11institutetext: Anton Pannekoek Institute for Astronomy, University of
Amsterdam, Postbus 94249, 1090 GE Amsterdam, The Netherlands
11email<EMAIL_ADDRESS>22institutetext: Leiden Observatory, Leiden
University, PO Box 9513, 2300 RA, Leiden, The Netherlands

#  TUVOpipe: A pipeline to search for UV transients with Swift-UVOT††thanks: A
reproduction package for this paper is available at
https://doi.org/10.5281/zenodo.5946940/.

David Modiano 11 Rudy Wijnands 11 Aastha Parikh 11 Jari van Opijnen 11 Sill
Verberne 1122 Marieke van Etten 11

Despite the prevalence of transient-searching facilities operating across most
wavelengths, the ultraviolet (UV) transient sky remains to be systematically
studied. Therefore, we recently initiated the Transient Ultraviolet Objects
(TUVO) project, with which we search for serendipitous UV transients in data
obtained using currently available UV instruments with a strong focus on the
UV and Optical (UVOT) telescope aboard the Neil Gehrels Swift Observatory (an
overview of the project is described in a companion paper). Here, we describe
the pipeline (named TUVOpipe) we constructed in order to find such transients
in the UVOT data, using difference image analysis. The pipeline is run daily
on all new public UVOT data (which are available 6-8 hours after the
observations are performed), so we discover transients in near real time.
Transients that last $>$0.5 days are therefore still active when discovered,
allowing for follow-up observations to be performed. From 01 October 2020 to
the time of submission, we used the TUVOpipe to process 75 183 individual UVOT
images, and we currently detect an average rate of $\sim$100 transient
candidates per day. Of these daily candidates, on average $\sim$30% are real
transients (separated by human vetting from the remaining ‘bogus’ transients
which were not discarded automatically within the pipeline). Most of the real
transients correspond to known variable stars, though we also detect a
significant number of known active galactic nuclei and accreting white dwarfs.
The TUVOpipe can additionally run in archival mode, whereby all the archival
UVOT data of a given field is scoured for ‘historical’ transients; in this
mode, we also mostly find variable stars. However, some of the transients we
find (in particular in the real-time mode) represent previously unreported new
transients or undiscovered outbursts of previously known transients,
predominantly outbursts from cataclysmic variables. In this paper, we describe
the operation of (both modes of) TUVOpipe and some of the initial results we
have obtained so far.

###### Key Words.:

transient – ultraviolet – analytical – data analysis – image processing –
photometric – stars

##  1 Introduction

In the last decade, there have been many advances in the field of time-domain
astronomy. An increasing range of facilities have been performing large-scale
surveys with the aim of detecting and studying transients and variable sources
(collectively referred to as ‘transients’ hereafter). These facilities have
operated across the electromagnetic (EM) spectrum, ranging from (very-high
energy) gamma rays down to low-frequency radio waves (see Sagiv et al., 2014
for a review of many of the existing transient facilities). Recently, this
wealth of knowledge has been further expanded with the development of multi-
messenger transient-searching facilities such as LIGO-Virgo (gravitational
waves; Abbott et al., 2016) and IceCube (neutrinos; IceCube Collaboration et
al., 2018).

A noticeable exception, however, exists in the ultraviolet (UV) regime.
Although UV observations are often used for follow-up studies of transients
discovered at other wavelengths (e.g. Cenko et al., 2012; see also Middleton
et al., 2017 for an overview of multi-wavelength astronomy including the
utility of the UV) and through other messengers (e.g. Abbott et al., 2017),
the UV has not been utilised for systematic large-scale searches for
serendipitous transients. Some relatively small projects have carried out
searches for UV transients, but with very limited scopes (e.g. Welsh et al.,
2005; Wheatley et al., 2008; Gezari et al., 2013 using data obtained with
GALEX). The primary reason for the lack of such UV transient studies is that
the Earth’s atmosphere is opaque to most UV radiation, prohibiting ground-
based facilities111Although most UV is blocked by the Earth’s atmosphere, a
significant fraction of the near-UV can still be observed from the ground, and
some new facilities are currently undertaking transient searches in bluer
bands (i.e. the U band) than typically used in transient surveys (e.g.
MeerLICHT; Bloemen et al., 2016, and in the near future, BlackGEM; Groot et
al., 2019).. UV telescopes mounted on satellites are therefore the primary
option for UV astronomy. However, there are no large UV surveying facilities
currently in operation, though some are planned or proposed (see, e.g.
ULTRASAT, Sagiv et al., 2014; CASTOR, Côte et al., 2012; Dorado, Singer et
al., 2021; UVEX, Kulkarni et al., 2021). See Kulkarni et al. (2021) for a
review of past and future missions with UV-transient searching capabilities.

This gap in our surveying capability is particularly noteworthy given that the
UV can provide valuable information about many types of interesting high-
energy sources, many of which are known to exhibit strong UV emission (and may
even peak in the UV at early times during their outbursts). Examples of such
phenomena include UV bright flares from active stars and interacting binaries,
outbursts from accreting white dwarfs, namely novae and dwarf novae (DNe),
outbursts from accreting neutron stars and black holes (e.g. X-ray binaries;
XRBs), supernovae (SNe), tidal disruption events (TDEs), variability of active
galactic nuclei (AGNs), and kilonovae (see Sagiv et al., 2014 for an overview
of the types of transients expected to show strong UV emission). Discovering
such sources in the UV can provide important information about the early UV
emission as well as how they evolve in time. Therefore, such studies could
significantly help to understand the physics of the UV emission processes in
these transients (which in many cases is still poorly understood) and
potentially uncover previously unknown behaviour. Perhaps most importantly,
since systematic, large-scale, blind UV transient searches have not been
undertaken, such studies would have the potential to discover completely new
types of sources.

Despite the lack of dedicated UV transient facilities, currently operational
UV telescopes mounted on satellites can be utilised to perform effective
searches for serendipitous UV transients. These facilities have relatively
small fields of view (FoV; typically up to a few arcminutes to at most a few
tens of arcminutes), reducing the number of possible transients discovered per
field studied. Nonetheless, one can take advantage of the repeating
observations of the same fields that these telescopes frequently carry out in
order to perform relatively large-scale transient surveys of the UV sky.

To this end, we initiated the Transient UV Objects (TUVO) project (see
Wijnands et al., in prep., for an overview of the TUVO project), with which we
aim to study the UV transient sky. The instrument we primarily use in the TUVO
project is the Ultraviolet and Optical Telescope (UVOT; see Roming et al.,
2005; Breeveld et al., 2010, 2011) aboard the Neil Gehrels Swift Observatory
(Swift; see Sect. 2.1 for a description of the observatory and instrument).
The reasons why, in the TUVO project, we so far have focused on the UVOT is
because of the high flexibility and rapid pointing capabilities of Swift
(which allow a large number of fields to be observed multiple times) in
combination with the accessibility of the data: all data are public and
accessible within a few hours of the observations being performed.
Furthermore, there are $\sim$17 years of archival UVOT data and up to a few
hundred new observations each day. Since the UVOT is primarily used to follow
up on individual previously discovered sources, this huge amount of data has
hardly been explored to search for serendipitous UV transients.

To undertake such a study, we built TUVOpipe222Note the dropped ‘S’ with
respect to the name of the pipeline in Modiano et al. (2020), which referred
to Swift. This designation of the facility was originally added to the name of
the pipeline because different pipelines were initially envisaged for the
different currently active UV instruments. However, we have now decided that
if additional UV instruments are implemented in the TUVO project in the
future, the processing of their data will be incorporated directly into the
current pipeline by making any necessary modifications to the code (for
details, see Wijnands et al., in prep.)., a pipeline that processes all newly
available UVOT data every day in order to search for UV transients. As the
main goal of the TUVO project is to discover and study currently active
transients, TUVOpipe primarily runs on the most recent Swift data (called the
’Quick-Look’ or ’QL’ data; see Sect. 2.3). We denote this default mode of
TUVOpipe as the real-time mode. A secondary mode of the pipeline is available
and can be used to process all archival observations of a given field,
allowing ‘historical’ unknown transients to be discovered (i.e. transients
that were active during archival images and were likely never studied).

##  2 Swift and UVOT

###  2.1 Swift

Swift was launched in 2004 with the primary science goal of understanding the
origin of gamma-ray bursts (GRBs; see Gehrels et al., 2004 for a detailed
description of Swift). The facility houses three instruments: a gamma-ray
detector called the Burst Alert Telescope (BAT; 15-150 keV; Barthelmy et al.,
2005); the X-ray Telescope (XRT; 0.2-10 keV; Burrows et al., 2005); and the
Ultraviolet and Optical Telescope (UVOT; 1700-8000Å; see Sect. 2.2 for more
details). When a GRB is detected by the BAT, Swift quickly repoints itself so
that the XRT and UVOT point at the position of this GRB in order to obtain
immediate X-ray, UV, and optical follow-up observations of the source. The
rapid pointing capability is one of the unique features of Swift. When GRBs
are not being observed, Swift carries out observations based on a schedule
consisting of targets from a wide variety of Guest Investigator and Target of
Opportunity observing programmes.

Each Swift observation is identified with a unique Observation ID (ObsID). The
data of each ObsID consist of all BAT, XRT, and UVOT data obtained during that
specific observation. Each observation is further split into the individual
exposures undertaken for that observation (i.e. Swift may observe a target
several times in one day, and all exposures are then part of the same ObsID;
see Sect. 2.3 and Fig. 1 for a full description of the structure of UVOT data
files). A few times per day, all of the newly obtained Swift data are down-
linked to the primary ground station in Malindi, Kenya, after which they are
processed and uploaded to the Quick-Look (QL)
page333https://swift.gsfc.nasa.gov/sdc/ql/. This means that preliminary Swift
data from all three instruments are typically accessible to the public just
4-6 hours after they are taken. We note that ‘preliminary’ here does not mean
raw; all public data have already been processed automatically by the Swift
reduction pipelines. However, the data on the QL page are updated as
additional exposures are carried out; these are then added to their
corresponding ObsID with every down-link of data. With every such down-link,
all QL data are also reprocessed with updated housekeeping information and
tagged with a new version number. When the data corresponding to an ObsID have
been on the QL page for $\sim$1 week, they are fully updated with all
exposures that the ObsID will comprise and are processed with the most up-to-
date housekeeping information. At this point, the data are transferred to the
High Energy Astrophysics Science Archive Research Centre
(HEASARC)444https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/swift.pl for
archiving and are not modified further (unless a calibration update is deemed
significant enough to warrant a ‘grand reprocessing’ of the whole
archive555See https://www.swift.ac.uk/archive/reprocessing.php for details..

###  2.2 UVOT characteristics

The UVOT is the primary instrument used by the TUVO project (see Roming et
al., 2005; Breeveld et al., 2010, 2011, also see the UVOT instrument
page666https://swift.gsfc.nasa.gov/about_swift/uvot_desc.html). In short, the
UVOT is a diffraction-limited, 30cm Ritchey-Chretien reflector with a 17’x17’
FoV and a photon-counting CCD detector, constructed with the primary science
goal of detecting and characterising the optical and UV afterglows of GRBs.
The UVOT is equipped with six filters covering the UV and optical wavelength
range 1700-6500 Å (see Table 1 for details of the central wavelengths and full
width at half maximum, FWHM, of the filters), plus a white filter covering the
1700-8000 Å range. In addition, a blocked filter, a magnifier, and two grisms
are available for use during UVOT observations, though data obtained using
these filters are not used in the TUVO project; we only process data that were
obtained using the seven primary UV and optical filters.

Figure 1: Data structure of Swift ObsIDs, which contain data obtained with the BAT, the XRT, and the UVOT. For the UVOT, the data are then further separated by filter, for all filters with which data were obtained for the given ObsID. We note that the UVOT is also equipped with a blocking filter, not shown in the figure. Within each filter, the data are separated into three primary files: the raw image file, the reduced sky image file, and the exposure map file (see Sect. 2.3). Each of these three files is composed of a number of extensions, which can be extracted and analysed independently of each other. Each extension is an image created from a single UVOT exposure in a particular filter (i.e. a snapshot). Extensions of the different file types correspond to each other (e.g. extension 1 of the raw image file is used to create extension 1 of the sky image file). Filter | Central Wavelength ($\AA$) | FWHM ($\AA$)
---|---|---
V | 5468 | 769
B | 4392 | 975
U | 3465 | 785
UVW1 | 2600 | 693
UVM2 | 2246 | 498
UVW2 | 1928 | 657
Table 1: Central wavelengths and FWHM of the six primary UVOT filters. Table
adapted from Breeveld et al. (2011).

Most Swift observations contain UVOT exposures777The rare exceptions are
typically fields that contain very bright stars that would damage the
detector, and hence the blocked filter is used.. UVOT observations are by
default performed using the ‘filter of the day’ (Page et al., 2014), which
involves cycling through the U, UVW1, UVM2, and UVW2 filters daily. This
filter strategy is not used if the principal investigator (PI) of an observing
programme specifies a different filter or set of filters. As with all Swift
ObsIDs, any UVOT ObsID is composed of one or multiple snapshots, which are
individual exposures typically taken throughout the course of one day.

