# Harnessing the Hubble Space Telescope Archives: A Catalogue of 21,926
Interacting Galaxies

David O’Ryan Department of Physics, Lancaster University, Bailrigg, Lancaster,
LA1 4YB, UK  European Space Agency (ESA), European Space Astronomy Centre
(ESAC), Camino Bajo del Castillo s/n, 28692, Villaneuva de la Cañada, Madrid
Bruno Merín European Space Agency (ESA), European Space Astronomy Centre
(ESAC), Camino Bajo del Castillo s/n, 28692, Villaneuva de la Cañada, Madrid
Brooke D. Simmons Department of Physics, Lancaster University, Bailrigg,
Lancaster, LA1 4YB, UK  Antónia Vojteková  European Space Agency (ESA),
European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, 28692,
Villaneuva de la Cañada, Madrid  Anna Anku  European Space Agency (ESA),
European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, 28692,
Villaneuva de la Cañada, Madrid  Mike Walmsley Jodrell Bank Centre for
Astrophysics, Department of Physics and Astronomy, University of Manchester,
Oxford Road, Manchester, M13 9PL, UK  Izzy L. Garland Department of Physics,
Lancaster University, Bailrigg, Lancaster, LA1 4YB, UK  Tobias Géron Oxford
Astrophysics, Department of Physics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford, OX1 3RH, UK  William Keel  Department of Physics
and Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35487  Sandor
Kruk Max-Planck-Institut für Extraterrestrische Physik (MPE),
Giessenbachstrasse 1, D-85748 Garching bei München, Germany  Chris J. Lintott
Oxford Astrophysics, Department of Physics, University of Oxford, Denys
Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK  Kameswara Bharadwaj
Mantha School of Physics and Astronomy, University of Minnesota, 116 Church
Street SE, Minneapolis, MN 55455, USA  Karen L. Masters Departments of Physics
and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford,
Pennsylvania 19041, USA  Jan Reerink  European Space Agency (ESA), European
Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, 28692, Villaneuva
de la Cañada, Madrid  Rebecca J. Smethurst Oxford Astrophysics, Department of
Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford,
OX1 3RH, UK  Matthew R. Thorne Department of Physics, Lancaster University,
Bailrigg, Lancaster, LA1 4YB, UK

###### Abstract

Mergers play a complex role in galaxy formation and evolution. Continuing to
improve our understanding of these systems require ever larger samples, which
can be difficult (even impossible) to select from individual surveys. We use
the new platform ESA Datalabs to assemble a catalogue of interacting galaxies
from the _Hubble Space Telescope_ science archives; this catalogue is larger
than previously published catalogues by nearly an order of magnitude. In
particular, we apply the Zoobot convolutional neural network directly to the
entire public archive of _HST_ $F814W$ images and make probabilistic
interaction predictions for 126 million sources from the _Hubble_ Source
Catalogue. We employ a combination of automated visual representation and
visual analysis to identify a clean sample of 21,926 interacting galaxy
systems, mostly with $z<1$. 65% of these systems have no previous references
in either the NASA Extragalactic Database or Simbad. In the process of
removing contamination, we also discover many other objects of interest, such
as gravitational lenses, edge-on protoplanetary disks, and ‘backlit’
overlapping galaxies. We briefly investigate the basic properties of this
sample, and we make our catalogue publicly available for use by the community.
In addition to providing a new catalogue of scientifically interesting objects
imaged by _HST_ , this work also demonstrates the power of the ESA Datalabs
tool to facilitate substantial archival analysis without placing a high
computational or storage burden on the end user.

Interacting galaxies (802) — Computational methods (1965) — Catalogs (205)

##  1 INTRODUCTION

Interacting and merging galaxies are important to our current theory of
$\Lambda$CDM cosmology, in which structure typically assembles hierarchically
(Abadi et al., 2003; Springel et al., 2005; De Lucia & Blaizot, 2007; Guo &
White, 2008). Galaxy interaction leads to highly disturbed morphologies
(Toomre & Toomre, 1972; Hernández-Toledo et al., 2005; Wallin et al., 2016),
intense starbursts (Mihos & Hernquist, 1996; Springel, 2000; Saitoh et al.,
2009; Moreno et al., 2021) and, potentially, quenching of some systems
(Hopkins et al., 2013; Smethurst et al., 2018; Hani et al., 2020; Das et al.,
2022). In general, galaxies undergoing interaction are observed to have higher
star formation rates than those that exist in the field (Ellison et al., 2008;
Scudder et al., 2012; Pearson et al., 2019). Interaction also has a direct
impact on the gas angular momentum within each galaxy, causing it to decrease.
This, potentially, leads to funnelling of gas into into their nuclear regions
and igniting activity. This could be a connection with active galactic nuclei
(Ellison et al., 2008; Li et al., 2008; Ellison et al., 2011; Comerford et
al., 2015). However, such a connection remains debated (Alonso et al., 2007;
McKernan et al., 2010; Marian et al., 2020). Thus, understanding galaxy
interaction is crucial to testing theories of galaxy evolution itself.

Interacting galaxies have long been explored with different samples of
galaxies. Examples include constraining merger rates as a function of redshift
(Lotz et al., 2008), inferring the contribution of minor mergers to the cosmic
star formation budget (Kaviraj, 2014a, b), and examining interactions as a
function of their local environments, internal properties and AGN activity
(Darg et al., 2010a). These studies (and many others; for further examples,
see Barton et al., 2000; Alonso et al., 2004; Ellison et al., 2013; Holincheck
et al., 2016; Silva et al., 2021) illustrate the complex parameter space
involved in understanding the role of interaction in galaxy evolution. Thus,
to effectively study interacting galaxies, we need observed datasets of such a
size that they can sample a wide range of various parameters of interest.

The first large-scale catalogues of interacting galaxies are from the mid 20th
century (Arp, 1966; Vorontsov-Velyaminov, 1959, 1977, hereafter VV). These
catalogues primarily used visual inspection to identify mergers (e.g., de
Mello et al., 1997; Nair & Abraham, 2010) and generally found from hundreds to
thousands of systems. The largest set of interacting galaxies identified by a
single expert classifier contains 2,565 relatively nearby systems (Arp &
Madore, 1987). Citizen science techniques can extend this number, as was
presented by Darg et al. (2010b) who used them to find a catalogue of 3,003
interacting galaxies.

The inclusion of automated classification shows promise to continue this
expansion. The use of machine learning in classifying galaxy morphology is
well established (Ardizzone et al., 1996; Abd El Aziz et al., 2017; Barchi et
al., 2020; Ghosh et al., 2020; Cheng et al., 2021). The workhorse algorithm is
the convolutional neural network (CNN; for an introduction, see O’Shea & Nash,
2015), most often used in image recognition and feature extraction. CNNs can
be used for general classification (e.g. early- versus late-type galaxies) or
to extract specific morphological features of galaxies, such as bars, spiral
arms, etc; many works have demonstrated their effectiveness at this (e.g.
Ackermann et al., 2018; Jacobs et al., 2019; Bickley et al., 2021; Buck &
Wolf, 2021; Walmsley et al., 2022a). Pearson et al. (2022) demonstrated the
power of CNNs for finding interacting and merging galaxies specifically,
finding 2,109 in 5.4 deg2 of Hyper Suprime-Cam imagery - a large sample for
the small area covered.

However, issues with using CNNs in classifying interacting galaxies have been
found on numerous occasions. The primary concern, is that - without due care -
classifying interacting galaxies by morphology alone can be highly
contaminated. For example, CNNs often confuse chance alignments of galaxy
pairs on the sky for interacting systems. This leads to many predicted
interacting systems being thrown away after visual inspection (in some cases
up to 60%; Bottrell et al. (2019); Pearson et al. (2022)).

In this work, we aim to use machine learning to create a large, high-
confidence catalogue of interacting systems, drawn entirely from existing
astronomical imagery. We search through the European Space Agency’s _Hubble
Space Telescope_ Science Archive111See http://hst.esac.esa.int/ehst/ using a
CNN to predict whether an image contains an interacting system, from among the
126 million extended objects in the _Hubble_ Source Catalogue (HSC; Whitmore
et al., 2016). The feature extraction we implement is focused on finding tidal
features or morphological disturbance caused by the interaction. The tidal
features prioritised include tidal tails, tidal bridges or tidal debris. As
stated previously, this runs the risk of introducing high levels of
contamination by close pairs. We thus implement further automated and manual
methods, which significantly reduce this. The systems we find are often in the
background of previous deep surveys (such as the Cosmic Evolution Survey,
COSMOS, Scoville et al. 2007; the Great Observatories Origins Deep Survey,
GOODS, Giavalisco et al. 2004; and the Pancromatic Hubble Andromeda Treasury
Survey, PHAT, Dalcanton et al. 2012), where spectroscopic coverage varies.
Therefore, while our final catalogue reduces contamination to $\sim 3\%$,
definitively removing all contamination by close pairs remains a challenge
following this work.

This paper is laid out as follows: Section 2 describes the HSC and all the
criteria we applied to create the images we predict over. This Section also
introduces ESA Datalabs222https://datalabs.esa.int/; a new platform which
allows the user to directly access the _Hubble_ Science Archive. Section 3
gives an in depth description of the Zoobot CNN we utilise for our
predictions, and how it differs from a commonly used CNN. Section 4 explains
the process of creating the training set for our CNN to find interacting
galaxies, with Section 5 showing how well it performed and providing the
diagnostics of the CNN. We also use this Section to investigate the
contamination in our catalogue. Section 6 describes our results and discusses
the final catalogue as well as define interesting systems or objects that we
have found. We also explore some basic properties of the catalogue here.
Finally, Section 7 summarises our results and conclusions.

