PASA 2024

# The ASKAP Variables and Slow Transients (VAST) Pilot Survey

Tara Murphy  1,2 David L. Kaplan<EMAIL_ADDRESS>3 Adam J. Stewart
1 Andrew O’Brien  3 Emil Lenc  4 Sergio Pintaldi  5 Joshua Pritchard  1,2,4
Dougal Dobie  2,6 Archibald Fox  1 James K. Leung  1,2,4 Tao An  7 Martin E.
Bell  8 Jess W. Broderick  9 Shami Chatterjee  10 Shi Dai  11 Daniele
d’Antonio  4,8 J. Gerry Doyle  12 B. M. Gaensler  13 George Heald  14 Assaf
Horesh  15 Megan L. Jones  3 David McConnell  4 Vanessa A. Moss  4,1 Wasim
Raja  4 Gavin Ramsay  11 Stuart Ryder  16,17 Elaine M. Sadler  1,4 Gregory R.
Sivakoff  18 Yuanming Wang  1,2,4 Ziteng Wang  1,2,4 Michael S. Wheatland  1
Matthew Whiting  4 James R. Allison  19,4 C. S. Anderson  20,4 Lewis Ball
4,21 K. Bannister  4 D. C.-J. Bock  4 R. Bolton  4 J. D. Bunton  4 R. Chekkala
4 A. P. Chippendale  4 F. R. Cooray  4 N. Gupta  22 D. B. Hayman  4 K.
Jeganathan  4 B. Koribalski  4,11 K. Lee-Waddell  23,4 Elizabeth K. Mahony  4
J. Marvil  24 N. M. McClure-Griffiths  25 P. Mirtschin  4 A. Ng  4 S. Pearce
4 C. Phillips4 and M. A. Voronkov4 1Sydney Institute for Astronomy, School of
Physics, The University of Sydney, NSW 2006, Australia  2ARC Centre of
Excellence for Gravitational Wave Discovery (OzGrav), Hawthorn, Victoria,
Australia  3Department of Physics, University of Wisconsin-Milwaukee, P.O. Box
413, Milwaukee, WI 53201, USA  4CSIRO, Space and Astronomy, PO Box 76, Epping,
NSW 1710, Australia  5Sydney Informatics Hub, The University of Sydney, NSW
2008, Australia  6Centre for Astrophysics and Supercomputing, Swinburne
University of Technology, Hawthorn, Victoria, Australia  7Shanghai
Astronomical Observatory, the Chinese Academy of Sciences, 80 Nandan Road,
Shanghai 200030, China  8University of Technology Sydney, 15 Broadway, Ultimo
NSW 2007, Australia  9International Centre for Radio Astronomy Research,
Curtin University, GPO Box U1987, Perth, WA 6845, Australia  10Cornell Center
for Astrophysics and Planetary Science, Ithaca, NY 14853, USA  11School of
Science, Western Sydney University, Locked Bag 1797, Penrith South DC, NSW
2751, Australia  12Armagh Observatory and Planetarium, College Hill, Armagh,
BT61 9DG, N. Ireland  13Dunlap Institute for Astronomy and Astrophysics,
University of Toronto, 50 St. George St., Toronto, ON M5S 3H4, Canada; David
A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, 50
St. George St., Toronto, ON M5S 3H4, Canada  14CSIRO, Space and Astronomy, PO
Box 1130, Bentley WA 6102, Australia  15Racah Institute of Physics, The Hebrew
University of Jerusalem, Jerusalem 91904, Israel  16Department of Physics and
Astronomy, Macquarie University, Sydney NSW 2109, Australia  17Astronomy,
Astrophysics and Astrophotonics Research Centre, Macquarie University, Sydney,
NSW 2109, Australia  18Department of Physics, University of Alberta, CCIS
4-181, Edmonton, AB T6G 2E1, Canada  19Sub-Dept. of Astrophysics, Department
of Physics, University of Oxford, Denys Wilkinson Building, Keble Rd., Oxford,
OX1 3RH, UK  20Jansky Fellow of the National Radio Astronomy Observatory, P.
O. Box 0,Socorro, NM 87801, USA  21SKA Observatory, Jodrell Bank, Lower
Withington, Macclesfield, Cheshire SK11 9FT, UK  22Inter-University Centre for
Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune University Campus,
Pune 411 007, India  23International Centre for Radio Astronomy Research
(ICRAR), The University of Western Australia, 35 Stirling Hwy, Crawley, WA,
6009, Australia  24National Radio Astronomy Observatory, P.O. Box O, Socorro,
NM 87801, USA  25Research School of Astronomy and Astrophysics, Australian
National University, Cotter Road, Weston Creek ACT 2611, Australia

###### Abstract

The Variables and Slow Transients Survey (VAST) on the Australian Square
Kilometre Array Pathfinder (ASKAP) is designed to detect highly variable and
transient radio sources on timescales from 5 seconds to $\sim 5$ years. In
this paper, we present the survey description, observation strategy and
initial results from the VAST Phase I Pilot Survey. This pilot survey consists
of $\sim 162$ hours of observations conducted at a central frequency of 888
MHz between 2019 August and 2020 August, with a typical rms sensitivity of
0.24 mJy beam-1 and angular resolution of $12-20$ arcseconds. There are 113
fields, each of which was observed for 12 minutes integration time, with
between 5 and 13 repeats, with cadences between 1 day and 8 months. The total
area of the pilot survey footprint is 5 131 square degrees, covering six
distinct regions of the sky. An initial search of two of these regions,
totalling 1 646 square degrees, revealed 28 highly variable and/or transient
sources. Seven of these are known pulsars, including the millisecond pulsar
J2039–5617. Another seven are stars, four of which have no previously reported
radio detection (SCR J0533–4257, LEHPM 2-783, UCAC3 89–412162 and 2MASS
J22414436–6119311). Of the remaining 14 sources, two are active galactic
nuclei, six are associated with galaxies and the other six have no
multiwavelength counterparts and are yet to be identified.

###### doi:

10.1017/pas.2024.xxx

###### keywords:

radio transient sources – sky surveys – pulsars – stars

##  1 INTRODUCTION

Radio variability is produced by a wide range of astronomical sources and
phenomena, including the Sun, planets, stellar systems, neutron stars,
supernovae, gamma-ray bursts (GRBs) and active galactic nuclei (AGN) (Cordes
et al., 2004). Among the most exciting and anticipated transient sources from
extragalactic imaging surveys are neutron star mergers, tidal disruption
events, and orphan GRB afterglows (Metzger et al., 2015). These can help
answer questions regarding the true rates of these events (independent of
beaming), their energetics, and the properties of their surrounding media.
However, because the source of the emission is similar in most cases (shock-
driven synchrotron emission), distinguishing these sources from other
transients can be hard, especially with small numbers of detections well after
the sources have faded. Therefore prompt identification and multi-wavelength
comparisons are crucial (Ofek et al., 2011; Stewart et al., 2018).

At the same time, we must distinguish between extragalactic transients and
Galactic foreground sources, primarily low-mass stars and pulsars (analogous
to the situation in searches for fast optical transients, where M star flares
are the dominant foreground; see e.g., Cowperthwaite & Berger 2015; Ho et al.
2018; Webb et al. 2020). Again, multi-wavelength identification can be
crucial, but in some cases the properties of the radio emission itself can be
used to identify high brightness temperatures and/or coherent emission that
rule out extragalactic sources (e.g., Kaplan et al., 2008; Roy et al., 2010;
Pritchard et al., 2021).

Early untargeted surveys generally used archival data, and consisted of either
many repeats of relatively small sky areas (e.g., Carilli et al., 2003; Bower
et al., 2007); a few repeats of large sky areas, such as the comparison
between the NRAO VLA Sky Survey (NVSS; Condon, 1997) and Faint Images of the
Radio Sky at Twenty-centimeters (FIRST; White et al., 1997) surveys (Gal-Yam
et al., 2006); or inconsistently sampled repeats over a range of sky areas
(e.g., Bannister et al., 2011a). These surveys typically resulted in a small
number of detections, but the historical nature of these detections, and the
lack of multi-wavelength data made it difficult to classify or confirm these
objects (e.g., Bower et al., 2007; Thyagarajan et al., 2011).

Despite the limitations of these surveys there have been a number of
interesting discoveries. For example, Hyman et al. (2005) and subsequent
papers identified several interesting sources towards the Galactic Centre
whose origins are still unknown (see also Hyman et al., 2009), and Ofek et al.
(2011) used near real-time reduction and multi-wavelength follow-up to
identify a single transient that may be consistent with a neutron star merger.

More recently, the advent of Square Kilometre Array (SKA) pathfinder
telescopes and upgrades of established telescopes have allowed a new wave of
radio transients surveys (e.g., Driessen, 2020). For example, the Caltech-NRAO
Stripe 82 survey (CNSS; Mooley et al., 2016) has used the upgraded Karl G.
Jansky Very Large Array (VLA) to repeatedly survey an equatorial strip and
identified a number of sources, including a tidal disruption event (TDE;
Anderson et al., 2020). Stewart et al. (2016) used repeated observations of
the north celestial pole at 60 MHz with the Low Frequency Array (LOFAR; van
Haarlem et al. 2013) and found a single transient event of unknown nature. The
ongoing VLA Sky Survey (VLASS; Lacy et al., 2020) has led to the
identification of several sources through comparison with archival surveys
(Law et al., 2018; Ravi et al., 2021). The ThunderKAT large survey project
(Fender et al., 2016) on the MeerKAT telescope (Jonas & MeerKAT Team, 2016)
will both conduct its own observations and analyse commensal data from other
projects, and has already identified new sources including a chromospherically
active K-type sub-giant (Driessen et al., 2020). Increasingly these detections
are being made closer to real-time (within days of observation), aided by
improved radio surveys (Lacy et al., 2020; McConnell et al., 2020) to allow
rapid identification of new (or missing; e.g., Ravi et al. 2021) sources.

This progress at radio wavelengths is coupled with significant improvements
(in depth, wavelength coverage, resolution, and temporal sampling) in multi-
wavelength coverage, such as the Panoramic Survey Telescope and Rapid Response
System (Pan-STARRS; Chambers et al. 2016), the Vista Hemisphere Survey (VHS;
McMahon et al. 2013), the VISTA Variables in the Via Lactea survey (VVV;
Minniti et al. 2010), SkyMapper (Keller et al., 2007), the Dark Energy Survey
(DES; Abbott et al., 2018), DESI Legacy Imaging Surveys (especially Dark
Energy Camera Legacy Survey, or DECaLS; Dey et al., 2019), the Wide-field
Infrared Survey Explorer (WISE; Wright et al. 2010) and, especially for
stellar science, the Gaia mission (Gaia Collaboration et al., 2016). These
surveys improve cross-matching and classification for a variety of sources
(Stewart et al., 2018).

The Australian Square Kilometre Array Pathfinder (ASKAP; Johnston et al.,
2007; Hotan et al., 2021) is an array of $36\times 12$-metre prime-focus
antennas located at the Murchison Radio-astronomy Observatory in Western
Australia. It operates over a frequency range of 0.7–1.8 GHz with an
instantaneous bandwidth of 288 MHz and a nominal field of view of 31 square
degrees. ASKAP was designed as a rapid wide-field survey instrument. The ASKAP
Variables and Slow Transients Survey (VAST111https://vast-survey.org.; Murphy
et al., 2013) is one of the key survey science projects
(SSPs)222https://www.atnf.csiro.au/projects/askap/ssps.html. approved for
ASKAP time allocation.

VAST was designed to detect astronomical phenomena that vary on timescales
accessible in the ASKAP imaging mode (from $\sim 5$ seconds to several years).
Since the original survey proposal described by Murphy et al. (2013), the
scope of the science case has been extended to incorporate detection of radio
emission from gravitational wave events (in particular binary neutron star
mergers) and fast radio bursts and their host galaxies (in the image domain).
More rapidly varying sources (on timescales of less than seconds) are being
explored as part of the Commensal Real-Time ASKAP Fast-Transients Survey
(CRAFT; Macquart et al., 2010).

In the Early Science period, we have demonstrated ASKAP’s capability to detect
time-variable phenomena in imaging mode. We have reported: an initial search
for transients and variables in a single 30 deg2 field using 8 daily epochs
and a subset of the final ASKAP array (Bhandari et al., 2018, also see Heywood
et al. 2016); the discovery of a new millisecond pulsar J1431$-$6328 (Kaplan
et al., 2019); a sample of 23 previously undetected flaring stars (Pritchard
et al., 2021); and radio bursts from UV Ceti (Zic et al., 2019) and Proxima
Centauri (Zic et al., 2020). In addition, ASKAP was used to follow-up several
gravitational wave events from LIGO/Virgo, notably GW190814 (Dobie et al.,
2019; Abbott et al., 2020).

During 2019–2020, each of the SSPs was allocated 100 hours of observing time
to conduct a Phase I Pilot Survey with ASKAP. In 2021, another 100 hours have
been allocated to conduct a Phase II Pilot Survey. The purpose of these pilot
surveys is to test the observing strategy and implementation, data processing
capabilities, and scientific analysis in preparation for the full surveys,
which are expected to begin in 2022.