###  2.3 UVOT data structure

The UVOT data within an ObsID are first separated by filter (see Fig. 2.3).
The primary data components of a UVOT observation for a particular filter are
a raw image, a sky image, and an exposure map888Exposure maps are arrays with
each pixel representing the effective exposure time of the corresponding pixel
in the sky image (see the instrument software guide at
https://swift.gsfc.nasa.gov/analysis/UVOT_swguide_v2_2.pdf for details). (see
Fig. 1 for a visual representation of the structure of UVOT data). Raw images
are known as ‘Level 1’ images; these data are processed by the standard UVOT
reduction
pipeline999https://swift.gsfc.nasa.gov/quicklook/swift_process_overview.html
in order to produce the sky images (the ‘Level 2’ images). Level 2 images have
been flat-fielded, cleared of bad pixels, and astrometrically solved to a
precision of a few arcseconds (Poole et al., 2008; Breeveld et al., 2010). The
standard UVOT reduction pipeline also attempts to astrometrically solve images
to $\leq 0.5$” (Poole et al., 2008; Breeveld et al., 2010). However, this
advanced solution may fail on certain fields; for example, when the UV sky is
very different from the optical (UVOT images are astrometrically solved by
matching source positions to sources in the USNO-B1 catalogue; Monet et al.,
2003a) or when fields are very crowded (Breeveld et al., 2010). In such cases,
the misalignment between UVOT images of the same field may be up to several
arcseconds.

Primary UVOT data files (sky images, raw images, and exposure maps) are in the
Flexible Image Transport System (FITS; Pence et al., 2010) format, and each
file type is comprised of up to several extensions (see Fig. 1). Each
individual extension is a single exposure, or snapshot (see Sect. 2.2).
Extensions of the three different file types correspond to each other: for
example, extension 1 of the raw image file is the raw image used to produce
extension 1 of the sky image file. QL ObsIDs are updated with each subsequent
data down-link: additional snapshots taken since the previous down-link may be
added to the ObsID, all images are reprocessed with the standard UVOT
reduction pipeline and updated housekeeping information, and each image is
tagged with a new version number (reflecting the number of times it has been
reprocessed). All data within an ObsID were typically obtained by Swift over
the course of one day, so the extensions within each file represent exposures
separated by a few hours to a day. In TUVOpipe, we made use of the Level 2 sky
images and their exposure maps.

##  3 TUVOpipe

To automatically process the UVOT data and search for serendipitous UV
transients, we used our purposely built pipeline, TUVOpipe. In this section,
we describe all aspects of the operation of TUVOpipein detail. The pipeline
can run in two different modes: real-time or archival. The real-time mode is
of prime interest to the TUVO project since it discovers (in the QL data)
sources that are currently active and can therefore be studied in detail with
follow-up observations. Therefore, in this paper when discussing TUVOpipe, we
generally refer to the real-time mode. The functionality of the two modes is,
however, very similar; any discrepancies are briefly discussed in the relevant
sections.

TUVOpipe functions in five parts, each with a different purpose. Part I (PI)
downloads all required data; Part II (PII) searches for candidate transients
using difference imaging and performs some tests to filter out false positive
detections; Part III (PIII) creates light curves of all remaining candidates;
Part IV (PIV) produces preliminary classifications of each candidate through
light-curve analysis and source matching with external astronomical
catalogues; and Part V (PV) creates long-term light curves of the most
interesting candidates by using all archival UVOT observations of the field.
By default, parts PI-PIV are linked, and thus each of these parts is initiated
upon completion of the previous part. PV is only run when a candidate
transient of sufficient interest is found. However, the scripts can also be
used independently; for example, when undertaking tests for improvements or
bug fixes.

TUVOpipe is written entirely in python (v3.7)101010Python Software Foundation,
https://www.python.org/ and makes use of several software packages. The
primary software components integrated in the pipeline include several
astropy111111https://www.astropy.org/(Astropy Collaboration et al., 2018)
packages, the image subtraction software hotpants (which is an abbreviation of
High Order Transform PSF and Template Subtraction; Becker, 2015; see Sect.
3.3.2), and HEASARC’s software suite
HEASoft121212http://heasarc.gsfc.nasa.gov/ftools (v6.28). The latter requires
the calibration database
CALDB131313https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/swift/ (we used
version 20201215). A few additional software packages are used at various
stages of the pipeline; these are described and referenced in the relevant
sections in our paper.

Currently, TUVOpipe is run daily by members of the TUVO project. In Figs. 2,
3, 8, 10 and 14, we show the workflow of the various parts of the pipeline.
The details of TUVOpipe are discussed in the following sections.

###  3.1 User inputs

TUVOpipe runs with minimal user input: only a parent directory on a user’s
local machine needs to be specified via a small input text file. There are
many adjustable parameters within the scripts that can be modified for testing
and improvements, though they are now generally considered optimal and are
therefore typically not modified from their default values. We note that
‘optimal’ concerns the best parameters for the majority of UVOT fields. The
pipeline may perform better with different parameters for some specific fields
- for example, very crowded fields such as globular clusters (see Modiano et
al., 2020) - but so far it has not been possible to implement this
programmatically within the code. This is both because it is difficult for the
code to automatically recognise fields as certain types (e.g. those with
diffuse emission) and because it is difficult to determine sets of parameters
that would work optimally for all fields of a given type. One item the user
can manually specify is which UVOT filters should be processed by the
pipeline. However, by default all filters are selected, causing the pipeline
to run on each filter independently, since only images taken in the same
filter can be compared to each other to search for transients. For the
archival mode of TUVOpipe, three additional user inputs are required (compared
to the real-time mode), namely the right ascension (RA) and declination (Dec)
of the field of interest and the search radius (see Sect. 3.2 for details).

###  3.2 Part I

Figure 2: Workflow of PI (discussed in detail in Sect. 3.2. This part of
TUVOpipe downloads all UVOT data used to search for transients. In the
archival mode, the RA and Dec of the field of interest is required as input.
In the real-time mode, no input is required, since it always downloads all
data available on the QL page at the time when PI is initiated. In the
archival mode, the ObsID version check is bypassed, since ObsID versions of
data in the archive are not further updated (except in rare cases when a
reprocessing of the entire archive is carried out; see Sect. 2.1). Upon
completion of PI, Part II (PII) of the pipeline (see Sect. 3.3) is
automatically initiated.

Part I (PI) of TUVOpipe downloads the UVOT data to be processed. Since the
data to be downloaded differ in real-time and archival mode, PI consists of
two versions: PIa for the real-time mode and PIb for the archival mode. The
downloading processes for the two versions are slightly different (see Fig. 2
for a chart displaying the workflow of PI in both modes).

In PIa, first a list is created of all the fields and ObsIDs available on the
current Swift QL page. As discussed, new data are transferred from the QL page
to the archive after around one week, so the list created in PIa includes all
data taken up to around one week before the time when PIa is initiated. This
typically consists of up to several hundred observations of many different
fields. PI then loops through each ObsID in the list to check if a download is
required. If there are UVOT observations for a given ObsID, then a download of
the Level 2 sky images and corresponding exposure maps is carried out if
either the ObsID has not been downloaded before (i.e. it is not present in the
user’s local archive), or if the ObsID has already been downloaded but a newer
version of the data is available (e.g. if in a previous run of PIa a given
ObsID was downloaded, and before the next run of PIa, a further image was
taken by UVOT and added to that ObsID). In the latter case, PIa detects the
newer version and performs the download, replacing the old version with the
new version. After looping through all available observations in this way, PIa
is completed. Since we run TUVOpipe once per day, with each run one day’s
worth of new observations are downloaded, as well as any updated versions of
older observations. Since the data remain on the QL page for around one week,
TUVOpipe is guaranteed to process all available data unless it runs less
frequently than once per week.

In the case of PIb, the user provides the RA and Dec of the field of interest.
The user also selects a search radius, which defines the distance from the
specified RA and Dec out to which UVOT data will be downloaded - in other
words, all UVOT images with pointings within the search radius from the input
coordinates. Typically, a radius of 12’ is chosen, meaning that all archival
UVOT images with pointings $<$12’ from the specified RA and Dec are downloaded
for processing. This value is chosen because the FoV of the UVOT is 17’x17’,
so even images whose centres are offset with respect to each other’s by 12’
will still have considerable overlapping areas. This is important because to
feasibly perform transient searches, images must have large overlapping areas.
This is necessary both in order to successfully align images (see Sect. 3.3.1)
and to avoid very inefficient searches, since searching for transients in
images with very small overlapping areas would yield very few transients. PIb
then creates a list of all such available observations from the Swift-UVOT
archive and downloads all corresponding ObsIDs similarly to the operation of
PIa. To avoid unnecessary downloads, checks for previously downloaded ObsIDs
in the user’s local directory are carried out.

###  3.3 Part II

Figure 3: Workflow of PII (discussed in detail in Sect. 3.3). This is the part
of TUVOpipe that searches for transient candidates in UVOT images through
difference image analysis. The operation of PII is identical for the real-time
and archival modes, since in archival mode it simply stops after the field of
interest is completed. When PII terminates, it has produced lists of candidate
transients for each field and filter. These are passed to Part III of the
pipeline (see Sect. 3.4), which is automatically initiated upon completion of
PII.

Once all new UVOT data have been downloaded, the search for candidate
transients is carried out through Part II (PII) of the pipeline (see Fig. 3
for a chart showing the workflow of PII). PII consists of several levels of
processing, taking UVOT sky images as input and ultimately producing lists of
viable candidate transients for each field and filter. The workflow of PII is
such that all observations of all fields present in the user’s local archive
are processed in sequence automatically. Each field is checked to determine if
it has already been processed by PII. If so, PII moves to the next field. When
a field is encountered that has not been processed by PII (i.e. the pipeline
determines that there are unprocessed ObsIDs within the field), all ObsIDs of
the field are checked. When an ObsID that has not been processed is
encountered, PII begins processing that ObsID. All PII processing steps are
described in this section and outlined in Fig. 3.

####  3.3.1 Data preparation

For each ObsID that requires processing, PII carries out some initial data
processing steps necessary for the image subtraction (see Sect. 3.3.2) to
perform optimally. For each ObsID, all extensions are extracted using the
HEASoft
fextract141414https://heasarc.gsfc.nasa.gov/lheasoft/ftools/fhelp/fextract.html
tool. This is done because the extensions may not always be perfectly aligned
to each other, so we later perform the image alignment on all extensions.
Additionally, by processing each extension separately we obtain a larger
number of data points for the light curves of each transient we detect (see
Sect. 3.4).

Transient searches in TUVOpipe require at least two images of the same field:
a template and a science image. The field for each image is defined by the
field name assigned to it by Swift, which refers to the target of the
observations. The science and template images must be taken in the same
filter; all filters are therefore processed independently in TUVOpipe. PII
begins by processing each field independently. In any field directory, one
image is selected as the template, and all other images are defined as science
images. The template is the image with respect to which all science images
will be aligned and subtracted (i.e. it is the image to which all the science
images will be compared when searching for transients). For each field, PII
thus begins by checking if a template image has already previously been
defined within a given set in an ObsID. If it has, then it processes that
ObsID. If no template image has been selected, it attempts to select a
suitable template image. First, a template image library, located in the
user’s local machine and containing many UVOT archival images, is searched. We
search for template images that are in the required filter, have $>200s$
exposure time (higher quality templates are preferred for image subtraction,
so we try to avoid using low-exposure images as templates), and have a
pointing within 12’ of the first image in the ObsID being processed. If no
such template is found in the local template library, PII undertakes the same
search but in the entire UVOT archive. Archival images are preferred as
templates because if a transient is variable on timescales larger than a few
days to a week, then comparing only QL images (that at most span a time of
approximately a week) may not reveal any variable behaviour. By selecting an
archival template taken weeks to years previously, sources that vary on a
larger range of timescales can be identified. If a suitable template is found
in the archive, it is downloaded and selected as the template for the set. It
is also copied to the local template image library. We note, therefore, that
the local template image library contains only copies of UVOT archival images;
it is simply a way of reducing processing time as it avoids downloads. If no
suitable template image is found in the archive, the first science image of
the field and filter being processed that passes the exposure time and image
quality checks (basic checks performed for every image we process, described
below) is selected as the template.