Where necessary, we use a Flat $\Lambda$CDM cosmology with $H_{0}$ = $70$
km/s/Mpc and $\Omega_{M}=0.3$. Hereafter in this paper, when referring to an
interacting galaxy we are referring to a galaxy which has undergone one or
multiple flybys by a secondary galaxy and caused tidal disturbance. A merging
galaxy is the final state of these flybys, where two or more systems have
coalesced to form a highly morphologically irregular system.

##  2 DATA

###  2.1 The _Hubble_ Archives & ESA Datalabs

The observational data is directly from the _Hubble_ Science Archive and is
accessed from the new ESA Datalabs platform. The repository contains
approximately 100TB of data from the _Hubble Space Telescope_ (_HST_). This
repository spans all _HST_ instruments and filters. ESA Datalabs provides a
direct interface between users and the data. On this platform, every
observations’ FITS file can be accessed. To streamline our pipeline, we
applied criteria to the observations as not all filters have the same number
of observations, some instruments are not as sensitive to the low surface
brightness regime as others or the field of view of certain instruments would
not be ideal for measuring galaxy morphology. Finally, we do not conduct
source extraction from each FITS file ourselves but use the _Hubble_ Source
Catalogue (Whitmore et al., 2016, hereafter HSC) to define the centre of each
source cutout.

The criteria we apply are: the observational data must be from the Advanced
Camera for Surveys (ACS), it must be final product data of _HST_ (i.e. within
a .drc file, where the data has been drizzle (Avila et al., 2014) combined and
had charge-transfer-efficiency corrections applied), observed within the
$F814W$ filter and must be flagged as an extended source in the HSC. This
offloads sky subtraction, cosmic ray rejection and charge efficiency
calculations to the original _HST_ pipeline and removes costly steps from our
cutout creation process. We utilise all final product data of the $F814W$
filter from _HST_ as this was the filter which contained the most FITS files,
and therefore observations. The $F814W$ filter contained 9,527 final product
FITS files which could be used for source extraction, whereas the closest
second (the $F606W$ filter) contained $\approx$6000\. By using the filter with
the most files, we are confident that we cover a majority of the HSC. Applying
this criteria gives 126 million sources to predict over.

We must create 126 million source cutouts from 9,507 different FITS files.
Creating a dataset of cutouts at this magnitude in conventional methods (such
as AstroQuery or Table Access Protocol (TAP) services) would be impractical
due to making many network calls and long FITS file download times. Instead,
we use the ESA Datalabs platform, which is due to be released in Q3 of 2023.
This platform has been developed to allow us to ‘mount’ the _Hubble_ Science
Archive onto it. In practice, providing access to the entire _Hubble_ Science
Archives as local files for the user to manipulate while on the platform. This
bypasses network calls to servers to download our required FITS files, a
process which could have taken minutes per download. Having direct access to
the files, and quickly matching source coordinates to FITS files (described in
Section 2.2) allows us to open a FITS file and create all source cutouts from
it without having to close or reopen it. Therefore, we were able to create on
the order of 10k cutouts in the same order of time taken to download a single
file.

The source cutouts were created as $F814W$ gray scaled 150x150 (7.5′′x7.5′′)
pixel images using the HSC source coordinates as the centre. The image size
was set and standardized to streamline the pipeline. The majority of cutouts
are centered on the source but, in a minority, misalignment between source and
image centre occurs. This is a result of the drizzling process, with incorrect
alignment sometimes being significant. However, the target source was always
present in the cutout and we, therefore, did not attempt to rectify this. A
ZScaleInterval with a hard set contrast of 0.05 and a LinearStretch following
the default parameters in the Astropy (Astropy Collaboration et al., 2013,
2018) package. These were binned to 300x300 pixels (pixel resolution is
3.25′′x3.25′′) with a linear interpolation from the CV2 python package. The
images were created at 150$\times$150 to minimise storage required on the
early version of ESA Datalabs being used. Creating the images at half the size
allowed us to scale up to 300$\times$300 pixels without any effects of the
interpolation.

###  2.2 The Shapely Python Package

A large computational expense in our pipeline was matching FITS files to
sources. Conventionally, the Astropy contains function would be used to match
source coordinates to the FITS file WCS. We instead use the Shapely333Shapely
docs: https://shapely.readthedocs.io/en/stable/manual.html Python package.
Shapely is a geometry orientated package primarily focused on geospatial data.
We found converting the FITS image footprints into Shapely Polygons and the
source coordinates to Shapely Points and then checking if they overlapped had
significant speed up. Per iteration, Astropy’s contained_by function matches a
source to a FITS file on the order of 500ms. Using Shapely’s contains
function, the same process is on the order of 6$\mu$s.

##  3 UTILISING A CONVOLUTIONAL NEURAL NETWORK

We must choose a CNN which would best suit our needs to classify them into
interacting galaxies or not. We select the newly developed CNN Zoobot444Zoobot
DOI: 10.5281/zenodo.6483176 (Walmsley et al., 2022a). Zoobot is a CNN
specifically trained to classify galaxies based on morphology into many
different types (spiral, disk, elliptical, barred, non-barred, etc). We
retrain it to only classify galaxies into interacting or non-interacting.
Instead of training Zoobot from scratch and creating a new model, we use
transfer learning to finetune existing Zoobot models to classify our data for
our particular question. This allows us to retain information from Zoobot’s
previous training. More importantly, it requires a significantly smaller
training set to achieve high accuracy.

###  3.1 Zoobot

The version of Zoobot we use is a deep CNN which was trained on Galaxy Zoo
volunteer classifications over three different Galaxy Zoo: DECaLS (GZD)(Dark
Energy Camera Legacy Survey, described in Dey et al., 2019) campaigns. These
were GZD-1, GZD-2 and GZD-5 - each number corresponding to the DECaLS data
release. For training Zoobot, DECaLS imaging was selected using the NASA-Sloan
Atlas (NSA), which was itself constructed with SDSS Data Release 8 (DR8)
images. This also introduced implicit cuts to the training data, as SDSS can
not get to the depths of DECaLS. This introduces implicit magnitude and
redshift cuts on the training data. Specifically, SDSS DR8 and the NSA cover
galaxies brighter than m${}_{r}>17.77$ and closer than z $<0.15$. In Section
3.2 we describe using transfer learning to use Zoobot effectively outside of
this magnitude and redshift range.

Walmsley et al. (2022a) use the 249,581 volunteer classifications from GZD-5
campaign to train Zoobot to answer all 34 questions (example shown in Figure 4
of Walmsley et al., 2022a) in the remaining campaigns. GZD-5 was used as it
had a slightly different volunteer decision tree, having an expanded question
on potential different galaxy merger stages. Each galaxy image had been shown
to volunteers as a 3-color (g,r,z) of 424$\times$424 cutout. Each images pixel
scale was an interpolation between the measured Petrosian 50%- and 90%-light
radius. The measured full Petrosian radius had to be at least 3′′ to be shown
to the volunteers. When inputting into Zoobot, these cutouts were scaled and
grayscaled to 300$\times$300$\times$1 images, averaging over the 3-color
channels to remove colour information and avoid biasing the morphology
predictions. Zoobot utilised the Adam (Kingma & Ba, 2014) optimizer to train.

By training Zoobot in this way, combining the approach of answering many
questions at once with Bayesian representation learning, it learns a
generalisable summary of many types of galaxies. These generalised summarys
are lower-dimensional descriptions of galaxy types and are referred to as
representations. These representations change depending on the galaxy type,
morphology or environment in an image and lead to similar images being closer
together in a representation space than dissimilar ones. This representation
approach on a very broad classification problem is found to increase accuracy
and generality of Zoobot, giving it an edge over conventional CNNs. A more
detailed breakdown of this approach, as well as further details about Zoobots’
architecture, can also be found in Walmsley et al. (2022a).

Zoobot was trained to give a prediction score to an image of a galaxy based on
the question it is answering. The type of prediction score is set by the users
choice of the model final layer in Zoobot. We elect to use a SoftMax output,
which returns an output score as a float between 0 and 1. This prediction
score is not a probability score, although it may seem analogous. A well
behaved prediction score will map to probability, though not necessarily
linearly. The mapping between prediction score and probability is not
considered in this work, and we use the prediction score as an indicator of
Zoobot’s confidence a source is an interacting galaxy.

We are only interested in the ‘Is the galaxy merging or disturbed?’ question
from the Galaxy Zoo: DECaLS workflow, where the answer can be ‘merging’,
‘major disturbance’, ‘minor disturbance’ or ‘None’, and only want our version
of Zoobot to return the answer to this. Our version of Zoobot is also not
trained to predict over _HST_ data which differs from DECaLS data (different
resolutions, filter bandwidths, etc). If we were to use our version of Zoobot
as downloaded we would likely lose accuracy. We utilise transfer learning to
optimise accuracy of just our question as well as to classify _HST_ data.
Since this work, Zoobot has been trained on _HST_ data so the transfer
learning step would not be needed in future with the new models. How we apply
transfer learning is discussed in the following Section, but an excellent
review and discussion of applying transfer learning for detecting galaxy
mergers can be found in Ackermann et al. (2018).

###  3.2 Transfer Learning

Transfer learning (or finetuning) is a method of applying the same machine
learning model to a similar problem that it was originally trained on. Rather
than having to completely retrain all parameters in a model and essentially
create a new one, we can use the original model architecture and the
parameters it has learned from its previous training. In the case of Zoobot,
we keep the parameters it has learned from training on the DECaLS dataset and
freeze all sections of the model responsible for feature extraction and
recognition.

We construct a classification section that maximises accuracy and only allow
the weights of this section to change. As the classification section has fewer
parameters than the feature extraction section (the classification section
contains 86,209 parameters compared to the feature extraction sections’
4,048,989 parameters) we need significantly less data to completely retrain it
(in our case, a factor of 15 less). Once this retraining is complete, the
weights of the feature extraction sections of the model can be unfrozen and
tweaked using our smaller dataset with a very low learning rate to further
boost overall model accuracy.

