# Bursts from Space: MeerKAT - The first citizen science project dedicated to
commensal radio transients

Alex Andersson,1 Chris Lintott,1 Rob Fender,1,2 Joe Bright,1 Francesco
Carotenuto,1 Laura Driessen,3,4,5 Mathilde Espinasse,6 Kelebogile
Gaseahalwe,2,7 Ian Heywood,1,8,9 Alexander J. van der Horst,10,11 Sara
Motta,12 Lauren Rhodes,1 Evangelia Tremou ,13 David R. A. Williams,4 Patrick
Woudt,2 Xian Zhang,14,15 Steven Bloemen,16 Paul Groot,2,7,16 Paul Vreeswijk,16
Stefano Giarratana,17,18 Payaswini Saikia,19 Jonas Andersson,20 Lizzeth Ruiz
Arroyo,20 Loïc Baert,20 Matthew Baumann,20 Wilfried Domainko,20 Thorsten
Eschweiler,20 Tim Forsythe,20 Sauro Gaudenzi,20 Rachel Ann Grenier,20 Davide
Iannone,20 Karla Lahoz,20 Kyle J. Melville,20 Marianne De Sousa Nascimento,20
Leticia Navarro,20 Sai Parthasarathi,20 Piilonen,20 Najma Rahman,20 Jeffrey
Smith,20 B. Stewart,20 Newton Temoke,20 Chloe Tworek20 and Isabelle Whittle20
1Astrophysics, Department of Physics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford OX1 3RH, UK
2Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch
7701, South Africa
3CSIRO, Space and Astronomy, PO Box 1130, Bentley, WA 6102, Australia
4Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy,
The University of Manchester, Manchester, M13 9PL, UK
5Sydney Institute for Astronomy, School of Physics, The University of Sydney,
NSW 2006, Australia
6AIM/CEA Paris-Saclay, Universite Paris Diderot, CNRS, F-91191 Gif-sur-Yvette,
France
7South African Astronomical Observatory, PO Box 9, Observatory 7935, South
Africa
8Department of Physics and Electronics, Rhodes University, PO Box 94, Makhanda
6140, South Africa
9South African Radio Astronomy Observatory, 2 Fir Street, Observatory 7925,
South Africa
10Department of Physics, The George Washington University, 725 21st Street NW,
Washington, DC 20052, USA
11Astronomy, Physics and Statistics Institute of Sciences (APSIS), 725 21st
Street NW, Washington, DC 20052, USA
12Istituto Nazionale di Astrofisica, Osservatorio Astronomico di Brera, via E.
Bianchi 46, 23807 Merate (LC), Italy
13National Radio Astronomy Observatory, Socorro, NM 87801, USA
14Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan
Road, Shanghai 200030, China
15University of Chinese Academy of Sciences, 19A Yuquanlu, Beijing 100049,
China
16Department of Astrophysics/IMAPP, Radboud University, P.O. 9010, 6500 GL,
Nĳmegen, The Netherlands
17Dipartimento di Fisica e Astronomia, Università degli Studi di Bologna, Via
Gobetti 93/2, 40129 Bologna, Italy
18INAF – Istituto di Radioastronomia, Via Gobetti 101, 40129 Bologna, Italy
19Center for Astro, Particle and Planetary Physics, New York University Abu
Dhabi, PO Box 129188, Abu Dhabi, UAE
20Citizen Scientist, Zooniverse c/o University of Oxford, Keble Road, Oxford
OX1 3RH, UK
E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

The newest generation of radio telescopes are able to survey large areas with
high sensitivity and cadence, producing data volumes that require new methods
to better understand the transient sky. Here we describe the results from the
first citizen science project dedicated to commensal radio transients, using
data from the MeerKAT telescope with weekly cadence. Bursts from Space:
MeerKAT was launched late in 2021 and received $\sim$89000 classifications
from over 1000 volunteers in 3 months. Our volunteers discovered 142 new
variable sources which, along with the known transients in our fields, allowed
us to estimate that at least 2.1 per cent of radio sources are varying at 1.28
GHz at the sampled cadence and sensitivity, in line with previous work. We
provide the full catalogue of these sources, the largest of candidate radio
variables to date. Transient sources found with archival counterparts include
a pulsar (B1845-01) and an OH maser star (OH 30.1–0.7), in addition to the
recovery of known stellar flares and X-ray binary jets in our observations.
Data from the MeerLICHT optical telescope, along with estimates of long time-
scale variability induced by scintillation, imply that the majority of the new
variables are active galactic nuclei. This tells us that citizen scientists
can discover phenomena varying on time-scales from weeks to several years. The
success both in terms of volunteer engagement and scientific merit warrants
the continued development of the project, whilst we use the classifications
from volunteers to develop machine learning techniques for finding transients.

###### keywords:

radio continuum: transients – radio continuum: galaxies – radio continuum:
general – surveys

††pubyear: 2022††pagerange: Bursts from Space: MeerKAT - The first citizen
science project dedicated to commensal radio transients–A

##  1 Introduction

The latest generation of radio telescopes provide us with unprecedented detail
of the radio sky. Regular, wide-field images from highly sensitive telescopes,
including Square Kilometre Array (SKA) pathfinders such as MeerKAT (Jonas &
MeerKAT Team, 2016) and the Australian SKA Pathfinder (ASKAP; Hotan et al.,
2021) allow us to probe a wide range of physics at novel time-scales and
depths. For example, stellar radio emission can provide insight into magnetic
re-connection (Rigney et al., 2022) and has implications for orbiting planet
habitability (Airapetian et al., 2017; Günther et al., 2020), whilst the radio
afterglows of gamma-ray bursts constrain jet physics and kinetic feedback of
the most violent eruptions in the Universe (e.g. Rhodes et al., 2021).
Similarly, fast radio bursts can probe the baryonic content of the Universe
(Macquart et al., 2020), whilst the afterglows of neutron star mergers can
provide key insights into the structure of relativistic jets (Margutti &
Chornock, 2021). The combination of sensitivity, regular cadence and
(crucially) wide field of view (FoV) allow for commensal, untargetted
strategies in order to search for these known transient phenomena and new
classes of objects as yet undiscovered.

Previous investigations from both MeerKAT and other instruments have found
that only a few per cent of point sources aretransient or variable above
sensitivity limits at 1.4 GHz (Ofek et al., 2011 and references therein), with
source classes spanning a wide range of time-scales and physical processes.
The majority of radio variables found are active galactic nuclei (AGN; see
e.g. Thyagarajan et al., 2011), whose variations can be attributed to a
combination of refractive scintillation (Rickett, 1990) and shock-induced
flaring in their jets (Mooley et al., 2016). Whilst these AGN dominate samples
of variables, active or flaring stars have been found in untargetted radio
surveys (Mooley et al., 2016; Driessen et al., 2020; Andersson et al., 2022),
as have supernovae and GRB orphan afterglow candidates (Levinson et al., 2002;
Gal-Yam et al., 2006). Pulsars can vary intrinsically in the image plane -
indeed some of the slowest known pulsars are discovered in imaging data (Tan
et al., 2018; Caleb et al., 2022). Futhermore, diffractive scintillation
through the interstellar medium can cause short time-scale, large amplitude
variations in observations of pulsars, whilst refractive scintillation can
produce lower amplitude variability occurring on time-scales of hours to years
for point-like sources (Rickett, 2001; Hancock et al., 2019). There are also
numerous accounts of radio transients being discovered without clear
progenitor systems or multiwavelength counterparts (Bower et al., 2007;
Stewart et al., 2016; Murphy et al., 2017). These include the elusive sources
near the Galactic centre (Davies et al., 1976; Zhao et al., 1992; Hyman et
al., 2005; Chiti et al., 2016) \- including the newest such transient found by
ASKAP (Wang et al., 2021). The serendipitous discoveries, elusive nature and
broad physics at play in this zoo of radio transients all point towards the
need for new searches and the development of novel methods to maximise the
science yield of our observations.

ThunderKAT111The Hunt for Dynamic and Explosive Radio transients with MeerKAT
(Fender et al., 2016) is a large survey project dedicated to monitoring radio
transients with MeerKAT. The ThunderKAT team regularly observes known
transients such as X-ray binaries, cataclysmic variables and gamma-ray bursts
(XRBs, CVs and GRBs respectively). The large field of view of MeerKAT ($\sim
1$ square degree at 1.28 GHz), sampled at approximately weekly cadences with
high sensitivity also allows for unprecedented commensal searches for
transients, variables and other ancilliary science. Driessen et al. (2020),
Driessen et al. (2022a) and Andersson et al. (2022) describe the first
commensal transients found with MeerKAT, detailing flaring and quiescent
behaviour from stellar systems. Similarly, Driessen et al. (2022b) and
Rowlinson et al. (2022, hereafter D22 and R22 respectively) make use of the
best-sampled ThunderKAT fields surrounding XRBs GX339$-4$ and MAXI J1820+070
to discover new radio variables including pulsars and variable AGN. Images of
short GRB fields have been searched for transients at both fast and slower
time-scales in Chastain et al. (submitted), wherein there are many newly
described variables, of which most are likely scintillating AGN. The
ThunderKAT survey also makes use of MeerLICHT (Bloemen et al., 2016), a
robotic facility (65cm primary mirror) whose goals include shadowing MeerKAT
observations, providing spatial and temporal coverage of the optical sky to
complement the radio data.

Despite these methods of searching for radio transients bearing fruit, they
are not optimal. Firstly, the volume of data to analyse is far greater than
any one person can achieve by eye on reasonable time-scales. In the 4 years
since operations began, ThunderKAT has observed over 30 XRBs in total at
weekly cadence, typically following each source for over a month. This results
in over 100 TB of raw data to reduce and analyse, producing over 500 final
images. Each image then contains of order several hundred sources, from just a
single 15 minute observation. ThunderKAT also has memoranda of understanding
with many of the other Large Survey Projects on MeerKAT, such as LADUMA (Blyth
et al., 2016), MIGHTEE (Jarvis et al., 2016) and MHONGOOSE (de Blok et al.,
2016), to use their data commensally. As a result there are many hundreds of
observations in the growing archive, in which transients may reside, probing
right down to 1$\sigma$ sensitivity limits $\sim 1\mu$Jy (Heywood et al.,
2022). These data overload issues are only exacerbated when imaging on shorter
time-scales, including down to the 8s integration time of MeerKAT, as is
currently being tested within ThunderKAT (e.g. Caleb et al., 2022; Chastain et
al. submitted; Fijma et al. in prep).