After selecting a template, PII begins processing all science images. For each
science image, PII carries out an exposure time check and an image quality
check. If the image has an exposure time $\leq$50s then it is flagged and not
processed in PII. We implemented this check because when using lower exposures
times, we found that the image subtraction performs poorly (typically, the
difference images produced contain many artefacts). We note that although
these science images are not processed in PII, they are not discarded, as they
are still used later in the pipeline to create light curves (see Sect. 3.4).
Next, an image quality check is carried out in order to improve the
performance of PII by rejecting science images that are not usable to search
for transients. The HEASoft tool
uvotdetect151515https://heasarc.gsfc.nasa.gov/ftools/caldb/help/uvotdetect.html,
the standard tool used for source detection in UVOT images, is run on the
image. For each detected source a measure of its elongation is obtained: if
the total mean source elongation in the image is larger than 1.4 (a user-
defined value chosen empirically), then the image is rejected and not
processed further at any stage of TUVOpipe. This check prevents low-quality
images (e.g. those in which stars may be elongated due to either a telescope
tracking error or the exposure commencing before the telescope had finished
slewing) from being processed. In Fig. 4, we show an example of such an image.
They represent $\lesssim$1% of UVOT data that we process, both by number of
images and by exposure time.

Figure 4: Example of a UW2 image (ObsID 00048753209, extension 2, field
ASASSN-20ni) that did not pass the image quality check and was therefore
rejected by PII and not processed further in TUVOpipe. The measured mean
elongation for sources in this image is 1.6 (i.e. greater than the cut-off of
1.4).

For each science image that passes these initial checks, the science and
template images are cropped such that two new images are created that cover
the maximum overlapping area between the original science and template images.
This is necessary because the image alignment routines used require input
images to be of the same shape and size, and due to the pointing accuracy of
the UVOT (up to a few
arcminutes161616https://swift.gsfc.nasa.gov/proposals/tech_appd/swiftta_v14/node23.html)
and the orientation of the images in subsequent images (usually the images are
obtained at different roll angles of the satellite), this is not usually the
case. To illustrate this effect, we stacked two UVOT images of the same field
and filter taken at different times, using the HEASoft tools
fappend171717https://heasarc.gsfc.nasa.gov/lheasoft/ftools/fhelp/fappend.txt
and
uvotimsum181818https://heasarc.gsfc.nasa.gov/lheasoft/ftools/headas/uvotimsum.html
(see Fig. 5 for an illustration of this effect).

Figure 5: Example of translational and rotational offset between two UVOT
images of the same field, taken around one month apart. The image is composed
of two stacked UVOT UVW1 exposures of the field of Nova Per 2020. The ObsIDs
and extensions of the images are 00013909005[1] and 00013909010[1]. The
position angle of the telescope was $\sim$40∘ offset between the two images
($320^{\circ}$ for ObsID 00013909005; $280^{\circ}$ for ObsID 00013909010).
The displayed images clearly show the result of this rotational offset, as
well as a translational offset due to the pointing accuracy of Swift. Searches
for transients can be performed within the overlapping area. To prepare images
for alignment and image subtraction in PII, the images are cropped (and saved
to two new images) so as to contain only the overlapping area.

Since image subtraction requires precisely aligned images, the next step in
PII is to align each (cropped) science image to the (cropped) template. Image
alignment is carried out using the image registration tool
register_translation of the scikit-image191919https://scikit-image.org/
package, which uses fast Fourier transforms and cross-correlation to perform
image translation with sub-pixel accuracy (see Guizar-Sicairos et al., 2008).
The tool returns the X and Y pixel translation required to align the science
image to the template image. This translation is then applied to both the
cropped science image and the original, uncropped science image. The former
will be used for image subtraction (since only the parts of the image that
overlap between the science and template are useful to search for transients);
the latter will be used to create light curves of our candidate transient
sources, where using full images is optimal in order to maximise the number of
data points per light curve (see Sect. 3.4).

We note that the template chosen by PII is not necessarily the image with the
highest astrometric precision. Solving UV images astrometrically can be
difficult, in particular when the UV field looks significantly different
compared to optical images of the same field. Since at the PII stage we are
only interested in detecting transients, having the most precise astrometric
coordinates for each candidate is not vital; thus, we simply ensure that
images are correctly aligned relatively to each other. This means that the
error on the position for each transient we detect (e.g. all the coordinates
given in the example transients we show in Sect. 4) can be up to a few
arcseconds (see Sect. 2.3; see also Poole et al., 2008; Breeveld et al.,
2010).

On some occasions, the image alignment process fails. It is not always clear
what the cause of a failed alignment is, though certain kinds of sky fields
are more likely to be problematic for alignment, such as very empty (meaning
very few stars detected), or conversely, very crowded fields. Although the
tool we use always returns some alignment (i.e. it does not explicitly state
when alignment has failed), we performed extensive tests to identify
incorrectly aligned images and found that these occur most frequently when the
returned translation necessary to align the images is found to be greater than
8 pixels. Therefore, if the returned translation required is above this value,
we do not align the images at all, and we continue to process them using the
coordinates automatically assigned by the UVOT reduction pipeline. This is
because UVOT images are often already well aligned before we process them, but
on occasion the alignment tool may still fail and attempt a $>8$ pixel shift.
In these cases, by refusing the translation but continuing to process the
images, we may still obtain useful data. In our pipeline, these images are
flagged as unaligned images, and corresponding data points in the light curves
we create are labelled as such (see Sect. 3.4). We note that on occasion, the
alignment tool may also not correctly align images even when the returned
translation is less than 8 pixels. Therefore, poor alignments are not always
guaranteed to be flagged automatically by our code, though this happens rarely
and is easily recognised by users when inspecting the image stamps in the
final output of the pipeline (see Fig. 11).

Figure 6: Examples of transient candidates found through the image subtraction
process. Each row displays examples for a particular field and filter in which
transients were found. From left to right, the columns show the science image,
the template image, the difference image, and a zoomed-in view of the
difference image to display one or more transients. The red square in the
difference images (third column) shows the region displayed in the fourth
column. The examples displayed were chosen to show different types of fields
observed by UVOT and thus processed by TUVO. From top to bottom, the rows show
a field with diffuse emission (where the transient is the supernova
SN2021pit), a very empty field (where the transient is the X-ray binary SWIFT
J1357.2-0933), a very crowded field with diffuse emission (where the transient
in the top left of the zoom stamp is the star Cl* NGC 5139 NJL 47 and the
transient in the bottom right is the star Cl* NGC 5139 NJL 14), and a crowded
field (where the transient shown is the RR Lyrae OGLE LMC-SC3 324213). In the
displayed stamps, positive flux is shown as black, so ‘negative’ transients,
that is, those that were brighter in the template image than in the science
image, appear white in the difference images; and ‘positive’ transients,
representing sources that are brighter in the science image than in the
template image, are black in the difference images. The field names, ObsIDs,
and extensions of the science and template images shown (from the top left)
are SN2001EL-HOST/00049929023[1]; NGC1448/00031031001[5];
SwiftJ1357.2-0933/00031918123[1]; SwiftJ1357.2-0933/00031918002[1];
OmegaCentauri1/00037520001[2]; OmegaCentauri1/00037520004[1];
LMCFIELD6/00094074015[1]; and LMCFIELD6/00094074007[1]).

####  3.3.2 Image subtraction

Once a science image is fully prepared (see Sect. 3.3.1), image subtraction is
carried out for the template with respect to it. This is done using using the
hotpants (Becker, 2015) software. This routine was constructed based on the
image subtraction algorithm developed by Alard & Lupton (1998) and Alard
(2000). It works by first constructing two matching stamps (stamp-pairs) from
the science and template images, and then convolving the point spread
functions (PSFs) of the pairs (on the assumption that the stamps are small
enough that PSF variations within it for a given image are negligible). The
PSF matching202020We note that, typically, PSF matching is crucial for image
subtraction using data from ground-based facilities because sky conditions can
significantly affect the PSF of different images; space-based telescopes such
as UVOT do not suffer from these sky effects. However, the PSF of sources in
UVOT images can also change, for example due to temperature changes in the
telescope during observations, or due to the location on the CCD in which a
particular source falls (i.e. the PSF of a source that falls on the centre of
the UVOT CCD in one image may be different to the PSF of that same source if
it is located near the corner of the CCD in another image). is then modelled
across the entire image by a combination of the functions obtained for each
stamp pair. By default, the science-image PSF is convolved to match that of
the template. In addition to the PSF convolution, hotpants carries out flux
normalisation (to account for exposure time differences in the science and
template images), before finally subtracting the two PSF-convolved and flux-
normalised images pixel-by-pixel, producing a difference image. We run the
image subtraction in both directions, that is, first subtracting the template
image from the science image and then vice-versa. This is because the source
detection software we use (see Sect. 3.3.3) only picks up positive sources in
the difference images, so to detect transients that were brighter in the
science than the template image and also vice-versa, we need two difference
images, with the second created by flipping the direction of the subtraction
used in the first (we denote this the ‘flipped difference image’).

In some cases, such as when processing very empty fields, hotpants cannot find
suitable sources with which to match the PSF of the stars in the science image
to those in the template image, and the subtraction fails. Within TUVOpipe,
this occurs on $\sim 10\%$ of subtractions. In these cases, PII performs a
‘manual’ subtraction by simply normalising the flux in the two images by their
exposure time and then carrying out a pixel-by-pixel subtraction. This
produces difference images that are less accurate than when using hotpants
(because no PSF matching was performed), but in these difference images
transients can still be detected. In Fig. 6 we show examples of difference
images produced.

####  3.3.3 Source extraction and candidacy tests

Figure 7: Example of a difference image and the outcome of our candidacy tests
for some examples of the detected sources. Left panel: Typical difference
image produced by TUVOpipe (science image: ObsID 00034748064, extension 2, u
filter; template image: ObsID 00035028027, extension 1, u filter). In this
difference image, 38 sources were detected. Only one passed all three tests
(Sect. 3.3.3) and was retained as a candidate transient (source d - this is a
real transient, the BL Lac prototype system, which is often monitored by
Swift). The labels indicate the example sources displayed in the right panel.
Right panel: examples of candidate transients detected in the difference image
that did not pass one of the tests (a-c), and the only source that passed all
tests (d). The elliptical regions are the source regions extracted by the
source detection software (in this case uvotdetect).

Once a difference image is produced, PII searches it for candidate transients.
Real variable sources will appear in the difference image as point sources
(either positive or negative, depending on whether the source was brighter in
the template or in the science image; see Fig. 6). A first set of candidates
is detected by running source detection software on the difference images.
This is a standard strategy used in transient searches which utilise image
subtraction methods (e.g. ZTF, Masci et al., 2019; MeerLICHT, Hosenie et al.,
2021, the Deeper Wider Faster Survey with the Dark Energy Camera, Andreoni et
al., 2017; Andreoni & Cooke, 2019; see also Hu et al., 2021).

We run both uvotdetect (the standard UVOT source detection tool) and
sextractor212121https://sextractor.readthedocs.io/en/latest/ (Bertin &
Arnouts, 1996) to detect sources in the difference images. We use both
methods, because after testing the software on many different fields and with
different settings, and inspecting the results by eye, we found that some
sources were only detected by one of the two methods. The clearest sources in
the difference images are picked up by both methods, and virtually all sources
are picked up by at least one of the methods (with sextractor typically being
more effective than uvotdetect, but the latter still occasionally detecting
sources that are missed by the former). We run both detection methods on both
the normal and flipped difference images (see Fig. 6 for examples of template,
science, and difference image sequences and the appearance of transients in
the difference images). Once both source detection methods have been run on
both difference images, the detected sources are compiled into a single
transient candidate list (i.e. removing multiple detections of the same
source) for that image.

In practice, image subtraction is not a perfect process, due to various
possible factors. First of all, there may be artefacts in the input data, such
as stripes, optical ghosts, saturated stars, or imperfect mod-8 noise
correction (i.e. related to bright or saturated stars; see Sect. 4 of Breeveld
et al., 2010 for details about the mod-8 noise) in the science and template
images, or imperfect alignments. These can result in artefacts being carried
over in some form to the difference images. However, the majority of the
issues exhibited in difference images are due to complexities in the image
subtraction process itself (in our case hotpants). To create the convolution
kernel to match PSFs, hotpants uses Gaussian functions, which are not exact
representations of the image PSFs (see Cao et al., 2016; Hu et al., 2021 for
descriptions of this caveat in hotpants and see Breeveld et al., 2010 for a
detailed description of the PSFs of UVOT images). More specifically, for
example, asymmetric variations in the PSFs of the two input images may cause
problems in the subtraction, since hotpants models the PSFs with circularly
symmetric Gaussian functions (Hu et al., 2021). The image subtraction
algorithm may also encounter significant issues when dealing with diffuse
emission. Collectively, these complications ultimately lead to imperfect
difference images, and hence a large number of candidate sources detected in
the difference images are ‘bogus’ transients (false positives). The next stage
of PII is therefore to reject candidates that are not likely to be true
transient sources. A series of tests is performed on each detected source in
order to produce a list of reliable transient candidates (see Fig. 7 for an
example of a difference image with all detected sources shown and examples of
the results of these tests).