An example of taking an existing model and applying it to a new problem with
transfer learning is shown in Walmsley et al. (2022b). Here, they take the
trained model and finetune it to finding ring galaxies. They retain an
accuracy of 89% while only needing to train the model on 103 ring galaxies.
This significantly reduces computational expense and training time of the
model, while keeping the required training set very small. Interacting
galaxies are rare, and interacting galaxy catalogues not expansive. So
retraining the full network on hundreds of thousands of interacting galaxies
is not feasible. Using transfer learning, and following the example from
Walmsley et al. (2022b), we only need to create a training set of 103 \- 104
interacting galaxies to achieve an accuracy of $\approx$90%.

##  4 CREATING THE TRAINING SET

We create a large training set of interacting galaxies following the criteria
described in Section 2 to train our model. Therefore, we need a large,
labelled set of interacting and non-interacting galaxies. We elect to follow
the methodology of finetuning as described in Walmsley et al. (2022b), and aim
to create a balanced training set. This has the advantage that it
significantly improves the performance and accuracy of machine learning
classifiers, but the disadvantage that it can bias our final model if few
interacting galaxies exist compared to the general population. However, such a
bias will be mitigated by using a high prediction cutoff to define an
interacting galaxy. This is discussed in in Section 5.1. To create this large
training set we use the Galaxy Zoo collaboration (initial data release
described in Lintott et al., 2008).

###  4.1 Interacting Galaxies and Galaxy Zoo

The data in Galaxy Zoo is volunteer classifications on galaxy images spanning
multiple projects. We incorporate classifications from all major Galaxy Zoo
projects; Galaxy Zoo 1 (Lintott et al., 2008), Galaxy Zoo 2 (Willett et al.,
2013), Galaxy Zoo: _Hubble_ (Willett et al., 2017), Galaxy Zoo: CANDELS
(Simmons et al., 2017) and Galaxy Zoo: DECaLS (Walmsley et al., 2022a). These
projects contain a total of 1,367,760 labelled galaxy images that we must
extract the interacting galaxies from. We only use labels that are from
citizen scientists, and no labels generated by previous versions of Zoobot. We
apply three criteria to each interacting or non-interacting label. Firstly, it
must have greater than 20 volunteer votes on it. Applying this allows us to
use a statistically robust weighted vote from a crowd answer rather than
trusting any volunteers individually. Secondly, the calculated weighted vote
(i.e. the combination of the 20 or greater votes) must then be greater than
75% in favour of being an interacting galaxy or less than or equal to 25% for
it not to be; this ensured purity in our training set. If the question given
to volunteers was more specific (such as ‘Is this a minor disturbance?’ and
‘Is this a major disturbance?’) then if either answer was the majority vote we
classified it as an interacting galaxy. Thirdly, the object must exist in the
_Hubble_ footprint so that we could make a cutout of it.

Checking if each training source existed in the _Hubble_ footprint was only
possible in an efficient way because of ESA Datalabs. Rather than having
querying every coordinate and make network calls to TAP services, we extract
every final product $F814W$ observation footprint and check if each labelled
galaxy exists in at least one file. We make this check by creating a Shapely
Polygon for each observational footprint and a Shapely Point for each labelled
galaxy central coordinate. Using the Shapely Polygon contains function, we
check if a labelled galaxy’s Point overlaps with an observations’ footprint
Polygon. This returns a list of files which contain the training source. If a
training source was not found in any observational footprint we discard it. We
make no attempt here to check if our sources have other photometry available
to them, and only create 1-color images with the $F814W$ data. We provide the
images to Zoobot as 1-color grayscaled cutouts.

Upon applying these criteria we find 3,167 labelled interacting galaxies in
Galaxy Zoo: _Hubble_ project, the largest contribution to our training set.
These were paired with 3,167 labelled non-interacting systems (following the
previous criteria) to balance the training set. From all other projects, we
find 869 labelled interacting systems which fitting the creation criteria. The
primary limiting factor for Galaxy Zoo’s 1 and 2 was that many found
interacting galaxies did not exist in the Hubble footprint. For Galaxy Zoo:
CANDELS and Galaxy Zoo: DECaLS the limiting factor was the required calculated
weighted vote. These labelled interacting systems were then paired with 869
labelled non-interacting systems, ensuring that each labelled non-interacting
system came from the same project as its labelled interacting system
counterpart.

Each of these projects has a varied redshift range: Galaxy Zoo: _Hubble_ is
$z<1$, Galaxy Zoo: CANDELS $1<\emph{z}<3$ and Galaxy Zoo’s 1, 2 and DECaLs are
$\emph{z}<0.15$. This introduces a redshift bias into our model, where the
morphology and brightness of interacting sources changes with a $z>1$. This is
only partially rectified by including Galaxy Zoo: CANDELS, which provided 322
labelled interacting systems.

Figure 1: Example images of the labelled interacting galaxy systems used to
train Zoobot. Each galaxy had a weighted vote fraction $\geq$0.75 in Galaxy
Zoo. Top Row: Three examples from the Galaxy Zoo: _Hubble_ project of the
training set. Bottom Row: Three examples from the other Galaxy Zoo projects.
These are, from right to left, Galaxy Zoo 2, Galaxy Zoo CANDELS and Galaxy Zoo
DECaLS. The priority with this training set was that the interactors had clear
tidal features and disruption so Zoobot would learn to highly weight them and
not misclassify close pairs. Figure 2: Example images of the labelled non-
interacting galaxy systems used to train Zoobot. Top Row: Three examples from
the Galaxy Zoo: _Hubble_ project of the training set. Bottom Row: Three
examples from the other Galaxy Zoo projects. These are, from right to left,
Galaxy Zoo 2, Galaxy Zoo CANDELS and a starfield from the active learning
cycle. Starfields/globular clusters/open clusters existed throughout the HSC
flagged as extended sources. 1,000 images of starfields were added to the
training set so Zoobot would give them a very low score.

From all Galaxy Zoo projects, we find a training set of 4,036 labelled
interacting galaxies and combine them with their matched 4,036 labelled non-
interacting galaxies giving a total training set size of 8,072. Figures 1 and
2 show six examples of our labelled interacting and non-interacting galaxy
training set. As we require Zoobot to learn to weight tidal features or
disturbances highly, it is important that such structures dominate the
training set. Previous works, such as Pearson et al. (2022), have found that
final catalogues produced by CNNs are often heavily contaminated by sources
which are simply close pairs by projection effects and chance alignment in the
sky. By focusing our CNN on tidal features, we aim to minimise this
contamination. We ran an initial test of the prediction pipeline on the first
500,000 sources that had been created from the HSC to initially test our
Zoobot model. We investigate any source which was given a prediction score
$\geq 0.75$ and, to further increase the size of our training set, conduct one
step of active learning.

###  4.2 One Active Learning Cycle

To enlarge our training set further, we conduct one step of active learning to
find interacting galaxies. An active learning cycle involves an ‘expert’
checking the predictions made by the model, correcting any incorrect
predictions and then feeding it back into the model as additional labelled
images to a training set. We complete finetuning of Zoobot on our initial
training set of 8,072 galaxies and make predictions on the first 500,000
sources from the HSC (created under the criteria previously discussed). We
visually inspect the sources Zoobot gives a prediction score $\geq 0.75$ and
correct any wrong predictions. These corrected labelled sources and those
Zoobot correctly labelled are then added to the training set. Not only does
this step allow us to add more labelled interacting galaxies to the training
set, but it also allows us to evaluate Zoobot’s behaviour and check if it
consistently predicts a type of source or galactic morphology incorrectly.

From the first 500,000 sources, a total of 6,198 sources were given a
prediction score of $\geq$0.75. We correct the predictions Zoobot made and
balance this set to 5,698. During this cycle, a large number of globular
clusters/starfields/open clusters were given a very high prediction score.
Figure 2 shows an example of these contaminating star fields. We created
sources of 1,250 star fields and added these into the training set, labelling
them as non-interacting. Adding the balanced 5,698 sources plus the 1,250
starfields to our training set gave us an unbalanced training set of 15,020
sources. To then balance the training set, we took 1,250 labelled interacting
galaxies from the Galaxy Zoo: _Hubble_ project and made random image
augmentations with the TensorFlow Python package. These augmentations were
simple rotations, cropping and resizing. With these extra sources, our
training set contains 16,270 sources. Of these, 50% (8,135) were labelled
images of interacting galaxy systems.

##  5 DIAGNOSTICS

###  5.1 Model Performance

Upon finetuning Zoobot we validate its performance. We reuse the validation
set that Zoobot automatically creates when training. This set is created by
putting aside a random set of 20% of the training set. Zoobots then uses it to
validate its performance in training. We record which images Zoobot selected,
and extract these from the training set for further diagnostics. This provides
us with a validation set of 3,270 images, containing 1,648 non-interacting
galaxies and 1,622 interacting galaxies.

Figure 3: The distribution of prediction scores given to our validation set of
3,270 labelled sources set aside by Zoobot in training. These were split into
1,648 non-interacting sources and 1,622 interacting sources. As can be seen
from the distribution, our model is often confident when a source does or does
not contain an interacting galaxy by the strong bi-modality. This is likely
due to the very stringent vote weightings used when selecting the training
set. Using this distribution, we decide the prediction score to use as a
cutoff to give us our final binary classification: interacting galaxy or not.

Zoobot gave a prediction score between 0 and 1 to each of the validation
images, Figure 3 shows the resulting distribution. This distribution shows
that our model has high confidence in what is or isn’t an interacting system
due to the high counts at very low and very high probability scores. It is
likely the use of a balanced training set, and the very low volunteer score
needed to define a source as non-interacting that leads to a strongly bi-model
prediction score distribution. Using a balanced training set is an intrinsic
trade off between ease of training, and potential biases introduced. Having a
balanced dataset does not reflect reality, and leads Zoobot to over-predict
interacting galaxies. Using very stringent volunteer classification cutoffs
also leaves few ambiguous systems in the validation set, further enhancing
this bi-modality.