Radio observations are not free from false positives. Two main causes of these
false positives are the non-Gaussian artefacts that typically occur around
bright sources in radio images, and the changes in the point spread function
(PSF, or restoring beam) caused by differing elevations over a set of
observations, which induces non-intrinsic variability in the measurements of
resolved objects. As these issues might plague only one observation in a
dataset, they can lead to measurements easily confused for bona fide
transients by automated methods.

One method to search for radio transients is by harnessing the power of
citizen science. Citizen science projects hosted on the
Zooniverse222zooniverse.org have been highly successful in transient
astrophysics and astronomy more generally, starting with the original Galaxy
Zoo (Lintott et al., 2008). Since then the hundreds of public projects have
received over 700 million classifications from 2.5 million users (taken from
the website’s live tracker). In transient astronomy specifically, Wright et
al. (2017)’s Supernova Hunters combined a neural network with human
classifications to outperform either classifier alone and is still discovering
supernovae, over six years since launch333see https://www.wis-
tns.org/object/2022aeee. Similarly, Citizen ASAS-SN users have discovered
$>$10,000 new variable sources that are not present in the existing star
catalogs of the southern hemisphere (Christy et al., 2022). The Zooniverse’s
Talk feature (project specific forums where users, moderators and experts can
discuss individual subjects, classifications and so on) provides room for
novel discovery space - classic Galaxy Zoo examples include the ‘Green Peas’,
a class of compact galaxies with extremely high star formation rates
(Cardamone et al., 2009) and ‘Hanny’s Voorwerp’, an extended region of gas
ionised by the now-faded AGN of IC 2497 (Lintott et al., 2009). Similar finds
from other projects include unusual variable stars and new classes of
systematic noise in LIGO/Virgo detectors (Christy et al., 2022; Zevin et al.,
2017). In this work we will describe the first citizen science project
dedicated to radio transients. The aims of this project are to discover new
transients, eliminate spurious false positives and provide complementary
analysis to other commensal search methods, as well as allowing us to assess
the viability of further citizen science work.

In section 2 we detail the observations and processing prior to the Zooniverse
project launch discussed in section 3, the results of which are found in
section 4. We search for counterparts to radio variables in section 5 before
the discussion and conclusions found in sections 6 and 7.

##  2 ThunderKAT Observations and Pre-processing

Our observations consist of a subset of ThunderKAT XRB images, based on which
datasets were available at the time of research and contained more than a few
epochs. The observations used in this work were taken between mid-2018 and
late 2021. Generally, the observing strategy is determined by reports from
X-ray facilities of activity from an XRB, which is then observed at weekly
cadence by ThunderKAT in 15 minute images. Each observing block consists of
first scanning a primary calibrator, then bookending source observations with
phase calibrator observations. A table of the 11 datasets used in this study,
with the number of observations in each, is given in Table 1. The varying
number of epochs between datasets is a direct result of the radio activity of
the central XRB i.e. if a source is seen to fade below detection it is removed
from the weekly scheduling block, whilst the central root mean square flux
density (RMS) varies due to baseline coverage and presence of diffuse
structures. The values presented are the median values of the RMS calculated
across all images of a given dataset, evaluated in the central 8th of an
image. It is worth mentioning that the GX339–4 field has been observed every
week since ThunderKAT observations began, in contrast to the one or two
outburst cycles followed for all other datasets. It is important to remember
that the commensal nature of this work constrains us to whatever observational
cadence was used for monitoring the XRB.

Table 1: Properties of the 11 ThunderKAT datasets used in this work. Each field’s approximate Galactic latitude is given for relevance in section 4 and Figure 5. The number of sources and central RMS values are calculated by the TraP (see section 2). Dataset/Central XRB | Galactic | Epochs | Duration | Number of | Average Central | XRB Paper
---|---|---|---|---|---|---
| Latitude$(\degr)$ |  |  |  TraP Sources | RMS ($\mu$Jy) |
GX339–4 | –04.33 | 167 | 2018-04-14 – 2021-10-31 | 714 | 35 |  Tremou et al. (2020);
|  |  |  |  |  | Tremou et al. in prep
MAXI J1820+070 | +10.16 | 77 | 2018-09-28 – 2020-11-22 | 1838 | 26 | Bright et al. (2020)
GRS 1915+105 | –00.22 | 60 | 2018-12-08 – 2021-04-10 | 510 | 136 | Motta et al. (2021)
MAXI J1348–630 | –01.10 | 51 | 2019-01-29 – 2020-09-26 | 533 | 45 | Carotenuto et al. (2021)
MAXI J1848–015 | –00.10 | 35 | 2021-02-28 – 2021-11-19 | 271 | 290 |  Tremou et al. (2021);
|  |  |  |  |  | Bahramian et al. submitted
MAXI J1803–298 | –03.84 | 28 | 2021-05-04 – 2021-11-19 | 1093 | 22 | Espinasse et al. (2021)
EXO1846–031 | –00.92 | 26 | 2019-08-04 – 2020-04-10 | 366 | 88 | Williams et al. (2022)
Swift J1858.6–0814 | –05.32 | 25 | 2018-11-10 – 2020-03-02 | 1512 | 22 | Rhodes et al. (2022)
4U1543–47 | +05.42 | 21 | 2021-06-19 – 2021-11-14 | 904 | 27 | Zhang et al. (2021)
|  |  |  |  |  | Zhang et al. in prep
H1743–322 | –01.83 | 11 | 2018-09-05 – 2018-11-10 | 379 | 52 | Williams et al. (2020)
SAX J1808.4–3658 | –08.15 | 6 | 2019-07-31 – 2019-08-31 | 754 | 25 | Gasealahwe et al. (2023)

ThunderKAT data is typically reduced using OxKAT (Heywood, 2020), a semi-
automatic set of scripts that perform calibration, flagging and imaging of
MeerKAT data. OxKAT makes use of several existing radio astronomy packages
including casa (Mcmullin et al., 2007) for tasks such as gain and bandpass
calibration, self calibration and flagging, Cubical (Kenyon et al., 2018) for
further self calibration procedures, tricolour for further flagging (Hugo et
al., 2022) and wsclean (Offringa et al., 2014) for imaging. These steps are
broken into 1st generation calibration (1GC; direction independent effects),
flagging and self-calibration (2GC), with optional 3GC steps to account for
direction dependent effects. Some of the earlier observations were reduced
prior to the release of OxKAT, however these still follow the same basic
reduction of flagging using aoflagger (Offringa, 2010), bandpass, phase
calibration and flux scaling in casa, and imaging with wsclean, DDFacet (Tasse
et al., 2018) or casa. The commensal nature of this work means that, due to
different science requirements and observational conditions for each dataset,
the resultant images are heterogeneous in their properties, although mostly
homogeneous within a particular field.

###  2.1 Pre-processing and subject generation

Each set of images was processed using the Transients Pipeline (TraP Swinbank
et al., 2015), first designed for detecting transients with LOFAR. Below is a
brief description of how the pipeline works and some of the key parameters
used. The TraP finds sources above a user-defined threshold in a set of
astronomical images, creating light curves of each unique source and
calculating statistics for each source. This is done by fitting a Gaussian
component to each source in every epoch and associating it with those found at
that position in all previous images, updating the database as new
observations are added. Most TraP parameters are kept at their default
values444see
https://tkp.readthedocs.io/en/latest/userref/config/job_params_cfg.html for
the list of all parameters.. The detection_threshold, the signal-to-noise
ratio (S/N) above which sources are detected, was fixed at 8 throughout as a
trade off between detecting false positives and missing genuine sources of
interest. Once a source has been found, a Gaussian component is fit from its
peak down to 3$\sigma$ above the noise (analysis_threshold = 3). The
expiration i.e. the number of force fits to a position where a source was
found in a previous epoch, was always kept at greater than the total number of
observations in a dataset, meaning wherever a source had been found, a light
curve with datapoints for all remaining time steps was created. We are
interested in unresolved, point sources and their variability, so we set all
source fits to be fixed at the size of the PSF via force_beam = True. For
extended regions of emission, the change in size and position angle of the PSF
between observations can lead to non-intrinsic variability measurements, as
discussed in section 3. To allow for deblending we set deblend_nthresh = 10,
which accounts for overlaps between nearby sources such as double lobed radio
galaxies. Finally, the extraction_radius_pix, describing how far ‘out’ in the
image to search for sources, was always kept to approximately 1.5$\times$ the
main lobe of the primary beam, in accordance with Sarbadhicary et al. (2021).

Of the TraP outputs, the most relevant are the light curves and two statistics
computed based on the time series. For a light curve consisting of $N$ data
points of flux density $F_{i}\pm\sigma_{i}$, observed at frequency $\nu$, the
two variability statistics are defined as

$\eta_{\nu}\equiv\chi^{2}_{N-1}=\frac{1}{N-1}\sum_{i=1}^{N}\frac{(F_{i,\nu}-\overline{F_{\nu}}^{*})^{2}}{\sigma_{i}^{2}}$
(1)

and

$V_{\nu}\equiv\frac{s_{\nu}}{\overline{F_{\nu}}}=\frac{1}{\overline{F_{\nu}}}\sqrt{\frac{N}{N-1}(\overline{F^{2}_{\nu}}-\overline{F_{\nu}}^{2})}$
(2)

where $s$, $\overline{F}$ and $\overline{F_{\nu}}^{*}$ denote standard
deviation, mean and weighted average respectively. Generally speaking we can
use $\eta$ and $V$ to determine the statistical significance and the amplitude
of variation respectively. We expect $\eta$ to correlate with average flux
density; the brightest sources will have the smallest statistical
uncertainties. Similarly, $V$ and $\overline{F}$ should be anti-correlated as
we are only able to measure small variations for the brightest sources. We
also note that flux calibration errors can lead to overinflated statistics for
bright sources - e.g. systematic differences between epochs with small
statistical uncertainties producing very large values of $\eta$, as equation 1
does not include systematic uncertainties in its calculation.

Once all observations have been processed by the TraP, we generate figures of
the light curves and local sky around every runningcatalog source - that is,
each of the 8874 unique entries to the database. The local sky figure is a
square arcminute image centred on the weighted mean Right Ascension and
Declination (RA,Dec) of each source, using the image where the source was
detected at the highest S/N. Finally, these figures are uploaded to the
Zooniverse platform along with some basic metadata (RA,Dec, median flux
density and the time stamp of the highest S/N observation), creating the
subjects for citizen scientists to classify.

##  3 Citizen Science platform

The Bursts from Space: MeerKAT (hereafter BfS:MKT) Zooniverse project
555https://www.zooniverse.org/projects/alex-andersson/bursts-from-space-
meerkat launched on 7th December 2021 with classifications concluding by early
March 2022. During this time 1038 volunteers classified our sources using the
workflow seen in Figure 1. Volunteers are given a tutorial to familiarise them
with the data and describe the figures shown in the project, as well as
accounting for common pitfalls due to figure processing - namely there are a
few visualisation issues that make classifications more difficult, discussed
further in section 6. There are also description pages for the project
detailing the team, the telescope and the kinds of objects we are searching
for so that citizen scientists can learn more about astronomy and the work we
do.