To begin with, for each image the overall performance of the image subtraction
is checked: if the number of sources detected in the difference image is
$>$90% of the number of sources in the science image, then it is considered a
bad subtraction. We note that here for consistency we run the test once for
each method; in other words, we compare the number of sources detected by
uvotdetect in the science and difference images, and then again for
sextractor. If either of the two methods fails the test, then it is considered
a bad subtraction. Typical causes of bad subtractions are images that were not
correctly aligned; if the template and science images are misaligned, every
source in both images will appear in the difference image. Very crowded fields
are also frequent causes of bad subtractions; crowded fields can cause severe
problems for image subtraction even on well-aligned images, and many stars may
not be subtracted properly, leaving residuals in the difference images that
are picked up as sources. Badly subtracted difference images are rejected and
not processed further, though their corresponding science images are kept for
creating light curves if any transients were detected in that field (see Sect.
3.4). These unsuitable subtractions occur on 20-30% of images222222This
problem is found to be very challenging to solve, though we are currently
working on methods to reduce the fraction of badly subtracted images..

If the difference image is accepted, PII proceeds to run three tests on each
individual detected source to determine whether it is a viable transient
candidate. If a source fails any test, it is discarded; if it passes all
tests, it is considered a viable candidate transient and is retained. The
three tests carried out are described in the following paragraphs, in order of
when they are processed within PII.

The first is the elongation test. If the source has an elongation higher than
a specific value then it fails the test (see Fig. 7, right panel, a). By
default, we use a value of 2.0 (i.e. higher than the elongation cut for low-
quality science images of 1.4; see Sect. 3.3.1; this number can be adjusted by
users if necessary) because during the image subtraction process, the
convolution of the PSF between the template and science images can cause real
transient sources to deviate slightly from point sources in the difference
images. Many poorly-subtracted sources in the differences images have
brightness profiles consisting of multiple positive and negative brightness
components. Most cases of detections of elongated sources in the difference
images are due to the source detection software identifying these components
as real sources (see Fig. 7, right panel, a).

Secondly, we have the yin-yang test. One of the most common artefacts in
difference images is when sources appear as distinct dipoles - the so-called
yin-yang pattern (see e.g. Hu et al., 2021). This is when a source appears
composed of both positive and negative contributions (see Fig. 7, right panel,
b). These patterns are often detected in the difference images (for sources
that are not truly variable) and are artefacts introduced by the image
subtraction method. Typically this occurs when there are asymmetric
differences in the PSFs of the input images, which are not well accounted for
by hotpants (Hu et al., 2021), or when the images were not properly aligned.
To reject these artefacts, a box (twice the size of the semi-major axis of the
source region detected by the source detection software) is created centred at
each source’s location; for a source to pass the yin-yang test, the total
negative counts in the box must not be comparable (less than a factor of 2.5,
tested empirically on many sources) to the total positive counts.

The brightness profile test provides a useful way of determining whether a
source is real by examining characteristics of its PSF; for example, by
fitting a Gaussian to the source profile. Although a Gaussian is not a perfect
match to the UVOT PSF (see Breeveld et al., 2010), extensive testing on our
sources has revealed that it is still adequate for our purposes. Sources in
the difference images that exhibit irregular brightness profiles that do not
resemble real sources could be the result of subtracting saturated stars,
blended sources, or hotpants failing to perfectly cross-convolve the PSFs of
the two images. The final test therefore uses the same small image stamp
created during the yin-yang test and fits a two-dimensional Gaussian to the
brightness profile of the source within the stamp. The source passes the test
if the full width at half maximum of the fitted Gaussian is between 2.8 and
3.7 pixels and the $\chi^{2}$ squared value of the fit is less than $10^{3}$.
The values for these cut parameters have been determined by performing tests
across many fields with different characteristics, including empty fields and
very crowded fields. These thresholds we chose for passing the test are
relatively loose in order to account for the fact that real source profiles
are not expected to exhibit perfect Gaussian shapes (see Fig. 7, right panel,
c, for an example of sources that did not pass this test).

The described process in PII so far (from extraction of the image from the
ObsID to the candidacy tests per source) is then repeated for every image in a
given field, after which PII moves to the next field. Therefore, when PII has
finished, a list has been produced of all the viable transient candidates for
each field and for each UVOT filter. These sources are passed to Part III of
TUVOpipe for further processing.

There are no major differences in the functionality of PII between the
archival and the real-time modes of the pipeline, since for the archival mode
it simply stops after the single field that is being processed is completed.
The only difference worth noting is that during the archival mode of the
pipeline, it runs on all archival data for a given field, and therefore there
is no need for the archival template selection process (see Sect. 3.2). The
first image to be processed that passes the image quality check has an
exposure time $>200s$ and is $<4^{\prime}$ from the position specified by the
user; this is always selected as the template. We also note that for any field
processed, in both the real-time and archival modes, the template can be
chosen manually by the user. By default, this does not happen since we aim to
minimise user intervention (as we process several tens of fields per day).
However, this can occasionally be useful, for example in the archival mode
where several hundreds of images may be processed with respect to the
template, because users may want to manually ensure that the template is of
good quality (e.g. it has high enough exposure time, no artefacts present, and
no elongated sources).

###  3.4 Part III

Figure 8: Workflow of PIII (discussed in detail in Sect. 3.4). This part of
TUVOpipe creates light curves and stamps for each transient candidate detected
in PII and which passes the $\Delta$M check. See Fig. 9 for an example of the
output of PIII. PIII operates in the same way for the real-time and archival
modes of TUVOpipe since it requires only a list of candidate transients that
were detected in PII. Upon completion of PIII, Part IV (PIV) of the pipeline
is automatically initiated.

The next step in TUVOpipe consists of creating light curves of all transient
candidates that passed all tests in PII. This is done in Part III (PIII) of
the pipeline, the workflow of which is displayed in Fig. 8.

PIII begins by running through each field and creating a global list of all
candidates per field that were output by PII and removing any duplicates.
Duplicates are sources detected by PII in different difference images of the
same field and the same filter, but which are close to each other and
represent the same source. The radius chosen inside which sources are
considered duplicates is 8”; this value was chosen empirically by examining
many duplicate sources. For each source in the global list, PIII performs
photometry at the source location. It does this using every science image of
the field available in the user’s directory (downloaded in PI), as well as the
template image232323For the template image, PIII also computes the on-sky
distance (in arcminutes) of the source from the centre of the image. This is
done to make users aware of occasions when the source is near the corners of
CCD in the template image. In those cases (typically when the distance is
¿11’), the photometry may not be completely reliable due to the decrease in
sensitivity of the detector towards the corners of the CCD. We find that this
effect is not always fully accounted for in the automatic UVOT calibration.
(which was selected in PII). PIII thus outputs the (AB) magnitude of the
source in each image. We note that PIII runs photometry on the uncropped,
aligned, science images. This is because if the cropped images were used, some
transients may be cut out of some images, therefore not allowing us to obtain
photometry on those images and thus reducing the number of data points we can
obtain. Despite this, for a given transient, in some images no photometric
measurement is possible (consider a transient that is originally detected near
the edge of a particular image). Since UVOT images of the same field do not
always cover the same exact sky area (see Sect. 3.3.1), in a subsequent image
of that field the location of that transient may fall outside the image. In
these cases, no photometric measurement can be made for the transient in that
image.

Photometry is carried out using the HEASoft UVOT photometry tool
uvotsource242424https://heasarc.gsfc.nasa.gov/lheasoft/ftools/headas/uvotsource.html.
As input, uvotsource requires an image, a source region file, and a background
region file. The regions are created automatically by PIII as each source is
processed. The source region file is defined by a circle with its centre at
the location of the source (as determined by the source detection method) and
a radius of 7”. The background region is then created as an annulus with inner
radius twice the size of the source region radius (to avoid the wings of the
source flux profile contaminating the background region) and outer radius
three times the size of the source region radius. This ideally results in a
suitable background region that is close to the target source, is
significantly larger than the target source region, and is devoid of other
sources (see Fig. 9 for an example). Unfortunately, the last criterion is
occasionally not met, for example in some crowded fields where a candidate can
have several nearby sources contaminating the background region. This results
in incorrect magnitude measurements, generally underestimating the target
source brightness252525This effect may also introduce fake variability or hide
true variability in the light curves of our sources. However, although it is
difficult to quantify, this is likely rare because it requires the sources
contaminating the background region to also be strongly variable themselves
within the time frames of our light curves.. However, the products of PIII
include a stamp of the source, displaying the source and background regions
(see Fig. 9). From these stamps, it is always apparent when there is
background contamination (see Fig. 11 for such an example), so the user
becomes directly aware when the displayed magnitudes are not completely
reliable. We note that for sources of interest, manual light curves can be
created using Part V (see Sect. 3.6) and a user-specified background region in
order to obtain the most accurate light curve possible. We also note that the
automatically produced light curves are not intended to be the most accurate
possible, but simply to provide good enough indications to the user to roughly
examine the variability behaviour of each candidate and determine whether or
not further investigation of the transient is warranted.

It is worth making a remark about how we deal with non-detections in PIII (the
transient might not be active during all the images of a field, so not all
images will necessarily have detections of the transient). For every
uvotsource run, a magnitude measurement is always produced, even when there is
no detection (in the cases where there is no detection and the flux in the
source region is negative, the magnitude will be set to 99). However, for each
uvotsource run an upper limit is also always given, representing the limiting
magnitude of the UVOT image at the source location (based on the local
background counts). Our method of determining whether there is a detection or
not is thus to compare the magnitude measurement and the upper limit; if the
upper limit value is lower (i.e. brighter) than the measured magnitude, then
we assume there was no detection, and we take the upper limit as the data
point for that image262626This method was suggested to us during communication
with the Swift help desk regarding a quick and rough check on whether a source
was detected.. We later label it as an upper limit in the light curve. If the
upper limit value is higher (i.e. fainter) than the measured magnitude, then
we assume there was a detection and take the magnitude measurement as the data
point for that image.

The output of each uvotsource run is a table including the source (AB)
magnitude, the upper limit or limiting magnitude at the source location, and
the $1\sigma$ error. The error is composed of both the statistical and
systematic errors on the magnitude measurement due to photometric calibration
errors of UVOT (see e.g. Breeveld et al. 2011 or the instrument software
guide272727https://swift.gsfc.nasa.gov/analysis/UVOT_swguide_v2_2.pdf).

Figure 9: Example of final product of PIII for a candidate transient. Top row
shows, from left to right, the (50”x50”) stamps of the source in one of the
science images, the template image, and the difference image. The figure at
the bottom shows the light curve created from the locally available QL data of
the source (plus the upper limit of the source in the template image). In the
left and middle stamps in the top row, the red circle indicates the source
region and the blue annulus indicates the background region used by
uvotsource. The data points in the light curve corresponding to the template
and science images are indicated with a ‘T’ and an ‘S’, respectively. The
value alongside the template label indicates the on-sky distance (in
arcminutes) of the source in the template image from the centre of the
template image. Inspection of the science, template, and difference image
stamps clearly show that this is a real transient source. The data point
derived at the source location in the template image is an upper limit, since
there was no clear detection. The transient was found at RA=00:42:46.65,
Dec=+41:14:26.6 in the UVW1 filter and is a known nova in M31 (AT2021jwr).

Before creating a light curve with the measured magnitudes and upper limits,
PIII determines the amplitude of the variability exhibited by the light curve.
The magnitude difference between the brightest and faintest data points is
determined and a check is carried out: if the difference is less than 0.4
magnitudes, a light curve is not created. This is done because we are only
interested in highly variable sources, and also because we noticed that
sources detected by TUVOpipe with $<$0.4 magnitude variability very rarely
pass our variability significance test (see Sect. 3.5.2).

For all transient candidates that pass these tests, PIII then uses all
measured magnitudes and upper limits obtained to construct light curves. The
data points in the light curve correspond to the brightness of the candidate
transient during all the QL data currently located in the local archive (this
may include data taken $>$1 week previously, e.g. if the user’s local drive
contains data from previous runs of the pipeline), as well as during the one
archival image chosen as the template (if it was available; see Sect. 3.3.1).
We denote these products ‘PIII light curves’. Additionally, corresponding
50”x50” stamps of the science, template, and difference images are displayed,
overlaid with the source and background regions used by uvotsource. This is
done in order to contextualise the candidate for the user and to help visually
distinguish between real and bogus sources (see Fig. 9). The difference image
stamp (and the corresponding science image stamp) shown is the first
difference image in which the candidate was detected by the pipeline (not all
the difference images of a given field will trigger a particular transient;
e.g. if the transient was not variable in a particular image with respect to
the template, or if the performance of the image subtraction or source
detection was poor for a particular image). All light-curve files and stamps
produced in PIII are passed to the next stage of the pipeline (see Sect. 3.5),
which attempts to further classify the sources.

There are no major functional differences in PIII when running TUVOpipe in
archival mode with respect to running it in real-time mode, except that during
the archival mode only sources from one field are processed, and all archival
data covering that field (within the chosen search radius) are analysed. To
maximise the potential for finding historical transients, we selectively run
the archival mode on fields that have been observed many times by UVOT, so
light curves produced when running in archival mode typically cover long
timescales and include many data points.