The prediction score must be reduced to a binary classification for our
problem. We use Figure 3 to define a prediction score above which a source is
classified as an interacting galaxy. We measure the accuracy of Zoobot for
different cutoffs, where the accuracy is the fraction of labels correctly
predicted over the total number of labels predicted on. Figure 4 shows this
change in accuracy. We find that our model is most accurate with a prediction
score cutoff of 0.55 with an accuracy of 88.2%. Figure 4 also shows the change
in the purity of our catalogue with changing prediction cutoff. Here, purity
is the ratio of number of true interacting galaxies to total sources in the
final catalogue. These scores can be combined into the F1 score of our model,
shown in Figure 15 in the Appendix.

Figure 4: A measure of accuracy and purity against prediction score. The
accuracy (in blue) is a direct measure of the number of sources Zoobot
correctly predicted vs the total number of predictions made. The measure of
purity (in orange) is the the number of predictions Zoobot correctly made vs
the total number of predictions for an interacting galaxy. The cutoff score
(in red) shows the point above which we would define an interacting galaxy and
below which we would not. At this point, the accuracy appears lower due to
Zoobot making many false negative predictions while successfully making true
negative predictions. This is confirmed by the maximisation of purity. Due to
the number of sources Zoobot is predicting over, the size of the catalogue
will exceed any previous catalogues. Therefore, we use this very conservative
cutoff to maximise purity over the completeness of our catalogue. These
measures can also be shown with the F1 score. Figure 15 shows this change with
prediction cutoff in the Appendix.

Figure 5 also shows a measure of accuracy for our model at different cutoffs
using confusion matrices. Importantly, it also shows how our model is getting
labels wrong: either giving false positives (where a labelled non-interacting
galaxy is predicted to be interacting) or false negatives (where a labelled
interacting galaxy is predicted to be a non-interacting). The number of
incorrect positive and negative predictions change based on the prediction
cutoff, with a very low cutoff giving many false positives and a very high
cutoff giving many false negatives. Figure 5 shows that with a cutoff of 0.50,
we would return a high level contamination in our final catalogue. Of the
1,622 galaxies predicted to be interacting, 218 would be non-interacting
systems - approximately 13%. Our main aim in this work is to present a highly
pure, large interacting galaxy catalogue that can be used for statistical
exploration of interacting galaxy parameter space. Therefore, we use a very
stringent cutoff of 0.95.

Figure 5: Confusion matrices of four different cutoffs of prediction score
defining a binary classification of interacting galaxy or not. Confusion
matrices break down our accuracy measurement into how Zoobot is misclassifying
sources. At a cutoff of 0.50, the accuracy is highest at 88.2%. However, at
this cutoff, $\approx$10% of our final catalogue would contain contamination.
We elect to use the very stringent prediction cutoff of 0.95 for the rest of
this work as it will return the lowest contamination.

Using a cutoff of 0.95 reduces contamination significantly. Figure 5 shows the
final contamination in our validation catalogue would be $\approx$2%, where
Figure 4 shows that we are maximising the purity in our sample at the expense
of accuracy. The aim of this work is not to create a general tool to be used
by the community, but to find a large catalogue of interacting galaxies. As we
are investigating 126 million sources, despite removing $\approx$50% of
interacting galaxies from the final catalogue, we are certain that we can find
a catalogue larger than previous works.

Using such a high cutoff also reduces any risk of any biases introduced by
using a balanced training set. While using such a training set often increases
the accuracy and speeds up training, it can bias the model towards one
conclusion. In our case, the true rate of interacting galaxies will be much
smaller than 50%. Therefore, our model will be biased to labelling a source as
an interacting galaxy. This will be particularly true for edge cases, which
could be ambiguous to even an expert classifier. By using such a high cutoff
score, this bias will be mitigated by only labelling the most clearly
interacting objects as interacting.

Figure 6: Flow diagram of our contamination and duplication removal process.
De-duplication used agglomerative clustering based on sky separation. The
first step of de-duplication uses a cutoff of 1.5′′. This significantly
reduced duplication in the catalogue, as well as the size of the catalogue to
54,757 interacting galaxies. We then applied contamination removal to this de-
duplicated catalogue. Upon visual inspection, a small number of duplicated
systems still existed in the catalogue. To ensure a pure catalogue of unique
systems, we applied a agglomerative clustering again with a cutoff of 5′′.
This gave us a catalogue of 27,720 unique interacting systems. The final step
to ensure purity was visual inspection by DOR, removing any remaining
contamination. This gave the final pure catalogue of 21,926 unique interacting
systems.

###  5.2 Duplication Removal

The fully trained Zoobot made predictions on $\approx$126 million extended
sources from the HSC that had passed our creation criteria. Of these, 195,688
sources were given a score of 0.95 or greater, $\approx$0.2% of the total
number of sources. Upon visually inspecting a subset of sources, it is clear
that our Zoobot model had predicted for an interacting galaxy even if it was
not the central (and, therefore, target) source in the image. This is due to
the misalignment of sources from the centre in the training set as described
in Section 4. Zoobot learned to classify an image as an interacting galaxy if
it contained one, and not just if it was the central source. Therefore, many
interacting systems were duplicated in our final catalogue, appearing in
cutouts were the central source was not interacting.

Another source of further duplication was the HSC itself. In the HSC, many
extended objects have multiple source IDs applied to them. This is due to
bright clumps in extended sources being assigned a new ID, sources which had
been found but did not exist in reality or background sources which existed in
extended systems. We find that of the 195,688 Source IDs given a prediction
score of 0.95 or greater, approximately 3.6 Source IDs were matched to a
single real object. To refine the catalogue and remove the duplication we use
spatial clustering of each source with agglomerative clustering (an
introduction and description of hierarchical clustering, including
agglomerative clustering, can be found in Nielsen, 2016).

Agglomerative clustering is a method of hierarchical clustering based on a
distance metric between the sources. We set the maximum distance between
points to define a cluster. i.e. any sources within a defined distance on the
sky from each other will be merged under one source ID. This approach means we
do not need any knowledge of how many cluster of sources exist in the dataset
or the level of duplication within it, as would be the case in many other
clustering approaches. We create distance matrices of the angular separation
of every source using the Astropy Python package. These projected sky
separations are then used as a euclidean distance in the clustering algorithm
with an euclidean_linkage. The new ID of a cluster is the first source ID in
the cluster.

Initially, we utilise a limiting sky separation of 1.5′′ to remove the
duplication. This reduced the size of our potential catalogue to 54,757
interacting galaxy candidates. We then applied contamination removal as
described in Section 5.3. Once contamination removal was completed, the
catalogue size was 41,065 interacting galaxies. Visual inspection found
further duplication, so our initial de-duplication had not been aggressive
enough. To ensure the catalogue was of unique systems, we opted to use a final
aggressive limiting sky separation of 5′′ completely removing the duplication
in our catalogue. This aggressive de-duplication further reduced the size of
our catalogue to 27,720 candidate interacting systems. However, we could be
certain that each of these candidate systems was unique. Figure 6 shows a full
breakdown of the steps in our de-duplication and contamination removal
process.

###  5.3 Bad Predictions & Removal

After the initial step of de-duplication we begin removal of contamination
from the catalogue. A major, and expected, source of contamination is by close
pairs of galaxies. These are systems where chance alignment in the sky appears
that galaxies are close together but are actually at different redshifts.
Other sources of contamination include large central galaxies with satellite
galaxies about them, star fields with extended sources in them and objects
with strange morphologies that Zoobot predicted were tidal features.

Upon applying the clustering by sky projection of 1.5′′, the catalogue
contained 54,757 candidate interacting galaxies. Our primary concern is
contamination by close pairs. Creating catalogues of interacting galaxies with
CNNs are notorious for suffering from this problem, where a significant number
of candidates must be removed from otherwise large final catalogues (Bottrell
et al., 2019; Pearson et al., 2022). The decisive way to remove this
contamination is to compare redshift measurements of each galaxy in the
candidate interacting system. However, this is impractical for our catalogue
where the majority of candidates have no redshift measurements. To find close
pairs, and remove them effectively, we take advantage of the representations
Zoobot learns of each image. As described previously, Zoobot was trained to
answer every question in Galaxy Zoo: DECaLS simultaneously for every galaxy.
It therefore learns a generaliseable representation of many kinds of galaxies.
In this representation space, morphologically similar galaxies will exist
close together in clusters while those that are dissimilar will be further
apart. We extract the features Zoobot has learned of each candidate, and plot
its representation.

We remove the classification head of Zoobot and directly output the final
layer of the feature learning section of the model. This gives 1,280 features
(the representations) for each of our 27,720 candidate systems. However, there
will be much redundant information in this very high dimensional feature
space. We compress this using incremental principal component analysis (PCA)
(Ross et al., 2008). An excellent demonstration of using this approach can be
found in Walmsley et al. (2022b). We reduce the dimensionality from 1,280 to
40(as in Walmsley et al. (2022b)), and input the resultant components into the
Auto-Encoder UMAP (McInnes et al., 2018). UMAP projects the 40 dimensional
components of each candidate system onto a 2 dimensional manifold. The
position of each galaxy on this manifold is directly linked to its visual
morphology. Close pairs have similar visual features which will then appear as
a cluster in our representation space.