Figure 1: Classification workflow for BfS:MKT, showing the light curve and
local sky figures for GX339–4.

Citizen scientists were given five classes to which they can assign a source,
examples of which can be seen in Figure 2. Stable sources are unresolved,
point sources whose light curves are judged to be (within uncertainties)
consistent with flat. The Extended Blob classification is intended to catch
the resolved, extended sources, regardless of variability. We know that the
changing size and angle of the PSF between epochs introduces non-intrinsic
variability and so we instructed all volunteers to classify subjects they deem
to be resolved as Extended Blobs - making use of the local sky figure,
comparing the source size to that of the PSF in the lower left corner. The
Artefact classification was implemented to account for any spurious, non-
astrophysical sources that may be present in our images. Transient/Variable
classifications are those we are searching for, which are point sources with
variable light curves. Finally, if volunteers are uncertain if a subject fits
into any of these classes - either due to visualisation issues, their own
interpretation or anything other reason - they can say they are Unsure. We
included Unsure to assess the confidence of volunteers - if a subject does not
clearly fit into one class this will be seen quantitatively (not just in e.g.
the Talk board). Also, without an ‘unsure’ option, volunteers may have settled
for classifying as either stable or transient, leading to an under- or over-
prediction of interesting sources. There is also a Field Guide (accessible on
the main project webpage) with examples to demonstrate the type of subjects
intended for each class, as well as some help text describing the rough
thought process behind each source. If volunteers feel a subject is
particularly interesting, or they have questions on a particular source, they
can create individual threads on the dedicated Talk forum for the project,
where (citizen and project) scientists can discuss.

Figure 2:  Examples of the four observational classes within our workflow on
the Zooniverse, showing both the light curve and an image of each. From top to
bottom these are: Stable \- no variation in the light curve given the error
bars and a point source; Extended \- variations caused by changes to the PSF
and a source that is larger than the beam (lower left); Transient \- a clear
variable light curve for a source the same size as the PSF; Artefact \- a
spuriously transient light curve and a faint source on the outskirts of a very
bright object, with non-Gaussian noise structure. The final class, Unsure, by
definition has no archetypal characteristic so we show no figure here.

We require 10 volunteers to classify a subject before we consider it
classified, resulting in a total of 88740 classifications. These were
classified over 90 days, or an average of 1 submission per 1-2 minutes. The
histogram of all classifications for the project to date can be seen in Figure
3, showing the expected steep power law distribution of votes (Spiers et al.,
2019), as well as a number of ‘super users’ who have classified thousands of
sources each. The median, mean and standard deviation of user classifications
are 4.5, 86 and 490 respectively. The chosen retirement value of 10 is enough
to mitigate outlier votes, but not so high that it would take many months for
a single subject to be fully classified. We note here that if further
iterations of the project gain as much traction as this first batch of
subjects we will be able to determine what the optimal trade off could be.

Figure 3: Histogram of the number of classifications made per user on the
BfS:MKT project. As might be expected, many users perform only a few
classifications, whilst a few volunteers dedicate thousands of votes to the
project.

Simple aggregation is performed for this one question workflow, using the
standard aggregation scripts666https://aggregation-
caesar.zooniverse.org/index.html, where we take the Boolean values for each
classification and sum over every vote for each subject. This gives us 10
votes for a given source, from which we can calculate fractional
classifications and determine how many subjects are deemed to be
transient/variable by some number of citizen scientists (TF = transient
fraction).

We set a threshold of 4/10 volunteers classifying a subject as
transient/variable, reducing our sample from 8874 to 381 sources. This 0.4
threshold was chosen as a trade off between having many sources to vet and
missing some low vote fraction variables. These 381 subjects were visually
inspected by project scientists to confirm or reject each source as one where
both volunteers and experts agree. This final vetting reduces our number of
transient candidates to 168 sources (i.e. a true positive rate of 168/381 =
0.44). Reasons for disagreement between citizen and project scientists are
numerous and include how much volunteers make use of error bars, PSFs and
other parts of the figures, as well as the existence of systematic variability
that is present in earlier MeerKAT observations (see e.g. the appendix of D22)
that would only be noticeable to experts who have compared many light curves
in a given field. We will discuss what information we get from the false
positives in section 6.

##  4 Results

Using citizen scientists to inspect a wealth of data from the MeerKAT
telescope produces 168 variable and transient radio sources which have also
been vetted by project scientists. Of these 168 variables and transients found
by volunteers, 142 are not detailed in previous commensal or XRB work. This
constitutes one of the largest samples of radio variable candidates to date,
and their positions are listed in Table A. This table provides the TF, median
1.28 GHz flux density and $\eta$ and $V$ statistics described in section 2.1
for each of our sources, as well as the date on which they were detected at
highest S/N. We strongly encourage follow-up of sources of interest to the
community.

In order to characterise these sources, we can examine the variability plane
defined by $\eta$ and $V$ (equations 1 and 2) seen in Figure 4. The structure
seen in this figure consists of many sources at low significance and
amplitude, some spurious artefacts at high $V$ and generally bright objects
detected at high significance ($\eta$) but low variability amplitudes.
Systematic differences between datasets due to their heterogeneous sampling
and imaging are also present (e.g. the sources not voted as transients at
log($V$) $\sim-0.6$). The most important thing to note is that there are many
variable objects whose parameters are ‘normal’ i.e. non-anomalous. This means
that, were we instead to take outliers above some $N\sigma$ threshold in
$\eta$ and $V$, we could have missed sources that, upon individual inspection,
appear variable or transient. So citizen scientists can find interesting
variable radio sources that could have been missed by other techniques or
without detailed analysis (e.g. D22, R22 and Chastain et al. submitted). The
previously known transient sources (i.e. the circled sources of Figure 4) with
high $\eta$ and $V$ are almost all recovered, as are many of those found in
previous studies (see sections 4.1 and 4.2 for more detail), meaning that
volunteers are able to recover or discover interesting sources across a range
in statistical parameters. So our volunteers have been able to analyse large
data volumes in just a few months and produce the largest sample to-date of
variables from a radio telescope.

We can use these discovered (142) and recovered (26) variables, along with the
other known transients (19) in our fields, to estimate that at least 2.1 per
cent of radio sources seen at 1.28 GHz with our approximately weekly
observations are transient or variable. This rate is in line with previous
work on radio transients; see Ofek et al. (2011) for a review, as well as D22
and Sarbadhicary et al. (2021). We also note that these different studies,
including this work, probe slightly different time-scales, though all of them
‘long’ ( $\geq$ a week). This means that our volunteers and project scientists
can find comparable transient rates as previous work. This 2.1 per cent is a
lower limit on the number of variables and we can estimate what fraction of
variables/transient are missed by assessing which known transients are not
recovered. The fraction of previously known variables not recovered is 19/45,
implying the true amount of variables in our fields could be as high as $\sim
5$ per cent. However, this estimate is only valid under the assumption that
new variables are recovered at the same rate as the known transients in our
field. In section 6 we discuss the selection effects that are evident in the
different kinds of light curves selected by volunteers when compared to the
known transients in our field.

Figure 4: Variability plane for the 168 sources found by citizen scientists to
be variable, along with those they find to not be so in grey. The colourbar
denotes the fraction of classifications as a transient/variable source, whilst
marginal distributions of $\eta$ and $V$ can also be seen. Known variable
sources (e.g. XRBs) in our fields are circled. Imaging artefacts appear at low
$\eta$ and high $V$, whilst flux calibration uncertainties can produce high
$\eta$, low $V$ sources (due to lack of systematic uncertainty in equation 1.
Most known transients are found by citizen scientists, whilst many new sources
are identified and show a wide spread of values in this parameter space.

The majority of these 142 variables that our volunteers discover show long
term variability, with light curves showing variations over weeks to months.
One cause of variability for extragalactic sources is scintillation through
the Galactic interstellar medium (ISM). All of our observations are within
latitude $|b|\lesssim 10$°of the Galactic plane (see Table 1) so this may be a
large contributor to the variability seen in our sample. We can use the model
from Hancock et al. (2019) to characterise the effect of refractive
interstellar scintillation (RISS) for our set of variables, using an
underlying H$\alpha$ map from Finkbeiner (2003) to quantify the electron
scattering along a given line of sight through the Galaxy. This model predicts
the level of variability for a given radio frequency. We can directly compare
this predicted maximum RISS-induced variability to our measured $V$ values, as
seen in Figure 5. For the majority (131) of our new variables we see that the
predicted modulation is equal to or greater than that measured by TraP i.e.
the observed variability is consistent with (though not exclusively explained
by) the scintillation of light from AGN. By contrast, the known Galactic XRBs
and their jets - whose variability is caused by shocks and particle
acceleration - show variability that is much greater than what would be
expected due to refractive scintillation. The predicted RISS variability is in
some cases much higher than our observed $V$ values. This is likely caused by
the heteregeneous sampling of our fields, the coarseness of the H$\alpha$
model grid and/or the assumptions of the model. Finally, we can calculate a
weighted average time-scale of variation for our sources, which we find to be
$t_{0}=8\pm 4$ months, where the weights used are propagated through from the
uncertainties in the underlying map and the quoted uncertainty is the standard
deviation. This range of time-scales of variation at 1.28 GHz matches well
with the length of typical observations for our XRB fields. Both these
matching amplitudes and time-scales of variability provide evidence for the
majority of our transients being scintillating AGN or other point-like
extragalactic radio sources.

Figure 5: The ratio between observed $V$ and the variability predicted by
Hancock et al. (2019)’s model for Refractive Interstellar Scintillation
(RISS), for our sample of transients and variables. Most variability can be
explained by this model of a scintillating, extragalactic source, apart from
the known XRBs and jetted systems.

###  4.1 Comparison to Target Sources

Of all the 8874 sources classified in this project, there are 45 known
variable/transient objects published in the literature, including the 11 XRBs
listed in Table 1. Of the 11 XRBs, 9 are classified as transient by citizen
scientists. The only two that are missed are Swift J1858 and SAX J1808, whose
light curves can be seen along with that of EXO 1846 for contrast in Figure 6
\- exactly as citizen scientists would have seen them. We can explain why
Swift J1858 was not classified as transient due to a combination of there
being only one significantly bright data point, as well as the figure
generation creating an overly large legend. Similarly, SAX J1808’s
misclassification can be understood as stemming from uncertainty surrounding
so few data points (especially compared to other datasets). Indeed, SAX J1808
received 1 ‘Unsure’ vote which, had it instead been for ‘Transient/Variable’
would have pushed this subject above our classification threshold.

