.7063 | 14 | NA | 82 | 153 | 0.5 | 2.8 | 5.5 | 2019-11-13 | $<6\,$d | $<12\,$d |  $-$1.4
| +194222 |  $\pm$24 |  $\pm$19 |  $\pm$2.2 |  $\pm$0.8 |  $\pm$1.6 | 13:01 |  $\pm$0.7
N1 | J181515 | 273.8166 | $-$49.4627 | 5 | NA | 555 | 282 |  $-$2.5 | 6.4 | 16.0 | 2019-11-08 | $>8\,$min | $>4\,$min | 1.3
| -492746 |  $\pm$28 |  $\pm$18 |  $\pm$5.7 |  $\pm$2.2 |  $\pm$4.7 | 17:22 |  $\pm$0.2
N2 | J070038 | 105.1588 | $-$11.2458 | 10 | NA | 344 | 152 |  $-$1.8 | 5.5 | 4.5 | 2019-12-14 | $<8\,$d | $>8\,$min | 1.9
| -111436 |  $\pm$30 |  $\pm$21 |  $\pm$7.2 |  $\pm$2.7 |  $\pm$5.2 | 21:30 |  $\pm$0.5
N3 | J200758 | 301.9965 | 16.1642 | 7 | 222 | 300 | 346 | -2.2 | 2.7 | 15.4 | 2018-09-11 | $<1\,$d | $>3\,$d | -0.4
| +160954 |  $\pm$53 |  $\pm$26 |  $\pm$18 |  $\pm$3.0 |  $\pm$1.1 |  $\pm$2.1 | 19:36 |  $\pm$0.2
2 |  | 316.0950 | $-$13.6699 | 20 | NA | 1050 | 1311 | 0.9 |  $-$2.7 |  $-$2.0 | 2017-09-27 | $<6\,$min | $<1\,$d |  $-$0.5
|  |  $\pm$56 |  $\pm$30 |  $\pm$4.5 |  $\pm$1.6 |  $\pm$3.0 | 00:21 |  $\pm$0.1
9 |  | 196.7753 | 16.6089 | 21 | 168 | 117 | 12 | 0.6 | 0.0 | 1.2 | 2019-07-19 | $<3\,$h | $>22\,$h | 2.1
|  |  $\pm$52 |  $\pm$19 |  $\pm$16 |  $\pm$1.7 |  $\pm$0.6 |  $\pm$1.1 | 19:23 |  $\pm$0.6
10 |  | 208.4267 | 6.7745 | 23 | 293 | 200 | 110 |  $-$1.5 | 0.2 | 0.3 | 2019-08-05 | $<19\,$h | $<1\,$d | 1.2
|  |  $\pm$57 |  $\pm$21 |  $\pm$19 |  $\pm$2.0 |  $\pm$0.7 |  $\pm$1.3 | 14:40 |  $\pm$0.3
13 |  | 60.9932 | 14.0094 | 26 | 920 | 542 | 603 |  $-$2.1 | 1.7 | 1.2 | 2019-08-14 | $<5\,$min | $<2\,$min | 1.6
|  |  $\pm$103 |  $\pm$52 |  $\pm$44 |  $\pm$3.3 |  $\pm$1.2 |  $\pm$2.3 | 09:27 |  $\pm$0.2
Table 3: Properties of the transient events. The candidates are listed in the
order of the detection time (or the detection of the first event for the
repeating candidates). Three single detections labeled as “N” are the
redetections of the transients published in Naess et al. (2021a). It is
difficult to fit for rise and fall time using light curves due to uneven
scanning cadence. Instead, we examine if the scan right before and after the
scans with the peak flux density has a $>5\sigma$ detection, and calculate the
time interval in between the scans. The rise time of Event 4a is left empty
because the time gap between the peak scan and the scan right before is longer
than 50 days. Spectral index is evaluated as described in text, using array-
wise flux density values, except for candidate 13. This transient event
completed the rise and fall process within the time the sky took to drift
across the array. We therefore evaluate the spectral index using flux values
taken by only one quarter of detectors in each array, to capture the peak
flux.

Flux densities for each scan are evaluated at the refined positions and used
to generate light curves (Figure 5) using the same method described in Section
3.2. Higher-resolution light curves (Figure 6) are made by dividing detectors
in each array into four groups by the order of observation time to study the
minute by minute change in flux density. When the flux density values are
available in both bands of PA4 and PA5, the spectral index $\alpha$ is
evaluated by the best-fit of a power law $S_{\nu}\propto\nu^{\alpha}$ of
frequency $\nu$ to the flux measurement $S_{\nu}$ at the three different
bands, taking into account the variance of each band flux. Under this
circumstance, PA6 is omitted because it scans across the candidates
approximately 10 minutes earlier (or later when the sky is rising) than PA4
and PA5. Events 2 and 15 do not have f220 measurements, so $\alpha$ is
directly calculated by

$\alpha=\frac{\log(S_{\nu_{1}})-\log(S_{\nu_{2}})}{\log(\nu_{1})-\log(\nu_{2})}$
(1)

using the f150 and f090 flux measured by PA5 or PA6, depending on which array
measures the peak flux.