In this paper we describe the VAST Phase I Pilot Survey, present some initial
results, and discuss the plan for the VAST Phase II Pilot Survey. In Section 2
we discuss our observational specifications and scheduling; in Section 3 we
discuss our transient detection pipeline software; in Section 4 we present
results from an untargeted variability search on two of the VAST Phase I Pilot
Survey regions (3 and 4); and in Section 5 we discuss our future plans.

##  2 OBSERVATIONS AND SURVEY STRATEGY

###  2.1 The Rapid ASKAP Continuum Survey

In addition to our dedicated VAST observations, we incorporated data from the
Rapid ASKAP Continuum Survey (RACS; McConnell et al., 2020) as our first
epoch. RACS is the first large-area survey to be conducted with the full
36-antenna ASKAP telescope. When complete, RACS will cover the sky south of
declination $+51^{\circ}$ (a total sky area of 36 656 deg2), across three
ASKAP bands (centred on 888, 1296 and 1656 MHz). The angular resolution is
$15-25$ arcsec in the low band and $8-$27 arcsec in the mid band, depending on
the declination.

The first data release of RACS consists of 903 images covering the sky south
of declination $+41^{\circ}$, observed in the low band (centred on 888 MHz).
Each field was observed for $\sim 15$ min, achieving a typical rms noise of
0.25 mJy beam-1. All four instrumental polarisation products (XX, XY, YX, and
YY) were recorded to allow images to be made in Stokes parameters I, Q, U, and
V. The data were processed using the ASKAPSoft pipeline (Cornwell et al.,
2012) and are available through the CSIRO ASKAP Science Data Archive
(CASDA333https://research.csiro.au/casda/.; Chapman et al., 2017) under
project code AS110.

We used the RACS low-band survey as ‘epoch 0’ of the VAST Phase I Pilot Survey
(VAST-P1) which was conducted entirely in the lowest frequency band. We will
use the RACS mid-band survey as ‘epoch 0’ of the VAST Phase II Pilot Survey
(VAST-P2), which will be conducted in both the low and mid-bands. This allows
us to take advantage of the RACS tiling pattern and survey design, which had
very similar parameters to those required for VAST. An important note is that
the publicly-released RACS images have had an additional flux correction
applied, as described by McConnell et al. (2020), so any epoch 0 flux
densities reported in this work may differ from the (improved) published RACS
catalogue (Hale et al., submitted).

In in this paper, we use the following terminology to describe the different
components of our survey and others:

beam footprint

The arrangement of phased-array feed (PAF) beams used to cover a single field.
We use the square_6x6 and closepack36 beam footprints (see Hotan et al. 2021).

field

A single observation or pointing.

tiling pattern

How individual fields are placed to cover a larger area.

region

A grouping of pointings (usually but not always covering adjacent) meant to
cover a particular area of the sky or type of source.

survey footprint

The total sky area covered by a survey (for instance, DES) or one epoch of a
survey (as in VAST-P1).

###  2.2 Phase I Pilot Survey observations

VAST-P1 consists of 113 fields taken from the RACS low-band tiling pattern.
The survey coverage of VAST-P1 is shown in Figure 1. The survey consists of
six regions, chosen to test some of the different VAST science goals. These
are summarised below.

Region 1: An extragalactic region of 40 fields (1840 deg2) along an equatorial
strip centred at RA of $0^{\rm h}$. This was chosen to overlap with the CNSS,
which itself had considerable overlap with FIRST. In addition, this overlaps
with part of the DES/DECaLS coverage.

Region 2: A second extragalactic region of 28 fields (1300 deg2) along an
equatorial strip centred at RA of $12^{\rm h}$. The equatorial regions have
the advantage of greater multi-wavelength coverage from telescopes in the
northern hemisphere. This region in particular has good overlap with FIRST and
DECaLS.

Region 3: An extragalactic region of 19 fields (820 deg2) chosen within the
larger DES survey footprint, centred at ${\rm RA}=4.5$ hr, ${\rm
Dec}=-50\deg$.

Region 4: A second region of 19 fields (830 deg2) within the DES survey
footprint, centred at ${\rm RA}=22$ hr, ${\rm Dec}=-50\deg$. Region 4 also
overlaps with the Evolutionary Map of the Universe (EMU; Norris et al., 2011)
Phase I Pilot Survey and the Polarisation Sky Survey of the Universe’s
Magnetism (POSSUM; Gaensler et al., 2010) Phase I Pilot Survey.

Region 5: A small 5-field (265 deg2) region covering the Galactic Centre.

Region 6: A small 2-field (135 deg2) region covering the Small and Large
Magellanic Clouds.

Note that the region area above is not the number of fields times 31 deg2 for
two reasons. First, the instantaneous field-of-view is typically more than 31
deg2 as it includes sky sampled by the PAF beams which is outside the region
over which the PAF can point (which is the origin of the 31 deg2 number). The
field-of-view is frequency dependent444See
https://confluence.csiro.au/display/askapsst/ASKAP+Observation+Guide, Figure
7.4. but can be up to 64 deg2 at the low-band frequency, including lower-
sensitivity wings of the PAF beams. Second, when fields are adjoining the
lower-sensitivity wings overlap, improving sensitivity but reducing total sky
coverage.

The ASKAP observing specifications in the context of VAST-P1 are summarised in
Table 1, and the full set of VAST-P1 observations is summarised in Table 2.
Our survey strategy was to observe five epochs of the VAST-P1 survey
footprint, with each epoch separated by months, and one pair of epochs
separated by a single day. However, there were also extra test observations
conducted, and some epochs in which extra fields were observed to accommodate
observations where the schedule initialisation failed (or was excessively
delayed). Hence, in addition to the six full epochs (epochs 1, 2, 8, 9, 12 and
13 in Table 2), there are a number of epochs that have partial coverage of the
VAST-P1 survey footprint. These epochs are labelled with an ‘x’ in their name
in Table 2 (3x, 4x, 5x, 6x, 7x, 10x and 11x). The data for epoch 12 (marked
with as asterisk in Table 2) only became available after submission of this
paper. We have included it in the table for completeness but the data is not
included in any of the analysis presented in this paper.

Table 1: VAST observing parameters. Note the number of epochs for VAST-P2 (as well as the minimum and maximum spacing) are planned estimates, and may change when the survey is conducted. The image rms and total area for VAST-P2 (mid) are estimated from early RACS-mid observations; these estimated values are marked in italics. See Hotan et al. (2021) for details about the beam footprints. Parameter | VAST-P1 | VAST-P2 (low) | VAST-P2 (mid)
---|---|---|---
Centre Frequency | 888 MHz | 888 MHz | 1296 MHz
Bandwidth | 288 MHz | 288 MHz | 288 MHz
Integration per tile | 12 min | 12 min | 12 min
Beam footprint | square_6x6 | square_6x6 | closepack36
Beam spacing | $1.05^{\circ}$ | $1.05^{\circ}$ | $0.9^{\circ}$
Total survey footprint area | 5131 deg2 | 5131 deg2 | 2638 deg2
Number of epochs | 5–13 | 2 | 3
Minimum spacing | 1 day | 1 month | 2 months
Maximum spacing | 12 months | 1 month | 4 months
Image rms per epoch | 0.24 mJy beam-1 | 0.24 mJy beam-1 |  $\mathit{\lesssim 0.2}$ mJy beam-1

The total combined area of a full VAST-P1 epoch is 5131 deg2. Each field was
observed for 12 minutes integration time (rather than 15 minutes for RACS).
Excluding RACS (as epoch 0) we observed 813 fields, for a total observing time
of $\sim 162$ hours. In this paper we present results from two of the regions
(3 and 4). The rest of the VAST-P1 survey footprint will be analysed in
subsequent papers.

Figure 1: The VAST-P1 survey footprint, showing the number of observations of each field. The green region shows the planned survey footprint of the VAST-P2 mid-band observations. VAST-P2 low-band will cover the same survey footprint as VAST-P1. The sky map is plotted with J2000 equatorial coordinates in the Mollweide projection and the background diffuse Galactic emission at 887.5 MHz is modelled from Zheng et al. (2017) using Price (2016). Table 2: Summary of VAST-P1 observations, giving the number of fields in each epoch, the start and end dates for the epoch, and the total sky area. Epochs with an ‘x’ in the name only have partial sky coverage, as discussed in the text. Epoch 0 is the RACS survey. Epoch $12^{*}$ was only available after submission of this paper and is included here for completeness but is not included in any of the analysis. Epoch | Num. | Start Date | End Date | Area
---|---|---|---|---
| Fields |  |  | deg2
0 | 113 | 2019 Apr 25 | 2019 May 08 | 5131
1 | 113 | 2019 Aug 27 | 2019 Aug 28 | 5131
2 | 108 | 2019 Oct 28 | 2019 Oct 31 | 4905
3x | 43 | 2019 Oct 29 | 2019 Oct 29 | 2168
4x | 34 | 2019 Dec 19 | 2019 Dec 19 | 1672
5x | 81 | 2020 Jan 10 | 2020 Jan 11 | 3818
6x | 49 | 2020 Jan 11 | 2020 Jan 12 | 2400
7x | 33 | 2020 Jan 16 | 2020 Jan 16 | 1666
8 | 112 | 2020 Jan 11 | 2020 Feb 01 | 5097
9 | 112 | 2020 Jan 12 | 2020 Feb 02 | 5097
10x | 13 | 2020 Jan 17 | 2020 Feb 01 | 803
11x | 11 | 2020 Jan 18 | 2020 Feb 02 | 695
$12^{*}$ | 109 | 2020 Jun 19 | 2020 Jun 21 | 5100
13 | 104 | 2020 Aug 28 | 2020 Aug 30 | 5028

###  2.3 Phase II Pilot Survey strategy

The VAST Phase II Pilot Survey will commence in 2021. The aims of VAST-P2 are:
to build on the scientific analysis from VAST-P1; to test faster turn-around
of imaging and transient detection; and to demonstrate commensality with ASKAP
mid-band surveys by observing some epochs at 1296 MHz.

We plan to observe five epochs spread over the six months of the pilot
program. We will observe two epochs at low-band, using the same survey
footprint as VAST-P1 and the square_6x6 beam footprint. The remaining three
epochs will be observed in the ASKAP mid-band. This will use the tiling
pattern from the RACS mid-band survey and the closepack36 beam footprint
(Hotan et al., 2021).

The size of the primary beams scales proportionally with wavelength. Since we
tile the sky with beam footprints that provide nearly uniform sensitivity
across the observed area, our mid-band survey will require a different tiling
pattern where each beam footprint will cover less area than the low-band beam
footprints. Just as we have done for our low-band survey, we select our mid-
band tiling pattern from the RACS mid-band survey tiling pattern. Note that
since the mid-band beam footprints are smaller, the low- and mid-band field
centres are not aligned.

Subtracting the duration of two low-band epochs from the total VAST-P2
observing time will leave enough time to observe 3 epochs of 91 mid-band
fields. This will not be enough to cover the entire low-band survey area, so
we have iteratively selected mid-band fields from RACS to optimise the overlap
with the low-band fields from the following VAST regions in order: 5 (Galactic
Centre), 6 (Magellanic Clouds), 1 (equatorial strip), then 4 (DES/EMU/POSSUM).
This results in 88 mid-band fields. The remaining 3 mid-band fields have been
selected from region 3 (DES) as it is adjacent to the already selected region
6 fields that cover the LMC. The full set of selected mid-band fields is shown
in Figure 1.

###  2.4 Data products

VAST-P1 data was processed using the same calibration and imaging strategy as
that used by the RACS survey (McConnell et al., 2020), the only difference was
that VAST used a standard Gaussian primary beam correction whereas RACS
applied a position-dependent correction based on holography data to correct
for slight non-Gaussianity of the PAF beams. The use of holographic data to
correct for primary beam attenuation was made available for general processing
in the ASKAPsoft pipeline after most VAST processing for VAST-P1 was
completed, however, it was used in the processing of epoch 12. There are two
main image data products:

  1. 1.

The individual field (or ‘tile’) images produced by ASKAPSoft. These were
created by combining the 36 individual beam images. We produced multi-
frequency synthesis (MFS) images for all four Stokes parameters I, Q, U and V.
Two Taylor-terms are used in Stokes I imaging to consider the spectral slope
of field sources across the observed band.

  2. 2.

Combined images made by mosaicing the individual field images together (these
are not produced by ASKAPSoft) across each region of VAST-P1. This means that
images at different times within a given epoch are combined, so they can only
be used for epoch-to-epoch comparisons.

A typical VAST-P1 image is shown in Figure 2. When imaging, we removed short
($<100$ m) baselines in order to; (i) to minimise solar interference; and (ii)
as there is insufficient uv-coverage to reliably recover extended structure in
short VAST-P1 observations.

A histogram of the rms noise in each individual VAST image in regions 3 and 4
is shown in Figure 3, where we separately show Stokes I and Stokes V. The
latter is dominated by thermal noise in the images, while the former also has
significantly more contributions from source confusion, sidelobe confusion,
and deconvolution artifacts. The median rms is $0.24\,{\rm mJy\,beam}^{-1}$ in
Stokes I, while it is $0.20\,{\rm mJy\,beam}^{-1}$ in Stokes V.