Figure 6: The TraP light curves of EXO 1846–031, Swift J1858.6–0814 and SAX
J1808.4–3658 (upper left, upper right and lower respectively). The foremost
was classified as transient by citizen scientists whilst the others were not.
We believe these to be caused by bad figure generation and lack of data points
respectively.

In addition to these central 11 sources, several of the XRBs also display
discrete, resolved jet components (MAXI J1820+070, MAXI J1348–630, MAXI
J1848–015 and 4U1543–47; see Table 1) that are counted as unique sources by
the TraP as they become resolved and move away from the core of the jet. In
total there are 6 moving, transient jet components that are detected by the
TraP as unique, of which 3 are classified as variable by citizen scientists.
Given that neither the software pipeline nor project classifications were
designed to pick up moving point sources, it is interesting to note that there
is room for unexpected discoveries even in such simple workflows.

###  4.2 Previous commensal studies

One of the largest works on variable sources in MeerKAT images to date is that
of D22. In that work, 21 new long term variables (LTVs) are detailed, along
with GX339–4, the first MeerKAT transient MKT J170456.2–482100 and the known
mode-changing pulsar PSR J1703–4851 (both described in Driessen et al., 2020).
Of these 24, excluding the XRB discussed earlier, MKT J170456.2–482100, PSR
J1703–4851 and 10 of the LTVs are missed by classifications i.e. only 11 are
marked as transient/variable by volunteers. One reason for this is that in
D22, each light curve is binned by a factor of 10 (i.e. every 10 datapoints
are represented by their uncertainty-weighted mean) to make long term
variability more apparent, something not done here. Furthermore, here we made
no use of a deep, stacked observation in order to follow sources through every
epoch (see section 2.1’s details on the expiration parameter), something that
will result in different light curve shape and in one case resulted in a
source not being detected at all in our set of images. In general, the LTVs
not identified by volunteers were generally fainter and have less smooth light
curve evolution. This may give some insight into a bias of our method; it is
easier to find light curves with long-term, clear evolution as opposed to more
stochastic variability.

Similarly, when comparing to the work of R22 we see that the three variables
found therein - NVSS sources J181849+062843, J181752+064638 and J182029+063419
- were recovered by citizen scientists. However, the three sources found in
the ‘transient hunt’ (where therein transient is defined as sources not
detected in a deep observation of the field, see their section 3.1) were not
identified by volunteers. As above we note that the variables recovered have
smooth light curve evolution over long time-scales, whilst those not
identified as transient are much fainter, with larger uncertainties and
figures that are more ‘cluttered’.

Finally, the M dwarf SCR 1746–3214 is a radio transient that exhibits flares,
serendipitously seen in early ThunderKAT data (Andersson et al., 2022). In
order to assess what light curves citizen scientists were most comfortable
with classifying, we provided two light curves of this source - one with more
datapoints and an additional detection and one with only two detections and
two upper limits (see Figure 3 in the above article). Interestingly, when
provided with the shorter light curve of the source , the transient source was
mostly classified as ‘Unsure’ or ‘Stable’. However, when given the full light
curve volunteers correctly identify the subject as transient. We can perhaps
use this to infer that citizen scientists were least unsure when classifying
sources with more data points and less reliant on upper limits, as is the case
for many of the new variables found in this study.

##  5 Counterparts and Associations

We make use of the MeerLICHT optical telescope to gain physical insight into
possible source classes of our radio variables and transients. We use
MeerLICHT for this due to its position in South Africa and its mission to
follow the radio observations of MeerKAT, resulting in highly complementary
spatial and temporal coverage of our radio sources (if observed at night). A
typical observing schedule consists of 1 minute exposures of a given field,
alternating between q-band (440–720nm) and each of 5 Sloan bands u, g, r, i
and z. Whilst MeerLICHT operates in these 6 bands, here we use only the q-band
due to its highest sampling rate and its broad wavelength coverage. We
crossmatch with the MeerLICHT database at a 2″search radius - large enough to
partially account for MeerKAT astrometric uncertainties (see D22) and proper
motion (e.g. of nearby stars) but not so large as to include many false
matches - using the the uncertainty-weighted mean position of each of our
candidates as returned by the TraP. Running this crossmatch returns 25
counterparts in the MeerLICHT database and 143 3$\sigma$ upper limits. This
low rate of optical counterparts is not surprising as all of our XRB fields
are within 10 degrees of the Galactic plane and so many optical counterparts
may be heavily extincted.

The radio-optical plane for our variables can be seen in Figure 7, with
comparison source types from Stewart et al. (2018)777Code for reproducing this
plot is available at https://github.com/4pisky/radio-optical-transients-plot.
The first thing to note is that the majority of these cross-matches exist in
the region where extragalactic sources have been detected - either quasars or
GRBs. If most of our candidates are extragalactic in nature this agrees with
previous studies, who find the vast majority of radio variables are
extragalactic (Thyagarajan et al., 2011; Sarbadhicary et al., 2021). There are
a few sources overlapping the ‘stellar’ region of the parameter space - these
include the transients already reported by ThunderKAT (in Driessen et al.,
2020 and Andersson et al., 2022). It is important to note that by comparing to
archival data in this way does not leave room for unknown astrophysical
classes, however this still gives an indication of the overall distribution of
multiwavelength counterparts.

Figure 7: The mean optical and radio flux densities of our sample of radio
variables, atop an underlying distribution of astrophysical classes (Stewart
et al., 2018). Black crosses denote counterparts within the MeerLICHT database
whilst grey triangles are upper limits. Diagonal lines denote a constant ratio
between radio and optical flux density, whilst the $A_{R}$ marker indicates
the horizontal displacement caused by 5 magnitudes of optical extinction. The
majority of our radio sources are likely extragalactic as they overlap in
parameter space with quasars and GRBs.

###  5.1 Highlights

We also search for counterparts at other wavelengths and with pre-existing
classifications, with the aid of pre-existing code available in Driessen
(2021). Crossmatching makes use of the astroquery package to search the
SIMBAD888The Set of Identifications, Measurements and Bibliography for
Astronomical Data, available online at http://simbad.cds.unistra.fr/simbad/
database (Wenger et al., 2000) and several catalogues within
Vizier999https://vizier.cds.unistra.fr/viz-bin/VizieR (Ochsenbein et al.,
2000). We again search at a 2″radius to our radio sources. We searched for
X-ray and gamma-ray counterparts to our radio variables. To do this we
crossmatched with catalogs from the Fermi (Schinzel et al., 2014), Chandra,
(Evans et al., 2010), Swift (Evans et al., 2020) and XMM-Newton (Traulsen et
al., 2020) facilities. There were no counterparts for any of our sample, aside
from the known transients of interest (i.e. the XRBs). Below we make note of a
few interesting objects returned in our search with IR or radio detections, or
otherwise known counterparts.

####  5.1.1 OH Maser - BfS 80

We detected transient emission from a source in the EXO 1846 field which
volunteers confidently classified as transient. Cross-matching reveals that
the source in question is a known maser star OH 30.1–0.7 otherwise known as
V1362 Aql, with radio observations stretching back almost 50 years (Evans et
al., 1976). This asymptotic giant branch (AGB) star is heavily dust-obscured
at optical wavelengths, but very bright at IR wavelengths - $W1=6.9\pm 0.1$
mag, or 279 Jy at 25 $\mu$m (Cutri et al., 2013; Gonidakis et al., 2014). A
comparison of a MeerKAT radio detection (contours) and Spitzer Glimpse imaging
can be seen in Figure 8, alongside its light curve. AGB stars are post-main
sequence systems, whose low surface temperatures, radii of several hundred
times the solar radius and stellar pulsations give rise to strong winds,
expelling as much as $\sim 10^{-5}M_{\odot}$yr-1 (Höfner & Olofsson, 2018).
These winds create an oxygen-rich, dusty circumstellar environment that
generate masers at 1612 and 1667 MHz as infrared photons pump OH molecules
formed through photodissociation of water by interstellar radiation.

Figure 8: Upper: Light curve of OH Maser 30.1–0.7 picked up by volunteers.
There were 4 epochs in which this source was not detected prior to this
observed variability. Lower: Overlay of MeerKAT radio contours over Spitzer
imaging from the GLIMPSE survey (Benjamin et al., 2003). Contours are spaced
linearly in 0.5 mJy increments from -0.5 to 3.5 mJy.

The cause of the variability of OH 30.1–0.7 is not clear. Perhaps the OH maser
emission is varying, due to stellar pulsations. The derived stellar period
from the General Catalog of Variable Stars is $\sim$1730d (Samus’ et al.,
2017), with more recent estimates from the WISE W1 and W2 bands at $1950\pm
50$ and $1520\pm 20$ days respectively (Groenewegen, 2022). These time-scales
are much longer than the variability seen in the radio light curve here (of
order a few months), so it is not clear if stellar pulsations are responsible
for variable maser emission (averaged across the entire L-band from Jy to mJy
levels). There could also be inhomogeneities at the site of the maser
emission. The second cause for variability could be due to binary interactions
- the system likely has a companion, as inferred from ALMA CO data in Decin et
al. (2019). This, combined with the lack of any optical counterpart implies
the system could be a dusty (D-type) symbiotic binary - all D-type symbiotics
host Mira stars (Whitelock, 1987). These binaries consist of a windy red giant
and a smaller companion onto which material is shed (Allen, 1984). Radio
emission has been seen from symbiotic binaries (Seaquist et al., 1984) and is
known to vary in some sources, typically interpreted as optically thick
emission from an inhomogeneous region in the red giant’s wind that is ionised
by its companion (Seaquist, 1988). The nature of OH 30.1–0.7’s companion is as
yet unknown due to the heavy extinction in this region and without evidence of
high ionisation (e.g. HeII or [OIII]) we cannot claim that this is a symbiotic
system. There is overlap between maser systems and Mira-type symbiotic
binaries (Seaquist et al., 1995) and so the observed variability could be due
to a combination of emission mechanisms discussed, or perhaps something else
entirely. One thing to note is that there were four observations of this field
prior to the initial data point of the light curve - i.e. there are 4 non-
detections before the system reached $\sim$3mJy as seen. Future radio
observations could help determine what the nature of the variability is
(including the initial non-detections), perhaps combined with spectroscopic
searches for the nature of the companion (e.g. if it is a white dwarf, or for
evidence for ionisation).