Figure 7: The representation distribution of 54,757 candidate interacting
galaxies. This distribution is the compressed 2D representation of the 1,280
dimensional representation that Zoobot has learned of each image. Each image
is a randomly selected one from sources within each bin in the distribution.
The X and Y axis on this plot are the 2D mapping on the manifold given by UMAP
for the 40 dimensional principal components of each source, and not physical
parameters. Three gradients are clear in this distribution: first; from the
left to right there is a distinct gradient in the contrast of the images. The
images to the left are local galaxies with low redshift, while those on the
right are dimmer sources at much higher redshift. This is an effect of how the
images are created using a linear scaling function and a fixed contrast. The
second feature, also from left to right, is a gradient of larger source size
to smaller source size. This is a feature Zoobot has learned based on the
redshift of the source as well. The third, from top to bottom, is a gradient
of the inclination of the source. With the most inclined (and even diffraction
spikes) of the sources appearing at the top, while at the bottom the sources
are face on. Along the bottom of the representation plot, there are close
paired sources as well as many star fields. Along the very top, there is
contamination in the form of isolated stars in star fields. Thus, we make
aggressive cuts along the top and bottom of our representation space to remove
as much contamination in a general way. The full representation plot, with all
sources and the cuts, is shown in Figure 8.  Figure 8: Scatter plot showing
the precise distribution of each representation of sources in the remaining
54,757 sources. This is the unbinned version of Figure 7. The two red lines
show the cutoffs utilised to remove the majority of close pairs by projection
as well as the very obvious contamination of stars and stellar fields at the
top of the representation distribution. The number of candidate interacting
systems in the catalogue was reduced to 41,065 systems.

Figure 7 shows the representation distribution of our 54,757 candidates after
compression with UMAP. A random image in each bin has been selected to show
the morphology of the objects within the bin. There are three clear gradients
that exist in the representation distribution: one of source size, one of the
source inclination and one of image contrast between the source and the
background. The gradient of source size is clear from left to right. This is
also true of contrast between the source and background. The gradient of
source inclination is from top to bottom. The top shows very inclined sources,
and even the diffraction spikes of stars, while along the bottom we find face
on sources which take up a larger part of the cutout centre. At the very
bottom of the figure (away from the main body) a cluster of very poorly
contrasted sources with the background that are face on are found. The
gradients of inclination and source size are expected while that of contrast
is less so. This gradient is likely a result of how we created our images
using a Linear Stretch with fixed contrast. The effect of this is that dimmer
sources have brighter backgrounds, a particular issue at high redshift.

Figure 7 has many areas of similar morphology. On the left, we have isolated
objects: disturbed spirals or large galaxies with tidal disturbance to them.
Along the bottom, we see isolated bright objects with satellites about them.
On the bottom right, we see our area of representation space dominated by
close pairs. In the centre, we see the population of interacting galaxies that
Zoobot was trained to find. The areas of representation space which are
dominated by clear sources of contamination are cut. Figure 8 shows a scatter
plot of the representation distribution and the cuts we make. They are made
such that any source with a Y Mapping of $-2\leq Y\leq 4.75$ will be kept in
the catalogue. The choice of these cuts has been made by eye, and then
bootstrapping the remaining images to check contamination removed. After
applying these cuts, we retain 41,065 systems in our catalogue.

We estimate $\approx$ 25% of sources in the greater than 0.95 prediction bin
are close pairs. This may seem lower than previous works, but is due to our
very conservative prediction cutoff. The general cuts to our population based
on their position in representation space makes it very likely that we retain
some close pairs in the catalogue, while also removing interacting galaxy
systems.

As described in Section 5.2, we then apply a 5′′ to the 41,065 remaining
candidates, further reducing our catalogue to 27,720 systems. With such an
aggressive sky projection cut, many individual interacting galaxies are now
identified under the same ID as the secondary galaxy in the system. To remove
remaining contamination in the catalogue, a final visual classification step
was conducted. This visual inspection was conducted by DOR. Any systems
removed at this stage were classified into three categories: interacting
system, contamination and gems. The gems sub-category became necessary as many
sources of contamination that were being removed were objects of other
astrophysical interest, and is described in Section 6.2.

##  6 RESULTS & DISCUSSION

###  6.1 An Interacting Galaxy Catalogue

Upon de-duplication and contamination removal described in Sections 5.2 and
5.3, our final catalogue contains 21,926 interacting systems. Figure 9 shows a
random sample of 50 of the systems from our catalogue. In these examples we
can see highly distorted or currently interacting systems, precisely what we
trained Zoobot to highly predict. Some cutouts are of the full interacting
system, containing both the primary and secondary galaxies in the interaction.
Some source cutouts only show one of the interacting galaxies, though these
systems remain highly disturbed. Due to the constraints in our training set,
so highly weighting disturbance or tidal features in our predictions, we are
sampling interaction from all epochs except the approach to the initial pass.
At this initial stage, there will be no tidal features formed or disturbance
in the disks as the two galaxies approach each other. Separating them from
close pairs would be difficult without kinematic or redshift information, not
available for the majority of these sources.

Figure 9: An example of 50 of the final interacting systems found with Zoobot.
These were selected randomly from the de-duplicated and de-contaminated 21,926
sources. Each of these examples have extended tidal features and distortion.
Not all of the final interacting systems have two galaxies within them (for
example, image 2), but are clearly very disturbed by a tidal event. These were
kept in as they would form a large part of the interacting galaxy population
and would be flagged as disturbed or interacting in Galaxy Zoo. Each of these
images is a 1-colour image using the $F814W$ _HST_ filter.

We investigate which of the systems in our catalogue have previous references
in the astrophysical literature. To search the literature, we use the
AstroQuery Python package with a coordinates based search of cutoff radius
5′′. We search the astronomical databases Simbad, the NASA Extragalactic
Database (NED) and ViZier for references to our interacting systems. These
return either a list of references, or an empty list showing no references
associated with the system. We find that 7,522 of our systems have at least 1
reference associated with them, while and 14,404 do not. A flag exists in the
catalogue data release which shows whether a system has references associated
with it or it could be considered a ‘new’ system. We, however, do not claim
that these systems are discovered by ourselves. These systems have always
existed in the backgrounds of large surveys or observations and been
discovered by others, it is only with ESA Datalabs that we can apply a
methodology such as in this work to extract those systems from these
observations. We also do not claim that these unreferenced systems are
particularly interesting or phenomenal. It is most likely that these systems
are the very faint background galaxies in surveys or observations whose main
objective was something other than finding interacting galaxies. This will be
further discussed in Section 6.3.

Figure 10 shows the distribution of our catalogue in the sky. The _HST_ is
able to observe the majority so the catalogue sources are scattered throughout
it. We find that the sources cluster in different parts of the sky which
correspond to major surveys conducted using the _HST_ involving ACS/WFC and
the $F814W$ filter. We also mark the centres of the seven surveys which
correspond to the major clustering of interacting systems in the sky. These
were the COSMOS, the GOODS North, GOODS South, PHAT, CANDELS, AEGIS and
Spitzer Space Telescope FLSv Region (Morganti et al., 2004) surveys.

The full catalogue and data product are found on Zenodo at the following DOI
where it is freely accessible to the community:
https://doi.org/10.5281/zenodo.7684876. Table 1 shows an example of the data
and format of the 50 sources shown in Figure 9. We also bootstrap the final
catalogue as an estimate of contamination remaining. As described in Section
5.3, the final step of contamination removal was visual inspection by DOR of
the 27,720 candidate interacting systems to remove the remaining 5,794
contaminants from the final catalogue. Visual inspection by a single expert at
this scale is not perfect. We extract random sources from the catalogue in
batches of 500 and manually re-classify them again. This bootstrapping reveals
that $\approx$3% of our interacting system in the final catalogue remains
contamination.