Figure 2: A VAST image within Region 4 (field VAST_2131$-$56A) from epoch 12
with two cutouts. Cutout A: a $1\deg$ image centered on (J2000) $\alpha$ =
21:49:26.5, $\delta$ = –55:17:52.87 containing several bright sources,
including the large radio galaxy 2MASX J21512991–5520124. Cutout B: a
$0.3\deg$ image centered on (J2000) $\alpha$ = 21:36:18.9, $\delta$ =
–58:00:12.68 containing a range of source morphologies. Figure 3: Distribution
of median image rms values (computed over the central half of each image) for
each field in each epoch of regions 3 and 4. We plot the rms values from
Stokes I (blue) and Stokes V (orange).

In addition to images, the ASKAPSoft pipeline also produces a catalogue of
groups of pixels of contiguous emission (‘islands’), a catalogue of Gaussian
components fit to the islands (‘components’, where each island may be fit with
one or more components), and an rms map and a median map corresponding to each
field. These are created by the Selavy source finding software (Whiting, 2012)
incorporated into ASKAPSoft. We ran Selavy separately on our combined images,
and used these additional data products as inputs to our VAST transient
detection pipeline, as described in Section 3. We used the default parameter
settings in ASKAPSoft, so sources were considered ‘detected’ if they exceeded
5 times the local rms (roughly 1.2 mJy in Stokes I for an unresolved source).

Two of the VAST-P1 epochs (8 and 9) were observed on consecutive days, and
were matched in local sidereal time (LST). This was done to explore whether
LST-matching improved the ability to search for transients using image
subtraction. The observations for the matched epochs were taken over a period
of two weeks, but within that period each individual field was LST matched and
observed on consecutive days.

###  2.5 Data quality analysis

Before images were released in CASDA, we performed some quality control checks
of the astrometric accuracy and flux density scale (both relative and
absolute). In addition, we visually inspected each image to check the overall
data quality (on the basis of this, a single field was excluded: VAST_0534–43A
in epoch 7x, which contains Pictor A). In this section, we summarise the
results for the two VAST-P1 regions (3 and 4) presented in this paper (see
Section 4.2).

####  2.5.1 Astrometric accuracy

To evaluate the astrometric accuracy of our images, we extracted bright (SNR
$>=7$), compact555Compactness is determined following the definition by (Hale
et al., submitted) of an integrated to peak flux ratio of
$S_{I}/S_{P}<1.024+0.69\times\mathrm{SNR}^{-0.62}$. sources using Selavy that
are isolated by a minimum of $150$ arcsec from other sources, and then
crossmatched them with the International Celestial Reference Frame (ICRF)
catalogue (Charlot et al., 2020). The ICRF consists of 4536 radio sources with
accurate positions measured using very long baseline interferometry (VLBI).

The left panel of Figure 4 shows the offsets for VAST-P1 sources associated
with the 41 ICRF sources in regions 3 and 4 (a total of 331 comparisons across
all epochs). The median and standard deviation of the positional offsets is
$-0.19\pm 0.53$ arcsec in right ascension and $0.07\pm 0.48$ arcsec in
declination, with a standard error of $29$ milliarcsec in both coordinates.
The median positions of each epoch of VAST-P1 are shown as coloured markers.

We also compared the VAST-P1 sources with the positions in the published RACS
catalogue (Hale et al., submitted). There were $378\,823$ unique measurements
of $32\,769$ compact, isolated RACS sources across all epochs within regions 3
and 4 with SNR $>=7$. The results of this comparison are shown in the right
panel of Figure 4. The median and standard deviation of the positional offsets
are $0.58\pm 1.01$ arcsec in right ascension and $0.14\pm 0.99$ arcsec in
declination, with a standard error of $1.6$ milliarcsec in both coordinates.

Figure 4: Astrometric accuracy for compact sources in regions 3 and 4 of
VAST-P1 compared to Left: sources from the ICRF catalogue and Right: sources
from the RACS catalogue, as described in Section 2.5.1. The image pixel size
of $2.5\times 2.5$ arcsec is shown as a red dashed box. The median offset for
each individual epoch is shown with coloured markers. The solid lines show the
overall median offsets, and the dashed lines are the median $\pm 1$ standard
deviation. For the comparison with RACS, the background is a 2D histogram of
the source counts where the colour scale represents the number of sources per
bin.

####  2.5.2 Flux density scale

In addition to the absolute flux density scale, for variability analysis it is
important that the flux density scale from epoch to epoch is consistent. To
evaluate both of these, we crossmatched compact, isolated sources with
$\mathrm{SNR}>=7$ in each epoch of VAST-P1 with sources in the published RACS
catalogue. Figure 5 shows the flux density ratio for these $378\,823$ sources.

Nine of the twelve epochs have a median VAST-P1/RACS flux density ratio
between 0.97 and 1.03 (within $3\%$). Epoch 10x has a ratio of 0.93 and Epochs
05x and 06x have ratios of 0.94 and 0.95 respectively. The overall ratio is
0.98 with a $1\sigma$ scatter of 0.15. This scatter is probably the result of
a few differences in the way that RACS and VAST-P1 data were processed. The
most significant of these is that the published RACS images had a holography
correction applied that improved the flux density scale, particularly in the
outer regions of images. These discrepancies will be corrected in future
analyses, but for the current work our conservative variability thresholds
(see below) mean that they are not significant.

Figure 5: Absolute and relative flux density scale comparison for $378\,823$
sources in regions 3 and 4, comparing those measured in each VAST-P1 epoch to
RACS (as discussed in Section 2.5.2). The median ratio for each epoch is shown
by the dotted lines. The background is a 2D histogram of the source counts
where the colour scale represents the number of sources per bin. The overall
median ratio is 0.98 with a scatter of 0.15.

####  2.5.3 Known issues

As discussed above, the observations presented here are part of a pilot study
for a full survey. Therefore some of the observations and analysis used were
less than optimal, and evolved as we discovered deficiencies. These lessons
will inform the full VAST survey. Nonetheless, the data are useful for
scientific analysis, but we note the various issues below.

From epoch 4x onwards, we made all observations within $\pm 1$ h of meridian
transit to ensure a consistent synthesised beam size for all fields. In epochs
1–3x, this did not occur and some fields were observed at large zenith angles,
resulting in very extended synthesised beams.

From epoch 5x onwards, an intermittent system issue at the initialisation of a
new field observation caused random phase delays on a few antennas on a small
subset of field observations. These were identified after processing, but
could not be corrected in all cases. Furthermore, even for corrected fields,
this occasionally resulted in increased astrometric and flux scale errors.
Fields that were affected by this issue are highlighted in the release notes
in CASDA. Other more subtle changes in the bandpass and beam-shape may also
have occurred in early observations as beam-forming and use of the on-dish
calibrator improved over the course of the pilot survey. As a result, more
recent data tend to be of higher quality than earlier data.

The process used to obtain an initial deconvolution model for use in self-
calibration in the ASKAP pipeline can introduce a slight positional offset in
each beam image. This is because self-calibration will tend to snap to a
dominant field source and place it at a pixel centre. This not only causes
astrometric errors, up to half a pixel (1.25 arcsec) in extent, but can also
cause apparent extension of a source if the shift is in opposite directions
for a source in two overlapping beams.

The source finding for the VAST pipeline input was performed on final mosaiced
images. In early pilot survey images, it was assumed that the point spread
function (PSF) of each beam would be consistent in each field. In practice,
the PSF can vary from beam to beam as a result of differences in flagging and
elevation. As only a single PSF is recorded in the image header, typically the
PSF associated with beam 0, this can affect the accuracy of the flux scale
across the entire mosaic.

Also, note that the RACS flux density scale correction (as described in
Appendix A of McConnell et al. 2020) has not been applied to the VAST data.
Hence we also used the pre–flux-corrected RACS data in epoch 0, for
consistency. For future work, we plan to convolve all fields to a consistent
PSF and apply these corrections to ensure consistency between RACS-low, RACS-
mid and both the VAST pilot surveys.

###  2.6 Data availability

The raw data and individual field images for the entire VAST-P1 survey are
publicly available through CASDA under project code AS110 for RACS and AS107
for VAST. Epoch 4x of VAST-P1 was conducted as a test observation and is
available under project code AS113. Higher order data products such as
variability catalogues will be released once full analysis is complete.

##  3 TRANSIENT DETECTION PIPELINE

The primary technical goal of the VAST Pilot Surveys is to demonstrate the
capability to detect highly variable and transient sources. One of the main
open-source software packages for image-based radio transient detection is the
LOFAR Transients Pipeline (TraP; Swinbank et al., 2015). TraP can be applied
to radio image data from any facility, and has been used successfully in a
number of transient projects (e.g., Stewart et al., 2016; Driessen et al.,
2020; Sarbadhicary et al., 2020).

Testing TraP on the early ASKAP datasets revealed some scalability issues.
After investigating a number of options, we decided to implement a new
pipeline to search ASKAP data in an efficient way. Most of the key logic of
this pipeline follows the approach of TraP, but with some implementation
differences that significantly improve the performance.

The main VAST Pipeline technology stack is: Python 3.7+,
PostgreSQL666https://www.postgresql.org. $12+$,
Astropy777https://www.astropy.org. $4+$,
Django888https://www.djangoproject.com. $3+$,
Pandas999https://pandas.pydata.org. $1.2+$, Dask101010https://dask.org. $2+$
and Bootstrap111111https://getbootstrap.com/. 4\. A more complete technical
description of the pipeline implementation is given by Pintaldi et al. (2021).
In this section we discuss the scientific functionality and resulting data
products in the context of the results presented here.

###  3.1 Pipeline processing method

The VAST pipeline takes as input a set of images, rms maps and mean
(background) maps (all as FITS files) from Selavy as well as the Selavy source
finding component list. These data products are all default outputs from the
ASKAPSoft continuum imaging process, which means the VAST pipeline can be run
immediately after the ASKAPSoft data reduction is completed.

For the work presented in this paper, we used the combined images (created by
mosaicing individual fields together) as the input. This means that only
epoch-to-epoch timescales could be explored. The entire Selavy source list was
read in, but any duplicate sources from overlapping fields within the same
epoch were filtered out using a tight crossmatching radius of 2.5 arcsec. In
principle it would also be possible to search for variability within epochs in
the regions where fields overlap: this will be the subject of future analysis.
Investigating faster variability (within a single observation, for example,
Wang et al. 2021) will also be explored in future work.

We ran the VAST pipeline on these combined images, following the steps below:

  1. 1.

Image ingest: Image metadata and Selavy catalogues were ingested for each
image.

  2. 2.

Uncertainties on flux densities and positions for every source component were
calculated following Condon (1997).121212The uncertainties calculated by
Selavy were not correct at the time we ran the ASKAPSoft pipeline. This is
being addressed for a future release (Whiting, private communication).

  3. 3.

Source association was done by crossmatching each epoch to the next epoch
using the de Ruiter method (Scheers (2011), based on de Ruiter et al. 1977).
We used the default beamwidth limit of 1.5 and the search radius of 5.68
(these are both unitless values). The beam size that is used for the epoch
mode association should be equal to the largest beam contained in the epoch
images.

  4. 4.

Forced extractions (where a flux density is measured at a specified position)
were performed for sources that were not detected in every
epoch131313https://github.com/askap-vast/forced_phot., so that a complete
light curve could be built for each source. Where there were multiple
observations of a given source within an epoch (e.g., due to image overlap),
the forced extractions were taken from the image whose centre was nearest to
the source position.

  5. 5.

New sources that had not appeared in previous epochs were identified.

  6. 6.

Variability metrics were calculated for all sources identified by the pipeline
(see Section 3.2). The forced extraction measurements were incorporated when
calculating all variability metrics.

  7. 7.

Results were saved in a database and output to
parquet141414https://parquet.apache.org/documentation/latest/. files, an open
source columnar data format that allows flexible custom post-processing
(Vohra, 2016).

The resulting data products are a light curve for every source detected in one
or more epochs, and a set of standard variability metrics. Figure 8 shows
light curves for the highly variable sources presented in this paper.

The pipeline results are available to users via either an interactive web
application (including some basic multi-wavelength information), or through
the parquet files that can be read by a variety of data-analysis software
packages. The VAST Tools python package, described in Section 3.3, offers a
streamlined method to explore the pipeline results in a Jupyter notebook
environment. For more detailed information about the VAST pipeline, see the
publicly available software and documentation151515https://github.com/askap-
vast/vast-pipeline..

###  3.2 Variability metrics

The VAST pipeline identifies highly variable and transient sources using the
two key light curve parameters identified by Rowlinson et al. (2019). These
are: the modulation index $V$, a measure of the variability in a light curve;
and the reduced $\chi^{2}$ relative to a constant model $\eta$, a measure of
the significance of the variability. These parameters are defined as:

$V=\frac{1}{\overline{S}}\sqrt{\frac{N}{N-1}(\overline{S^{2}}-\overline{S}^{2})},$
(1)

$\eta=\frac{N}{N-1}\left(\overline{wS^{2}}-\frac{\overline{wS}^{2}}{\overline{w}}\right),$
(2)

where $N$ is the number of data points, the flux density and uncertainty on
the $i$-th epoch are $S_{i}$ and $\sigma_{i}$, means are denoted by overbars
(i.e., $\overline{S}\equiv\frac{1}{N}\sum_{i}S_{i}$) and in calculating $\eta$
the flux density measurements are weighted by the uncertainties according to
$w_{i}=1/\sigma_{i}^{2}$ (also see Swinbank et al. 2015).