Figure 5: Light curves for 18 detected transient events on day time scales from the peak. Each frequency is denoted by a different color. We see that in most cases the peak is correlated with all frequencies. Figure 6: High-resolution light curves for transient events on minute time scales. Events 1, 2, 5, 6, 7, 8, 9, 10, 13, 14 and 15 show strong signal variation within a space of four minutes. | pa4f220 | pa4f150 | pa5f150 | pa6f150 | pa5f090 | pa6f090 |  |  | pa4f220 | pa4f150 | pa5f150 | pa6f150 | pa5f090 | pa6f090
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
1 |  |  |  |  |  |  |  | 8 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
3 |  |  |  |  |  |  |  | 7 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
4a |  |  |  |  |  |  |  | 11 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
4b |  |  |  |  |  |  |  | 12a |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
5a |  |  |  |  |  |  |  | 12b |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
5b |  |  |  |  |  |  |  | 14 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
6 |  |  |  |  |  |  |  | 15 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
2 |  |  |  |  |  |  |  | 9 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
10 |  |  |  |  |  |  |  | 13 |  |  |  |  |  |
|  |  |  |  |  |  |  |  |  |  |  |  |  |
Figure 7: 10$\arcmin$x10$\arcmin$ 3-day thumbnail maps for each transient. The
upper row is the intensity map with $\pm$5000 $\mu$K color range. The bottom
row is the $S/N$ map after applying a matched filter, with $\pm$5 color range.
Due to the conjugate gradient iteration used to solve the maximum-likelihood
map-making equation only being run for 10 steps, these maps are effectively
mildly highpass filtered. The affected scales have negligible weight in the
matched filter. Events 2, 9, 10, 13 at the bottom of the table are the four
events that are difficult to determine if they are real transients.

###  4.2 Counterparts

We searched for counterparts for each transient event in the
SIMBAD555https://simbad.cds.unistra.fr/simbad/ (Wenger et al., 2000) database.
The results of this search are summarized in Table 4. We searched for any Gaia
objects (Mou & Webster, 2021) within one arcminute of each event and list the
SIMBAD identification. We calculate the probability of a chance association
given the density of Gaia stars (Mou & Webster, 2021) with the counterpart’s
magnitude or lower within the counterpart’s separation of the transient’s
position. We exclude any counterparts with a chance association probability
greater than ten percent. We also searched for counterparts of the transients
found in Naess et al. (2021a) that are recovered in this analysis. The
associations agree with the original findings.

In addition to the Gaia source catalog, we search for galactic objects in the
Gaia extragalactic catalog (Mou & Webster, 2021) using the VizieR (Ochsenbein
et al., 2000). Only events 10 and 14 have associations from this catalog.
Event 10 is coincident with an AGN with a 1.1 percent chance of a false
association (see Table 4). Event 14 is coincident with a RR Lyrae star with a
2.4 percent chance of a false association. This association is not listed
because there is a much more obvious Gaia star associated with event 14.

In Figure 8 we plot the positions of the transients overlayed on combined ACT
data from 2007–2019. This plot includes color images in the optical from the
Digitized Sky Surveys (DSS)666https://archive.stsci.edu/cgi-
bin/dss_form777These images were acquired using the online tool Aladin Lite.
See Boch & Fernique (2014). that show there are bright stellar counterparts
for most of the events.