###  3.5 Part IV

Part IV (PIV) of TUVOpipe carries out further investigation of all the
candidate transients for which light curves were produced in PIII. It does
this by obtaining additional information both from external catalogues and
from further analysis of the transient and its light curve. The workflow of
PIV is displayed in Fig. 10. An example of the PIV product, that is by default
the final output of TUVOpipe with which users are presented for each detected
transient, is shown in Fig. 11.

Figure 10: Workflow of PIV (discussed in detail in Sect. 3.5). This is the
part of TUVOpipe that collects information about each transient to supplement
the UVOT light curve in order to obtain a preliminary classification and help
determine whether further investigation is warranted. The operation of PIV is
identical between the archival and real-time modes of the pipeline. The output
of PIV (shown in Fig. 11) is all the light curves produced in PIII
supplemented with basic source information, information from external
catalogue queries, and variability information. Figure 11: Output of PIV for a
transient detected by TUVOpipe. The image stamps and light curve are produced
by PIII (see Fig. 9 for an example). PIV supplements this with information
about the UVOT observations (panel A), basic analysis of the light curve
(panel B), and results from the catalogue queries performed (panel C). The
information displayed in panels A, B, and C is discussed in detail in Sections
3.5.1, 3.5.2, and 3.5.3, respectively. In this example, with all the
information provided we can deduce a fairly robust classification of this
source as a Mira variable (FQ Cas), based primarily on Simbad, ASAS-SN, VSX,
and GCVS. Note that for most catalogues, magnitudes are provided in the AB
system; where this is not the case (e.g. Gaia), we specify that the magnitude
stated is in the Vega system.

####  3.5.1 Basic source information

For each transient candidate, PIV provides information about the Swift
observation(s) used to detect it. In the final product, PIV displays the
coordinates of the source (both in RA and Dec, J2000, and in Galactic
longitude and latitude), visibility information relevant for follow-up
observations (maximum altitude during the night at the time of discovery) with
the three ground-based observatories with which we undertake follow-up studies
(see Wijnands et al., in prep., for a description of our ground-based follow-
up programmes), and the number of archival observations available (total and
specifically for the filter in which the transient was detected; this helps to
determine whether a long-term light curve would be useful; see Sect. 3.6).
Figure 11, panel A, shows an example of the basic source information obtained
for a transient candidate.

####  3.5.2 Variability test

Part IV then carries out a test for each source to determine a rough estimate
of the level of variability we detect. The purpose of this test is for users
to have an idea of the significance of the variability of each transient
candidate that they are presented with, and also to automatically discard
sources for which the observed variability is not significant.

To do this, we fit a line of constant brightness (i.e. assuming no
variability) to the light curve using a weighted least-squares approach. We
determine the goodness of fit using the $\chi^{2}$ statistic, which is then
converted to a significance. The significance gives an indication of the
likelihood that the variability displayed represents true variability, so
sources with variability significance ¡1.0 are discarded at this stage, and
the PIV output is not created for these sources. If there are any upper limits
in the light curve, then the variability significance is not computed and the
transient is retained. This is because upper limits do not have errors and
cannot be incorporated into this test; however, highly interesting transients
may not be detected in some images, and we do not want to discard those. We
note that, due to the underlying assumptions of the method, it should only be
seen as a tool to limit the number of transients picked up that do not show
strong enough variability to warrant further investigation and not as a tool
to determine any highly accurate statistics regarding each transient. The
standard deviation and variance of the magnitudes in the light-curve data is
also computed in order for users to have a quantitative indication of the
scatter in the data. The following quantities derived in this analysis are
then displayed alongside the light curve (see Fig. 11, panel B): the
significance of the variability, the maximum amplitude of the variability
(‘Amp’, obtained by subtracting the brightest from the faintest magnitudes in
the displayed data), the timescale of the light curve (‘$\Delta$T’; the time
between the earliest and latest data point displayed), the standard deviation,
and the variance.

####  3.5.3 Probing external catalogues

PIV automatically probes public catalogues from a variety of facilities and
surveys for previous detections and potential classifications at the locations
of the sources we detect. Most queries are performed using
astroquery282828https://astroquery.readthedocs.io/en/latest/, an astropy tool
designed to query astronomical databases using Python. If astroquery is not
available for a particular catalogue, we attempt to query it through Vizier,
for which astroquery allows access to all catalogues. If a catalogue is also
not available in Vizier, a query of the catalogue’s online database is done
via Python-based web-scraping with the package Beautiful
Soup292929https://beautiful-soup-4.readthedocs.io/en/latest/. We currently
probe 22 different catalogues, including several all-sky catalogues in various
wavelengths as well as transient and variable star catalogues (see Table 2 for
a full list of all catalogues we probe, including brief descriptions and
references for each). The code is set up such that when new catalogues or new
versions of catalogues are published, we can easily create a new query and add
the information to the PIV output.

For each transient candidate, PIV queries each catalogue for any sources
within 5” of the position of the candidate. The value 5” was chosen to account
for errors in positions of our sources, which may be up to a few arcseconds
(see Sect. 2.3). If matches exist, relevant source information is extracted
from the results of the query and displayed alongside the light curve (see
Fig. 11, panel C). This information can include any known designation,
classification, or measured magnitudes (always in the AB system except where
noted otherwise) of the source. For example, we query the full Gaia catalogue,
and we can thus immediately check for each transient whether there is a likely
Gaia counterpart, and if there is one, we know its G-band magnitude, and if
available, temperature and distance values (distances are queried from an
associated Gaia catalogue that contains distances to 1.33 billion Gaia
sources, see Bailer-Jones et al., 2018). Under the Gaia information, ‘Parallax
Sig’ gives the significance of Gaia’s parallax measurement for the source; so
high numbers indicate robust distance measurements. We also query the MARS
alert stream303030https://mars.lco.global/ of the Zwicky Transient Facility
(Bellm et al., 2019), which is useful in order to know whether ZTF detected a
variable source at the position of our transient, and if so, at what optical
magnitude. The ZTF query can sometimes also help to confirm the source as a
real transient, given its real-bogus (‘rb’) score of 1.0 (the score ranges
from 0 to 1, where 1 corresponds to the highest probability of it being real;
see Fig. 11, panel C).

If there are multiple sources in a given catalogue within 5” of our transient,
PIV extracts the information from the catalogue source with the smallest on-
sky separation to the transient candidate. Since the UVOT astrometric
uncertainty can be up to a few arcseconds (see Sect. 2.3), in such cases there
is a chance that PIV collects the information of a catalogued source that is
not the real counterpart of the TUVO source. However, the number of sources in
the catalogue within 5” of the transient candidates is given, so, in such
cases, the information given in the figure is interpreted more tentatively
than if only one match had been found. We further note that these automatic
catalogue matches are not intended to be conclusive, but simply to add
potentially useful information for a preliminary classification of the TUVO
source.

Finally, PIV assigns to each transient a name in the format TUVO-[YY][id]
where [YY] is the last two digits of the year in which it was triggered by
TUVOpipe and [id] is a unique identifier composed of letters starting with ‘a’
and rising alphabetically, adding additional characters when necessary (this
is the standard scheme for naming transient alerts, as recommended by the
IAU313131https://www.iau.org/public/themes/naming_stars/). For sources we deem
to be of high interest and warrant further investigation, we manually check
the results obtained with the catalogue queries to ensure that the matches are
correct (e.g. by astrometrically solving the UVOT images with
astrometry.net323232https://astrometry.net/ and ensuring that the match
obtained is still accurate).

####  3.5.4 Asteroids

It is worth noting the regular detection of Solar System objects by TUVOpipe,
which are triggered by the pipeline as transients due to their high proper
motion. The UV to U-band properties of asteroids are so far poorly studied
(e.g. only a handful of asteroids have been studied at ¡220nm, mostly
spectroscopically, see Becker et al., 2020). Therefore, although asteroids are
not prime targets of TUVO (since they are not eruptive or intrinsically
variable), TUVOpipe detections of such objects may be useful for a better
understanding of asteroids, in particular when UVOT observations in multiple
filters are available, allowing us to study the colour properties of these
sources.

In Fig. 12, we show an example of how an asteroid detection typically appears
in our pipeline: a light curve in which there is only a detection in one image
(the object detected is named Alleghenia 457). To separate asteroids from
astrophysical transients, we probe the
SkyBot333333https://vo.imcce.fr/webservices/skybot/ (Berthier et al., 2006)
database, a large catalogue of known Solar System objects. To account for the
higher uncertainty in the positions of many asteroids, we query this database
with a 20” radius from our transients, rather than 5” as used for all other
catalogue queries in PIV. In addition, due to the high proper motion of
asteroids, the time in which the detection occurred is required as input in
the query (we take this from the UVOT image which triggered the detection).

Figure 12: Example of TUVO (PIII) light curve in which an asteroid is detected
in one of the images. This source was detected at position RA=09:49:16.30,
Dec=+00:26:48.3 with the exposure starting on 30 November 2021 at 16:21:55 in
the field of PMNJ0948+0022 in the U filter, and was matched to the known Solar
System object Alleghenia 457 with the SkyBot database.

For most asteroids we detect, a match is found in the database. Additionally,
very bright detections present only in one image (i.e. transients with a
single detection), may be indicative of asteroids. Therefore, in cases where a
transient is detected in only one image and there is no match in the SkyBot
database, we inspect consecutive UVOT images by eye. If we see a source
appearing at different positions in consecutive UVOT images (usually taken a
few minutes to a few hours apart; potentially including different filters), we
can confirm that it is a Solar System object. We invite the reader to consult
Fig. 13 for an example of an asteroid detection in consecutive UVOT images
(the source shown was also matched with the SkyBot database).

So far, we have been able to identify a few tens of TUVO transient candidates
as asteroids, either by matches with the SkyBot database or by inspecting the
UVOT images. We have, however, also found a handful of transients that were
not in the SkyBot database and were only seen in a single image (i.e. they do
not appear as moving sources in consecutive UVOT images). These sources could
be real astrophysical transients (and therefore potentially of great interest
for TUVO), though they could also still be asteroids that were not listed in
the SkyBot database and do not appear in multiple UVOT images (because the
time between the images was long enough that the asteroid was no longer in the
FoV). We refer the reader to Wijnands et al. (in prep.) for an example and a
more in-depth discussion of these types of detections in the TUVO project.

Figure 13: Example of multiple UVOT images in which our pipeline detected an
asteroid. In the template image, no asteroid is observed. In the two science
images, which were taken a few hours apart, a source appears at different
positions, indicated by the red arrows. Our pipeline detected the source in
both images as two separate transients, with both having used the left image
as the template for the image subtraction. This source was matched using the
SkyBot database to the known object Alleghenia 457. All images were in the
field of the known AGN PMNJ0948+0022 and taken in the U filter. The ObsIDs and
extension numbers for the three images are, from left to right:
00031306073[1], 00031306074[1], 00031306074[2]. The exposures in the images
began, from left to right, on 13 November 2021 at 05:54:39, 20 November 2021
at 16:21:55, and 20 November 2021 at 19:43:05. All three images are 2.5’x2.5’.
North is up and east is to the left.

####  3.5.5 Vetting transients

All transients that are processed in PIV are vetted manually by the users. For
each transient, we visually examine the output of PIV (see Fig. 11) and
attempt to determine first if the source is likely real (as described in Sect.
3.3.3, despite the various tests performed within the code, a number of
‘bogus’ sources are not successfully filtered out and are output as viable
transient candidates), and second if it is of sufficient interest to warrant
further investigation. Bogus transients are often identified by significant
artefacts visible in science, template, or difference image stamps. Typically
$\sim$20-30$\%$ of the candidates are vetted as ‘real’ transients, that is,
sources that display real, significant variability in the UVOT data. This
fraction depends in part on how well TUVOpipe performed on the fields
processed (e.g. if the alignment does not succeed on many images of a
particular field observed on a given day, then a relatively high number of
bogus transients may be output from that field). This leaves us with around a
few tens to $\sim$100 real transients per day.

If a candidate is identified as real, the users proceed to determine whether
it is of sufficient interest to study further based on examination of the
light-curve behaviour and the results of the probed catalogues. If the
transient displays one or more very bright (¿1-2 mag) outbursts, it is likely
to be considered for further investigation. If the source has no clear
identification in the probed catalogues, or if there are disagreements between
reported classifications of the source, then we also consider the transient to
be potentially interesting. Transients that we consider highly interesting, of
which we typically detect a handful every week, are manually passed to the
final part of the pipeline (see Sect. 3.6).

###  3.6 Part V

Figure 14: Workflow of PV (discussed in detail in Sect. 3.6). Since most PV
functionalities are identical to previous parts of the pipeline, the workflow
shown here is highly simplified, and we refer the reader to Figs. 2, 3, 8, and
10 for details. This part of TUVOpipe creates a light curve of any source of
interest using all archival UVOT data in all filters. The output of PV (shown
in Fig. 15) for a given source is the long-term, multi-band light curve
created using all UVOT data.