The parameter $V$ selects sources with high variability without assessing the
statistical significance of that variability. In contrast, the parameter
$\eta$ acts as a measure of the statistical significance of that variability
without knowledge of its absolute amplitude. Used in combination, these
parameters select a sample of statistically significant, highly variable
objects.

The pipeline also allows for two-epoch variability searches (where the
variability is calculated based on pairs of observations), but this
functionality was not used in the results presented here.

###  3.3 VAST Tools software

VAST Tools161616https://github.com/askap-vast/vast-tools. is a python package
containing tools for accessing and exploring VAST data products. The current
functionality at the time of latest release (Version 2.0.0) is outlined below.
VAST Tools can be used via a command line interface or in Jupyter notebooks.
VAST Tools can also be used as an interface to analyse results from a VAST
Pipeline run.

####  3.3.1 Survey information

The VAST Tools package includes the complete details of the spatial and
temporal coverage of VAST-P1. The beam centres for all fields are listed in
comma-separated-values (CSV) files on a per-epoch basis, while Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix; Górski et al. 1999) Multi-Order
Coverage (MOC171717http://ivoa.net/documents/MOC/.; Fernique et al. 2019) maps
contain the spatial coverage of each survey field, and each epoch. We have
also created a space-time MOC for the full pilot survey coverage. The MOCs can
be accessed though a mocpy181818https://github.com/cds-astro/mocpy/. wrapper
included in the VAST Tools package, which also allows users to load MOCs and
also query VizieR tables for sources within the MOC coverage.

####  3.3.2 Querying coordinates

The Query class allows users to search the VAST pilot for observations of a
given location. Three main query types exist: (1) finding the fields
containing the specified coordinates; (2) searching for radio emission at the
specified coordinates; and (3) finding all sources within a given radius of
the specified coordinates. Query types 2 and 3 require VAST tools to be run on
a machine with access to the relevant FITS files and Selavy catalogues.

VAST tools can crossmatch coordinates with the Selavy catalogues, and produce
light curves, FITS cutouts and postage stamp images with various overlays
(e.g., Figure 11). Forced fitting is available, and there are options to
produce light curves with upper limits (based on Selavy rms maps), forced fits
for non-detections, or forced fits for all epochs.

####  3.3.3 Pipeline interface

Users are able to explore the results from the VAST Pipeline in an interactive
manner using the Pipeline class, which contains built-in methods to perform
tasks such as transient analyses, plotting lightcurves and querying external
resources. This is achieved by using the parquet files that are provided as
output by the pipeline, and using data exploration libraries such as Pandas
and vaex191919https://vaex.io..

##  4 UNTARGETED VARIABILITY SEARCH

Some of the early results from VAST focus on the time-domain behaviour of
samples of known objects selected in radio or other wavebands, for example the
search for radio afterglows from Swift GRBs that resulted in a detection of
the late-time afterglow from GRB 171205A (Leung et al., 2021).

In contrast, in this section we present initial results from an untargeted
search for highly variable sources and transients in regions 3 and 4 of the
VAST Phase I Pilot Survey. This demonstrates the functionality of the VAST
pipeline, and shows some of the diversity of variable sources that the full
VAST survey will detect. Analysis of the other survey regions will be
presented in subsequent papers.

###  4.1 Data analysis

We ran the VAST pipeline on the fields from regions 3 and 4, following the
process outlined in Section 3. We then selected compact, isolated sources by
excluding sources that satisfied any of the following criteria:

  * •

fewer than two measurements (forced or Selavy);

  * •

neighbouring sources within 30 arcseconds;

  * •

extended (a ratio of average integrated flux to average peak flux greater than
1.5);

  * •

a SNR of $<7$;

  * •

negative modulation index (caused by a negative mean flux density).

After applying these criteria, our remaining sample of compact, isolated
sources consisted of 155 071 unique sources each detected in at least one
epoch. This is a very conservative selection. For instance, requiring no
nearby neighbours helps to eliminate imaging artifacts but will also exclude
sources in a galaxy with a detectable nucleus or sources in crowded regions.
Therefore the results below should be taken as a lower limit, and future
searches should identify more objects even in the same regions of the sky.

Figure 6 shows the distribution of sources in the two key variability metrics
$V$ and $\eta$ (defined in Equations 1 and 2). Using $2\sigma$ cutoffs in both
of these parameters ($V>0.51$, $\eta>5.53$) we identified 171 sources as
highly variable. These appear in the shaded top right-hand quadrant of Figure
6.

We manually inspected the radio light curves, images and multi-wavelength data
for each of these sources, and identified 108 as artefacts near bright
sources. Another 14 were in regions of poor data quality, and 21 were
detections of marginal significance. This left 28 reliable astronomical
transients and variables, which are marked as red in Figure 6. We discuss
these variable sources in the next section.

Figure 6: A plot of the two key variability metrics, $V$ and $\eta$. Sources
that appear in the shaded top-right quadrant are variable candidates as they
exceed the $2\sigma$ thresholds on $V$ and $\eta$ calculated by fitting a
Gaussian function to the sigma-clipped distributions of each metric. The
thresholds are $V>0.51$ and $\eta>5.53$. Sources that have been classified as
variables after manual inspection are marked in red.

###  4.2 Results

Our 28 highly variable and transient sources are listed in Table 4 with light
curves in Figure 8 and image cutouts in Figure 11. Seven of these sources are
known pulsars and seven are radio stars. We discuss each of these classes
below. The remaining sources are discussed in Section 4.2.3.

####  4.2.1 Pulsars

Seven of our highly variable sources are known pulsars: PSRs J0255$-$5304,
J0418$-$4154, J0600$-$5756, J2039$-$5617, J2144$-$5237, J2155$-$5641, and
J2236$-$5527\. Pulsars can be variable for intrinsic reasons (nulling,
intermittency, e.g., Backer 1970; Kramer et al. 2006; Wang et al. 2020),
external reasons (interstellar scintillation; e.g., Rickett 1970; Stinebring &
Condon 1990; Bell et al. 2016; Kumamoto et al. 2021), or because of absorption
by gas in binary systems (e.g., Broderick et al., 2016; Polzin et al., 2020;
Kudale et al., 2020). In fact this variability can help separate pulsars from
the background of continuum sources (e.g., Dai et al., 2017).

Three of the sources are in binary systems: PSRs J2039$-$5617, J2144$-$5237,
and J2236$-$5527\. Of these, PSR J2039$-$5617 does have eclipses that cause
radio pulsations to disappear for half the orbit (Corongiu et al., 2021). That
source also has significant variability in the pulsed amplitude, which
Corongiu et al. (2021) largely ascribe to scintillation but also argue could
be caused by more ‘exotic’ mechanisms (such as absorption by intra-binary
gas). PSR J2144$-$5237 is a binary with a low-mass companion (Bhattacharyya et
al., 2019) that, based on its mass alone, could be a ‘redback’ system. Such
systems are often eclipsing (Roberts, 2011), but there is no sign of eclipses
in the timing data and so a helium-core white dwarf seems more likely. PSR
J2236$-$5527 is similarly presumed to have a helium-core white dwarf companion
(Burgay et al., 2013). Therefore binary companions do not seem to be causing
the variability in these systems, although PSR J2039$-$5617 may merit further
analysis.

Figure 7: Predicted modulation index (for interstellar scintillation) $V$
versus 888-MHz flux density for pulsars. We show all of the pulsars in the
ATNF pulsar catalogue (Manchester et al., 2016) as grey points, with the flux
densities computed from the catalogued values at 1.4 GHz or 430 MHz assuming a
spectral index of $-1.6$ (Jankowski et al., 2018). Predicted modulation index
is calculated using the Galactic electron-density model of Yao et al. (2017)
and diffractive scintillation following Lorimer & Kramer (2012) and Cordes &
Lazio (1991). The pulsars contained but not detected in VAST-P1 regions 3 and
4 are black outlined circles. The highly variable pulsars identified here are
blue circles (based on catalogue values, with the addition of Corongiu et al.
2021 for PSR J2039$-$5617 and Bhattacharyya et al. 2019 for PSR J2144$-$5237)
that are connected to their observed properties in VAST-P1 (blue squares).
Rather than mean flux density we show the maximum flux density, as that
governs detectability. Our thresholds of flux density $\geq 1.2\,$mJy (based
on the median image rms and a $5\sigma$ detection threshold) and modulation
index $\geq 0.51$ are the dashed lines. The black open circle at the right
near $300\,$mJy is PSR J0437$-$4715: it was not identified in our sample
because it is below our $V$ threshold (it has $V=0.4$) and it has an unrelated
source within the 30 arcsec neighbour limit.

For the other four pulsars, there are no exotic phenomena noted in the
literature (e.g., Newton et al., 1981; Xie et al., 2019; Johnston et al.,
2021). Therefore, in the absence of any other reason we would expect these
detections to be due to variability via refractive interstellar scintillation
on longer timescales (e.g., Kumamoto et al., 2021), plus diffractive
interstellar scintillation on shorter timescales (Rickett, 1990; Cordes &
Rickett, 1998) although since not all pulsars have been observed extensively
we cannot rule out other effects. To examine this in more detail, in Figure 7
we plot for all known pulsars the predicted modulation index for interstellar
scintillation versus the flux density, and highlight those identified here.
The predictions are far from perfect. We have assumed a single spectral index
for our flux density estimates, and a rough electron density model (there is
more than an order of magnitude variation in scattering timescale as a
function of dispersion measure; Kumamoto et al. 2021) along with a single
source velocity for our scintillation estimates. However, the sources
identified through variability are among those expected to be brighter and
more heavily modulated. We also highlight the difference between the predicted
properties (which will reflect the mean flux densities) and the observed
properties, which will be biased to high flux densities and modulation levels
because of the nature of these detections.

The maximum dispersion measure (DM) in regions 3 and 4 is about $30\,{\rm
pc\,cm}^{-3}$ (Yao et al., 2017), which is also consistent with the DMs of the
pulsars we identified (they span 14–30$\,{\rm pc\,cm}^{-3}$). As the
scintillation timescale and bandwidth are largely dependent on the DM (e.g.,
Kumamoto et al., 2021), the DM range can be used as a proxy to help identify
which other sources will be detected as variable in the remainder of our
survey.

####  4.2.2 Stars

Seven of our highly variable sources are identified with known stars. Two of
them (CD–44 1173 and UPM J0409–4435) were found in the circular polarisation
search conducted by Pritchard et al. (2021), and are discussed there. Our new
detections (AB Dor A/C, SCR J0533–4257, UCAC3 89–412162, LEHPM 2-783 and 2MASS
J22414436$-$6119311) are discussed below and the images are shown in Figure
11.

