11institutetext: Leibniz-Institut für Astrophysik Potsdam (AIP), An der
Sternwarte 16, 14482 Potsdam, Germany 22institutetext: Observational
Astrophysics, Division of Astronomy and Space Physics, Department of Physics
and Astronomy, Uppsala University, Box 516, 75120 Uppsala, Sweden
33institutetext: Max Plank Institut für Radioastronomie (MPIfR), Auf dem Hügel
69, 53121 Bonn, Germany 44institutetext: Physics Department, University of
Crete, GR 71003, Heraklion, Greece 55institutetext: Institute of Astrophysics,
Foundation for Research and Technology – Hellas, GR-70013 Heraklion, Greece
66institutetext: Harvard-Smithsonian Center for Astrophysics, 60 Garden
Street, Cambridge, MA 02138, USA

# An expanded ultraluminous X-ray source catalogue††thanks: Based on
observations obtained with XMM-Newton, an ESA science mission with instruments
and contributions directly funded by ESA Member States and NASA.

M. C. i Bernadich 112233 A. D. Schwope 11 K. Kovlakas 4455 A. Zezas 445566 I.
Traulsen 11

(Received ; accepted )

###### Abstract

Context. Ultraluminous X-ray sources (ULXs) are non-nuclear, extragalactic,
point-like X-ray sources whose luminosity exceeds that of the Eddington limit
of an accreting stellar-mass black hole
($M_{\textrm{BH}}<10\textrm{M}_{\odot}$, $L_{\textrm{X}}>10^{39}$ erg s-1).
They are excellent laboratories for extreme accretion physics, probes for the
star formation history of galaxies, and constitute precious targets for the
search of intermediate-mass black holes. As the sample size of X-ray data from
modern observatories such as XMM-Newton and Chandra increases, producing
extensive catalogues of ULXs and studying their collective properties has
become both a possibility and a priority.

Aims. We build a clean, updated ULX catalogue based on one of the most recent
XMM-Newton X-ray serendipitous survey data releases, 4XMM-DR9, and the most
recent and exhaustive catalogue of nearby galaxies, HECATE. We perform a
preliminary population study to test if the properties of the expanded XMM-
Newton ULX population are consistent with previous findings.

Methods. We perform positional cross-matches between XMM-Newton sources and
HECATE objects to identify host galaxies. We filter out known foreground and
background sources and other interlopers by finding counterparts in external
catalogues and databases such as Gaia DR2, SSDS, PanSTARRS1, NASA/IPAC
Extragalactic Database and SIMBAD. Manual inspection of image data from
PanSTARRS1 and the NASA/IPAC is occasionally performed to investigate the
nature of some individual sources. We use distance and luminosity arguments to
identify ULX candidates. Source parameters from 4XMM-DR9, galaxy parameters
from HECATE, and variability parameters from 4XMM-DR9s are used to study the
spectral, abundance and variability properties of the updated XMM-Newton ULX
sample.

Results. We identify 779 ULX candidates, 94 of which hold
$L_{\textrm{X}}\gtrsim 5\times 10^{40}$ erg s-1. We find that spiral galaxies
are more likely to host ULXs. For early-spiral galaxies we also find that the
number of ULX candidates per star forming rate that is consistent with
previous studies, while we also attest the existence of a significant ULX
population in elliptical and lenticular galaxies. Candidates hosted by late-
type galaxies tend to present harder spectra and to undergo more and more
extreme inter-observation variability than the ones hosted by early-type
galaxies. $\sim$30 candidates with $L_{\textrm{X}}>10^{41}$ erg s-1 are also
identified, constituting the most interesting candidates for intermediate-mass
black hole searches.

Conclusions. We have built the largest ULX catalogue to this date. Our results
regarding the spectral and abundance properties of ULXs confirm the findings
made by previous studies based on XMM-Newton and Chandra data, while our
population-scale study on variability properties is unprecedented. Our study,
however, provides limited insight on the properties of the bright ULX
candidates ($L_{\textrm{X}}\gtrsim 5\times 10^{40}$ erg s-1) due to the small
sample size. The expected growth of X-ray catalogues and potential future
follow-ups will aid in drawing a more clear picture.