####  5.1.2 Pulsar - BfS 20

Pulsar PSR B1845-01 (J1848–0123), whose light curve can be found in Figure 9,
was seen in the field surrounding XRB MAXI J1848. In our MeerKAT observations
we measure a mean flux density of $\sim$15 mJy, in close agreement with that
measured by the Parkes radio telescope recently (15.2 $\pm$ 0.9 mJy; Johnston
& Kerr, 2018). However, reports of this pulsar’s flux density have been as low
as 8.9 $\pm$ 0.9 mJy at 1400 MHz (Hobbs et al., 2004), indicating long term
changes in the received brightness from the source. The pulsar is brighter at
lower frequencies (e.g. measured to be $79\pm 6$ mJy at 408 MHz by Lorimer et
al., 1995), with a spectral index $\alpha$ \- defined at frequency $\nu$ as
flux density $F\propto\nu^{\alpha}$ \- of $-1.3\pm 0.3$ (McEwen et al., 2020).

Figure 9: Light curve of PSR B1845–01 with MeerKAT. 7 of 10 volunteers voted
for this as a transient/variable source. The low amplitude variability
observed over a time-scale of 100s of days is consistent with RISS.

The observed variability is consistent with RISS (see section 4), with a
predicted modulation greater than the measured $V\sim 0.02$ and the observed
time-scale of variation matching the estimated $11\pm 2$ months. Furthermore,
this pulsar has a relatively high dispersion measure (DM) of $159.1\pm 0.2$ pc
cm-3 (McEwen et al., 2020; compared to typical DMs of order 10s of pc cm-3 for
pulsars with $|b|>25\degr$; Manchester et al., 2005), which is known to be
linked to long time-scale, low amplitude refractive scintillation (Stinebring
et al., 1990; Stinebring et al., 2000). Similarly, the pulsar’s location in
the Galactic plane is in keeping with its radio emission traversing a large
free electron content, hence the high DM and clear scintillation. We note that
B1845-01’s spin period of $\sim$0.65943s is much shorter than that of our
observations (typically 15 minute epochs consisting of 8s correlator sampling)
so this cannot be contributing to the observed variability.

This pulsar adds to the diverse range of behaviours seen in pulsars spotted by
MeerKAT in imaging data. Similar examples include the mode changing pulsar
observed in the GX339–4 field (see D22) and, in the most extreme case, one of
the slowest pulsars discovered (Caleb et al., 2022).

####  5.1.3 VLASS1 J181955.28+074418.7 - BfS 146

Radio source VLASS1 J181955.28+074418.7 is an object spotted by our volunteers
not only in our aggregated results (scoring 7/10 votes as a
transient/variable) but also in our Talk
board101010https://www.zooniverse.org/projects/alex-andersson/bursts-from-
space-meerkat/talk/4567/2263526. This source is in the field of MAXI J1820 but
outside the 0.5 degree radius set by R22. Its light curve can be seen to vary
very smoothly in Figure 10, where it is worth noting that the non-detections
are all due to drops in data quality in those images, which should be filtered
out in future work. This variation is not similar to any nearby source in our
data, nor is it correlated with PSF size or shape. There is a counterpart to
this source in the Very Large Array’s Sky Survey (VLASS QuickLook Epoch 1;
Lacy et al., 2020; Gordon et al., 2021), with a 3 GHz flux density of $1.9\pm
0.3$mJy. There are no counterparts to this source in any higher energy band,
despite this field being our furthest from the Galactic plane ($b\sim
10\degr$). This kind of source is very typical of our sample - scant extra
data but an intriguing radio light curve. Further information at other
wavelengths would help determine source type, as would simultaneous radio data
- e.g. to determine a precise spectral index $\alpha$ and see if this points
towards the source being an AGN or a pulsar. Our RISS analysis (see section 4)
produces a ratio of 1.17 between the observed $V$ and predicted scintillation
amplitude. This could indicate the source has some intrinsic variation however
the scintillation parameters were not exhaustively tested, so this ratio could
be explained by an incorrectly assumed distance to, or relative motion
between, the model scattering screen and the observer.

Figure 10: Light curve of source VLASS1 J181955.28+074418.7, showing smooth,
near sinusoidal variations. The epochs denoted upper limits are in error and
due to not filtering out low quality images in our pre-processing.

##  6 Discussion

Using citizen science to discover transients is a fruitful endeavour, as shown
by both the uptake of our Zooniverse project and the novel results produced.
For our volunteers, the project provides new experiences with a branch of
observations perhaps less familiar to their perception of astronomy (e.g.
compared to galaxy morphologies or solar physics). The uptake of our project
demonstrates a clear appetite for further development of citizen science for
radio transients, as evidenced by >1000 citizen scientists applying their time
to our science case. The time taken to individually check several years worth
of data is much greater than the 3 months taken by our volunteers, so citizen
scientists help project scientists analyse their observations more
efficiently. In this study we have been able to recover or discover a broad
range of astrophysical transients that occur over several orders of magnitude
in time-scale, so the science results discussed here also give merit to
developing BfS:MKT further. These results include flaring from stellar
systems, the discrete and compact jets of XRBs, maser emission, pulsars, the
long-term variation in likely AGN and potentially new source classes. Given
these successes, we plan to launch a second wave of classifications to the
Zooniverse site shortly, using different data from surveys on MeerKAT to
explore the range of parameter space for both citizen and project scientists.
Of course this method is not restricted to MeerKAT data and any such set of
(radio) images and light curves could be analysed in this way.

However, citizen science is not without its challenges and we can understand
some of these by comparing our findings to those of previous work from the
ThunderKAT team - namely D22 and R22, as well as the published XRB work (see
Table 1). The first thing to note is that not all of the transients from D22
and R22 were recovered here. Some of this can be explained by the difference
in pre-processing of images and TraP light curves (discussed in 4.2). However,
several of the transients missed are due to how we have produced subject
images for the Zooniverse project. For example, the two XRBs not classified as
transients can be explained due to the lack of clarity in Figure 6 (see
section 4.1). Similarly, the image of source MKT J182015.5+071455 (R22’s
source 2) provided to volunteers was automatically scaled to the brightest
pixel in the image, not be the central source of interest. As such, the source
appears not to be present and was classified mostly as an artefact. We will
use these issues to improve our procedure for future batches of data e.g. by
manually setting the pixel scale in images, altering legend sizes etc.

Aside from these pre-processing issues, there are still some known transients
not recovered, including the pulsars in D22 and R22. When we compare the light
curves of these pulsars to variables that are recovered we see a clear trend -
recovered transients show long term, smooth variations, and are typically
brighter, with smaller uncertainties, resulting in clearer patterns on display
to scientists. By contrast, the faint, transient pulsar light curves show very
‘noisy’ light curves, with less eye-catching patterns, despite being precisely
the kinds of transients we want to discover. R22’s pulsar received 3 votes as
a transient/variable and 1 unsure classification - this could be due to the
heterogeneous nature of our classifiers, or it could reflect the uncertainty
surrounding a less clear pattern in a light curve. As mentioned in section
4.2, we provided two light curves of Andersson et al. (2022)’s M dwarf to
volunteers, one with force fit measurements (more data points and an
additional detection) and one without. The light curve with more data points
passed our threshold of 0.4, whilst the latter did not, perhaps indicating
that classifiers are more comfortable with longer light curves. These
unrecalled transients give insight into the limitations of this dataset: our
sample of transients and variables is likely biased towards brighter, slower
evolving objects that occur in our most sampled fields. We hope to alleviate
some of this bias in future Zooniverse runs by emphasising the use of non-
detections and by encouraging volunteers to label things as transient. We
could also implement the transients and variables discussed in this work into
the Field Guide for volunteers as more examples of the types of sources for
which we are looking.

We can quantify how scalable our method is by comparing our observations to
the MeerKAT MIGHTEE survey. MIGHTEE’s observations produce $\sim 6000$ sources
per square degree on the sky (Heywood et al., 2022), for which our TraP
processing would produce a light curve. For comparison, in this work we
produce $\sim 1000$ subjects from the ThunderKAT fields that are devoid of
large diffuse structures or exceedingly bright sources. If we assume that only
the same 1000 volunteers contribute to all future data releases as with this
study, at the same classification rate, then it would take approximately 60
days to receive 10 classifications on every source at MIGHTEE’s sensitivity
($1\sigma$ RMS noise $\sim$ 1 $\mu$Jy), per square degree. Multiplying this
across the sky coverage of just the ThunderKAT fields used in this study
($\sim 1.5$ square degrees over 11 fields) results in a required volunteer
classification time of 990 days. This is far greater than the time taken for
the observations (e.g. of order tens of 8-hour epochs) and which is needed to
image the data and process them to form light curves. If the volunteer results
were only analysed when all classifications were finished then this would also
be too late for real-time follow-up of transients. To bring the classification
time to that of this work (90 days) would require an order of magnitude more
volunteers, which is achievable for Zooniverse projects, particularly when
disseminated widely. For example, Galaxy Zoo variants receive many 10s of
thousands of volunteers, whilst Gravity Spy has had over 30,000 participants.
However, for any considerable survey area the overall time required would
again balloon to far larger time-scales than reasonable, particularly if we
process datasets and classifications in batches once observations are
complete. Finally, observations at the 8s integration time for MeerKAT produce
far fewer sources to classify compared to deeper images, however the imaging
and processing time prior to volunteer classification increases hugely so the
overall time-scale remains long. So this kind of analysis will not scale
easily to the most sensitive observations of MeerKAT fields (at 1.28 GHz), let
alone those expected from the SKA, ngVLA (Hallinan et al., 2019) or DSA-2000
(Selina et al., 2018).

One way to alleviate this data deluge might be to develop machine learning
methods to remove ‘bogus’ and ‘boring’ sources in favour of the rarer
variables and transients for which we are searching, as has been done for e.g.
supernovae and galaxy morphologies (Wright et al., 2017; Walmsley et al.,
2022b). Active learning, where humans feed back to machine learning techniques
in order to prioritise sources of interest and optimise precious human
attention, has been shown to uncover unique light curves (Ishida et al., 2021)
and radio morphologies (Lochner et al., 2023), and are able to optimise the
volunteer classification of optical galaxies (Walmsley et al., 2022a). We are
currently applying Lochner & Bassett (2021)’s Astronomaly active learning
framework to the data presented in this work in the hopes that we can use the
combined power of human classifiers and machine processing to extract the most
science from our wealth of data (Andersson et al. in prep).