Figure 8: Lightcurves of the variables identified in Regions 3 and 4 of
VAST-P1. Blue round points are peak flux density measurements from Selavy.
Grey triangles are the $3\sigma$ upper-limits of the forced-fitted flux
density for images where there was no Selavy detection. Figure 9: (continued)
Lightcurves of the variables identified in Regions 3 and 4 of VAST-P1. Blue
round points are peak flux density measurements from Selavy. Grey triangles
are the $3\sigma$ upper-limits of the forced-fitted flux density for images
where there was no Selavy detection. Figure 10: (continued) Lightcurves of the
variables identified in Regions 3 and 4 of VAST-P1. Blue round points are peak
flux density measurements from Selavy. Grey triangles are the $3\sigma$ upper-
limits of the forced-fitted flux density for images where there was no Selavy
detection. Figure 11: Images of the variable sources identified in this paper.
The left panel shows the VAST Stokes I image for the epoch with the maximum
flux density. The middle panel shows the Stokes V image for the same epoch
where positive flux density corresponds to right handed circular polarisation
and negative to left handed. The ellipse in the lower left corner of each
radio image shows the FWHM of the restoring beam. The right panels show Stokes
I contours at 30, 60, and 90 per cent of the peak Stokes I flux density
overlaid on an RGB image of optical data from either DES DR1 (DES) or
SkyMapper (SM; Onken et al., 2019) with red=i-band, green=r-band, blue=g-band.
All images have been centred on a frame aligned with the position of the radio
source. Optical data have been astrometrically corrected to the VAST epoch
according to the proper motion of the target when available. Figure 12:
(continued) Images of the variable sources identified in this paper. The left
panel shows the VAST Stokes I image for the epoch with the maximum flux
density. The middle panel shows the Stokes V image for the same epoch where
positive flux density corresponds to right handed circular polarisation and
negative to left handed. The ellipse in the lower left corner of each radio
image shows the FWHM of the restoring beam. The right panels show Stokes I
contours at 30, 60, and 90 per cent of the peak Stokes I flux density overlaid
on an RGB image of optical data from either DES DR1 (DES) or SkyMapper (SM;
Onken et al., 2019) with red=i-band, green=r-band, blue=g-band. All images
have been centred on a frame aligned with the position of the radio source.
Optical data have been astrometrically corrected to the VAST epoch according
to the proper motion of the target when available. Figure 13: (continued)
Images of the variable sources identified in this paper. The left panel shows
the VAST Stokes I image for the epoch with the maximum flux density. The
middle panel shows the Stokes V image for the same epoch where positive flux
density corresponds to right handed circular polarisation and negative to left
handed. The ellipse in the lower left corner of each radio image shows the
FWHM of the restoring beam. The right panels show Stokes I contours at 30, 60,
and 90 per cent of the peak Stokes I flux density overlaid on an RGB image of
optical data from either DES DR1 (DES) or SkyMapper (SM; Onken et al., 2019)
with red=i-band, green=r-band, blue=g-band. All images have been centred on a
frame aligned with the position of the radio source. Optical data have been
astrometrically corrected to the VAST epoch according to the proper motion of
the target when available. Figure 14: (continued) Images of the variable
sources identified in this paper. The left panel shows the VAST Stokes I image
for the epoch with the maximum flux density. The middle panel shows the Stokes
V image for the same epoch where positive flux density corresponds to right
handed circular polarisation and negative to left handed. The ellipse in the
lower left corner of each radio image shows the FWHM of the restoring beam.
The right panels show Stokes I contours at 30, 60, and 90 per cent of the peak
Stokes I flux density overlaid on an RGB image of optical data from either DES
DR1 (DES) or SkyMapper (SM; Onken et al., 2019) with red=i-band, green=r-band,
blue=g-band. All images have been centred on a frame aligned with the position
of the radio source. Optical data have been astrometrically corrected to the
VAST epoch according to the proper motion of the target when available. Figure
15: (continued) Images of the variable sources identified in this paper. The
left panel shows the VAST Stokes I image for the epoch with the maximum flux
density. The middle panel shows the Stokes V image for the same epoch where
positive flux density corresponds to right handed circular polarisation and
negative to left handed. The ellipse in the lower left corner of each radio
image shows the FWHM of the restoring beam. The right panels show Stokes I
contours at 30, 60, and 90 per cent of the peak Stokes I flux density overlaid
on an RGB image of optical data from either DES DR1 (DES) or SkyMapper (SM;
Onken et al., 2019) with red=i-band, green=r-band, blue=g-band. All images
have been centred on a frame aligned with the position of the radio source.
Optical data have been astrometrically corrected to the VAST epoch according
to the proper motion of the target when available. Figure 16: (continued)
Images of the variable sources identified in this paper. The left panel shows
the VAST Stokes I image for the epoch with the maximum flux density. The
middle panel shows the Stokes V image for the same epoch where positive flux
density corresponds to right handed circular polarisation and negative to left
handed. The ellipse in the lower left corner of each radio image shows the
FWHM of the restoring beam. The right panels show Stokes I contours at 30, 60,
and 90 per cent of the peak Stokes I flux density overlaid on an RGB image of
optical data from either DES DR1 (DES) or SkyMapper (SM; Onken et al., 2019)
with red=i-band, green=r-band, blue=g-band. All images have been centred on a
frame aligned with the position of the radio source. Optical data have been
astrometrically corrected to the VAST epoch according to the proper motion of
the target when available. Figure 17: (continued) Images of the variable
sources identified in this paper. The left panel shows the VAST Stokes I image
for the epoch with the maximum flux density. The middle panel shows the Stokes
V image for the same epoch where positive flux density corresponds to right
handed circular polarisation and negative to left handed. The ellipse in the
lower left corner of each radio image shows the FWHM of the restoring beam.
The right panels show Stokes I contours at 30, 60, and 90 per cent of the peak
Stokes I flux density overlaid on an RGB image of optical data from either DES
DR1 (DES) or SkyMapper (SM; Onken et al., 2019) with red=i-band, green=r-band,
blue=g-band. All images have been centred on a frame aligned with the position
of the radio source. Optical data have been astrometrically corrected to the
VAST epoch according to the proper motion of the target when available.

VAST J052644.9$-$652650 is identified as AB Dor, a quadruple system composed
of a K1V pre-main sequence star + M5.5V and M3.5 + M4.5 pair of binary systems
(Close et al., 2007). The pre-main sequence star AB Dor A, is by far the
brightest star in the system; it has a rotation period of 0.514 d, and shows
optical super-flares (Schmitt et al., 2019) and long-term X-ray variability
(Lalitha & Schmitt, 2013). VLBI radio observations at
$1.4\text{\,}\mathrm{GHz}$, $8.4\text{\,}\mathrm{GHz}$, and
$22.3\text{\,}\mathrm{GHz}$ have revealed milliarcsecond radio structure
(Climent et al., 2020). The source is detected in VAST-P1 in 11 epochs in
Stokes I with a maximum flux density of 7.8 mJy beam-1, and in three epochs in
Stokes V showing both right- and left-handed circular polarisation with
circular polarisation fraction ranging from $52\%$ to $60\%$. We speculate
that the three events with high circular polarisation are associated with
known prominence formation in young active stars such as AB Dor, HK Aqr, BO
Mic and PZ Tel (Doyle & Collier Cameron, 1990; Barnes et al., 2001; Jardine et
al., 2001). AB Dor lies in the Transiting Exoplanet Survey Satellite (TESS;
Ricker et al. 2015) southern continuous viewing zone, allowing near continuous
optical photometry at 2 min cadence extending over a year (Ioannidis &
Schmitt, 2020).

VAST J053328.0$-$425719 is identified as SCR J0533–4257, an M4.5 dwarf (Riaz
et al., 2006). Ultraviolet activity (Schneider & Shkolnik, 2018) and X-ray
flares (Fuhrmeister & Schmitt, 2003) have been observed from this star, though
there are no previous radio detections reported in the literature. The source
is detected in three epochs. The maximum flux density is $2.83\pm 0.24$ mJy
beam-1. The circular polarisation fraction ranges between $33\%$ and $57\%$.
It was observed using TESS (TIC 302962949) in 2 min cadence during sectors 5
and 6 and in 20 sec cadence in sector 33 (total duration 67.9 d). The TESS
data show evidence for modulations at periods of 1.25 and 0.46 d, hinting it
may be a binary. Optical flares are frequently seen, most of which have a peak
amplitude of a few $\sim$0.01 mag but up to 0.2 mag on one occasion.

VAST J201949.8$-$581650 is identified as LEHPM 2–783, an M6 dwarf (Riaz et
al., 2006) with high proper motion ($\mu_{\alpha}$=–23.4,
$\mu_{\delta}$=–341.1 mas yr-1) (Gaia Collaboration et al., 2018). There are
no previous radio detections reported in the literature. The source is only
detected in a single epoch with a flux density of $2.50\pm 0.27$ mJy beam-1
and a circular polarisation fraction of $77\%$. It was observed using TESS
(TIC 387220832) in 2 min cadence during sectors 13 and 27 (total duration 50.4
d). The TESS sector 13 data shows evidence for a period at 6.7 d whilst the
sector 27 data suggest a longer period of 11.6 d. It is flare-active, with
several optical flares having amplitudes greater than 0.5 mag. It has a
location on the Gaia colour-magnitude diagram red-ward of the main sequence,
indicating it maybe a binary or a young star. The high circulation
polarisation of 77% could point towards auroral magnetospheric activity,
similar to that seen by Zic et al. (2019) in UV Ceti, a star of similar
spectral type.

VAST J212217.5$-$454631 is identified as UCAC3 89–412162, which has a distance
of 31.6 pc and with a Gaia colour $(\mathrm{BP}-\mathrm{RP})=2.49$ and
absolute magnitude MG $=10.0$ (Gaia Collaboration et al., 2018) is consistent
with an M3V spectral type (Pecaut & Mamajek, 2013). There are no previous
radio detections reported in the literature. This source is also detected in a
single epoch with a flux density of $2.31\pm 0.25$ mJy beam-1 and a circular
polarisation fraction of $87\%$. It was observed using TESS (TIC 200645542) in
2 min cadence during sectors 27 and 28 (total duration 41.1 d). There is no
evidence for a periodic modulation in the optical light curve or flaring
activity. The lack of flaring activity in this object is consistent with
reduced or minimum spot coverage. This of course does not mean a lack of
flaring, e.g., Schmitt et al. (2019) suggested that large flares only have
$\sim$14% of their energy in the TESS passband. The fact that we detect a
radio outburst could suggest that this object may have flares that have a peak
flux in the blue, since the contrast over the background emission is much
higher at shorter wavelengths and the TESS wavelength band in centred at 7865
Å.

VAST J224144.7$-$611933 is associated with 2MASS J22414436$-$6119311\. It has
a high proper motion ($\mu_{\alpha}=150.2$, $\mu_{\delta}=-87.9$ mas yr-1,
from Gaia), a colour ($\mathrm{BP}-\mathrm{RP}=2.81$) consistent with a M3.5V
spectral type and has a distance of 28.42$\pm$0.06 pc (Gaia Collaboration et
al., 2021). It was observed in TESS sectors 1, 27 and 28 (TIC 232064182) in 2
min cadence and shows evidence for a periodic modulation on a period of 0.721
d. Short duration optical flares are seen with amplitudes typically of a few
percent, but on one occasion with an amplitude of $\sim$60 percent.

All of the stars in our sample show radio emission with high fractional
circular polarisation ($>30\%$), making this a useful tool for distinguishing
these sources from extragalactic transients. They have optical magnitudes
between V=17.17 and V=10.9, and span a wide range in brightness and colour.
With the exception of SCR J0533$-$4257, these stars have high proper motions
($>100$ mas/yr) suggesting that in the absence of an optical identification,
VLBI proper motion measurements are another way in which they can be
distinguished from extragalactic sources.

####  4.2.3 Other variables

We found 14 highly variable sources that were not associated with a known
pulsar or star. For these we searched for optical or infrared counterparts in
several WISE catalogs, Gaia EDR3 (Gaia Collaboration et al., 2021) and the
Dark Energy Survey DR1. Proper motions from Gaia were applied if available.
Photometric redshifts were retrieved from the Photometric Redshifts for the
Legacy Surveys catalogue (Zhou et al., 2021) when available. For sources that
have unclear WISE counterparts due to resolution limits, we used forced-
photometry measurements from the unWISE images (Lang, 2014; Meisner et al.,
2016, 2017, for W1 and W2) and WISE images (for W3) provided in the DESI
Legacy Imaging Surveys DR8 catalogue (Dey et al., 2019).

None of these 14 sources have a detection in circular polarisation within the
VAST-P1 data, although many of the limits are not very constraining, with
$3\sigma$ Stokes V/I fractional polarisation limits between $11\%$ and $56\%$.
The multi-wavelength properties of these objects are summarised in Table 3.
Sources with a WISE counterpart (including some of the stars discussed in
Section 4.2.2) are plotted on a WISE colour-colour plot in Figure 18.

Table 3: Multiwavelength properties of the 14 variable sources not identified as known stars or pulsars; see Section 4.2.3. We provide the WISE cross-ID, or for sources with unclear WISE counterparts, the unique LS object ID from the DESI Legacy Imaging Surveys DR8 catalogue. The median of the photometric redshift probability distributions are provided when available from Zhou et al. (2021) along with the limits of the 95% confidence interval. Source Name | Cross-ID | photo-z | Class
---|---|---|---
VAST J031648.1$-$625825 | Gaia EDR3 4722095986792487296 | 1.16 (0.81, 1.50) | QSO
| LS-DR8 8796095391534556 |  |
| WISEA J031648.22$-$625825.5 |  |
VAST J041908.3$-$472233 | LS-DR8 8796098726269814 | 1.06 (0.82, 1.28) | Galaxy
| WISEA J041908.43$-$472233.1 |  |
VAST J051151.0$-$623054 | LS-DR8 8796095481781222 | – | Galaxy
| WISEA J051151.13$-$623053.6 |  |
VAST J051452.1$-$440838 | – | – | ?
VAST J055639.5$-$614106 | LS-DR8 8796095615927489 | 0.83 (0.39, 1.12) | Galaxy
VAST J202339.9$-$561604 | Gaia EDR3 6469160387838360704 | 0.28 (0.27, 0.31) | Galaxy
| LS-DR8 8796096719948142 |  |
| WISEA J202340.02$-$561604.8 |  |
VAST J204100.8$-$500727 | – | – | ?
VAST J204421.6$-$495935 | – | – | ?
VAST J205441.9$-$401059 | – | – | ?
VAST J210110.3$-$451649 | LS-DR8 8796099359737883 | 0.48 (0.41, 0.50) | Galaxy
| WISEA J210110.42$-$451648.9 |  |
VAST J212623.6$-$461124 | Gaia EDR3 6575524939389655808 | 2.32 (0.19, 2.68) | Seyfert
| LS-DR8 8796099096154771 |  |
VAST J212758.6$-$470528 | – | – | ?
VAST J212857.7$-$395953 | – | – | ?
VAST J225351.7$-$630834 | LS-DR8 8796095383739344 | 1.00 (0.86, 1.16) | Galaxy
Figure 18: WISE colours of the variable sources identified in this paper
plotted on top of the classification regions from Wright et al. (2010).
Sources classified as stars are shown in orange, and those classified as other
variables are shown in blue. Sources that did not have a clear counterpart in
WISE but had unWISE forced-photometry available in the DESI Legacy Imaging
Surveys DR8 catalogue are shown with open markers.

Two of the objects are associated with AGN. VAST J031648.1–625825 is
coincident with the IR source WISEA J031648.22–625825.5 (and Gaia source EDR3
472209598679248729). The WISE colours of this object (see Figure 18) suggest
it is a QSO with a photometric redshift202020Redshifts are quoted as the
median of the photo-$z$ probability distribution with the 95% confidence
interval. of $z=1.16\>(0.82,1.50)$. There are only two detections in VAST-P1.
VAST J212623.6-461124 is coincident with the Gaia source EDR3
657552493938965580, and unWISE colours suggest this is a Seyfert galaxy. There
are four detections of this source in VAST-P1. Both of these Gaia sources have
parallaxes and proper motions consistent with zero. In the absence of further
information it is not possible to determine the cause of the observed
variability, but it can likely be explained by scintillation or intrinsic AGN
variability.