###### Key Words.:

accretion, accretion disks – black hole physics – catalogs – stars: black
holes – X-rays: binaries

##  1 Introduction

Ultraluminous X-ray sources (ULXs) are extragalactic, point-like, X-ray
sources whose luminosity exceeds that of the Eddington limit of an accreting
stellar-mass black hole (StBH, $M_{\textrm{BH}}<10\textrm{M}_{\odot}$,
$L_{\textrm{Edd}}{\approx}10^{39}$ erg s-1), and that are not the central
source of their host galaxy. These objects have been the subject of active
research since the advent of X-ray astronomy (see Kaaret et al. 2017 for a
review) and, for a time, due to the scaling of the Eddington limit with mass,
it was believed that they hosted accreting BHs of masses between those of
StBHs and supermassive BHs. Nowadays, they are more commonly associated with
super-Eddington accretion onto common compact objects such as StBH and neutron
stars (NSs), with a few exceptions where BHs of higher mass need to be invoked
(Bachetti 2016).

The current understanding comes from three main lines of evidence. Firstly,
predictions of the formation rate for intermediate-mass BHs (IMBHs,
$100\textrm{M}_{\odot}<M_{\textrm{BH}}<10^{6}\textrm{M}_{\odot}$) with donor
companions fall short of explaining the observed abundances of ULXs (King
2004; Madhusudhan et al. 2006). Secondly, ULXs are most commonly associated
with star-forming galaxies (Swartz et al. 2011), or even young elliptical
galaxies with recent star formation events, rather than old elliptical ones
with no star formation (Kim & Fabbiano 2004, 2010). This is consistent with
ULXs belonging to a high-luminosity extrapolation of the X-ray binary
population, whose abundances correlate with the star-formation rate (SFR) and
the stellar mass ($M_{*}$) of their host galaxies (Gilfanov 2004; Grimm et al.
2003; Mineo et al. 2012). And finally, but perhaps most importantly, the
direct observation of regular pulsations in some ULXs unmistakably points
towards accretion onto a NS in binary systems. Famous examples of neutron
star-powered ULXs (NS-ULXs) are M82 X-2 (Bachetti et al. 2014), NGC 7793 P13
(Fürst et al. 2016; Israel et al. 2016), NGC 5907 ULX1 (Israel et al. 2017) or
NGC 300 ULX1 (Carpano et al. 2018). Since some of these objects present
$L_{\textrm{X}}>10^{40}$ erg s-1, they are clear cases of super-Eddington
accretion. In hand with these observations, population synthesis models even
suggest that NS-ULXs constitute a significant fraction of the ULX population
(Wiktorowicz et al. 2017).

The prospect of super-Eddington in ULXs accretion has sparked a lot of
interest for the modelling of ULX systems. Many works have shown that
sufficiently extreme accretion rates to power ULXs can be achieved during
certain phases of regular HMXB and LMXB evolution. In fact, these rates can
explain most of the ULX population (King 2002; Rappaport et al. 2005;
Wiktorowicz et al. 2015; Pavlovskii et al. 2017). Yet, many aspects of the
physics of super-Eddington accretion itself are poorly understood, specially
in cases where the material falls on top of strongly magnetized neutron stars,
and very rich theoretical research is being performed to model this phenomenon
(see Kaaret et al. 2017 for a comprehensive discussion of this topic, which
otherwise lies outside of the scope of this introduction).

Nonetheless, ULXs are still good sources where to look for IMBHs, as a handful
of specially bright candidates still remain. ESO 243-49 HLX-1 has been
detected with both luminosities of $L_{\textrm{X}}{\approx}10^{42}$ erg s-1and
spectral state transitions typical of sub-Eddington Galactic BH binaries
(GBHBs, Remillard & McClintock 2006), being consistent with a mass of
$\sim$$10^{3}$ M⊙ (Servillat et al. 2011). M82 X-1 showcases a similar
behavior (Kong et al. 2007), with the addition of quasi-periodic oscillations
also characteristic from GBHBs (QPO, Remillard & McClintock 2006) and that
grant it a mass estimate of $400\leavevmode\nobreak\ \textrm{M}_{\odot}$
(Pasham et al. 2014). And just like in GBHBs, powerful radio jets are also
present in the IMBH candidates ESO 243-49 HLX-1 (Webb et al. 2012; Cseh et al.
2014) and NGC 2276-3c (Mezcua et al. 2015).