Table 1: An example of the format of the final catalogue for the 50 example images presented in this paper.  Image No. | SourceID | RA (deg) | Dec (deg) | Interaction Prediction | References | Status
---|---|---|---|---|---|---
1 | 4001014298177 | 261.292845 | 37.162387 | 0.983999 | No entry | Unreferenced
2 | 4001444190958 | 183.527536 | 33.183451 | 0.998016 | [1994PASP..106..646K] | Referenced
3 | 4000809226818 | 93.960150 | -57.813401 | 0.982266 | [2019ApJ…878…66C] | Referenced
4 | 4553390202 | 73.581297 | 2.903528 | 0.968280 | No entry | Unreferenced
5 | 4000907600174 | 259.037474 | 59.657617 | 0.999978 | No entry | Unreferenced
6 | 4575187799 | 150.001883 | 2.731942 | 0.974649 | [2007ApJS..172…99C] | Referenced
7 | 4000717342023 | 149.527791 | 2.126945 | 0.993912 | [2007ApJS..172…99C] | Referenced
8 | 4001174802281 | 28.593114 | -59.643515 | 0.982890 | No entry | Unreferenced
9 | 4182689774 | 186.709991 | 21.835419 | 0.973232 | [2016ApJS..224….1R, 2011ApJS..193….8B] | Referenced
10 | 4000958398690 | 186.719496 | 23.961225 | 0.999288 | No entry | Unreferenced
11 | 4266881925 | 344.730228 | -34.799824 | 1.000000 | No entry | Unreferenced
12 | 4001084105393 | 150.128198 | 2.623949 | 0.982739 | [2018ApJ…858…77H, 2007ApJS..172…99C] | Referenced
13 | 4000961670486 | 345.337556 | -38.985521 | 0.954961 | No entry | Unreferenced
14 | 4000719687395 | 338.173538 | 31.189718 | 0.974724 | No entry | Unreferenced
15 | 4001435343326 | 331.771500 | -27.826175 | 0.986885 | No entry | Unreferenced
16 | 4001268932937 | 8.856781 | -20.271978 | 0.986329 | No entry | Unreferenced
17 | 4651336656 | 149.836709 | 2.141702 | 0.984389 | [2007ApJS..172…99C] | Referenced
18 | 4000877021787 | 116.211231 | 39.462563 | 0.979178 | No entry | Unreferenced
19 | 4000878525229 | 149.834893 | 2.516816 | 0.963694 | [2007ApJS..172…99C, 2009ApJS..184..218L] | Referenced
20 | 6000290755870 | 186.774907 | 23.866311 | 0.981961 | No entry | Unreferenced
21 | 4000806637434 | 210.253419 | 2.854869 | 0.960790 | No entry | Unreferenced
22 | 4001215753971 | 135.898809 | 50.487130 | 0.998386 | No entry | Unreferenced
23 | 4000813961830 | 163.678042 | -12.776815 | 0.958405 | [2005ApJ…630..206F] | Referenced
24 | 4001200639012 | 54.037618 | -45.170026 | 0.991404 | No entry | Unreferenced
25 | 4000921402261 | 150.417634 | 2.313781 | 0.990775 | [2018ApJ…858…77H, 2012ApJ…753..121K] | Referenced
26 | 4001224732336 | 337.217339 | -58.444885 | 0.955972 | No entry | Unreferenced
27 | 4000781402752 | 216.968619 | 34.575819 | 0.974076 | No entry | Unreferenced
28 | 4001283017901 | 120.202582 | 36.058927 | 0.994169 | [2016ApJS..224….1R] | Referenced
29 | 4000833486119 | 116.260049 | 39.457642 | 0.971092 | No entry | Unreferenced
30 | 4000949659908 | 146.342493 | 68.730869 | 0.961113 | No entry | Unreferenced
31 | 4000982920478 | 53.084832 | -27.765379 | 0.983472 | [2010A&A…512A..12B] | Referenced
32 | 4001189505548 | 192.492491 | 2.436292 | 0.992574 | No entry | Unreferenced
33 | 4001060882070 | 89.700725 | -73.049783 | 0.962839 | No entry | Unreferenced
34 | 4000889750512 | 151.176470 | 41.214096 | 0.962205 | No entry | Unreferenced
35 | 6000322363510 | 53.149367 | -27.823945 | 0.963889 | [2016ApJ…830…51S] | Referenced
36 | 4000722901091 | 28.257843 | -13.928090 | 0.982778 | No entry | Unreferenced
37 | 6000198293960 | 264.488431 | 60.101798 | 0.986865 | No entry | Unreferenced
38 | 4001095660911 | 258.587670 | 59.970358 | 0.955193 | No entry | Unreferenced
39 | 4000972775076 | 330.960020 | 18.796346 | 0.989131 | No entry | Unreferenced
40 | 4001132466571 | 126.545810 | 26.456196 | 0.997077 | No entry | Unreferenced
41 | 4000933395648 | 312.810365 | 2.288410 | 0.976252 | No entry | Unreferenced
42 | 4000932940918 | 218.066960 | 32.997228 | 0.990737 | No entry | Unreferenced
43 | 4001048433104 | 93.880689 | -57.754746 | 0.957755 | No entry | Unreferenced
44 | 4001039919651 | 53.111470 | -27.673717 | 0.994424 | [2011ApJ…743..146C] | Referenced
45 | 4001282607544 | 333.765783 | -14.006097 | 0.999520 | No entry | Unreferenced
46 | 4000922341052 | 260.723839 | 58.849293 | 0.995477 | No entry | Unreferenced
47 | 4000731518210 | 194.869144 | 14.146223 | 0.994651 | No entry | Unreferenced
48 | 4001082523786 | 311.703084 | -12.869002 | 0.976454 | No entry | Unreferenced
49 | 4000767041112 | 149.784518 | 2.172233 | 0.991335 | [2007ApJS..172…99C] | Referenced
50 | 4001024667142 | 150.661685 | 1.718587 | 0.967865 | [2007ApJS..172…99C] | Referenced
Figure 10: Sky Distribution of our catalogue, with marked positions of well
known deep surveys conducted by _HST_. _HST_ is able to observe almost the
entire sky and therefore the interacting galaxies are scattered throughout.
Large clusters of sources are found in the locations of surveys. This shows
that often our sources are in the background of larger surveys and
observations.

###  6.2 The Gems

By conducting a visual inspection of the 27,720 candidate systems we were able
to directly identify many other objects of astrophysical interest. As Zoobot
was trained to highly predict objects with irregular morphologies, we also
find many other astrophysical objects with strange morphologies which may be
of interest to the community. We call these sources of contamination gems. We
make 16 sub-categories of these: active galactic nuclei (AGN)/quasars,
submillimetre galaxies, galaxy groups, high redshift galaxies, jellyfish
galaxies, galaxy jets, gravitational lenses/lensing galaxies, Lyman-$\alpha$
Emitters, overlapping galaxies, edge on proto-planetary disks, radio halos,
ringed galaxies, supernova remnants, transitional young stellar objects, young
stellar clusters and unknown objects.

Each sub-category has been defined by checking Simbad and ViZier for
references within a 5′′ radius of each source and using the astrophysical
literature for a definition of the source. DOR classified any unreferenced
objects by morphological similarity to other defined objects. The platforms
ESASky555ESASky: https://sky.esa.int/(Merín et al., 2017), NASA Extragalactic
Database (NED) and the Sloan Digital Sky Survey were also used to investigate
any unreferenced objects. ESASky was of paramount importance as we could
investigate many objects across a range of wavelengths with many instruments.

The only objects which were classified by other means than visual morphology
were AGN/quasars, submillimetre galaxies and the six unknown objects. We
attempt to confirm the unreferenced AGN/quasar as candidates by investigating
the source in Chandra or XMM-Newton for hard or soft X-Ray emission. The
submillimetre candidates were also investigated using Herschel or Planck
measurements. If there was a positive signal in their positions, they were
classified as such. Further work will be needed to confirm these
classification.

The final category which required further inspection was that of the unknown
objects. These are objects which have unusual morphology which mark them out
from the rest of the sample, but no references associated with them in Simbad
or ViZier. They also did not appear in NED, meaning they could not be
confirmed to be galaxies. These objects are shown in appendix C.

Table 2 shows a breakdown of the total number of objects found and the number
of which were referenced or unreferenced. We have released catalogues of each
sub-category in the same format as that of the main catalogue without the
interaction prediction column. Each of these catalogues can also be found at
the same Zenodo link.

Table 2: A breakdown of gems found in the visual inspection stage of contamination. Each gem category has been classified based on the references associated with each object.  Category | Total Found | Referenced | Unreferenced
---|---|---|---
AGN/Quasars | 35 | 21 | 14
Submillimetre Galaxies | 11 | 8 | 3
Galaxy Groups | 6 | 6 | 0
High Redshift Galaxies | 10 | 7 | 3
Jellyfish Galaxies | 18 | 5 | 13
Galaxy Jets | 25 | 10 | 15
Gravitational Lenses/Lensing Galaxies | 189 | 64 | 125
Lyman-Alpha Emitters | 1 | 1 | 0
Overlapping Galaxies | 221 | 92 | 129
Edge-on Protoplanetary Disks | 9 | 2 | 7
Radio Halos | 1 | 1 | 0
Ringed Galaxies | 6 | 1 | 5
Supernova Remnants | 4 | 3 | 1
Transitional Young Stellar Objects | 2 | 1 | 1
Unknown Objects | 6 | 0 | 6
Young Stellar Clusters | 2 | 1 | 1

###  6.3 Source Redshifts and Photometry

We investigate the redshift distribution and photometric properties of sources
in our catalogue. We extract all sources with pre-existing data, querying
Simbad, ViZieR, the HSC via the Milkulski Archive for Space Telescopes (MAST)
and NED. Our queries use a $5^{\prime\prime}$ search radius within the Python
package AstroQuery. The existing data from each of these databases has
undergone heterogeneous selection and analysis procedures by the various
studies we extract them from; we do not try to reconcile these here. Rather
than a detailed physical analysis of these sources, our priority in this
subsection is to highlight how to explore and use this catalogue, as well as
any difficulties which may arise.

Of the 21,926 interacting systems in our high-confidence sample, 3,037 of the
7,522 referenced sources have a measured redshift. Figure 11 shows the
redshift distribution of this subset of our catalogue. 42.5% of the sources
have a redshift $z\leq 0.5$, 45.1% have a redshift $0.5<z<1$ and 12.4% have a
redshift $z>1$. In fact, a small fraction (15) of these sources are found to
be at z$\geq 5$. Upon investigation of these sources two of their redshifts
have been measured photometrically, while the remaining 13 sources did not
have the method of measurement recorded in the archive. Therefore, this
finding of very high redshift interacting galaxies are uncertain at best.

It is important to note that the small sample with redshift information is
affected by the selection biases of the combined studies publishing these
values, and therefore the distribution may not be representative of the full
sample. In addition, above redshift $z=1$ the $F814W$ filter begins to only
capture rest-frame UV flux, and therefore $z>1$ galaxies with low star
formation rates are more likely to fall below the flux limits of our detection
images. Sampling only the rest-frame UV also changes a galaxy’s observed
brightness and morphology (e.g., Ferreira et al., 2022) – the latter being how
Zoobot identifies interacting galaxies. For example, tidal features whose
initial starburst has faded may be undetected; conversely, a single galaxy
with irregular star-forming clumps may appear to be multiple interacting
galaxies, which we noted as a particular source of contamination during the
visual inspection stage. High-redshift interacting galaxies that are detected
initially by Zoobot but have unusual morphologies compared to $z\sim 1$
sources may be removed during prediction (Section 3), given that finetuning is
based primarily on the $z\lesssim 1$ imagery of Galaxy Zoo: _Hubble_.
Therefore, the currently measured redshift distribution in Figure 11 is likely
due to some combination of selection bias and training bias.

Figure 11: The redshift distribution of a subsample of our catalogue. Of the
7,583 referenced systems, 3,037 of them had redshift measurements in the NED,
MAST or Simbad. This redshift distribution shows that our model confidently
predicted interacting systems primarily for $z<1$ systems. This was
anticipated, as the model was primarily trained on systems at these redshifts.
There are fifteen sources with a reported $z>5$. Figure 12: The distribution
of redshift with magnitude for all sources with available data. This shows the
parameter space we are sampling in this catalogue. Panel A shows that the
majority of our sources are dim, background sources at low redshift. Panel B
shows the faintest objects we find are at the limiting magnitudes of the
different surveys this data is from.