Six of the sources are associated with galaxies, based on their WISE or DES
DR1 identifications. None of these are offset from the central positions of
their optical or infrared counterparts and so could be due to nuclear
emission, but the limited angular resolution means we cannot rule out other
sorts of transients (supernovae, GRBs). Therefore, we also searched for
possible associations with known transients at other wavelengths. We checked
against catalogues of GRBs detected by the International Gamma-Ray
Astrophysics Laboratory (INTEGRAL; Mereghetti et al., 2003), the Swift Burst
Alert Telescope (BAT; Lien et al., 2016) and the Fermi Large Area Telescope
(LAT; Ajello et al., 2019) and Gamma-ray Burst Monitor (GBM; von Kienlin et
al., 2020). Possible associations with known supernovae were checked against
the Open Supernova Catalog212121https://sne.space/. (Guillochon et al., 2017)
and supernovae reported in the Transient Name Server222222https://www.wis-
tns.org/.. None of the sources are associated with known GRBs or supernovae.
Further monitoring during VAST-P2 may provide more insight into the origin of
the variability in these objects.

The remaining six sources have no obvious optical/IR counterparts. None are
detected in circular polarisation (although again the limiting ratios are not
very constraining, with $3\sigma$ Stokes V/I ratios ranging between 24% and
56%). Again, none of these sources are associated with known GRBs or
supernovae. With the exception of VAST J204421.6$-$495935, none exhibit a
light curve with a temporal rise or decay that is physically consistent with
an uncatalogued GRB afterglow or radio supernova. Further observations of
these sources are ongoing.

We used the lightcurves and redshifts (where available) to compute brightness
temperatures for all of the sources in Table 3 (as in Pietka et al. 2015,
Stewart et al. 2016, Bhandari et al. 2018, Bell et al. 2019, and others). For
a timescale, we took the minimum timescale over which the two-point modulation
index (e.g., Mooley et al., 2016) exceeded 0.5. Given the sparsity of our
temporal sampling, these timescales do not have good resolution, but range
from $\sim 1-2$ d (e.g., VAST J051452.1$-$440838) to 266 d (VAST
J212857.7$-$395953). Using the photometric redshifts (or $z=1$ when not
available) we found brightness temperatures of $10^{13}\,$K for the most
slowly-varying sources to $>10^{18}\,$K for the most quickly-varying. However,
we do not believe those timescales to be intrinsic to the sources, as they
would violate the Compton catastrophe limit (Kellermann & Pauliny-Toth, 1969)
or the stricter equipartition brightness temperature limit (Readhead, 1994).
Instead we look at three possibilities: extrinsic variability, Doppler
boosting, or coherent emission.

For extrinsic variability, we used NE2001 (Cordes & Lazio, 2002) to calculate
the scintillation transition frequency, $\nu_{0}$ for extragalactic sources
along the lines of sight contained by regions 3 and 4. Both regions have
similar values with the values in region 3 spanning $7.8-12.3$ GHz and the
values in region 4 spanning $8.1-14.1$ GHz with median values of 9.2 GHz and
9.8 GHz respectively. This quantity can then be used to estimate the expected
level of variability caused by refractive scintillation using the scaling
relations of Walker (1998). We find that compact sources in both regions will
exhibit $\sim 25\%$ variability at 888 MHz due to refractive scintillation on
timescales of $\sim 10$ days (the all-sky model for refractive scintillation
from Hancock et al. 2019 predicts a similar timescale, but a mean modulation
index about a factor of two higher than that predicted from NE2001). In most
cases this is reasonably consistent with what we observe, although as we are
selecting the most variable sources we are likely probing the tail of the
modulation-index distribution.

However, there are sources that seem inconsistent with this interpretation.
For instance, VAST J051452.1$-$440838 and VAST J212758.6$-$470528, which vary
by a factor of $>2$ on timescales as fast as 1 d. If such variability were to
be intrinsic and not violate the Compton catastrophe limit, then both sources
would be nearby ($\lesssim$5 Mpc); one may have then expected that a
counterpart should be detectable at other wavelengths. Instead, in these
cases, diffractive scintillation (which has higher modulation indices and
faster variability; e.g., Walker 1998 and Macquart & de Bruyn 2006) may be
operating if the source size is very compact. Or, we may be seeing a
relativistic jet beamed toward us with a Doppler factor of $\sim 100$ (e.g.,
Readhead, 1994; Lähteenmäki & Valtaoja, 1999). Finally, some of the the
sources without optical counterparts may be coherently-emitting Galactic
objects such as pulsars, where diffractive scintillation is expected and
intrinsic variability can be very fast. For most of regions 3 and 4 the most
sensitive pulsar survey is the high-latitude portion of the High Time
Resolution Universe (HTRU) survey (Keith et al., 2010), which is sensitive to
flux densities as low as 0.2 mJy at 1.4 GHz for low DMs. This is a factor of
10 below the flux densities of our sources, but especially if they are
scintillating strongly or otherwise varying, previous surveys may have missed
them. Further observations to probe the spectral energy distribution, small-
scale structure, and precise position of the varying source will help
distinguish between these scenarios, as can optical spectroscopy to search for
signatures of AGN (in those classified as ‘Galaxy’ in Table 3) and pulsar
searches (for those classified as ‘?’).

Table 4: Highly variable sources identified in the VAST-P1 regions 3 and 4. The coordinate of each source is given as the weighted average of all Selavy detections, where the weight is the inverse square of the positional error. $\sigma_{\text{pos}}$ is the averaged positional uncertainty. $\eta$ and $V$ are the variability parameters described in the text. nE gives the number of epochs (observations) that cover the source location. nD gives the number of detections. $|{\rm V}|/{\rm I}$ is the ratio of Stokes V to Stokes I flux density measured in the epoch for which this is a maximum, or the most constraining 3 sigma upper limit in the case of non-detections in Stokes V. Source Name | RA | Dec | $\sigma_{\text{pos}}$ | $\eta$ | $V$ | nE | nD | $S_{\text{max}}$ | $|{\rm V}|/{\rm I}$ | ID
---|---|---|---|---|---|---|---|---|---|---
| (J2000) | (J2000) | (arcsec) |  |  |  |  | (mJy beam-1) |  |
Pulsars |  |  |  |  |  |  |  |  |  |
VAST J025556.2$-$530421 | 02:55:56.3 |  $-$53:04:21 | 0.4 | 2997.26 | 1.40 | 7 | 7 | 52.6$\pm$0.4 | $<0.01$ | PSR J0255$-$5304
VAST J041803.7$-$415414 | 04:18:03.8 |  $-$41:54:14 | 0.4 | 14.76 | 0.80 | 9 | 4 | 4.2$\pm$0.2 | $<0.15$ | PSR J0418$-$4154
VAST J060046.5$-$575654 | 06:00:46.5 |  $-$57:56:54 | 0.3 | 6.82 | 2.02 | 10 | 1 | 1.8$\pm$0.2 | $<0.29$ | PSR J0600$-$5756
VAST J203934.8$-$561709 | 20:39:34.8 |  $-$56:17:10 | 0.4 | 6.22 | 1.12 | 6 | 2 | 1.9$\pm$0.2 | $<0.27$ | PSR J2039$-$5617
VAST J214435.6$-$523707 | 21:44:35.7 |  $-$52:37:07 | 0.5 | 153.22 | 1.06 | 6 | 5 | 9.6$\pm$0.3 | $<0.08$ | PSR J2144$-$5237
VAST J215501.2$-$564158 | 21:55:01.3 |  $-$56:41:59 | 0.4 | 5.99 | 1.11 | 7 | 1 | 1.7$\pm$0.2 | $<0.25$ | PSR J2155$-$5641
VAST J223651.8$-$552748 | 22:36:51.8 |  $-$55:27:48 | 0.4 | 15.28 | 1.08 | 7 | 1 | 2.6$\pm$0.2 | $<0.20$ | PSR J2236$-$5527
Stars |  |  |  |  |  |  |  |  |  |
VAST J033155.6$-$435914 | 03:31:55.7 |  $-$43:59:15 | 0.4 | 25.14 | 2.21 | 6 | 1 | 4.2$\pm$0.3 | $0.88\pm 0.29$ | CD$-$44 1173
VAST J040932.1$-$443538 | 04:09:32.2 |  $-$44:35:38 | 0.3 | 9.77 | 1.55 | 10 | 1 | 2.6$\pm$0.2 | $0.93\pm 0.40$ | UPM J0409$-$4435
VAST J052844.9$-$652650 | 05:28:44.9 |  $-$65:26:50 | 0.4 | 56.17 | 0.56 | 11 | 11 | 7.8$\pm$0.4 | $0.60\pm 0.17$ | V* AB Dor B
VAST J053328.0$-$425719 | 05:33:28.0 |  $-$42:57:20 | 0.4 | 9.58 | 1.11 | 9 | 3 | 2.8$\pm$0.2 | $0.57\pm 0.24$ | SCR J0533$-$4257
VAST J201949.8$-$581650 | 20:19:49.8 |  $-$58:16:50 | 0.4 | 8.03 | 1.48 | 6 | 1 | 2.5$\pm$0.3 | $0.78\pm 0.36$ | LEHPM 2$-$783
VAST J212217.5$-$454631 | 21:22:17.6 |  $-$45:46:31 | 0.4 | 9.51 | 2.92 | 7 | 1 | 2.3$\pm$0.2 | $0.87\pm 0.39$ | UCAC3 89$-$412162
VAST J224144.7$-$611933 | 22:41:44.8 |  $-$61:19:33 | 0.5 | 29.89 | 0.78 | 6 | 4 | 4.4$\pm$0.3 | $0.60\pm 0.34$ | 2MASS J22414436$-$6119311
Other variables |  |  |  |  |  |  |  |  |  |
VAST J031648.1$-$625825 | 03:16:48.1 |  $-$62:58:25 | 0.5 | 10.92 | 0.98 | 5 | 2 | 2.0$\pm$0.2 | $<0.35$ | –
VAST J041908.3$-$472233 | 04:19:08.4 |  $-$47:22:33 | 0.4 | 7.83 | 0.95 | 10 | 4 | 3.1$\pm$0.5 | $<0.47$ | –
VAST J051151.0$-$623054 | 05:11:51.1 |  $-$62:30:54 | 0.5 | 7.57 | 0.54 | 6 | 3 | 2.6$\pm$0.2 | $<0.21$ | –
VAST J051452.1$-$440838 | 05:14:52.1 |  $-$44:08:38 | 0.3 | 18.43 | 0.84 | 11 | 3 | 4.1$\pm$0.2 | $<0.11$ | –
VAST J055639.5$-$614106 | 05:56:39.5 |  $-$61:41:07 | 0.4 | 6.01 | 0.52 | 10 | 6 | 2.5$\pm$0.3 | $<0.24$ | –
VAST J202339.9$-$561604 | 20:23:40.0 |  $-$56:16:05 | 0.5 | 10.71 | 0.55 | 6 | 5 | 2.9$\pm$0.2 | $<0.18$ | –
VAST J204100.8$-$500727 | 20:41:00.8 |  $-$50:07:27 | 0.4 | 5.90 | 0.65 | 6 | 2 | 2.3$\pm$0.2 | $<0.27$ | –
VAST J204421.6$-$495935 | 20:44:21.7 |  $-$49:59:35 | 0.5 | 63.09 | 0.56 | 6 | 6 | 5.4$\pm$0.2 | $<0.14$ | –
VAST J205441.9$-$401059 | 20:54:42.0 |  $-$40:11:00 | 0.4 | 21.59 | 1.92 | 7 | 1 | 8.7$\pm$0.5 | $<0.09$ | –
VAST J210110.3$-$451649 | 21:01:10.4 |  $-$45:16:49 | 0.4 | 6.06 | 0.54 | 7 | 4 | 2.6$\pm$0.2 | $<0.24$ | –
VAST J212623.6$-$461124 | 21:26:23.7 |  $-$46:11:25 | 0.4 | 10.17 | 0.67 | 7 | 4 | 4.6$\pm$0.3 | $<0.15$ | –
VAST J212758.6$-$470528 | 21:27:58.7 |  $-$47:05:29 | 0.4 | 8.86 | 1.28 | 7 | 1 | 2.0$\pm$0.2 | $<0.24$ | –
VAST J212857.7$-$395953 | 21:28:57.8 |  $-$39:59:53 | 0.7 | 6.35 | 0.93 | 3 | 2 | 4.0$\pm$0.4 | $<0.56$ | –
VAST J225351.7$-$630834 | 22:53:51.7 |  $-$63:08:35 | 0.5 | 14.02 | 0.58 | 5 | 2 | 4.7$\pm$0.3 | $<0.41$ | –

###  4.3 Variability analysis and transient source density

We found 28 highly variable or transient sources out of 155 071 compact
sources detected in at least one epoch of the VAST-P1 survey. This is a lower
limit as we used relatively strict variability thresholds (Figure 6) and also
exclude any source with neighbours within $30\,$arcsec (Section 4.1). In
particular, about 2% of the surveyed area was not searched for ‘transients’
because it was within 30 arcsec of an existing source, but as many as 8% of
sources were not searched for variability as they had neighbours. Future
analyses will relax these requirements and so should discover more transient
and variable sources. The variability metrics we used in this analysis
correspond to a variability of $\sim 50\%$. This implies that only an
extremely small percentage ($0.02\%$) of sources above $\sim 1.75$ mJy are
variable at this level on timescales of a few months. This is consistent with
(although much lower than) upper limits from previous studies at 1.4 GHz that
showed that the fraction of variables on timescales between minutes and years,
and flux densities above 0.1 mJy is less than $\sim 1\%$ (Bannister et al.,
2011a, b; Thyagarajan et al., 2011; Mooley et al., 2013) as summarised by
Mooley et al. (2016).