##  7 Conclusions

In this work we have presented the first citizen science project for finding
transients in image plane radio surveys. The uptake of the project was very
strong, with $>1000$ volunteers taking part, demonstrating a healthy appetite
for further Zooniverse data releases. We were also able to use the known
transients in our fields to understand some reasons why interesting sources
may be missed and will fold this learning through to future iterations of the
project. Citizen and project scientists uncovered a large sample of
interesting transient and variable sources, some of which we may have not
uncovered were it not for our volunteers’ dedication. We provide the full
catalogue of 168 radio transients and variables, the largest catalogue of
candidate radio variables to date. This includes links to images and light
curves, and we encourage others to follow up these sources, and additional
future catalogues that this project will deliver. We made use of archival
multiwavelength data, including the MeerLICHT telescope, to help classify the
systems found. The sources found span a broad range of physical phenomena
including pulsars, radio-loud stars, XRBs and a large set of AGN - likely
varying due to scintillation. These results demonstrate the wealth of science
possible with new radio facilities. Finally, we hope to use volunteer
classifications to develop anomaly detection algorithms, with an eye towards
current and future surveys such as the SKA.

## Acknowledgements

The authors would like to thank our reviewer Matthew Bailes for his helpful
comments that have improved this study. We also thank Patricia Whitelock for
her helpful discussion points around OH masers, Mira variables and symbiotic
variables. AA acknowledges the support given by the Science and Technology
Facilities Council through a STFC studentship. CJL acknowledges support from
the Alfred P. Sloan foundation. FC acknowledges support from the Royal Society
through the Newton International Fellowship programme (NIF/R1/211296). LND
acknowledges support from the European Research Council (ERC) under the
European Union’s Horizon 2020 research and innovation programme (grant
agreement No 694745). PJG acknowledges support from NRF SARChI Grant 111692.
MeerKAT is operated by the South African Radio Astronomy Observatory (SARAO),
which is a facility of the National Research Foundation, an agency of the
Department of Science and Innovation.

This publication uses data generated via the Zooniverse.org platform,
development of which is funded by generous support, including a Global Impact
Award from Google, and by a grant from the Alfred P. Sloan Foundation.

We thank the SARAO staff involved in obtaining the MeerKAT observations. We
acknowledge the use of the ilifu cloud computing facility – www.ilifu.ac.za, a
partnership between the University of Cape Town, the University of the Western
Cape, the University of Stellenbosch, Sol Plaatje University, the Cape
Peninsula University of Technology and the South African Radio Astronomy
Observatory. The ilifu facility is supported by contributions from the Inter-
University Institute for Data Intensive Astronomy (IDIA – a partnership
between the University of Cape Town, the University of Pretoria, the
University of the Western Cape and the South African Radio astronomy
Observatory), the Computational Biology division at UCT and the Data Intensive
Research Initiative of South Africa (DIRISA). The MeerLICHT consortium is a
partnership between Radboud University, the University of Cape Town, the
Netherlands Organisation for Scientific Research (NWO), the South African
Astronomical Observatory (SAAO), the University of Oxford, the University of
Manchester and the University of Amsterdam, in association with and, partly
supported by, the South African Radio Astronomy Observatory (SARAO), the
European Research Council and the Netherlands Research School for Astronomy
(NOVA).

This research has made use of the SIMBAD database, operated at CDS,
Strasbourg, France (Wenger et al., 2000), as well as the ATNF Pulsar catalog
(Manchester et al., 2005) available at
https://www.atnf.csiro.au/research/pulsar/psrcat/.

This research has made use of Astropy, an Astronomy-based, community-developed
Python package (Astropy Collaboration et al., 2013, 2018, 2022).

## Data Availability

ThunderKAT raw data are available on the SARAO archive
(https://archive.sarao.ac.za/). The data for each light curve mentioned in
Table A and throughout the text will be available at
https://github.com/AnderssonAstro/BfS-MKT-Analysis and we encourage readers
interested in particular sources to investigate them in further detail, giving
credit to this work. The data used to produce our figures will also be in said
repository, excluding personal data from volunteers, which can be shared on
reasonable request to the authors.

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##  Appendix A Table of Results