Name | ID | Object Type | Magnitude | Pos Err (”) | Sep (”) | Chance
---|---|---|---|---|---|---
1 | V* CP Ori | Eclipsing Binary, G0 | 10.52 | 22 | 8.60 | 5.66e-04
3 | V* TX PsA | Eruptive Variable, M5IVe | 11.84 | 14 | 4.24 | 1.21e-04
4a/b | V* IM Peg | RS CVn Variable, K2III | 5.66 | 8 | 1.48 | 4.23e-08
5a/b | HD 182928 | Rotating Variable, G8IIIe | 9.37 | 3 | 2.15 | 5.79e-06
6 | ** SKF 1810A | Young Stellar Object Candidate | 12.73 | 10 | 7.00 | 5.05e-03
7 | G 9-38 | High Proper Motion Binary, M7V/M8Ve | 12.49/11.97 | 4 | 4.03 | 1.56e-04
8 | StKM 1-1155 | Low-mass Star, M0.0Ve | 10.91 | 8 | 2.71 | 1.78e-05
11 | V* TY Pic | RS CVn Variable, G8/K0III+F | 7.29 | 20 | 6.80 | 9.82e-06
12a/b | HD 22468 | RS CVn Variable, K2:Vnk/K4 | 5.60/8.51 | 5 | 4.10 | 2.59e-07
14 | Gaia DR2 3927810990205301504 | Star | 12.43 | 14 | 2.88 | 5.96e-05
15 | HD 347929 | Rotating Variable, K0 | 9.04 | 14 | 7.66 | 8.04e-05
N1 | 2MASS J18151564-4927472 | High Proper Motion Star, M3 | 11.72 | 5 | 4.25 | 5.47e-04
N2 | HD 52385 | Star, K0/1III | 8.11 | 10 | 7.46 | 5.15e-05
N3 | HD 191179 | Spectroscopic Binary, G5 | 7.96 | 7 | 7.05 | 4.41e-05
2 | Gaia DR2 6885400713762009216 | Star | 16.74 | 20 | 13.02 | 5.96e-02
9 | Gaia DR2 3936693910286240000 | Star | 13.87 | 21 | 33.51 | 2.11e-02
10888Field too dense to identify stellar counterpart | 1636148068921376768 | AGN | – | 23 | 17.40 | 1.20e-02
13 | Gaia DR2 38908044312695424 | Star | 14.34 | 26 | 23.58 | 2.51e-02
Table 4: Possible counterparts for each transient event from the SIMBAD
database. If known, the spectral type is given next to the object type. The
chance of a false association is calculated using the density of Gaia sources
with the same magnitude or brighter of the counterpart. If the Gaia object is
not found in the SIMBAD database, then the Gaia identification number is
listed. All separations are calculated using the Gaia coordinates. This table
also includes counterparts associated with the three previously published ACT
transients from Naess et al. (2021a). When two stars from the same system are
resolved, as is the case with events 7 and 12, we quote the average separation
weighted by one minus the chance association and we list the lowest of the two
chance associations. Figure 8: Combined ACT data from 2007–2019 overlayed with
the transient positions. Images from DSS at each location, indicated by a in
the center of each image, are also shown with a one arcminute contour plotted
for scale. The majority of the transient events have bright stellar
counterparts seen in the optical.

##  5 Discussion and Conclusions

We have demonstrated a pipeline of transient detection and characterization
using ACT data, and presented a catalog of 21 millimeter transients from 3-day
maps, including three of which that were previously detected, and four events
that cannot be confirmed as real transients. After assessing the spectral
indices and counterpart associations of these events, two classes of objects
emerge: flares associated with stars with flat or falling spectra, indicating
radiation from sychrotron emission, and stellar flares with rising spectra,
indicating thermal emission. Performing a statistical analysis on such a small
sample size is difficult, but in the future as we discover more millimeter
stellar transients, we will be able to better characterize them. For example,
we will be able to determine whether these events reside near the galactic
plane or scattered across the sky, or whether two classes of transients,
rising versus falling spectra, really exist or if there is simply a wide
distribution of spectral indices. Tests of these statistics with our small
number of events are inconclusive.

Events 3, 4a, 4b, 5a, 5b, 11, 12a, 12b, and 15 are associated with rotating
variable stars. These stars have large dark spots on their surface from
magnetic activity in their chromospheres. These spots cause intrinsic
variability in flux in optical and IR wavelengths but also cause flares in
radio wavelengths from synchrotron radiation (Hjellming & Gibson, 1980).
Events 4, 11, 12, and 15 are coincident with a special type of rotating
variable stars called RS Canum Venaticorum variables (RSCVn). These are binary
systems that also exhibit variability due to dark spots (See Hall 1976, Zeilik
et al. 1979). Similar events were seen in the millimeter systematic transient
search from SPT-3G (Guns et al., 2021).

Event 1 is also associated with a binary system. Although it is not classified
as rotationally variable, it most likely flares in the microwave with a
similar mechanism. This association is similar to ACT-T J200758+160954 (N3)
which was previously published in Naess et al. (2021a).

Events 7, 8, and 15 are all coincident with cool G, K, and M type stars. Event
14 is associated with a Gaia star but its spectral type is unknown. Since all
of these events also have negative spectral indices, these flares are most
likely from magnetic activity producing synchrotron radiation. This is
consistent with findings from Guns et al. (2021) who reported microwave flares
from M and K dwarfs. These cool stars have convective envelopes which cause
increased magnetic activity but do not exhibit the same dark spots found in
rotationally variable stars (Yang et al., 2017).