Of our 28 highly variable sources, nine were detected in only a single epoch
of our survey (see the sources with ${\rm nD}=1$ in Table 4) and hence would
have been considered ‘transients’ in previous works that calculated transient
source densities and rates (e.g., Mooley et al., 2016). Of these nine single-
epoch transients, seven are known pulsars or stars, suggesting that a majority
of candidates detected in searches for extragalactic transients at this
sensitivity will be foreground Galactic sources, even at Galactic latitudes of
$|b|\approx 45\deg$ sampled here. These are easy to identify through cross-
matching with existing catalogues, or, in some cases by their circularly
polarised emission.

The other 19 sources were detected in multiple (2–7) epochs, but some of them
could still have been considered ‘transients’ in previous surveys due to the
range of temporal scales probed. For instance VAST J041908.3$-$472233 has 4
detections (Figure 8) but they all occur within a 15 d period, with
constraining non-detections before and after. So for a typical synchrotron
transient with timescale $>30\,$d at these frequencies (e.g., Metzger et al.
2015), it could represent a single event. Even some sources that appear
multiple times could be considered ‘transients’ in the context of a 2-epoch
transient rate. VAST J031648.1$-$625825 was visible, disappeared, and then
reappeared 480 d later. For a 2-epoch survey metric it could then represent 2
independent transients.

For the purposes of comparison with previous work, we have calculated the two-
epoch transient source density for extragalactic synchrotron sources, on
timescales of $>30\,$d (Metzger et al., 2015; Carbone et al., 2017). Future
works will explore longer and shorter timescales as well as more detailed
rates calculations. We considered only the unclassified sources from Table 4
and computed two numbers of ‘transients’: total source pairs based on a
constraining non-detection (upper limit lower than the lowest detection)
combined with a detection, and unique sources, where in both cases all
observations had been binned to 30 d samples. The former more accurately
represents a 2-epoch snapshot rate, while the latter accounts for multiple
detections of individual objects. We found 20 total ‘transients‘ comprising 6
unique sources. The equivalent sky area probed was 41 034.6 deg2, based on
unique pairs of observations binned to 30 d samples (Carbone et al., 2016).
This then gives a density of $(4.9\pm 1.1)\times 10^{-4}$ deg-2 (total
transients) or $(1.5\pm 0.6)\times 10^{-4}$ deg-2 (unique sources). These are
at a flux density of $>1.2$ mJy (based on our median sensitivity and a
5$\sigma$ detection threshold).

Figure 19 shows this two-epoch transient source density compared to other
results (and constraining limits) from the literature232323Compiled from
http://www.tauceti.caltech.edu/kunal/radio-transient-surveys/index.html
(Mooley et al., 2013)..

Figure 19: Two-epoch transient source surface density limits. Black wedges
denote upper limits from previous searches for transients on week-month
timescales (Bower & Saul, 2011; Dobie et al., 2019; Thyagarajan et al., 2011),
while black markers show rates from searches with detected transients
(Bannister et al., 2011a; Anderson et al., 2020) with associated two-sided 95%
confidence interval (Gehrels, 1986). Cyan wedges show the phase space that the
proposed VAST Deep single field (VAST-DS), Deep multi-field (VAST-DM) and VAST
wide surveys will be sensitive to (note that these predictions come from
Murphy et al. (2013) and will change once the full VAST survey specifications
are confirmed). The result of this work is shown in red. The predicted rates
of neutron star mergers (NSM), magnetars, long and short gamma ray bursts
(LGRB, SGRB) and tidal disruption events (TDE) from Metzger et al. (2015) are
shown with dashed lines.

##  5 SUMMARY

We have introduced the VAST Pilot Survey and described the survey strategy and
observations to date. We outlined the VAST transient detection pipeline, and
as a demonstration of its functionality, used it to analyse two regions (3 and
4) of the Phase I Pilot Survey.

In this analysis we found 28 highly variable and transient sources out of a
total of 155 071 sources detected in these regions. These sources included
seven known pulsars, seven stars (three of which have no previous radio
detections), two AGN, six galaxies and six sources yet to be identified.

We found a transient source density of $(1.5\pm 0.6)\times 10^{-4}$ deg-2 for
a flux density limit of $>1.2$ mJy and a 5$\sigma$ detection threshold. This
is consistent with rates measured in other surveys.

Future work on the VAST pipeline will incorporate better methods for reducing
the false positive rate, in particular for excluding artefacts due to nearby
bright sources. We are also developing a pipeline to enable faster continuum
imaging of visibility datasets (following a similar method as that of Wang et
al. 2021) so that we can process data as soon as possible after the
observations.

In the coming year, we will conduct the Phase II Pilot Survey, incorporating
both low-band and mid-band observations. The results presented here are a
significant milestone in demonstrating ASKAP’s capability to detect transient
sources once VAST begins full survey operations in 2022.

###### Acknowledgements.

TM acknowledges the support of the Australian Research Council through grants
FT150100099 and DP190100561. JL and JP are supported by Australian Government
Research Training Program Scholarships. DK and AO are supported by NSF grant
AST-1816492. SC acknowledges support from the NSF (AAG 1815242). MJ was
supported by NSF Physics Frontiers Center award number 1430284. TA
acknowledges support from the NSFC (12041301). BMG acknowledges the support of
the Natural Sciences and Engineering Research Council of Canada (NSERC)
through grant RGPIN-2015-05948, and of the Canada Research Chairs program. GRS
is supported by NSERC Discovery Grants RGPIN-2016-06569 and RGPIN-2021-04001.
SD is the recipient of an Australian Research Council Discovery Early Career
Award (DE210101738). AH acknowledges support by the I-Core Program of the
Planning and Budgeting Committee and the Israel Science Foundation, and
support by ISF grant 647/18. This research was supported by Grant No. 2018154
from the United States-Israel Binational Science Foundation (BSF). AH
acknowledges support by the Sir Zelman Cowen Universities Fund. Parts of this
research were conducted by the Australian Research Council Centre of
Excellence for Gravitational Wave Discovery (OzGrav), project number
CE170100004. This research was supported by the Sydney Informatics Hub (SIH),
a core research facility at the University of Sydney. This work was also
supported by software support resources awarded under the Astronomy Data and
Computing Services (ADACS) Merit Allocation Program. ADACS is funded from the
Astronomy National Collaborative Research Infrastructure Strategy (NCRIS)
allocation provided by the Australian Government and managed by Astronomy
Australia Limited (AAL). The Dunlap Institute is funded through an endowment
established by the David Dunlap family and the University of Toronto. The
Australian Square Kilometre Array Pathfinder is part of the Australia
Telescope National Facility which is managed by CSIRO. Operation of ASKAP is
funded by the Australian Government with support from the National
Collaborative Research Infrastructure Strategy. ASKAP uses the resources of
the Pawsey Supercomputing Centre. Establishment of ASKAP, the Murchison Radio-
astronomy Observatory and the Pawsey Supercomputing Centre are initiatives of
the Australian Government, with support from the Government of Western
Australia and the Science and Industry Endowment Fund. We acknowledge the
Wajarri Yamatji as the traditional owners of the Murchison Radio-astronomy
Observatory site. Armagh Observatory & Planetarium is core funded by the N.
Ireland Executive through the Department for Communities. This work has made
use of data from the European Space Agency (ESA) mission Gaia
(https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and
Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement. The national facility
capability for SkyMapper has been funded through ARC LIEF grant LE130100104
from the Australian Research Council, awarded to the University of Sydney, the
Australian National University, Swinburne University of Technology, the
University of Queensland, the University of Western Australia, the University
of Melbourne, Curtin University of Technology, Monash University and the
Australian Astronomical Observatory. SkyMapper is owned and operated by The
Australian National University’s Research School of Astronomy and
Astrophysics. The survey data were processed and provided by the SkyMapper
Team at ANU. The SkyMapper node of the All-Sky Virtual Observatory (ASVO) is
hosted at the National Computational Infrastructure (NCI). Development and
support the SkyMapper node of the ASVO has been funded in part by Astronomy
Australia Limited (AAL) and the Australian Government through the
Commonwealth’s Education Investment Fund (EIF) and National Collaborative
Research Infrastructure Strategy (NCRIS), particularly the National eResearch
Collaboration Tools and Resources (NeCTAR) and the Australian National Data
Service Projects (ANDS). This research made use of:
Astropy,242424http://www.astropy.org a community-developed core Python package
for Astronomy (Astropy Collaboration et al., 2013, 2018); matplotlib (Hunter,
2007); and numpy (Harris et al., 2020).