The final part of TUVOpipe, Part V (PV), is used for creating long-term UVOT
light curves of any sources of interest, using all archival data available
(spanning up to $\sim$17 years, reflecting the operation time of Swift). The
only input required for PV is the RA and Dec of a source. Since this is a
useful tool in itself, PV was built to function both as a continuation of PI-
PIV (i.e. to examine transients detected by our pipeline), and also entirely
independently of the rest of the pipeline (i.e. to examine any source of
interest). We therefore highlight two different use cases for PV, described in
this section, which are implemented depending on whether the source of
interest was determined by TUVOpipe or externally. In both cases, the
functionality and the final product of PV is the same (see Fig. 14 for a chart
displaying the workflow of PV). We note that most of the functionalities of PV
are identical to previous parts of the pipeline; so, to avoid repetition and
for aesthetic purposes (i.e. to avoid a very complicated flow diagram), the
displayed workflow for PV is highly simplified, and within the diagram we
refer to the relevant workflows for details. An example of the output of PV is
shown in Fig. 15: the source was a known, highly active dwarf nova that was
initially triggered by our pipeline due to strong variability. There is no
separation between real-time and archival modes in PV, since all archival data
are always processed.

Figure 15: UVOT light curves created a variable star using PV for the highly
active dwarf nova AX J1549.8-5416 (see Zhang et al., 2017 for a detailed study
of this source). The data includes all archival UVOT data covering the
position of the source, which in this case included a total of 1097 images, in
the four filters U (209 data points), UVW1 (322 data point), UVM2 (264 data
point), and UVW2 (302 data point), spanning 14 years. The light curves for
different filters are shown in different sub-plots to avoid confusion due to
the high number of data points.

In the first use of PV, it takes as input the coordinates of a source detected
with TUVOpipe and which was found to be highly interesting when vetting the
PIV product (see Sect. 3.5 for a description of how we determine sources of
high interest). This allows further information, namely past variability or
outburst behaviour (or lack thereof), to be gathered about each discovered
source, aiding in classifying the source and in deciding whether further
investigation (e.g. follow-up observations) is warranted.

In the second mode, we input the coordinates of any source for which we are
interested in the long-term UV behaviour; in other words, sources which were
not necessarily transients detected by our pipeline. This mode can be used for
any source which has been observed at any time by UVOT, and it is therefore
very useful for studying the long-term UV variability of any source discovered
with other methods and by other facilities, whether transient or persistent,
previously classified or not. For many source classes (e.g. X-ray binaries,
cataclysmic variables/dwarf novae, and variable stars), long-term UV
variability remains largely understudied compared to other wavelengths, so PV
is a very useful tool.

In both cases, PV works in the same way, making use of the various
functionalities that exist in previous parts of TUVOpipe. It uses the input RA
and Dec of the source of interest, plus a user-defined search radius as input
(the latter value is by default 12’; see Sect. 3.2 for the reasons for
selecting this radius). PV then downloads all archival UVOT data within the
search radius of the source position (i.e. similarly to how PI works when
operating in the archival mode). All downloaded images are then cropped and
aligned (see Sect. 3.3.1 for details of how our pipeline carries out cropping
and image alignment), and PV then performs photometry at the location of the
source in each image using uvotsource (see Sect. 3.4 for details regarding how
TUVOpipe performs photometry). Uvotsource requires a source extraction region
and a background region as input; by default, PV automatically creates these
regions in the same way as in PIII (thus, see Sect. 3.4 for a description of
how these are constructed). However, PV also has the option for the user to
input manually created regions. This can improve the reliability of the
photometry, because, for example, users can ensure that the background region
is devoid of contaminating sources that may be present in the automatically
created background region.

Finally, using PV we attempt to obtain a better handle on the amplitude of the
variability of the source of interest by stacking images in which there was no
detection. Using the HEASoft tools fappend and uvotimsum, all images in which
there was no detection (see Sect. 3.4 for how we determine whether there is a
detection with uvotsource) are automatically stacked, independently for each
filter. PV then runs uvotsource on the stacked images. For each filter, this
results in either a deeper upper limit (i.e. fainter than the upper limits for
individual images in which there was no detection) or potentially a detection.
In both cases, this feature allows us to obtain a better estimate of the
amplitude of variability because we have a stronger constraint on the
quiescent level (and if the stacked images result in a detection of the
source, we even know the exact value). However, we note that this method
assumes that at any time when there is no detection, the source is in
quiescence, and that the quiescent level is constant (which is not necessarily
the case). In practice, this means that although the quiescent level we
determine in this way is indicative, it may not always be completely accurate,
and it is useful for the user to keep this in mind when examining PV light
curves. However, with PV the user can also manually select a range of
observations (either by ObsID or by MJD) to use for the summing of images.
Therefore, when we detect highly interesting sources, we manually select the
intervals for which to sum images, and thus we obtain deeper upper limits (or
detections) as needed for the particular source. In Fig. 16, we show an
example of PV light curves of a source which underwent an outburst that was
detected by TUVOpipe. When in quiescence, the source is too faint to be
detected in individual images in the UVW1, UVM2, and UVW2 bands, but the
stacking of all non-detection images led to detections (shown as horizontal
lines in the light curves). This allowed us to characterise the amplitude of
the outburst with more precision. The source is a CV we discovered with
TUVOpipe (see Sect. 4.2.1 for a brief discussion and Modiano et al., in prep.,
for a detailed study about this source).

Figure 16: UVW1, UVM2, and UVW2 PV light curves for a source triggered by
TUVOpipe, denoted as TUVO-21acq, due to an outburst detected in February 2021
(see the data points around MJD 59300). The quiescent level of the source is
too faint to be detected by individual UVOT images, as seen by the upper
limits displayed in the light curves. However, when all the images in which no
detection was made are stacked, uvotsource detects a source in the stacked
image due to the increased depth. This quiescent level is shown by the
horizontal line, with the error shown by the shaded region. This long-term
light curve shows that the recent outburst has so far been the only outburst
of this source observed using the UVOT. Analysis of the outburst and further
spectra obtained (see Sect. 4.2.1) allowed us to securely classify this source
as a CV undergoing a DN outburst.

There is one minor difference in operation between the two use cases of PV. As
discussed in Sect. 3.3.1, UVOT images may be misaligned with respect to each
other by up to a few arcseconds. When passing a source’s coordinates to PV, we
need to ensure that the coordinates will match the position of the source in
all newly downloaded images to process in PV. In the mode where we run PV on a
source found by TUVOpipe, the input coordinates are those determined in PII
based on the template used there, so PV copies the template used in PII to the
PV directory. It selects this as the template to which all other images in PV
will be aligned. In the mode where we input any source of interest (not found
by our pipeline), the coordinates are taken from external sources. In this
mode, the template is selected as the first science image to be processed that
has an exposure time of at least 200s and which passes the image quality
checks (see Sect. 3.3.1). We need to ensure that this template image is
correctly astrometrically solved so that the coordinates of the source of
interest fall at the right location on the image. We do this by using
astrometry.net and by manually comparing the position of the source in the
UVOT template image to images of the source available in the literature or in
online databases. We then use the obtained UVOT position of the target as
input for PV.

##  4 Results and discussion

Figure 17: Sky map (Mollweide projection) of all data obtained using the UVOT
which were processed by our pipeline (from 01 October 2020 until 23 December
2021). The colour bar indicates the number of images taken for each field
(‘fields’ here are based on the coordinates of the centres of the images,
rather than on field name). The very high number of observations at -60° and
-45°were obtained at the position of the SMC, which Swift has been observing
extensively as part of the S-CUBED survey program (Kennea et al., 2018). The
total number of images included in the plot is 75 183, representing a total
exposure time of 22 686 ks (see also Fig. 18).

Figure 18: Bar charts displaying UVOT filter usage of all data processed by
our pipeline. In the left panel, the filters are separated by number of
images; in the right panel, filter usage is separated by total exposure time.

TUVOpipe has been running daily since 01 October 2020 (and continues to be run
daily), and we have detected several thousand real UV transients, including a
few particularly interesting sources which lack clear previous identifications
(see Sect. 4.2.1). In this section, we describe the results obtained since the
pipeline began full operation (01 October 2020) to the time of submission (23
December 2021). TUVOpipe has processed all individual exposures which were on
the Swift-UVOT QL page during this period343434A small fraction of images is
not processed by us, including images which do not pass the elongation test
(see Section 3.3.1), or due to errors such as repeated failed downloads or
data that our software fails to read correctly (the reasons behind these
failures are not always clear). In this section, we only discuss UVOT results
obtained by our TUVO project; however, it is worth noting that since we
process the vast majority of UVOT data (since 01 October 2020), some of the
information we have obtained, such as the distribution of exposures per
filter, is also a good indication of the typical characteristics of the UVOT
observations., and typically we process all data between a few hours and a few
days after the observations are obtained. In Sect. 4.1, we discuss TUVOpipe
statistics and present an overview of all the data processed by the pipeline.
We also present breakdowns of the detected transients by filter. In Sections
4.2 and 4.3, we discuss some specific examples of interesting transients we
have detected using TUVOpipe in the real-time and archival modes,
respectively.

###  4.1 TUVOpipe statistics

####  4.1.1 Observations processed

Figure 19: Histograms of UVOT exposure times per filter processed by TUVOpipe.
The x-axes are logarithmic, and the bins are evenly spaced in logarithmic
space. Figure 20: Histograms of UVOT exposure times per wavelength regime
(optical and UV) processed by TUVOpipe. This is the same data as shown in Fig.
19, but with the three optical filters combined and the three UV filters
combined (the white filter is not included). The x-axes are logarithmic and
the bins are evenly spaced in logarithmic space.

So far, TUVO has processed 75 183 individual UVOT snapshots (we refer to these
individual exposures simply as images), or a total of $\sim$22 686 ks of UVOT
exposure time. In Fig. 17, we show a sky map of all the fields processed by
TUVO, with the colour indicating the total number of images per field. These
fields are scattered throughout the sky, with concentrations in the Galactic
plane and on the SMC (-60°, -45°), the latter mainly due to the S-CUBED survey
(Kennea et al., 2018), an ongoing survey of the Small Magellanic Cloud with
Swift.

In Fig. 18, we show how the number of images (left panel) and total exposure
time (right panel) are distributed between the UVOT filters. The majority of
data were taken in the three UV filters ($69.8\%$ of UVOT observations by
number of images and $77.6\%$ by exposure time). This reflects the primary
usage of the instrument as a UV telescope in the form of the mentioned ‘filter
of the day’ strategy (see Section 2.2). Total exposure time is similar similar
between these three UV filters ($\sim$22-32$\%$ each, with UVM2 being the most
used filter by exposure time with $31.4\%$). Only a relatively small fraction
of UVOT observations are performed with the V, B, and U filters: $27.9\%$ by
number of images and $20.1\%$ by total exposure time. By number of images, the
distinctions between filter usage are slightly less pronounced than by
exposure time. This implies that UV exposure times are on average longer than
optical exposure times (see also Figs. 19 and 20, where this is clearly
visible).

Figures 19 and 20 show histograms of the exposure times used in UVOT
observations per filter (the latter combines the UV filters and combines the
optical filters). These clearly show the preference for short exposure times
for the optical filters (the distribution peaks at $<$100s) and for long
exposure times for the UV filters (the bulk of the distribution peaks around a
few hundred seconds; the narrow peak at $\sim$50s in the UVW1 is largely due
to the SMC survey that uses short exposures). As seen from the histograms, the
white filter is the least used filter, similar in total usage to the V and B
filters by exposure time, and significantly less by number of images (also
indicating that exposures with the white filter are typically longer than
those in the optical filters).

In Fig. 21, we display the average and median exposure times per filter. The
mean exposure times for the V and B filters are significantly lower than those
of the U and UV filters (¡150s vs. ¿250s), which are relatively flat (the
lower average value for the UVW1 filter than for the other UV filters is again
due in part to the SMC survey being carried out in that filter with short
exposures). The median values are roughly flat at around 100s for the V, B, U,
and UVW1 filters and jump to around 250s for the UVM2 and UVW2 bands. The
higher values in the average compared to the median indicate that the latter
is dominated by a few long exposures (several hundred seconds).

Figure 21: Mean and median exposure times per image for each UVOT filter for
all data processed by TUVOpipe. Figure 22: Magnitudes of all detected sources
(when using a threshold of $5\sigma$) in all science images processed using
TUVOpipe, separated out by filter. Magnitudes are in the UVOT magnitude system
(see Breeveld et al., 2011). The data are not stacked, i.e. histograms are
directly overplotted on each other. Each histogram contains 300 bins of equal
width. Figure 23: Transient candidate rates determined by TUVOpipe in each
filter. The rates are computed independently per filter and are the ratios of
the total number of transients detected to: the total exposure time processed
by TUVOpipe (rate per second exposure time); the total number of sources
detected at $5\sigma$ in all the science images processed (rate per detected
source); and the total number of images processed (rate per image).