The relevance of ULXs as proxies of recent star formation, in accretion
physics and in IMBHs searches has been so far exposed. However, ULX studies
are limited by their small numbers and large distances. For this reason, a lot
of effort has been put into building compilations of ULX candidates from X-ray
surveys. Early works relied on ROSAT data (e.g. Roberts & Warwick 2000; Liu &
Bregman 2005; Liu et al. 2006; Colbert & Ptak 2002), yielding up to $\sim$100
candidates. More recent ones have been based on XMM-Newton and Chandra data.
XMM-Newton-based catalogues have been highly successful, yielding up to $\sim$
300 ULX candidates (Walton et al. 2011; Earnshaw et al. 2019), but they are
somewhat hampered by the low angular resolution of the observatory. On the
other hand, works based on the Chandra telescope have provided better
identification of individual sources thanks to its crisp angular resolution
(e.g. Swartz et al. 2004, 2011; Wang et al. 2016; Kovlakas et al. 2020).

Of particular interest to us are the works of Earnshaw et al. (2019) and
Kovlakas et al. (2020), since they both constructed ULXs samples from the
largest and most recent XMM-Newton and Chandra samples available at the time.
Earnshaw et al. (2019) is, in fact, our main predecessor and inspiration
regarding methodology. They identify 384 ULX candidates within the fourth XMM-
Newton data release111https://xmmssc-www.star.le.ac.uk/Catalogue/3XMM-
DR4/UserGuide˙xmmcat.html (3XMM-DR4, Rosen et al. 2016), using the Third
Reference Catalogue of Bright Galaxies (RC3, de Vaucouleurs et al. 1991) and
the Catalogue of Neighbouring Galaxies (Karachentsev et al. 2004) as
references for the host galaxies. Their study focuses mostly on the spectral
properties of ULXs, and they find that ULXs tend to be somewhat harder in
late-type galaxies, and that their hardness ratios in general alike to those
of X-ray binaries below the Eddington threshold. Kovlakas et al. (2020) find
629 ULX candidates within the Chandra Source Catalog
2.0222https://cxc.harvard.edu/csc2/ (CSC2) using the Heraklion Extragalactic
CATaloguE (HECATE Kovlakas et al. 2021) as the reference for host galaxies,
which comes with valuable information such as the SFR and $M_{*}$. Their study
focuses heavily on the scaling properties of the ULX content of galaxies with
the SFR and $M_{*}$ parameters, and finds that SFR is the determinant
parameter for late-type galaxies while $M_{*}$ rules over the abundance in
late-type galaxies, in accordance with recent population synthesis models
(Wiktorowicz et al. 2017).

Now, the ninth XMM-Newton data release333http://xmmssc.irap.omp.eu (4XMM-DR9,
Webb et al. 2020), its stacked version (4XMM-DR9s, Traulsen et al. 2020) and
the HECATE catalogue are available to us, making it possible to build the
largest catalogue of extragalactic, non-nuclear, point-like X-ray sources
based on XMM-Newton data. We then use the catalogue to preliminarily explore
the distribution of ULX spectral and variability properties, and the
dependence of ULX abundances galaxies with the SFR and $M_{*}$ on XMM-Newton’s
sky.

This paper is organized as follows: in the following section we explain the
specifics of selection in archival data, in Section 3 we describe our
methodology for identifying ULX candidates and other interloping objects, in
Section 4 we expose our main results and in Section 5 we discuss some of the
limitations and implications of our work and compare it with previous ones.

##  2 Data samples

###  2.1 The X-ray samples

We used the Fourth XMM-Newton Serendipitous Source Catalog, Ninth Data
Release, (4XMM-DR9, Webb et al. 2020) as the basis for our ULX catalogue. It
lists 810 795 individual detections of 550 124 unique sources discovered
across 11 204 observations, and it represents an increase of 177 396 sources
with respect to 3XMM-DR4, the resource used in the previous ULX study based in
XMM-Newton data (Earnshaw et al. 2019). The columns we used during the
construction are:

  * •

`DETID` and `SRCID`, the identification number of detections and sources,

  * •

`SC_RA`, `SC_DEC` and `SC_POSERR`, the source J2000.0 sky coordinates and
their positional uncertainty,

  * •

`SC_EXTENT`, the measured extension of the source,

  * •

`SC_DET_ML`, the detection likelihood of sources, taken from the highest value
of `DET_ML` among their detections,

  * •

`EP_8_FLUX` and `EP_8_FLUX_ERR`, the measured X-ray flux of detections and its
uncertainty in the 0.2$-$12 keV band, derived from the EPIC photon counts,

  * •

`SC_EP_8_FLUX` and `SC_EP_8_FLUX_ERR`, the averaged X-ray flux of sources and
its uncertainty in the 0.2$-$12 keV band, derived from the EPIC photon counts,

  * •

`SC_HR`$i$, where $i$ runs from 1 to 4, the source hardness ratios from the
source count rates in the respective energy bands,

  * •

and `SC_SUM_FLAG`, the summary quality flag of a unique source, which runs
from 0 to 5. 0 means that all detections of the source are trustworthy, while
5 means that at least one detection is most likely spurious. Its value is
given by the highest value of `SUM_FLAG` among all detections of the source.