## References

  * Abbott et al. (2018) Abbott T. M. C., et al., 2018, ApJS, 239, 18
  * Abbott et al. (2020) Abbott R., et al., 2020, ApJ, 896, L44
  * Ajello et al. (2019) Ajello M., et al., 2019, ApJ, 878, 52
  * Anderson et al. (2020) Anderson M. M., et al., 2020, ApJ, 903, 116
  * Astropy Collaboration et al. (2013) Astropy Collaboration et al., 2013, A&A, 558, A33
  * Astropy Collaboration et al. (2018) Astropy Collaboration et al., 2018, AJ, 156, 123
  * Backer (1970) Backer D. C., 1970, Nature, 228, 42
  * Bannister et al. (2011a) Bannister K. W., Murphy T., Gaensler B. M., Hunstead R. W., Chatterjee S., 2011a, MNRAS, 412, 634
  * Bannister et al. (2011b) Bannister K. W., Murphy T., Gaensler B. M., Hunstead R. W., Chatterjee S., 2011b, MNRAS, 418, 2813
  * Barnes et al. (2001) Barnes J. R., Collier Cameron A., James D. J., Donati J. F., 2001, MNRAS, 324, 231
  * Bell et al. (2016) Bell M. E., et al., 2016, MNRAS, 461, 908
  * Bell et al. (2019) Bell M. E., et al., 2019, MNRAS, 482, 2484
  * Bhandari et al. (2018) Bhandari S., et al., 2018, MNRAS, 478, 1784
  * Bhattacharyya et al. (2019) Bhattacharyya B., et al., 2019, ApJ, 881, 59
  * Bower & Saul (2011) Bower G. C., Saul D., 2011, ApJ, 728, L14
  * Bower et al. (2007) Bower G. C., et al., 2007, ApJ, 666, 346
  * Broderick et al. (2016) Broderick J. W., et al., 2016, MNRAS, 459, 2681
  * Burgay et al. (2013) Burgay M., et al., 2013, MNRAS, 433, 259
  * Carbone et al. (2016) Carbone D., et al., 2016, MNRAS, 459, 3161
  * Carbone et al. (2017) Carbone D., van der Horst A. J., Wijers R. A. M. J., Rowlinson A., 2017, MNRAS, 465, 4106
  * Carilli et al. (2003) Carilli C. L., Ivison R. J., Frail D. A., 2003, ApJ, 590, 192
  * Chambers et al. (2016) Chambers K. C., et al., 2016, arXiv e-prints, p. arXiv:1612.05560
  * Chapman et al. (2017) Chapman J. M., Dempsey J., Miller D., Heywood I., Pritchard J., Sangster E., Whiting M., Dart M., 2017, in Lorente N. P. F., Shortridge K., Wayth R., eds, Astronomical Society of the Pacific Conference Series Vol. 512, Astronomical Data Analysis Software and Systems XXV. p. 73
  * Charlot et al. (2020) Charlot P., et al., 2020, A&A, 644, A159
  * Climent et al. (2020) Climent J. B., Guirado J. C., Azulay R., Marcaide J. M., Jauncey D. L., Lestrade J. F., Reynolds J. E., 2020, A&A, 641, A90
  * Close et al. (2007) Close L. M., Thatte N., Nielsen E. L., Abuter R., Clarke F., Tecza M., 2007, ApJ, 665, 736
  * Condon (1997) Condon J. J., 1997, PASP, 109, 166
  * Cordes & Lazio (1991) Cordes J. M., Lazio T. J., 1991, ApJ, 376, 123
  * Cordes & Lazio (2002) Cordes J. M., Lazio T. J. W., 2002, arXiv e-prints, pp astro–ph/0207156
  * Cordes & Rickett (1998) Cordes J. M., Rickett B. J., 1998, ApJ, 507, 846
  * Cordes et al. (2004) Cordes J. M., et al., 2004, New Astronomy Reviews, 48, 1459
  * Cornwell et al. (2012) Cornwell T., et al., 2012, Technical report, ASKAP Science Processing Document. CSIRO Astronomy and Space Science
  * Corongiu et al. (2021) Corongiu A., et al., 2021, MNRAS, 502, 935
  * Cowperthwaite & Berger (2015) Cowperthwaite P. S., Berger E., 2015, ApJ, 814, 25
  * Dai et al. (2017) Dai S., Johnston S., Hobbs G., 2017, MNRAS, 472, 1458
  * Dey et al. (2019) Dey A., et al., 2019, AJ, 157, 168
  * Dobie et al. (2019) Dobie D., et al., 2019, ApJ, 887, L13
  * Doyle & Collier Cameron (1990) Doyle J. G., Collier Cameron A., 1990, MNRAS, 244, 291
  * Driessen (2020) Driessen L., 2020, Astronomy and Geophysics, 61, 5.12
  * Driessen et al. (2020) Driessen L. N., et al., 2020, MNRAS, 491, 560
  * Fender et al. (2016) Fender R., et al., 2016, in MeerKAT Science: On the Pathway to the SKA. p. 13 (arXiv:1711.04132)
  * Fernique et al. (2019) Fernique P., Boch T., Donaldson T., Durand D., O’Mullane W., Reinecke M., Taylor M., 2019, MOC - HEALPix Multi-Order Coverage map Version 1.1, IVOA Recommendation 07 October 2019
  * Fuhrmeister & Schmitt (2003) Fuhrmeister B., Schmitt J. H. M. M., 2003, A&A, 403, 247
  * Gaensler et al. (2010) Gaensler B. M., Landecker T. L., Taylor A. R., POSSUM Collaboration 2010, in American Astronomical Society Meeting Abstracts #215. p. 470.13
  * Gaia Collaboration et al. (2016) Gaia Collaboration et al., 2016, A&A, 595, A1
  * Gaia Collaboration et al. (2018) Gaia Collaboration et al., 2018, A&A, 616, A1
  * Gaia Collaboration et al. (2021) Gaia Collaboration et al., 2021, A&A, 649, A1
  * Gal-Yam et al. (2006) Gal-Yam A., et al., 2006, ApJ, 639, 331
  * Gehrels (1986) Gehrels N., 1986, ApJ, 303, 336
  * Górski et al. (1999) Górski K. M., Wandelt B. D., Hansen F. K., Hivon E., Banday A. J., 1999, arXiv e-prints, pp astro–ph/9905275
  * Guillochon et al. (2017) Guillochon J., Parrent J., Kelley L. Z., Margutti R., 2017, ApJ, 835, 64
  * Hancock et al. (2019) Hancock P. J., Charlton E. G., Macquart J.-P., Hurley-Walker N., 2019, arXiv e-prints, p. arXiv:1907.08395
  * Harris et al. (2020) Harris C. R., et al., 2020, Nature, 585, 357
  * Heywood et al. (2016) Heywood I., et al., 2016, MNRAS, 457, 4160
  * Ho et al. (2018) Ho A. Y. Q., et al., 2018, ApJ, 854, L13
  * Hotan et al. (2021) Hotan A. W., et al., 2021, PASA, 38, e009
  * Hunter (2007) Hunter J. D., 2007, Computing in Science & Engineering, 9, 90
  * Hyman et al. (2005) Hyman S. D., et al., 2005, Nature, 434, 50
  * Hyman et al. (2009) Hyman S. D., Wijnands R., Lazio T. J. W., Pal S., Starling R., Kassim N. E., Ray P. S., 2009, ApJ, 696, 280
  * Ioannidis & Schmitt (2020) Ioannidis P., Schmitt J. H. M. M., 2020, A&A, 644, A26
  * Jankowski et al. (2018) Jankowski F., van Straten W., Keane E. F., Bailes M., Barr E. D., Johnston S., Kerr M., 2018, MNRAS, 473, 4436
  * Jardine et al. (2001) Jardine M., Collier Cameron A., Donati J. F., Pointer G. R., 2001, MNRAS, 324, 201
  * Johnston et al. (2007) Johnston S., et al., 2007, PASA, 24, 174
  * Johnston et al. (2021) Johnston S., et al., 2021, MNRAS, 502, 1253
  * Jonas & MeerKAT Team (2016) Jonas J., MeerKAT Team 2016, in MeerKAT Science: On the Pathway to the SKA. p. 1
  * Kaplan et al. (2008) Kaplan D. L., Hyman S. D., Roy S., Bandyopadhyay R. M., Chakrabarty D., Kassim N. E., Lazio T. J. W., Ray P. S., 2008, ApJ, 687, 262
  * Kaplan et al. (2019) Kaplan D. L., et al., 2019, ApJ, 884, 96
  * Keith et al. (2010) Keith M. J., et al., 2010, MNRAS, 409, 619
  * Keller et al. (2007) Keller S. C., et al., 2007, PASA, 24, 1
  * Kellermann & Pauliny-Toth (1969) Kellermann K. I., Pauliny-Toth I. I. K., 1969, ApJ, 155, L71
  * Kramer et al. (2006) Kramer M., Lyne A. G., O’Brien J. T., Jordan C. A., Lorimer D. R., 2006, Science, 312, 549
  * Kudale et al. (2020) Kudale S., Roy J., Bhattacharyya B., Stappers B., Chengalur J., 2020, ApJ, 900, 194
  * Kumamoto et al. (2021) Kumamoto H., et al., 2021, MNRAS, 501, 4490
  * Lacy et al. (2020) Lacy M., et al., 2020, PASP, 132, 035001
  * Lähteenmäki & Valtaoja (1999) Lähteenmäki A., Valtaoja E., 1999, ApJ, 521, 493
  * Lalitha & Schmitt (2013) Lalitha S., Schmitt J. H. M. M., 2013, A&A, 559, A119
  * Lang (2014) Lang D., 2014, The Astronomical Journal, 147, 108
  * Law et al. (2018) Law C. J., Gaensler B. M., Metzger B. D., Ofek E. O., Sironi L., 2018, ApJ, 866, L22
  * Leung et al. (2021) Leung J. K., et al., 2021, MNRAS,
  * Lien et al. (2016) Lien A., et al., 2016, ApJ, 829, 7
  * Lorimer & Kramer (2012) Lorimer D. R., Kramer M., 2012, Handbook of Pulsar Astronomy. Cambridge University Press
  * Macquart & de Bruyn (2006) Macquart J. P., de Bruyn A. G., 2006, A&A, 446, 185
  * Macquart et al. (2010) Macquart J., et al., 2010, PASA, 27, 272
  * Manchester et al. (2016) Manchester R. N., Hobbs G. B., Teoh A., Hobbs M., 2016, VizieR Online Data Catalog, p. B/psr
  * McConnell et al. (2020) McConnell D., et al., 2020, PASA, 37, e048
  * McMahon et al. (2013) McMahon R. G., Banerji M., Gonzalez E., Koposov S. E., Bejar V. J., Lodieu N., Rebolo R., VHS Collaboration 2013, The Messenger, 154, 35
  * Meisner et al. (2016) Meisner A. M., Lang D., Schlegel D. J., 2016, The Astronomical Journal, 153, 38
  * Meisner et al. (2017) Meisner A. M., Lang D., Schlegel D. J., 2017, The Astronomical Journal, 154, 161
  * Mereghetti et al. (2003) Mereghetti S., Götz D., Borkowski J., Walter R., Pedersen H., 2003, A&A, 411, L291
  * Metzger et al. (2015) Metzger B. D., Williams P. K. G., Berger E., 2015, ApJ, 806, 224
  * Minniti et al. (2010) Minniti D., et al., 2010, New Astronomy, 15, 433
  * Mooley et al. (2013) Mooley K. P., Frail D. A., Ofek E. O., Miller N. A., Kulkarni S. R., Horesh A., 2013, ApJ, 768, 165
  * Mooley et al. (2016) Mooley K. P., et al., 2016, ApJ, 818, 105
  * Murphy et al. (2013) Murphy T., et al., 2013, PASA, 30, e006
  * Newton et al. (1981) Newton L. M., Manchester R. N., Cooke D. J., 1981, MNRAS, 194, 841
  * Norris et al. (2011) Norris R. P., et al., 2011, PASA, 28, 215
  * Ofek et al. (2011) Ofek E. O., Frail D. A., Breslauer B., Kulkarni S. R., Chandra P., Gal-Yam A., Kasliwal M. M., Gehrels N., 2011, ApJ, 740, 65
  * Onken et al. (2019) Onken C. A., et al., 2019, PASA, 36, e033
  * Pecaut & Mamajek (2013) Pecaut M. J., Mamajek E. E., 2013, ApJS, 208, 9
  * Pietka et al. (2015) Pietka M., Fender R. P., Keane E. F., 2015, MNRAS, 446, 3687
  * Pintaldi et al. (2021) Pintaldi S., Stewart A., O’Brien A., Kaplan D., Murphy T., 2021, arXiv e-prints, p. arXiv:2101.05898
  * Polzin et al. (2020) Polzin E. J., Breton R. P., Bhattacharyya B., Scholte D., Sobey C., Stappers B. W., 2020, MNRAS, 494, 2948
  * Price (2016) Price D. C., 2016, PyGSM: Python interface to the Global Sky Model (ascl:1603.013)
  * Pritchard et al. (2021) Pritchard J., et al., 2021, MNRAS, 502, 5438
  * Ravi et al. (2021) Ravi V., et al., 2021, arXiv e-prints, p. arXiv:2102.05795
  * Readhead (1994) Readhead A. C. S., 1994, ApJ, 426, 51
  * Riaz et al. (2006) Riaz B., Gizis J. E., Harvin J., 2006, AJ, 132, 866
  * Ricker et al. (2015) Ricker G. R., et al., 2015, Journal of Astronomical Telescopes, Instruments, and Systems, 1, 014003
  * Rickett (1970) Rickett B. J., 1970, MNRAS, 150, 67
  * Rickett (1990) Rickett B. J., 1990, ARA&A, 28, 561
  * Roberts (2011) Roberts M. S. E., 2011, in Burgay M., D’Amico N., Esposito P., Pellizzoni A., Possenti A., eds, American Institute of Physics Conference Series Vol. 1357, Radio Pulsars: An Astrophysical Key to Unlock the Secrets of the Universe. pp 127–130 (arXiv:1103.0819), doi:10.1063/1.3615095
  * Rowlinson et al. (2019) Rowlinson A., et al., 2019, Astronomy and Computing, 27, 111
  * Roy et al. (2010) Roy S., Hyman S. D., Pal S., Lazio T. J. W., Ray P. S., Kassim N. E., 2010, ApJ, 712, L5
  * Sarbadhicary et al. (2020) Sarbadhicary S. K., et al., 2020, ApJ, submitted, arXiv:2009.05056
  * Scheers (2011) Scheers L. H. A., 2011, PhD thesis, University of Amsterdam
  * Schmitt et al. (2019) Schmitt J. H. M. M., Ioannidis P., Robrade J., Czesla S., Schneider P. C., 2019, A&A, 628, A79
  * Schneider & Shkolnik (2018) Schneider A. C., Shkolnik E. L., 2018, AJ, 155, 122
  * Stewart et al. (2016) Stewart A. J., et al., 2016, MNRAS, 456, 2321
  * Stewart et al. (2018) Stewart A. J., Muñoz-Darias T., Fender R. P., Pietka M., 2018, MNRAS, 479, 2481
  * Stinebring & Condon (1990) Stinebring D. R., Condon J. J., 1990, ApJ, 352, 207
  * Swinbank et al. (2015) Swinbank J. D., et al., 2015, Astronomy and Computing, 11, 25
  * Thyagarajan et al. (2011) Thyagarajan N., Helfand D. J., White R. L., Becker R. H., 2011, ApJ, 742, 49
  * Vohra (2016) Vohra D., 2016, in , Practical Hadoop Ecosystem. Apress, Berkeley, CA, pp 325–335, doi:10.1007/978-1-4842-2199-0_8
  * Walker (1998) Walker M. A., 1998, MNRAS, 294, 307
  * Wang et al. (2020) Wang S. Q., et al., 2020, ApJ, 897, 8
  * Wang et al. (2021) Wang Y., Tuntsov A., Murphy T., Lenc E., Walker M., Bannister K., Kaplan D. L., Mahony E. K., 2021, MNRAS, 502, 3294
  * Webb et al. (2020) Webb S., et al., 2020, MNRAS, 498, 3077
  * White et al. (1997) White R. L., Becker R. H., Helfand D. J., Gregg M. D., 1997, ApJ, 475, 479
  * Whiting (2012) Whiting M. T., 2012, MNRAS, 421, 3242
  * Wright et al. (2010) Wright E. L., et al., 2010, AJ, 140, 1868
  * Xie et al. (2019) Xie Y.-W., et al., 2019, Research in Astronomy and Astrophysics, 19, 103
  * Yao et al. (2017) Yao J. M., Manchester R. N., Wang N., 2017, ApJ, 835, 29
  * Zheng et al. (2017) Zheng H., et al., 2017, MNRAS, 464, 3486
  * Zhou et al. (2021) Zhou R., et al., 2021, MNRAS, 501, 3309
  * Zic et al. (2019) Zic A., et al., 2019, MNRAS, 488, 559
  * Zic et al. (2020) Zic A., et al., 2020, ApJ, 905, 23
  * de Ruiter et al. (1977) de Ruiter H. R., Willis A. G., Arp H. C., 1977, A&AS, 28, 211
  * van Haarlem et al. (2013) van Haarlem M. P., et al., 2013, A&A, 556, A2
  * von Kienlin et al. (2020) von Kienlin A., et al., 2020, ApJ, 893, 46