Figure 12 shows the basic parameter space sampled by the sub-sample of the
catalogue with existing photometry and redshifts. We show the distributions of
redshift with the measured apparent $F814W$ magnitude and the calculated
absolute $F814W$ magnitude. The faintest objects are, as expected, observed at
approximately the limiting magnitude of the deepest observations in our
catalog. Other observations have brighter limits; those wishing to select a
uniform or volume-limited sample from our catalog must consider the variable
flux limits across the sample.

We finally focus on sources from our high-confidence sample that have multi-
band photometry, focusing on commonly-observed filters. By construction, 100%
of the sample has $F814W$ measurements, with 45% of the catalogue having
$F606W$ and only 11% having measured fluxes in $F475W$. Table 3 summarizes the
filter coverage of our catalogue. 6.1% (1336 sources) have complete 3-band
photometric information in the HSC. We use these to create examples of color
images from the catalogue (using the algorithm of Lupton et al., 2004). We
used a scaling factor $Q=2$ and $\alpha=0.75$, with ($F814W$, $F606W$,
$F475W$) as RGB channels and multiplicative factors of (1.25, 0.95, 2). The
resultant images are shown in Appendix B.

Table 3: Percent of sources in the final catalogue which have observations in the relevant _Hubble_ filter.  Filter (s) | Sources Covered
---|---
F814W | 100%
F606W + F814W | 45.0%
F475W + F814W | 11.0%
F475W + F606W + F814W | 6.1%

We extract the measured magnitudes of the $F606W$ and $F814W$ filters, giving
us two-band photometry for 9,876 sources. Cross referencing with each source
that had a redshift yields 2,993 sources from our catalogue. We calculate the
color of each source and plot it against the absolute magnitude in the $F814W$
filter. Figure 13 shows the resulting color-magnitude distribution in Panel A.
The resultant distribution is very hard to interpret due to the high scatter
of the sources. We extrapolate from this panel that there is little
contamination from sources other than galaxies. If levels of contamination
were high we would expect a second locus of sources with a very different
color-magnitude distribution.

Plotting the color-magnitude distribution in this way captures a wide range of
rest-frame wavelengths in the observed filters, which is the primary reason
that panel A of Figure 13 is hard to interpret. In this first-look study, we
do not have full spectral energy distributions (SEDs) of most sources, so
K-correction of individual colors within this sample would involve assuming a
template SED for each galaxy. Given that a high fraction of galaxies in our
sample of mergers may deviate from standard SED templates, we wish to avoid
this method. Instead, we choose redshift ranges within which to examine
subsamples, such that the observed $F606W$ and $F814W$ bands cover consistent
rest-frame colors within that subsample. Figure 13B shows only sources with
$z<0.18$, within which the observed filters can be taken to be approximately
rest-frame filters, which we define as at least 50% of the flux captured in
the observed band being emitted at rest-frame wavelengths covered by that
band. At $0.24<z<0.56$, the observed $F606W$ filter captures at least 50%
rest-frame $F475W$ flux, and the observed $F814W$ filter captures at least 50%
rest-frame $F606W$ flux, so Figure 13C is approximately a rest-frame
$F475W-F606W$ vs $F606W$ plot. At $0.62<z<1$, Figure 13D is approximately a
rest-frame NUV-Blue plot ($F336W-F475W$ vs $F475W$).

Figure 13: The color-magnitude distribution of sources with a redshift
measurement associated. Panel A shows the distribution of all galaxies,
without controlling for redshift or dust extinction. The remaining panels then
split these sources into distinct redshift bins where the $F606W$ and $F814W$
filters are observing in different rest frames. Panel B shows the color-
magnitude distribution in the local universe, where the rest frame
observations are $F606W$ and $F814W$ flux. This bin reveals a blue population.
Panel C shows the redshift bin where at 50% - 100% of observed $F606W$ and
$F814W$ flux is rest frame $F475W$ and $F606W$ flux. This bin reveals a larger
distribution of interacting galaxies, with a dominating population of blue
systems and a minor population of red systems. Panel D shows the redshift bin
where 50% to 100% of observed $F606W$ and $F814W$ flux is rest frame $F336W$
and $F475W$ flux. These filter bands are very sensitive to star formation, and
reveal a broad distribution in color of red and blue systems.

The galaxies in Panel B are observed in approximately the rest frame $F606W$
and $F814W$ filters. Nearly all are blue systems (by general definitions at
various redshifts, _e.g._ , Kauffmann et al., 2003; Whitaker et al., 2012;
Schawinski et al., 2014). This is expected for interacting systems with enough
gas to fuel a starburst. The lack of many red systems is due to few gas-poor
(“dry”) interactions in the (relatively) local volume (López-Sanjuan et al.,
2009). In Figure 13C, the $F606W$ and $F814W$ filters are still detecting
rest-frame optical ($F475W$ and $F606W$) emission, and we find a much broader
population. There are both blue and red interacting systems, with the redder
mergers occurring in more luminous (likely higher mass) systems, broadly
consistent with expectations (van Dokkum, 2005; Lotz et al., 2008). The rest-
frame filters approximately captured in Panel D ($F336W$ and $F475W$) sample
emission across the $4000$ Å break. Sensitivity to NUV means this panel
effectively splits systems according to very recent star formation history
(Schawinski et al., 2014; Smethurst et al., 2015). There is a significant
spread in color, with equivalent red and blue systems. We, therefore, find
many young blue systems undergoing star formation and bright brighter,
elliptical, massive systems also undergoing interaction in this bin.

This initial examination of the subsample of systems with easily retrievable
redshifts has revealed that the interacting galaxies in the sample broadly
agree with previous studies of colors in merging systems. This demonstrates
the underlying promise of the catalogue. A detailed study is beyond the scope
of this work, but there is considerable potential for new astrophysical
insights using this high-confidence catalog with nearly an order of magnitude
more sources than those previously published.

##  7 CONCLUSION

We present a large, pure catalogue of 21,926 interacting galaxy systems found
from the _Hubble_ Source Catalogue. This catalogue is a factor of six larger
than previous works. Each interacting system was found using the European
Space Agency’s new platform ESA Datalabs, which allowed us to directly apply
an advanced CNN - Zoobot \- to the entire _Hubble_ science archive. This
corresponds to predicting over 126 million sources. The compiled catalogue has
a contamination rate of $\approx$3% as found by bootstrapping. Table 1 shows
an example of 50 entries in our new catalogue, Figure 9 showing the
corresponding images. The new catalogue and all corresponding images can be
downloaded from Zenodo: _Zenodo link will be added_.

Each of our interacting galaxies were given a prediction score $\geq$0.95 by
Zoobot, with such a conservative score chosen to limit contamination and
maintain purity in the catalogue. Contamination was removed by applying cuts
in representation space (shown by Figure 8) and visual inspection. Upon visual
inspection, many contaminating images were found to be objects of other
astrophysical interest. These have been compiled into separate catalogues, and
Table 2 shows a breakdown of the objects found. These sub-catalogues have been
released alongside our interacting galaxy catalogue. With the priority of
purity in this catalogue creation, we will aim in future work to use it in the
statistical analysis of interacting galaxies and begin linking the underlying
parameters of interaction to the complex physical processes that occur in
them. A secondary purpose of this catalogue is to serve as a training set for
future models which may wish to search for interacting or merging galaxies.

With the use of ESA Datalabs, this project was conducted quickly. The entire
process, from creating the source cutouts, to training Zoobot, to making
predictions on 126 million sources took three months to complete. Using
conventional methods, such as AstroQuery or TAP services, downloading the data
would have likely taken on this timescale. By bringing the user to the data,
rather than vice versa, catalogues of a similar size - and many times larger
than previous catalogues - of many different objects can be created quickly.

None of the the interacting systems in this work are ‘new’; every one of them
exists in the background of large scale _HST_ surveys and observations since
their release. However, the method to directly search for them has been
impractical until the release ESA Datalabs. By directly applying machine
learning to existing astrophysical data repositories, a new method to creating
significantly larger catalogues has been achieved.

This shows the importance of archival work, and the power that ESA Datalabs
will bring to the field of astronomy. ESA Datalabs is expected to be released
in Q3 and with it, the ability for large scale exploration of archival data.
It will be released with introductory tutorials, step-by-step guides and
different Python environments for ease of use for different telescopes and
instruments the ESA is involved in. It will have a full cluster of GPUs at its
disposal and a storage capability in the range of hundreds of Terabytes. In
future, this entire project - from training set creation to predictions -
could be conducted on ESA Datalabs.

Such a setup as ESA Datalabs also allows the creation of large observational
catalogues, comparable to that we create from cosmological simulations. This
is incredibly important to further constraining already existing results. In
the current period of astronomy where large survey instruments are awaiting
first light, or the beginning of future telescopes is uncertain, the ability
to get ever more information out of the archives is paramount.

## ACKNOWLEDGEMENTS

DOR gratefully acknowledges the support from European Space Agencies Visitor
Archival Research program, and hosting at the European Space Astronomy Centre.
DOR thanks Bruno Merín for supervising this project and Sarah Kendrew for
aiding its creation. This project was conducted as part of DORs PhD program
supported by the UK Science and Technology Facilities Council (STFC) under
grant reference ST/T506205/1. BDS acknowledges support through a UK Research
and Innovation Future Leaders Fellowship [grant number MR/T044136/1]. ILG
acknowledges support from an STFC PhD studentship [grant number ST/T506205/1]
and from the Faculty of Science and Technology at Lancaster University. MW
gratefully acknowledges support from the UK Alan Turing Institute under grant
reference EP/V030302/1. MRT acknowledges the support from an STFC PhD
studentship [grant number ST/V506795/1] and from the Faculty of Science and
Technology at Lancaster University.