Since the XMM-Newton FWHM resolution is of 6″ (ESA: XMM-Newton SOC 2019), we
included only sources with $\verb|SC_EXTENT|<6\arcsec$ to work with point-like
sources only. We also considered only sources with $\verb|SC_DET_ML|>8$. As
these parameters are based on the worst result for all the detections of a
source, it can happen that an otherwise point-like source is counted as
extended if in one of its detections it is measured as such. We nevertheless
excluded them from the catalogue because other source parameters that consist
in averaged detection parameters are most likely unreliable. This left 452 602
sources available for the catalogue.

Additionally, we used the stacked version of 4XMM-DR9. This is the second XMM-
Newton serendipitous source catalogue built from overlapping observations
(4XMM-DR9s, Traulsen et al. 2020), which compiles variability information for
218 283 sources detected in 6 604 overlapping XMM-Newton observations, an
improvement of 146 332 sources with respect to the first version (Traulsen et
al. 2019). The strength of 4XMM-DR9s is that, from the overlapping
observations, extra source variability parameters that are not included in the
bare 4XMM-DR9 are computed. From it, we used the following columns:

  * •

`SRCID`, the source identification number,

  * •

`RA`, `DEC` and `RADEC_ERR`, the source J2000.0 sky coordinates and their
uncertainty,

  * •

`N_CONTRIB`, the number of times a source contributes to the computation of
variability, usually equal to the number of times it has been observed,

  * •

`VAR_PROB`, the probability of a source not showing inter-observation
variability, computed from the reduced $\chi^{2}$ of EPIC flux variability,

  * •

`FRATIO`, the $F_{\textrm{max}}/F_{\textrm{min}}$ ratio between the highest
and lowest observed fluxes in a single source.

###  2.2 The galaxy sample

We used the Heraklion Extragalactic CATaloguE (HECATE, Kovlakas et al. 2021,
submitted) compilation of 204 733 galaxies within 200 Mpc. Built from the
HyperLEDA catalogue (Makarov et al. 2014) and other databases such as NED,
SDSS and 2MASS, it is a much more complete galaxy compilation than the Third
Reference Catalogue of Galaxies (RC3, de Vaucouleurs et al. 1991),
traditionally used during previous ULX studies (Swartz et al. 2011; Wang et
al. 2016; Earnshaw et al. 2019). A more detailed description of its contents
is available in Kovlakas et al. 2021, but here we mention the columns we used:

  * •

`PGC`, the Principal Galaxy Catalogue identification number, originating from
the Principal Catalogue of Galaxies (Paturel et al. 1985) and still used in
HyperLEDA,

  * •

`RA` and `DEC`, the J2000.0 coordinates of the galactic center,

  * •

`R1` and `R2`, the minor and major D25 isophotal radii,

  * •

`PA`, the North-to-East position angle of the major axis,

  * •

`D` and `D_ERR`, the galaxy distance and its uncertainty,

  * •

`T`, the Hubble Type value ($T_{\textrm{H}}$) of a galaxy,

  * •

`logSFR_HEC`, the decimal logarithm of the SFR estimate,

  * •

and `logM_HEC`, the decimal logarithm of $M_{*}$.

The SFR values in the HECATE are based on infrared calibrations, which tend to
overestimate the SFR in early-type galaxies (Kovlakas et al. 2021). Therefore,
SFR values were only inspected for galaxies with $T_{\textrm{H}}\geq 0$.

###  2.3 The interloper samples

Our X-ray catalogue is inevitably populated by a fraction of non-ULX
contaminating objects such as background AGNs or foreground stars. Therefore,
we built a filtering pipeline (see section 3.1) to identify interlopers in
external catalogues and databases of already known objects. These consist of:
the second Gaia data release (GaiaDR2, Gaia Collaboration 2018), the Tycho-2
catalogue of bright stars (Tycho2, Høg et al. 2000), the 14th Sloan Digital
Sky Survey data release (SDSS-DR14, Blanton et al. 2017), the 13th edition of
the Véron-Cetty & Véron catalogue of QSOs and AGNs (VéronQSO, Véron-Cetty &
Véron 2010), the SIMBAD database (Wegner et al. 2000), the Panoramic Survey
Telescope and Rapid Response System database444https://panstarrs.stsci.edu
(PanSTARRS1, Flewelling et al. 2016) from the PanSTARRS1 surveys (Chambers et
al. 2016) and the NASA/IPAC Extragalactic
Database555https://ned.ipac.caltech.edu/ (NED).