Events 6, N1, and N2 all have rising spectra which are associated with thermal
emission. In these cases, some mechanism other than synchrotron radiation is
driving the flares. N1 and N2 are associated with cool M and K stars
respectively but the spectral types of the star associations for 6 and 9 are
unknown. Guns et al. (2021) also observed two events with M-dwarf counterparts
with rising spectra indicating this is a common class of millimeter
transients.

Although we cannot be certain events 2, 9, 10, and 13 are real transients, we
still present possible counterparts for them in Table 4. There is a six
percent chance that the Gaia counterpart for Event 2 is a chance association.
If the association is correct this transient is likely due to synchrotron
radiation from stellar magnetic fields. The field around Event 10 is too dense
to pinpoint a stellar counterpart. However, there is a small chance this event
is associated with an AGN. Eftekhari et al. (2022) predicts ACT will see on
the order of ten gamma ray bursts but this event is not associated with any
known GRB flares listed in the Fermi All-sky Variability Analysis (Abdollahi
et al., 2017). The associations for Events 9 and 13, are also unclear. Both
events have rising spectra so they would most likely be similar to events 6,
N1, and N2. The Zwicky Transient Facility (ZTF; Masci et al. 2018) detected a
flare with the same Gaia counterpart associated with event 13 twelve days
after and 23$\arcsec$ away from the ACT detection. ZTF also detects repeating
flares that are 34$\arcsec$ away from Event 9, although they did not occur
close in time to the ACT detection.999This cross detection was done using
software developed by Matheson et al. (2021).

This sample likely only represents a fraction of microwave transient events
present in the ACT data. The 3-day maps were not made for a systematic
transient search. Recently, single observation maps that span more seasons
than the 3-day maps have been made for the purpose of ACT time domain study.
Major advantages of the single observation maps include the relative
consistency in noise performance in each map and the freedom of stacking
single scan maps for different periods of time. We will perform a similar
analysis to search for transient events within those maps to gain a more
accurate event rate for ACT. This will inform current and future large sky-
area millimeter surveys such as The South Pole Telescope Carlstrom et al.
(2011), Simons Observatory (Ade et al., 2019), CCAT-prime (CCAT-Prime
Collaboration et al., 2023) and CMB-S4 (Abazajian et al., 2019) on how to best
detect and study these events.

##  6 Acknowledgements

The Authors would like to thank Carles Badenes for his helpful discussions and
comments.

This work was supported by the U.S. National Science Foundation through awards
AST-0408698, AST-0965625, and AST-1440226 for the ACT project, as well as
awards PHY-0355328, PHY-0855887 and PHY-1214379. Funding was also provided by
Princeton University, the University of Pennsylvania, and a Canada Foundation
for Innovation (CFI) award to UBC. ACT operates in the Parque Astronómico
Atacama in northern Chile under the auspices of the Agencia Nacional de
Investigación y Desarrollo (ANID; formerly Comisión Nacional de Investigación
Científica y Tecnológica de Chile, or CONICYT).

Computations were performed using Princeton Research Computing resources at
Princeton University. This research has made use of data and/or services
provided by the International Astronomical Union’s Minor Planet Center. This
research has made use of the SIMBAD database, operated at CDS, Strasbourg,
France.

YL acknowledges support from KIC Postdoctoral Fellowship. This work was
supported by a grant from the Simons Foundation (CCA 918271, PBL). EC
acknowledges support from the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (Grant
agreement No. 849169). SKC acknowledges support from NSF award AST-2001866.
R.D. thanks CONICYT for grant BASAL CATA FB210003. CHC acknowledges FONDECYT
Postdoc fellowship 3220255. ADH acknowledges support from the Sutton Family
Chair in Science, Christianity and Cultures and from the Faculty of Arts and
Science, University of Toronto. KMH acknowledges support by NSF awards
AST-1815887 and AST-2206344. JPH gratefully acknowledges support from the
estate of George A. and Margaret M. Downsbrough. CS acknowledges support from
the Agencia Nacional de Investigación y Desarrollo (ANID) through FONDECYT
grant no. 11191125 and BASAL project FB210003. EMV acknowledges support from
NSF award AST-2202237.

The Digitized Sky Surveys were produced at the Space Telescope Science
Institute under U.S. Government grant NAG W-2166. The images of these surveys
are based on photographic data obtained using the Oschin Schmidt Telescope on
Palomar Mountain and the UK Schmidt Telescope. The plates were processed into
the present compressed digital form with the permission of these institutions.
This research has made use of “Aladin sky atlas” developed at CDS, Strasbourg
Observatory, France. This research has made use of the VizieR catalogue access
tool, CDS, Strasbourg, France (DOI : 10.26093/cds/vizier). The original
description of the VizieR service was published in 2000, AAS 143, 23

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