Table 2: Table of transients and variables found by volunteers during BfS:MKT, with their associated positions, median flux densities ($F_{\mathrm{med}}$) and variability parameters $\eta$ and $V$. The observed date given here is that in which the source was detected at highest S/N by the TraP, whilst the distance recorded is how far an entry is from the pointing centre of that observation. The Transient Fraction (TF) is the fraction of classifications as a transiet/variable, from 10 volunteers. Note that their Zooniverse subject ID is a unique identifier from which one can find the image and light curve online and freely accessible at https://www.zooniverse.org/projects/alex-andersson/bursts-from-space-meerkat/talk/subjects/ID, replacing ID with the numeric value below. This table will be available in machine-readable form online. Name | Subject ID | Right Ascension | Declination | Date Obs. | TF | $F_{\mathrm{med}}$ | $\eta$ | $V$ | Distance | Known?
---|---|---|---|---|---|---|---|---|---|---
|  | (°) | (°) |  |  | (mJy) |  |  | (′) |
BfS 0 | 70785323 | 255.424 | -48.776 | 14/04/2018 | 0.4 | 13.2 | 83.7 | 0.05 | 11.2 | 0
BfS 1 | 70785357 | 255.364 | -48.970 | 14/04/2018 | 0.9 | 11.2 | 395.4 | 0.11 | 17.3 | 1
BfS 2 | 70785384 | 254.710 | -48.877 | 14/04/2018 | 0.5 | 4.7 | 26.8 | 0.07 | 39.7 | 0
BfS 3 | 70785398 | 255.441 | -48.675 | 14/04/2018 | 0.6 | 0.7 | 1.8 | 0.11 | 12.5 | 1
BfS 4 | 70785401 | 255.369 | -48.499 | 14/04/2018 | 0.4 | 0.4 | 0.6 | 0.14 | 22 | 1
BfS 5 | 70785440 | 255.156 | -48.946 | 14/04/2018 | 0.5 | 1.7 | 6.6 | 0.09 | 23.6 | 1
BfS 6 | 70785461 | 255.634 | -48.850 | 14/04/2018 | 0.9 | 0.6 | 4.3 | 0.19 | 4.6 | 0
BfS 7 | 70785641 | 255.291 | -48.597 | 14/04/2018 | 0.7 | 3.9 | 100.6 | 0.15 | 20.1 | 1
BfS 8 | 70785723 | 255.117 | -48.429 | 14/04/2018 | 0.6 | 0.6 | 1.1 | 0.11 | 31.9 | 1
BfS 9 | 70785821 | 256.988 | -48.972 | 14/04/2018 | 0.4 | 8.8 | 130.5 | 0.09 | 51.8 | 0
BfS 10 | 70785881 | 255.917 | -48.670 | 14/04/2018 | 0.4 | 0.5 | 0.9 | 0.11 | 11.1 | 1
BfS 11 | 70785898 | 255.706 | -48.790 | 01/02/2020 | 0.7 | 2.5 | 22427.6 | 1.86 | 0 | 1
BfS 12 | 70785951 | 255.983 | -48.932 | 14/04/2018 | 0.6 | 3.6 | 62.6 | 0.13 | 13.9 | 1
BfS 13 | 70785952 | 255.557 | -48.560 | 14/04/2018 | 0.8 | 1.4 | 3.6 | 0.08 | 15 | 1
BfS 14 | 70785973 | 256.443 | -48.806 | 21/03/2020 | 0.5 | 0.2 | 0.6 | 0.23 | 29.2 | 1
BfS 15 | 70786097 | 289.084 | 10.836 | 28/02/2021 | 0.9 | 0.8 | 7.6 | 0.32 | 18 | 0
BfS 16 | 70786101 | 288.964 | 10.940 | 05/04/2021 | 0.4 | 0.2 | 8.7 | 0.49 | 9.8 | 0
BfS 17 | 70786128 | 288.798 | 10.946 | 24/07/2020 | 0.6 | 37 | 66097.3 | 1.51 | 0.3 | 1
BfS 18 | 70786366 | 288.400 | 10.354 | 08/12/2018 | 0.4 | 8.1 | 8002.1 | 0.24 | 42.3 | 0
BfS 19 | 70786703 | 282.208 | -1.497 | 08/11/2021 | 0.4 | 4.8 | 37 | 0.46 | 0.3 | 1
BfS 20 | 70786771 | 282.098 | -1.400 | 28/02/2021 | 0.7 | 15.2 | 5 | 0.05 | 8.6 | 0
BfS 21 | 70786879 | 282.207 | -1.499 | 08/11/2021 | 0.9 | 15.7 | 984.2 | 0.67 | 0.4 | 1
BfS 22 | 70786931 | 282.080 | -1.679 | 04/10/2021 | 0.5 | 80.4 | 12.5 | 0.02 | 13.6 | 0
BfS 23 | 70789583 | 271.374 | -30.120 | 22/05/2021 | 0.4 | 9.1 | 23 | 0.02 | 36.4 | 0
BfS 24 | 70789643 | 270.424 | -29.801 | 31/07/2021 | 0.6 | 0.5 | 1 | 0.07 | 17.6 | 0
BfS 25 | 70789787 | 271.225 | -29.740 | 07/05/2021 | 0.6 | 1.9 | 2.9 | 0.03 | 24.7 | 0
BfS 26 | 70789902 | 270.687 | -30.534 | 12/06/2021 | 0.6 | 2.1 | 2.1 | 0.02 | 42.6 | 0
BfS 27 | 70789937 | 271.029 | -30.293 | 15/08/2021 | 0.5 | 127.3 | 99.8 | 0.02 | 31.2 | 0
BfS 28 | 70790055 | 270.934 | -29.951 | 12/06/2021 | 0.5 | 1.6 | 2.4 | 0.04 | 11.7 | 0
BfS 29 | 70790141 | 270.681 | -29.466 | 12/06/2021 | 0.4 | 2 | 3.8 | 0.03 | 22 | 0
BfS 30 | 70790174 | 270.699 | -29.561 | 07/05/2021 | 0.8 | 2.4 | 3.5 | 0.03 | 16.2 | 0
BfS 31 | 70790384 | 271.383 | -30.411 | 12/06/2021 | 0.4 | 6.2 | 20.9 | 0.03 | 47.7 | 0
BfS 32 | 70790438 | 271.424 | -30.144 | 07/05/2021 | 0.5 | 8 | 11.9 | 0.02 | 39.3 | 0
BfS 33 | 70790547 | 270.539 | -29.637 | 19/06/2021 | 0.7 | 1.9 | 8.5 | 0.05 | 16.2 | 0
BfS 34 | 70790666 | 270.559 | -29.467 | 05/09/2021 | 0.4 | 3 | 1.8 | 0.02 | 24 | 0
BfS 35 | 70790672 | 271.167 | -29.189 | 19/06/2021 | 0.5 | 6.2 | 36.7 | 0.04 | 43.7 | 0
BfS 36 | 70790754 | 270.762 | -29.830 | 15/05/2021 | 0.4 | 0.1 | 13026.9 | 2.38 | 0.2 | 1
BfS 37 | 70790813 | 270.399 | -29.923 | 12/07/2021 | 0.4 | 1 | 2.2 | 0.05 | 19.7 | 0
BfS 38 | 70790848 | 270.833 | -29.289 | 23/10/2021 | 0.5 | 0.8 | 2.4 | 0.07 | 32.5 | 0
BfS 39 | 70790883 | 271.545 | -30.141 | 27/05/2021 | 0.7 | 2.1 | 4.5 | 0.03 | 44.9 | 0
BfS 40 | 70790918 | 270.650 | -29.852 | 05/06/2021 | 0.5 | 2.8 | 14.3 | 0.05 | 6 | 0
BfS 41 | 70790962 | 270.488 | -30.022 | 05/09/2021 | 0.5 | 0.3 | 6.6 | 0.28 | 18.4 | 0
BfS 42 | 70790975 | 270.723 | -29.525 | 14/11/2021 | 0.8 | 1.7 | 3.5 | 0.04 | 18.2 | 0
BfS 43 | 70791011 | 270.369 | -29.511 | 15/08/2021 | 0.4 | 2.5 | 4.1 | 0.03 | 27.9 | 0
BfS 44 | 70791023 | 270.899 | -29.293 | 15/05/2021 | 0.4 | 1.5 | 3.4 | 0.04 | 32.8 | 0
BfS 45 | 70791355 | 272.603 | -36.434 | 31/08/2019 | 0.5 | 2.3 | 6.4 | 0.04 | 40.3 | 0
BfS 46 | 70791434 | 272.517 | -37.488 | 16/08/2019 | 0.5 | 14.9 | 64.9 | 0.03 | 36.1 | 0
BfS 47 | 70791686 | 273.023 | -36.568 | 23/08/2019 | 0.4 | 17.7 | 200.6 | 0.06 | 50.1 | 0
BfS 48 | 70791761 | 271.248 | -37.247 | 31/08/2019 | 0.4 | 3 | 9.9 | 0.05 | 44.5 | 0
BfS 49 | 70791852 | 272.860 | -37.199 | 31/08/2019 | 0.4 | 2.1 | 6.2 | 0.04 | 38 | 0
BfS 50 | 70792117 | 272.207 | -37.403 | 31/08/2019 | 0.4 | 2.7 | 1.5 | 0.02 | 25.8 | 0
BfS 51 | 70792231 | 237.067 | -48.184 | 31/07/2021 | 0.5 | 1 | 10.4 | 0.1 | 32.6 | 0
BfS 52 | 70792375 | 235.628 | -47.398 | 17/10/2021 | 0.4 | 0.9 | 3.3 | 0.07 | 49.7 | 0
BfS 53 | 70792445 | 236.614 | -47.322 | 31/10/2021 | 0.4 | 20.4 | 27.5 | 0.02 | 22.2 | 0
BfS 54 | 70792463 | 236.839 | -47.273 | 17/10/2021 | 0.4 | 0.3 | 1.8 | 0.18 | 24.1 | 0
BfS 55 | 70792507 | 236.701 | -47.932 | 19/06/2021 | 0.5 | 0.7 | 2.9 | 0.1 | 15.9 | 0
BfS 56 | 70792542 | 235.884 | -47.378 | 17/10/2021 | 0.4 | 4.7 | 29.7 | 0.05 | 40.6 | 0
BfS 57 | 70792578 | 236.787 | -47.672 | 27/09/2021 | 0.5 | 0.2 | 12.3 | 0.76 | 0.2 | 1
BfS 58 | 70792640 | 236.045 | -47.552 | 17/10/2021 | 0.4 | 4.2 | 8.3 | 0.04 | 30.8 | 0
BfS 59 | 70792656 | 236.271 | -47.244 | 07/08/2021 | 0.4 | 4.3 | 5.7 | 0.03 | 33.1 | 0
BfS 60 | 70792673 | 237.774 | -47.166 | 31/07/2021 | 0.6 | 1.3 | 3.1 | 0.05 | 50.4 | 0
BfS 61 | 70792680 | 237.816 | -47.732 | 31/07/2021 | 0.6 | 2.6 | 8.3 | 0.04 | 41.8 | 0
BfS 62 | 70792689 | 236.974 | -48.085 | 23/10/2021 | 0.4 | 5.8 | 21.4 | 0.03 | 25.8 | 0
BfS 63 | 70792709 | 236.908 | -47.147 | 23/10/2021 | 0.4 | 4.1 | 11.6 | 0.04 | 32 | 0
BfS 64 | 70792753 | 237.413 | -47.324 | 31/10/2021 | 0.6 | 8 | 83 | 0.06 | 33 | 0
BfS 65 | 70792803 | 237.946 | -47.945 | 31/07/2021 | 0.6 | 0.6 | 1.4 | 0.07 | 49.6 | 0
BfS 66 | 70792836 | 237.318 | -48.068 | 19/06/2021 | 0.4 | 0.1 | 0.7 | 0.18 | 32 | 0
BfS 67 | 70792856 | 235.627 | -47.468 | 17/10/2021 | 0.6 | 0.6 | 4.1 | 0.13 | 48.5 | 0
BfS 68 | 70792874 | 236.501 | -47.879 | 31/07/2021 | 0.7 | 4 | 31.8 | 0.06 | 16.8 | 0
BfS 69 | 70792923 | 235.831 | -47.516 | 05/09/2021 | 0.5 | 0.7 | 0.9 | 0.05 | 39.7 | 0
BfS 70 | 70792950 | 237.244 | -48.086 | 23/10/2021 | 0.6 | 5.7 | 23.6 | 0.03 | 30.9 | 0
BfS 71 | 70792982 | 237.261 | -47.217 | 31/10/2021 | 0.6 | 0.5 | 6.4 | 0.2 | 33.5 | 0
BfS 72 | 70792990 | 237.207 | -47.712 | 31/07/2021 | 0.4 | 4 | 2.9 | 0.02 | 17.2 | 0
BfS 73 | 70793015 | 235.650 | -47.575 | 04/10/2021 | 0.4 | 0.9 | 2.7 | 0.06 | 46.3 | 0
BfS 74 | 70793035 | 235.482 | -47.815 | 31/10/2021 | 0.6 | 1.9 | 18.9 | 0.08 | 53.2 | 0
BfS 75 | 70793087 | 282.321 | -3.065 | 07/09/2019 | 0.6 | 1 | 9528.9 | 1.53 | 0 | 1
BfS 76 | 70793134 | 282.315 | -2.990 | 30/11/2019 | 0.4 | 74.9 | 11.7 | 0.02 | 4.5 | 0
BfS 77 | 70793169 | 282.957 | -3.192 | 10/04/2020 | 0.4 | 5.9 | 8 | 0.04 | 38.9 | 0