In Fig. 22, we show the magnitude distributions of all sources detected (using
a threshold of $5\sigma$) in the UVOT science images processed by TUVOpipe so
far. Magnitudes were extracted using uvotdetect, which performs photometry on
all detected sources in a UVOT image353535We note that although uvotsource is
typically preferred for UVOT photometry, uvotdetect runs significantly faster
per source, and it is thus well suited to obtaining an indication of the
overall statistics for a large number of sources and observations. Uvotdetect
photometry measurements of point sources have been found to agree well with
those derived from uvotsource. See
https://www.swift.ac.uk/analysis/uvot/pos.php for details. Magnitudes shown
here are in the UVOT magnitude system (see Breeveld et al., 2011) since this
is the only system in which magnitudes are given by uvotdetect.. The
histograms of the different filters are not stacked; i.e. the data overlap.
The distributions peak between magnitudes 16 and 18, with a slight trend of
increasing magnitudes (fainter sources) towards the UV. This may be due to a
combination of the longer exposure times used in the UV bands on average
resulting in greater depth and the different zero points of the filters (see
Table 2 in Breeveld et al., 2011).

####  4.1.2 Transient rates

In this section, we discuss candidate transients detected by TUVOpipe. These
are transient detections that meet all automatic criteria within the pipeline
and have thus made it to PIV. However, they are still ‘candidates’, because
most of the sources that pass all automatic tests are not real variable
sources (typically $\sim$70% of our transient candidates are bogus), and must
be separated from real transients by eye (see Sect. 3.5.5). Therefore, we
emphasise that the information provided in this section is mostly useful in
terms of describing the output of the pipeline, and does not allow for
detailed statistics about true transient rates detected by UVOT.

So far, TUVOpipe has made a total of 28 061 transient candidate detections;
this represents 15 154 independent candidates found (many detections are
duplicate; for example, when multiple filters find the same transient or when
a transient is active for many UVOT observations). Typically, between several
tens and a few hundred candidates are detected by TUVOpipe every day, though
the daily output is highly variable as it can depend on the fields observed by
UVOT (e.g. crowdedness, diffuse emission, sparse fields). For example, when
images from the S-CUBED SMC survey (Kennea et al., 2018) are processed, the
number of candidates we detect is significantly higher than average, due to
crowded fields resulting in both more real sources (having many sources in the
field increases the chances of finding variable sources) and more bogus
sources (due to relatively more frequent problems with alignment; see Sect.
3.3).

In Fig. 23, we display the rates of TUVO transients in each filter, using
three different measurements. The transient rate per second of exposure time
(i.e. the ratio of the number of transients detected to seconds of exposure
time) gradually drops towards the UV in the V, B, U, and UVW1 bands and then
decreases significantly for the UVM2 and UVW2 filters (the UVM2 filter is
around 6 times less prolific than the V filter by this metric). The transient
rate per detected source (i.e. the ratio of the number of transients to the
total number of sources detected in the science images) shows the fraction of
sources that are variable in each filter and which our pipeline detects. Here,
we see an increasing rate in the U and UV filters compared with the redder B
and V optical bands. The transient rate per image (i.e. the ratio of the
number of transients detected to the number of images) gives an indication of
the average number of images required in each filter for TUVOpipe to detect a
transient. This rate peaks in the U and UVW1 bands, where we detect, on
average, one transient in every two-to-three images, and is lowest in the UVM2
band, where we detect one transient in every eight-to-nine images.

The average rates across all filters are: 0.0011 (rate per second of exposure
time), 0.0031 (rate per detected source), and 0.24 (rate per image). As
mentioned, since the majority of the transient candidates discussed here are
not real, we cannot make conclusive statements about any potential intrinsic
astrophysical reasons for the varying rates across bands, due to the
significant effect of the pipeline’s performance on these rates.

###  4.2 Initial results from Quick-Look searches

The primary goal of the TUVO project is to serendipitously discover new
transients that have not been previously detected, even during quiescence.
Unfortunately, no robust, confirmed astrophysical transient in this category
has been discovered by the project so far, although some candidates have been
detected (note, however, that these candidates were only detected in a single
UVOT image, so we cannot rule out that they are uncatalogued asteroids; see
Wijnands et al., in prep., for a more in-depth discussion and an example).
However, transients we discover that have previously known quiescent
counterparts (such as detections from deep sky surveys), but which do not have
any known classification or variable behaviour, are also considered to be very
interesting by the TUVO project. Finally, detecting new outburst behaviour of
known variable sources is a further aim of the project.

In this section we describe some specific examples of different kinds of
transients we have detected so far with TUVOpipe. For each example transient,
we only provide a brief summary of the key information that can be extracted
and what can be learned; in-depth discussions of each source are beyond the
scope of this paper. We note that for formatting and aesthetic purposes, we
omit the detailed information obtained with PIV (e.g. catalogue information,
variability information, etc.) from the figures shown for each transient.
Instead, we display the PIII light curves and science, template, and
difference image stamps (the PIII products), and where relevant, the long-term
light curves (the PV products), and we note any important information about
each source in the caption(s). When displaying PV light curves, we show data
for all available filters in a single plot and omit the (potential) detection
in quiescence obtained by stacking the individual images in which no detection
was made (see Sect. 3.6 and Fig. 16 for a description and example of this
feature of PV). We note that the coordinates we give for all the transients in
this section are those obtained by the pipeline and therefore may have errors
of up to a few arcseconds (due to the uncertainties on the astrometry of UVOT
images; see Sect. 3.3.1).

####  4.2.1 New outburst discoveries

Figure 24: UVW1 light curve and different image stamps of TUVO-21acq, the
transient detected by TUVOpipe at RA=13:05:44.74, Dec=-49:32:58.4. The source
was previously detected in quiescence but had no classification. We later
classified it as a CV using UVOT data and a SALT spectrum we obtained, which
was taken during quiescence (see Modiano et al., in prep., for a full report
about this source).

The source shown in Fig. 24, TUVO-21acq, is a TUVO discovery of a previously
unknown transient, and we refer the reader to Modiano et al., in prep., for a
detailed study of this source. Though there are archival detections of a
source at the position of TUVO-21acq when the source was in quiescence (e.g.
Gaia and XMM-OM catalogues), the nature of the source has so far not been
determined. From the timescale and amplitude of the UV outburst we detected
(which were, respectively, 7-21 days and ¿3.9 magnitudes in the UVW1 filter),
we suspected that this was a DN outburst from a cataclysmic variable (CV). We
created a long-term light curve using PV, which is displayed in Fig. 16. The
PV light curves allowed us to confirm that no previous outbursts of this
source have been detected by UVOT, and we also did not find any previous
outbursts reported by transient searching facilities such as ZTF, ASAS-SN, or
ATLAS (i.e. in any of the transient or variable star catalogues we probe with
PIV; see Sect. 3.5.3). Additionally, by stacking the individual UVOT images in
which no detection was made (see Section 3.6 for a description of this feature
of PV), we detected the source during quiescence and measured its brightness
at 23.20 $\pm$ 0.27, 23.07 $\pm$ 0.18, and 23.02 $\pm$ 0.15, in the UVW1,
UVM2, and UVW2 bands, respectively. This allowed us to better characterise the
outburst amplitude. After our discovery of this source, we obtained a spectrum
with the Southern African Large Telescope (SALT; Buckley et al., 2006) during
quiescence, which we used to securely classify it as a CV.

The source shown in Fig. 25, for which we refer the reader to Verberne et al.
(2020) for a detailed discussion, is a TUVO discovery of the first reported UV
outburst of the known CV ASASSN-18eh363636http://www.astronomy.ohio-
state.edu/asassn/transients.html. Using TUVOpipe, we detected this transient
in October 2020 and found the UV brightness in the UVW1, UVM2, and UVW2 to
increase by at least 6.0 magnitudes compared to quiescent levels. This is
consistent with relatively high-amplitude DN brightenings, which are typically
in the two-to-six magnitude range in the optical (though they may be stronger
in the UV; see e.g. Giovannelli, 2008 and Neustroev et al., 2017). Due to
solar constraints, we could not obtain further observations, so we only have a
lower limit on the outburst duration of just over one day. The only previously
known outburst of this source was reported by ASAS-SN and occurred in February
2018, indicating a maximum recurrence time of around 2.7 years. This is
consistent with known DN recurrence times of days to decades (although several
outbursts may of course have been missed, so the recurrence time is likely
lower than our upper limit). For further examples of new outbursts we detected
with TUVOpipe, including a bright UV transient that was previously seen in
quiescence but with no previous identification (Modiano & Wijnands 2021), we
invite the reader to consult Wijnands et al. (in prep).

Figure 25: UVW1 light curve and the different image stamps of the transient
detected using TUVOpipe at RA=14:28:33.52, Dec=-46:11:26.5. This source was
associated with a known CV (ASASSN-18eh), and it was not the target of the
Swift observations.

####  4.2.2 Recovered known transients

Using TUVOpipe, we recover known transients daily, and in particular sources
that are the target of the Swift observations and are undergoing known
outbursts (UVOT is used extensively to follow up on transients discovered both
by Swift’s XRT and BAT and by other facilities), but also serendipitous
detections of outbursts that happened to be in the FoV of a UVOT observation
but are being monitored by other facilities. These sources are usually not of
prime interest since they are not new TUVO discoveries. However, aside from
demonstrating the effectiveness of the pipeline, the recovery of these known
transients is still very useful, because they may have exhibited past UV
outbursts (or any other potentially interesting UV behaviour) that were
observed by UVOT but have not yet been studied or reported previously.
TUVOpipe therefore still provides valuable information in these cases. Here,
we describe some examples of different types of such known transients detected
by TUVOpipe (see Figs. 26 to 30). For additional examples, we suggest
consulting Wijnands et al. (in prep.).

_MAXIJ1820+070_ is a black hole XRB source, which has been monitored
extensively by Swift in recent years (see Sai et al., 2021 for an optical and
UV monitoring study of this source using only a part of the UVOT data
currently available). It was the target of the observations in which our
pipeline detected it as a transient. In Fig. 26, we show the stamps and light
curve from the TUVOpipe detection, and in Fig. 27 we show the long-term PV
light curve in optical and UV bands. The primary outburst and several
subsequent reflares are clearly visible in the PV light curve, most of which
have so far not been studied (Sai et al., 2021 described only the main
outburst and the first reflare). Similar reflaring behaviour has been observed
for other neutron star and black hole X-ray transients (e.g. in the black hole
XRB MAXIJ1535-571, see Russell et al., 2019; or in the neutron star system SAX
J1808.4-658, see Wijnands et al., 2001 and Campana et al., 2008). The physical
processes responsible for these outbursts and the subsequent reflares are
still poorly understood (for an in-depth discussion about the possible
mechanisms responsible for these reflares and an overview of systems that show
this kind of behaviour, including accreting white dwarfs, see Patruno et al.,
2016). In addition, only a limited number of systems in these reflaring states
have been observed in the optical and UV. Therefore, our extended optical/UV
light curve of this source will be helpful in further constraining the
mechanisms responsible for these reflares (although this is beyond the scope
of our paper; see Patruno et al., 2016 for an example of the benefits of
obtaining quasi-simultaneous UV, optical, and X-ray data for these types of
sources).

Figure 26: UVW1 light curve and the different image stamps of the transient
detected using TUVOpipe at RA=18:20:21.94, Dec=+07:11:07.3. This source was
identified as the known source MAXIJ1820+070, a black hole X-ray binary, and
was the target of the Swift observations. In Fig. 27, we show the long-term
(PV) light curve of this source. Figure 27: Long-term light curves created
using PV of MAXIJ1820+070, a transient that was detected by our pipeline (see
Fig. 26). The few data points obtained from images taken in the white filter
are not shown. This source was targeted very often by Swift due to the
outburst and several reflares in recent years. Figure 28: UVM2 light curve and
the different image stamps of the transient detected using TUVOpipe at
RA=22:02:43.26, Dec=+42:16:40.0. This source is the prototype BL Lacertae
object, a highly variable blazar that is frequently observed by Swift and is
therefore often detected as a transient by our pipeline. It was the target of
the Swift observations. The red points indicate data points obtained on images
that may not have been successfully aligned in the pipeline (see Sect. 3.3.1).
In Fig. 29, the long-term (PV) light curve of this source is displayed.

The prototype BL Lac object, BL Lacertae, has been heavily monitored by Swift
over the past, $\sim$13, years. Due to its highly variable and flaring
properties, it is regularly picked up by our pipeline whenever new
observations are obtained (see Fig. 28 for the PIII light curve of one such
TUVOpipe detection in the UVM2 filter and for observations in which BL Lac was
the target). To investigate the long-term variability of this system across
the UV-optical range, we also created a PV light curve, which is displayed in
Fig. 29. From the PV light curve, it can be seen that this source was observed
extensively in all six UV and optical filters, with observations obtained up
to several times per day. The long-term light curve thus shows significant
variability behaviour both on long timescales (e.g. the rise over the last few
years resulting in the brightest known state of this source in the optical;
see also Marchini et al., 2021) and on short (hours to days) timescales (e.g.
the variability shown in the inset axes in Fig. 29). Long-term, quasi-
simultaneous, multi-wavelength photometry can help to elucidate some of the
still unanswered questions about BL Lac and similar objects. For example, in a
monitoring study of this object, Stalin et al. (2005) find that when examining
short-timescale (¡1 day) variability, the source was seen to become bluer when
it brightened. This effect was not significantly detected when looking at
longer timescales (days to months). A tentative comparison of the colours (we
examined UVW2-V) of BL Lac using the UVOT data averaged over one day indeed
shows an increase in blueness when the source intensity increases. However, we
notice a similar effect for longer term brightenings (e.g. weeks to months). A
complete, detailed understanding of the physical mechanisms causing the
variability in BL Lac (and other blazars) at all timescales may therefore
require the characterisation of this property, though a detailed study of this
source is beyond the aim of this paper.