Much of the intense computation was conducted at the High End Computing
facility at Lancaster University. This publication uses data generated via the
Zooniverse.org platform, and the unending enthusiasm of citizen scientists and
volunteers in classifying galaxies. We also thank the many PIs who’s archival
data we have used to create this catalogue. All data containing astrophysical
objects of interest found in this work are public on MAST:
http://dx.doi.org/10.17909/wfke-n133 (catalog 10.17909/wfke-n133).

This research made use of many open-source Python packages and scientific
computing systems. These included Matplotlib Hunter (2007), scikit-learn
(Pedregosa et al., 2012), scikit-image (van der Walt et al., 2014), Pandas
(McKinney, 2010), Shapely (Gillies et al., 2007), UMAP (McInnes et al., 2018)
and numpy (Harris et al., 2020). This work also extensively used the
community-driven Python package Astropy (Astropy Collaboration et al., 2018).
Zoobot utilises the underlying code Tensorflow (Abadi et al., 2016) Python
package.

This project used data from the _Hubble_ Space Telescope and stored in the
archives at the European Space Astronomy Centre. These observations are
obtained from the Space Telescope Science Institute, which is operated by the
Association of Universities for Research in Astronomy, Inc, under NASA
contract NAS 5-26555. All sources were found using v3.1 of the _Hubble_ source
catalogue (Whitmore et al., 2016) and accessed using the ESA Datalabs science
platform. ESA Datalabs is directly connected to the ESA _Hubble_ Science
Archive. This study makes use of data from AEGIS, a multiwavelength sky survey
conducted with the Chandra, GALEX, Hubble, Keck, CFHT, MMT, Subaru, Palomar,
Spitzer, VLA, and other telescopes and supported in part by the NSF, NASA, and
the STFC.

For the purpose of open access, the authors have applied a Creative Commons
Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

DOR would like to thank those in the ESA Traineeship program cohort of 2022.
They created a wholly welcoming environment and space of support. A special
thanks must go to Karolin Frohnapfel and Emma Vellard for much technical
discussion. Finally, DOR would like to acknowledge Aurélien Verdier.

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##  Appendix A Further Model Diagnostics

In Section 5 we present diagnostic properties of our model. These include the
accuracy measurements, purity measurements as well as confusion matrices at
different cutoffs of our model. Here, we present the Receiver Operating
Characteristic (ROC) curves, the precision-recall (PR) curves, and measures of
true and false positive rates vs the cutoff threshold.

Figure 14 shows the ROC and PR curves of the final Zoobot model we applied to
the the _Hubble_ archives. The ROC shows the rate of change of finding true
positives and false positives with changing cutoff. The PR curve shows the
changes of precision against recall. Precision is the ratio of true positives
(interacting galaxies correctly predicted as so) to the sum of true and false
positives (non-interacting galaxies incorrectly predicted as interacting). The
recall is then the ratio of true positives to the sum of true positives and
false negatives (interacting galaxies that have been misclassified as non-
interacting). The red crosses in both plots shows how the model was behaving
when we use a cutoff of 0.95.

These are both as expected. Both curves show that the model behaves well, and
are much better than a random classifier (which would have a 1:1 relation).
The ROC plot shows that we are minimising our false positive rate when using a
prediction score cutoff of 0.95. However, we are misclassifying approximately
50% of interacting galaxies as non-interacting galaxies. The contamination
rate in our final catalogue (False Positives rate) will be very low (close to
zero in this ideal validation set). The PR curve shows a similar result. Here,
we are operating with a high precision (finding a pure catalogue) while
keeping our recall minimal.

We also present the changing F1 score for the model used in this work, shown
in Figure 15. The F1 score is twice the ratio of precision multiplied by
recall upon precision summed to recall. This combines our measure of accuracy
and purity into a single metric. The cutoff we use in this work is at the
point where the F1 score has began to decline. This is because we are
beginning to lose recall rapidly, but gaining significantly in precision. As
discussed in Section 5, this was an acceptable trade off in this work for a
very large, pure interacting galaxy catalogue.

Figure 14: The Receiver-Operator and Precision-Recall Curve for the Zoobot
model that was used to explore the Hubble archives. The blue curves are the
measured curves. These curves measure the relevant rates or characteristics
based on the changing cutoff applied to how Zoobot defines an interacting
galaxy. The red crosses are where the prediction score cutoff is for this
work. We can see in the Reciever-Operator Curve that the prediction score
cutoff we use would have an incredibly low false positive rate, while it would
be misclassifying $\approx$50% of interacting galaxies. This also shown in the
precision recall curve where our recall is $\approx$50%. Figure 15: The F1
score found during the diagnostics of the model used in this work. The F1
score is a measure combing the measure of accuracy and purity into one metric.
The cutoff we use is at the point where the F1 score begins to rapidly
decline. This point is shown by the red vertical line.

##  Appendix B Examples of Sources with 3-Band Information

Of the full catalogue of 21,926 interacting systems, only 1336 of them had got
all 3-band information. Six examples are shown in Figure 16. These were
created using the Lupton et al. (2004) algorithm, with a scaling factor $Q=2$
and $\alpha=0.75$, with ($F814W$, $F606W$, $F475W$) as RGB channels and
multiplicative factors of (1.25, 0.95, 2).

Figure 16: Example of six interacting systems in the catalogue with full
3-band imagery.

##  Appendix C Unknown Objects

From the final catalogue, there were six sources which we could not visually
identify. These objects were also not referenced anywhere in the astrophysical
literature. $F814W$ cutouts of the six objects are shown in Figure 17. Their
Source IDs are shown in the upper left of each image, and a separate catalogue
has been released of these with all other objects. This catalogue can be found
at the data release on Zenodo.

Four of the six objects (40001156424176, 4001368788120, 4001418076626 and
6000398415347) have a bright central source, followed by a low-surface
brightness tail. Initially, it was assumed that these were solar system
objects such as comets. This, however, could not be confirmed. The first of
these four sources is also thought to potentially be a highly disruped system
with a significantly elongated tidal feature. The final two unknown sources
(6000186797547 and 6000341449179) have no clear central source, though there
is extended structure to them. These are likely to be highly irregular
galaxies, but no confirmation could be found.

These objects are released to the community for identification and
investigation, as the authors cannot find definitive agreement on what they
are.

Figure 17: The six unknown systems found in this work. These have no reference
in Simbad or in NED, and their morphology could not be classified by the
authors. Investigation into these six objects are presented to the community,
with the authors hoping that future work and investigation of them can be
conducted by them.

##  Appendix D Acknowledging PIs

In the final section of this work, we wish to acknowledge all of the PIs whose
observations we have used. A machine readable table containing the proposal
IDs, the DOIs and the references (if provided/found) is presented with this
work. Table 4 shows the first twenty observations used in this work and is an
example of this table.

Table 4: Twenty example of the accompanying data table of observations used.  Proposal ID | Observation ID | Observation Date | DOI | references
---|---|---|---|---
8183 | hst_8183_54_acs_wfc_f814w_j59l54 | 18/07/2002 | https://doi.org/10.5270/esa-88k8vcj |
9075 | hst_9075_2a_acs_wfc_f814w_j6fl2a | 24/07/2002 | https://doi.org/10.5270/esa-gsxhb4b |
9351 | hst_9351_11_acs_wfc_f814w_j8d211 | 31/03/2003 | https://doi.org/10.5270/esa-5lba8bo |
9361 | hst_9361_03_acs_wfc_f814w_j8d503 | 22/07/2003 | https://doi.org/10.5270/esa-ecmnqgh |
9363 | hst_9363_09_acs_wfc_f814w_j8d809 | 02/07/2002 | https://doi.org/10.5270/esa-ethtec5 |
9367 | hst_9367_02_acs_wfc_f814w_j8ds02 | 10/06/2003 | https://doi.org/10.5270/esa-3j404ll |
9373 | hst_9373_02_acs_wfc_f814w_j6la02 | 05/07/2002 | https://doi.org/10.5270/esa-ztsq94u | Rejkuba et al. (2005)
9376 | hst_9376_02_acs_wfc_f814w_j8e302 | 13/07/2002 | https://doi.org/10.5270/esa-h90iavd | Keel et al. (2006)
9381 | hst_9381_02_acs_wfc_f814w_j8fu02 | 13/03/2003 | https://doi.org/10.5270/esa-vlapyea |
9400 | hst_9400_04_acs_wfc_f814w_j6kx04 | 29/05/2003 | https://doi.org/10.5270/esa-39rnout |
9403 | hst_9403_02_acs_wfc_f814w_j8fp02 | 09/07/2002 | https://doi.org/10.5270/esa-k5mv9ct |
9405 | hst_9405_6k_acs_wfc_f814w_j8iy6k | 22/05/2003 | https://doi.org/10.5270/esa-zy9phm1 |
9409 | hst_9409_03_acs_wfc_f814w_j6n203 | 29/06/2003 | https://doi.org/10.5270/esa-vjngw7r | Goudfrooij et al. (2004)
9411 | hst_9411_09_acs_wfc_f814w_j8dl09 | 11/02/2003 | https://doi.org/10.5270/esa-debpiln |
9427 | hst_9427_13_acs_wfc_f814w_j6m613 | 21/10/2002 | https://doi.org/10.5270/esa-bw1b97v |
9438 | hst_9438_01_acs_wfc_f814w_j6me01 | 16/01/2003 | https://doi.org/10.5270/esa-e5eaam5 | Gregg & West (2017)
9450 | hst_9450_02_acs_wfc_f814w_j8d402 | 25/08/2002 | https://doi.org/10.5270/esa-9ttmykz | York et al. (2005)
9453 | hst_9453_02_acs_wfc_f814w_j8f802 | 03/12/2002 | https://doi.org/10.5270/esa-1xvyjfy | Brown et al. (2003)
9454 | hst_9454_11_acs_wfc_f814w_j8ff11 | 23/03/2003 | https://doi.org/10.5270/esa-xsdowj9 |