BfS 78 | 70793209 | 282.732 | -2.533 | 30/11/2019 | 0.5 | 6.7 | 7.6 | 0.03 | 40.3 | 0
BfS 79 | 70793222 | 282.107 | -3.352 | 19/10/2019 | 0.4 | 37.6 | 8.1 | 0.01 | 21.5 | 0
BfS 80 | 70793281 | 282.175 | -2.841 | 07/12/2019 | 0.4 | 3.6 | 54.5 | 0.32 | 16 | 0
BfS 81 | 70793303 | 282.491 | -3.292 | 10/04/2020 | 0.4 | 0.8 | 3.2 | 0.21 | 17 | 0
BfS 82 | 70793382 | 282.355 | -3.728 | 07/09/2019 | 0.4 | 13.8 | 9.9 | 0.03 | 39.8 | 0
BfS 83 | 70793482 | 266.565 | -32.234 | 21/09/2018 | 0.6 | 0.4 | 146.3 | 1.09 | 0 | 1
BfS 84 | 70793565 | 266.151 | -32.564 | 27/10/2018 | 0.4 | 0.6 | 1.5 | 0.22 | 28.9 | 0
BfS 85 | 70793663 | 266.671 | -32.234 | 19/10/2018 | 0.4 | 0.2 | 26.8 | 1.18 | 5.4 | 1
BfS 86 | 70793688 | 266.761 | -32.828 | 27/10/2018 | 0.5 | 0.8 | 1.6 | 0.12 | 37 | 0
BfS 87 | 70793773 | 266.127 | -32.060 | 28/09/2018 | 0.4 | 1 | 6.7 | 0.23 | 24.6 | 0
BfS 88 | 70793847 | 284.523 | -9.106 | 05/08/2019 | 0.5 | 2.1 | 8.9 | 0.05 | 52.6 | 0
BfS 89 | 70793894 | 284.524 | -7.907 | 05/08/2019 | 0.6 | 3.3 | 6.4 | 0.03 | 21.1 | 0
BfS 90 | 70793933 | 284.260 | -8.211 | 05/08/2019 | 0.7 | 2.4 | 8.1 | 0.04 | 22.9 | 0
BfS 91 | 70794074 | 285.144 | -8.774 | 05/08/2019 | 0.6 | 0.8 | 9.2 | 0.11 | 43.7 | 0
BfS 92 | 70794096 | 283.910 | -8.295 | 05/08/2019 | 0.4 | 0.9 | 4.2 | 0.08 | 43.8 | 0
BfS 93 | 70794108 | 284.531 | -8.706 | 05/08/2019 | 0.7 | 3.1 | 14.9 | 0.04 | 28.9 | 0
BfS 94 | 70794119 | 284.483 | -8.148 | 05/08/2019 | 0.4 | 5.6 | 16.9 | 0.02 | 11 | 0
BfS 95 | 70794176 | 284.696 | -7.772 | 05/08/2019 | 0.4 | 0.6 | 1 | 0.07 | 28.1 | 0
BfS 96 | 70794327 | 284.412 | -7.658 | 05/08/2019 | 0.6 | 0.4 | 1.1 | 0.08 | 37.4 | 0
BfS 97 | 70794440 | 284.789 | -8.391 | 05/08/2019 | 0.4 | 0.3 | 0.7 | 0.11 | 12.5 | 0
BfS 98 | 70794613 | 285.194 | -8.346 | 05/08/2019 | 0.5 | 1.5 | 5.5 | 0.05 | 33.2 | 0
BfS 99 | 70794684 | 284.574 | -8.078 | 05/08/2019 | 0.7 | 3.3 | 30.7 | 0.05 | 10.5 | 0
BfS 100 | 70794685 | 284.858 | -7.609 | 05/08/2019 | 0.7 | 1.1 | 55.5 | 0.23 | 39.8 | 0
BfS 101 | 70794791 | 284.623 | -8.379 | 05/08/2019 | 0.9 | 2.3 | 21.8 | 0.05 | 8.6 | 0
BfS 102 | 70794807 | 285.090 | -8.988 | 05/08/2019 | 0.8 | 0.5 | 5.1 | 0.14 | 52.1 | 0
BfS 103 | 70794826 | 284.444 | -8.611 | 05/08/2019 | 0.9 | 1.8 | 12.5 | 0.06 | 25.4 | 0
BfS 104 | 70794918 | 285.358 | -8.161 | 05/08/2019 | 0.6 | 1.2 | 6.6 | 0.07 | 42.5 | 0
BfS 105 | 70794964 | 284.783 | -8.853 | 05/08/2019 | 0.4 | 1.1 | 2.3 | 0.05 | 37.8 | 0
BfS 106 | 70795034 | 284.820 | -8.057 | 05/08/2019 | 0.8 | 1.1 | 6.7 | 0.07 | 15 | 0
BfS 107 | 70795054 | 285.000 | -8.120 | 05/08/2019 | 1 | 2.3 | 21.6 | 0.07 | 22.2 | 0
BfS 108 | 70795071 | 285.019 | -8.570 | 05/08/2019 | 0.4 | 0.5 | 2.9 | 0.12 | 29.8 | 0
BfS 109 | 70795100 | 284.877 | -8.091 | 05/08/2019 | 0.7 | 0.7 | 5 | 0.13 | 16.3 | 0
BfS 110 | 70795197 | 284.812 | -8.673 | 05/08/2019 | 0.8 | 1.9 | 8.4 | 0.06 | 27.9 | 0
BfS 111 | 70795265 | 284.676 | -8.695 | 05/08/2019 | 0.4 | 1.8 | 2.1 | 0.03 | 27.5 | 0
BfS 112 | 70795290 | 284.448 | -7.598 | 05/08/2019 | 0.4 | 2.9 | 14.3 | 0.05 | 40.1 | 0
BfS 113 | 70795327 | 284.871 | -8.073 | 05/08/2019 | 0.8 | 9 | 103 | 0.05 | 16.6 | 0
BfS 114 | 70795334 | 284.800 | -7.904 | 05/08/2019 | 0.4 | 1.8 | 1.9 | 0.03 | 22 | 0
BfS 115 | 70795365 | 284.708 | -8.128 | 05/08/2019 | 0.8 | 3.3 | 15.3 | 0.03 | 7.5 | 0
BfS 116 | 70795426 | 284.341 | -7.904 | 05/08/2019 | 0.6 | 0.5 | 2.4 | 0.11 | 27 | 0
BfS 117 | 70795476 | 284.619 | -7.956 | 05/08/2019 | 0.7 | 1.7 | 3.4 | 0.04 | 17 | 0
BfS 118 | 70795913 | 207.308 | -62.922 | 17/11/2019 | 0.5 | 2 | 2.9 | 0.07 | 22.2 | 0
BfS 119 | 70795974 | 207.569 | -63.676 | 09/04/2019 | 0.4 | 20.9 | 50.9 | 0.04 | 27.8 | 0
BfS 120 | 70796049 | 207.055 | -63.273 | 09/04/2019 | 0.7 | 0.2 | 434.9 | 1.72 | 0.1 | 1
BfS 121 | 70796442 | 207.054 | -63.274 | 01/02/2019 | 0.4 | 0.5 | 2936.6 | 1.88 | 0 | 1
BfS 122 | 70796444 | 207.993 | -63.692 | 01/03/2019 | 0.4 | 2.3 | 3.7 | 0.07 | 35.6 | 0
BfS 123 | 70796614 | 274.548 | 7.188 | 14/10/2018 | 0.4 | 4.2 | 434.7 | 0.24 | 32.3 | 0
BfS 124 | 70796677 | 275.168 | 7.804 | 14/10/2018 | 0.6 | 8 | 487.9 | 0.24 | 37.4 | 0
BfS 125 | 70796699 | 274.869 | 6.912 | 14/10/2018 | 0.4 | 0.2 | 1.4 | 0.29 | 21.1 | 0
BfS 126 | 70796701 | 274.509 | 7.811 | 14/10/2018 | 0.6 | 1.3 | 81.5 | 0.26 | 51.1 | 0
BfS 127 | 70796797 | 275.120 | 6.698 | 14/10/2018 | 0.5 | 0.7 | 22.5 | 0.26 | 29.3 | 0
BfS 128 | 70796831 | 274.919 | 6.673 | 14/10/2018 | 0.4 | 0.2 | 3.4 | 0.4 | 32.4 | 0
BfS 129 | 70796842 | 274.773 | 7.007 | 14/10/2018 | 0.8 | 0.6 | 21.6 | 0.31 | 21.7 | 0
BfS 130 | 70796845 | 274.883 | 7.940 | 14/10/2018 | 0.4 | 0.2 | 3.3 | 0.31 | 47 | 0
BfS 131 | 70796863 | 274.588 | 7.085 | 14/10/2018 | 0.4 | 0.3 | 8.9 | 0.3 | 30.6 | 0
BfS 132 | 70796900 | 275.511 | 7.878 | 14/10/2018 | 0.4 | 0.8 | 28.2 | 0.31 | 48.5 | 0
BfS 133 | 70796970 | 274.727 | 7.523 | 14/10/2018 | 0.4 | 0.9 | 39.5 | 0.26 | 29.7 | 0
BfS 134 | 70796978 | 275.166 | 7.056 | 14/10/2018 | 0.6 | 0.6 | 14.5 | 0.31 | 8.9 | 0
BfS 135 | 70797098 | 275.110 | 6.915 | 14/10/2018 | 0.4 | 1.4 | 63.7 | 0.27 | 16.3 | 0
BfS 136 | 70797121 | 275.105 | 7.526 | 14/10/2018 | 0.4 | 0.5 | 6.1 | 0.28 | 20.5 | 0
BfS 137 | 70797142 | 275.235 | 6.442 | 14/10/2018 | 0.4 | 0.2 | 5.6 | 0.36 | 45.4 | 0
BfS 138 | 70797182 | 274.707 | 6.479 | 14/10/2018 | 0.6 | 2.4 | 322.8 | 0.36 | 48.2 | 1
BfS 139 | 70797198 | 275.717 | 7.629 | 14/10/2018 | 0.5 | 0.3 | 4.8 | 0.31 | 45.7 | 0
BfS 140 | 70797292 | 274.609 | 6.771 | 14/10/2018 | 0.5 | 0.4 | 9.2 | 0.32 | 38 | 0
BfS 141 | 70797295 | 274.819 | 6.766 | 14/10/2018 | 0.5 | 0.4 | 3.3 | 0.24 | 29.9 | 0
BfS 142 | 70797361 | 275.038 | 7.447 | 13/11/2018 | 0.7 | 0.5 | 14.2 | 0.32 | 16 | 0
BfS 143 | 70797376 | 274.875 | 7.784 | 14/10/2018 | 0.6 | 8.7 | 1052.6 | 0.26 | 38.2 | 0
BfS 144 | 70797391 | 275.126 | 7.338 | 14/10/2018 | 0.4 | 0.4 | 6.3 | 0.3 | 9.4 | 0
BfS 145 | 70797458 | 275.649 | 6.797 | 14/10/2018 | 0.7 | 0.8 | 8.2 | 0.32 | 40.5 | 0
BfS 146 | 70797482 | 274.980 | 7.739 | 14/10/2018 | 0.7 | 0.7 | 36.4 | 0.37 | 33.8 | 0
BfS 147 | 70797595 | 275.125 | 6.572 | 14/10/2018 | 0.7 | 3.8 | 520.9 | 0.28 | 36.9 | 1
BfS 148 | 70797626 | 274.716 | 7.064 | 14/10/2018 | 0.6 | 0.1 | 2 | 0.32 | 23.5 | 0
BfS 149 | 70797723 | 274.544 | 7.789 | 14/10/2018 | 0.4 | 0.3 | 8.7 | 0.28 | 48.7 | 0
BfS 150 | 70797725 | 275.532 | 7.768 | 14/10/2018 | 0.5 | 1.1 | 35.8 | 0.26 | 43.7 | 0
BfS 151 | 70797752 | 275.091 | 7.186 | 05/10/2018 | 0.7 | 0.2 | 1546.9 | 1.78 | 0 | 1
BfS 152 | 70797802 | 275.526 | 6.549 | 14/10/2018 | 0.6 | 0.6 | 10.2 | 0.27 | 46.1 | 0
BfS 153 | 70797809 | 275.818 | 7.101 | 14/10/2018 | 0.5 | 0.5 | 3.2 | 0.28 | 43.6 | 0
BfS 154 | 70797837 | 274.955 | 7.512 | 14/10/2018 | 0.4 | 0.9 | 18.1 | 0.25 | 21.2 | 0
BfS 155 | 70797847 | 275.930 | 7.263 | 14/10/2018 | 0.6 | 0.7 | 28.6 | 0.3 | 50.2 | 0
BfS 156 | 70797867 | 274.860 | 7.798 | 14/10/2018 | 0.7 | 0.6 | 6.8 | 0.24 | 39.2 | 0
BfS 157 | 70797932 | 275.014 | 7.750 | 14/10/2018 | 0.5 | 1.9 | 79.4 | 0.26 | 34.2 | 0
BfS 158 | 70798003 | 275.227 | 7.283 | 14/10/2018 | 0.4 | 12 | 853.4 | 0.26 | 10 | 0
BfS 159 | 70798081 | 275.063 | 7.475 | 14/10/2018 | 0.4 | 0.8 | 13.2 | 0.24 | 17.5 | 0
BfS 160 | 70798095 | 274.520 | 6.958 | 14/10/2018 | 0.6 | 0.9 | 25.4 | 0.24 | 36.7 | 0
BfS 161 | 70798104 | 274.469 | 6.777 | 10/04/2020 | 0.7 | 31.5 | 1686.3 | 0.3 | 44.4 | 1
BfS 162 | 70798126 | 274.372 | 7.327 | 14/10/2018 | 0.4 | 0.4 | 13 | 0.3 | 43.6 | 0
BfS 163 | 70798130 | 274.748 | 7.214 | 14/10/2018 | 0.6 | 1.3 | 81.3 | 0.27 | 20.5 | 0
BfS 164 | 70798239 | 275.506 | 7.480 | 14/10/2018 | 0.5 | 0.3 | 4.4 | 0.31 | 30.4 | 0
BfS 165 | 70798392 | 274.511 | 7.213 | 14/10/2018 | 0.5 | 1.1 | 96.5 | 0.3 | 34.6 | 0
BfS 166 | 70798400 | 275.445 | 7.451 | 14/10/2018 | 0.5 | 0.1 | 1.3 | 0.38 | 26.4 | 0
BfS 167 | 70798559 | 275.166 | 7.145 | 14/10/2018 | 0.6 | 8.9 | 409.4 | 0.24 | 5 | 0