Figure 29: Long-term light curves created using PV of the flaring blazar BL
Lacertae with all six primary UVOT filters. This transient was detected with
TUVOpipe (see Fig. 28).

Due to the rate of occurrence and typical brightness of novae in M31, and the
relatively frequent Swift observations of this galaxy, TUVOpipe regularly
detects novae in M31 serendipitously. The observations are sometimes
specifically targeting active novae in M31 for follow-up, but additional novae
active at the same time and which are located within the UVOT FoV are also
often detected by our pipeline. In Fig. 30, we show an example of one of these
detections. Although there have been several studies of novae in M31 in the
optical (see e.g. Shafter et al., 2011 and Cao et al., 2012), they have not
been studied extensively in the UV. Studying nearby (i.e. Galactic) novae may
provide better constraints on the outburst physics than more distant objects;
nonetheless, the advantage of M31 is that we can find a relatively large
number of novae at a known distance. This can help to perform studies of the
general properties of the UV emission of novae. To study UV light curves of a
sample of novae in detail (i.e. with more frequent observations than the
typical non-uniform monitoring of M31 with Swift), we obtained additional UVOT
observations for five of the novae we detected, every two weeks (Modiano et
al., 2021). We also refer the reader to Wijnands et al. (in prep.) for a
description of archival UVOT studies of novae in M31 in the TUVO project.

Figure 30: UVW2 light curve and science, template, and difference image stamps
of a transient detected using TUVOpipe at RA= 00:43:05.81, Dec=+41:15:47.5.
This source was identified as a known nova in M31 (AT2021pap; Zhang & Gao,
2021), and it was not the target of the Swift observations.

####  4.2.3 Variable stars

Due to their prevalence in the Galaxy, the majority of transients detected by
TUVOpipe are variable stars of many different classes (including RR Lyrae,
Cepheids, Mira’s and other long-period variables, delta-Scuti variables, and
eclipsing binaries). Most of these are previously known sources (as determined
from catalogue matches in PIV; see Sect. 3.5.3), though some sources exhibit
clearly variable behaviour as seen in their TUVOpipe light curves, but they do
not have any known classification as variable sources. For many classes of
variable stars, the UV emission is not well understood, and UV observations
may help to explain the underlying physics of the variability (see e.g. Sagiv
et al., 2014 for an overview of the scientific potential of UV studies of
several types of variable stars; see also Siegel et al., 2015 for a UV study
of RR Lyrae in globular clusters). Here, we briefly discuss some examples of
periodic variables that were detected by our pipeline. For additional examples
of some irregular variables we find, we suggest Wijnands et al. (in prep.).

A particularly interesting case regards the class of pulsating variables known
as RR Lyrae stars (see Modiano et al., 2020 for a discussion of an RR Lyrae
star we detected with TUVOpipe in the globular cluster 47 Tuc), for which the
strong UV variability can be up to several magnitudes (Siegel et al., 2015).
This is much larger than the amplitude of their pulsations in the optical,
which is typically ¡2 mags (see e.g. Smith et al., 2011; Hoffman et al.,
2021). Near-UV (NUV) light curves are highly sensitive to underlying
atmospheric models, temperatures, and surface gravities (Siegel et al., 2015),
so extensive UV observations of RR Lyrae can provide much better constraints
on these parameters compared to what is possible using only optical bands (in
particular when complementing the NUV with optical data; see Siegel et al.,
2015). Our lack of a detailed, complete understanding of the pulsations in RR
Lyrae is in part due to a deficiency of UV observations. The frequency of RR
Lyrae we detect with TUVOpipe indicates that the UVOT archive likely contains
a wealth of long-term UV data of these (and other) variable stars, therefore
making it a valuable data set for the study of RR Lyrae. In Fig. 31, we show
an example of the known RR Lyrae star ASASSN-V J183735.24-334960.0, recovered
serendipitously by TUVOpipe (i.e. it was not a target of the Swift
observations). We note the amplitude of variability of this RR Lyrae exhibited
in the UVW2 filter is of at least 1.5 magnitudes, while the ASAS-SN light
curve373737https://asas-
sn.osu.edu/variables/6687137d-360f-57ed-b386-dfafc94bc581 shows a V-band
amplitude of only 0.4 magnitudes.

Figure 31: UVW2 light curve and the different image stamps of the transient
discovered by TUVOpipe at RA=18:37:35.25, Dec=-33:49:59.6. This source could
be securely associated with the known RR Lyrae variable ASASSN-V
J183735.24-334960.0, and was not the target of the Swift observations.

With the real-time pipeline, we also frequently detect eclipsing binaries, due
to their significant changes in brightness. Often, when we trigger these
sources, PV light curves reveal potentially eclipsing behaviour. In these
cases, we can attempt to verify the nature of the variable source by creating
Lomb-Scargle periodograms for the light curves383838We note that in these
cases, to obtain the best possible period we apply barycentric corrections to
the data before determining the period, using tools available within astropy.
By default, we do not automatically barycentre-correct the data in TUVOpipe;
we only do this when we find a variable for which we are interested in
accurately measuring its period. and inspecting, by eye, the folded light
curves that we create using the best periods found. We note that this is done
independently of the rest of pipeline; it is not part of the automatic
software. An example of an eclipsing binary we found using TUVOpipe is shown
in Fig 32. The strong variability (by over 1 optical magnitude) led us to
create a long-term light curve; the PV light curves of all the used UVOT
filters are shown in Fig. 33. The source is a known source that appears in
many sky survey catalogues, including Gaia, where its G-band magnitude is
listed as 14.1 and its inferred temperature is $\sim$5000K (see Andrae et al.,
2018 for how the listed temperature we extract from the Gaia catalogue is
inferred from the Gaia data). This G-band magnitude is brighter than the U-UV
bands seen in the UVOT light curve, indicating (along with the temperature)
that it is a relatively red source. The source is also present in the ATLAS
catalogue of variable stars Heinze et al. (2018), where it is denoted as AT
J098.1962+05.6837. The ATLAS catalogue gives a period of 1.985 days. However,
no classification of the source was so far known, so the cause of the found
periodicity is not clear. Our method gave a best period of 1.9899 days, so we
used this period to fold the UVOT U-band light curve, which is shown in Fig.
34. The light curve clearly reveals an eclipse in which the source becomes
dimmer by around 1.5 magnitudes, and the shape of the light curve confirms it
as an eclipsing binary. There may also be additional very weak minima seen
halfway between the deep eclipses (see around phase -0.5 and 0.5 in the plot
shown). The fact that any potential secondary minima are very weak may suggest
that this is an Algol-type system (these are known as EA binaries; see Carmo
et al., 2020 for a review of these systems and example light curves; the
inferred Gaia temperature is also consistent with this being an EA system).

We also note that the proximity of the found period to exactly two days may
suggest an artificial effect due to the Earth’s rotation. However, the period
is consistent within data obtained from Earth (ATLAS) and from a satellite
(Swift), the latter of which in itself is unlikely to introduce artefacts in
the periodicity due to Earth’s rotation (moreover, we have not found the same
period in any other source, suggesting this is not a systematic effect in the
UVOT data). Additionally, the shape of the folded light curve clearly
resembles that of an eclipsing binary. Therefore, we are relatively confident
that the found period is real and the classification as an eclipsing binary is
correct.

Figure 32: U-band light curve and different image stamps of a transient of a
transient triggered by TUVOpipe at RA=06:32:47.14, Dec=+05:41:02.5. We
identified the source as the known variable source AT J098.1962+05.6837 and
confirmed its nature by creating a long-term light curve with PV (see Fig. 33
and 34). It was not the target of the Swift observations. Figure 33: Long-
term, multi-band PV light curve (created using all available UVOT data) of the
variable source AT J098.1962+05.6837 (see Fig. 32 for the original TUVOpipe
detection). Figure 34: Light curve in the U filter of the eclipsing binary
shown in Figs. 32 and 33 (AT J098.1962+05.6837), folded with a period of
1.9899 days and repeated to show two entire cycles. The times have been offset
such that the deepest point occurs at phases 0 and 1.

###  4.3 Initial results from archival searches

As discussed in Sect. 3, TUVOpipe can also be run in archival mode, whereby
all archival UVOT data of any user-identified field is searched in order to
look for historical transients. Archival searches are not of the highest
priority for the TUVO project, since transients discovered in archival data
are usually no longer active at the time of discovery and therefore cannot be
investigated further with follow-up observations. However, these transients
can still be highly interesting, so the archival mode of TUVOpipe is also of
prime importance for the TUVO project.

We determined fields of interest based both on fields containing what we call
‘confined stellar populations’ (fields that harbour many stars or galaxies
that fit within one or few UVOT FoVs, such as globular clusters and nearby
galaxies; see Wijnands et al. (in prep.) and on the number of UVOT
observations. In both cases, the selection of the fields has the aim of
maximising the potential for the discovery of historical transients. Similarly
to the real-time mode, the vast majority of transients we discover in the
archival mode are known variable stars; however, using this mode we also
discovered several previously unknown transient sources. Here, we briefly
discuss initial results from two types of fields we selected on which to run
the archival pipeline, where we targeted fields that have been observed many
times by Swift. For results regarding archival pipeline runs on confined
stellar populations, we direct the reader to Modiano et al. (2020) and
Wijnands et al. (in prep.).

Some fields that are very frequently observed with Swift are part of
monitoring programmes designed to regularly observe flaring sources detected
by Fermi-Lat (mostly blazars although also several Galactic transients),
denoted as ‘sources of interest’393939https://www.swift.psu.edu/monitoring/
(Stroh & Falcone, 2013). Swift observations of each of these fields amount to
between a few tens and several hundred kiloseconds of exposure time. We ran
the archival pipeline on all 23 fields in the original list404040Besides the
23 primary fields in the list of Fermi sources of interest, there is also a
secondary list of additional sources, which also have extensive Swift
monitoring. We continue to run the archival mode of TUVOpipe on the large
number of these additional fields., and we mostly detected known variable
stars, typically a few tens in each field (see Fig. 35 for an example of a
long-period variable we detected with an archival TUVOpipe run on the field of
the blazar PKS 1622-297).

Figure 35: V-band light curve and the different image stamps of an archival
transient discovered using TUVOpipe at RA=16:25:29.82, Dec=-29:51:35.6. This
source was identified as the long-period variable candidate ASASSN-V
J162529.87-295134.3, and it was recovered serendipitously (i.e. it was not the
target of the Swift observations) with TUVOpipe running in archival mode in
the field of the blazar PKS 1622-297. The red points indicate data points
obtained on images that may not have been successfully aligned in the pipeline
(see Sect. 3.3.1). We note that the diffuse structure seen in the difference
image stamp is likely the result of artefacts in the science image caused by
very bright stars in the FoV.

In this search, when processing the field of the blazar 3C 454.3 in the
archival mode, we detected a possible M-dwarf flare shown in Fig. 36 (UVW1
PIII light curve with image stamps) and Fig. 37 (PV light curves in six
filters). The PV light curves show that this is a very red source (as seen by
the quiescent brightness across the optical-UV range) that undergoes
occasional brightenings in all the UVOT bands.

Figure 36: Archival UVW1 PIII light curve of the M-dwarf Sp2254+1606 detected
with TUVOpipe at RA=22:54:11.09, Dec=+16:06:55.5. The light curve shows the
source possibly exhibiting several flares. The target of the observations was
the known blazar 3C 454.3; this M-dwarf was detected with TUVOpipe
serendipitously in the archival mode. The red points indicate data points
obtained on images that may not have been successfully aligned in the pipeline
(see Sect. 3.3.1). See Fig. 37 for the PV light curves in all filters for this
source. Figure 37: PV light curves, in all available filters created, of the
M-dwarf and possible flaring star Sp2254+1606, detected serendipitously with
TUVOpipe running in archival mode. For all filters, there were similar numbers
of observations, but for figure clarity, upper limits derived from images in
which the source was not detected are omitted. Therefore, the lower numbers of
displayed data points in the UV light curves compared with the optical light
curves simply indicate that the persistent source was not detected in many
individual UV images. The horizontal lines represent the brightness (and the
shaded regions the corresponding error) of the source detected in the stacked
images, which were created by combining all individual images in which there
was no detection (this value is therefore not shown for V and B filters,
because the source was always detected in those filters). The inset plot in