All the cross-matches were performed using the
TOPCAT666http://www.star.bris.ac.uk/mbt/topcat/ and
STILTS777http://www.star.bris.ac.uk/~mbt/stilts/ software tools to manipulate
the tables in all of the following steps (Taylor 2005).

##  3 Methods

###  3.1 The automatic filtering process

Correlation of sources with galaxies. We selected all 4XMM-DR9 sources that
overlapped with the isophotal ellipses of HECATE within their positional
uncertainty. Catalogue entries matched to galaxies with available measurements
of `R1`, `R2` and `PA` were labeled with $\verb|MATCH_FLAG|=0$. When `PA` was
unknown, detections of sources matched with the minor isophotal circle were
labelled as $\verb|MATCH_FLAG|=1$, while detections of sources within the
annulus drawn by `R1` and `R2` were given $\verb|MATCH_FLAG|=2$. When both
`PA` and `R2` were unlisted, sources matched to the circle drawn by `R1` were
labeled as $\verb|MATCH_FLAG|=3$. Additionally, we stored in `n_Galaxies` the
number of galaxies a source has been matched with. Detections of sources with
$\verb|n_Galaxies|>1$ are listed more than once and treated independently for
each galaxy, being identifiable by the unique `DET_PGC_ID` and `SRC_PGC_ID`
detection/source-galaxy identity numbers. This way, a comprehensive catalogue
of 50 446 entries was built, out of which 49 816 have $\verb|n_Galaxies|=1$
and 48 206 present $\verb|MATCH_FLAG|=0$.

Figure 1: Top: XMM-Newton observation 0762610401, including galaxy NGC 3631
with all detected sources. Bottom: optical Pan-STARRS1 (Chambers et al. 2016)
image of the same galaxy. Non-nuclear sources are highlighted with red
crosses, while a green cross signals the central source. The image in the
visible range covers a slightly smaller sky area including the four most
central sources from the X-ray image.

Identification of central sources. At low redshifts, AGNs typically present
luminosities of $L_{\textrm{X}}>10^{41}$ erg s-1 (Brandt & Alexander 2015).
However, some have been observed to overlap with the ULX luminosity regime (Ho
et al. 2001; Eracleous et al. 2002; Ghosh et al. 2008; Zhang et al. 2009).
Therefore, we identified all sources whose position overlapped within three
times of their uncertainty with a circle of radius 3” around the center of
their host galaxy. 3 658 entries in our catalogue were thus labeled as central
sources. We also checked for sources with more than one potential host galaxy
($\verb|n_Galaxies|>1$) that had been flagged as central-source candidates in
one of their iterations, and flagged them as central-source candidates on all
of their other galaxy associations to indicate their potentially nuclear
nature. This highlighted 137 further entries, all of which are composed by
optically coincident galaxies, whether related or not. Figure 1 illustrates
how essential it is to identify these central sources before classifying them
as ULXs, as otherwise they would easily slip through and contaminate our
sample.

Identification of foreground stellar objects.

X-ray emission from stars is ubiquitous across the Hertzsprung-Russell diagram
and has manifold reasons, be it active coronae, hot stellar winds or binary
accretion, which can lead to the misclassification of Galactic objects as
extragalactic ULXs. We performed a cross-match of unflagged objects with
GaiaDR2 making use of the query tool provided by CDS, Strasbourg, and then
with Tycho2, to identify potential contaminating foreground stars. In both
cases, a positional overlap within three times the positional uncertainty was
required on both sides of the cross-match. Assuming that all matched objects
were stars, a very stringent constraint of
$\log(F_{\textrm{X}}/F_{\textrm{V}})<-2.2$ was used to decide whether the
matched object can explain the X-ray luminosity of the XMM-Newton source,
where $F_{\textrm{X}}$ is the source X-ray flux provided by `SC_EP_8_FLUX` and
$F_{\textrm{V}}$ its flux in optical light. This limit is established in
consistency with the work of Freund et al. (2018), were
$\log(F_{\textrm{X}}/F_{\textrm{BOL}})=-2.2$ is considered the maximal
luminosity ratio for early-type stars, the ones presenting the largest share
of X-ray luminosity. However, being the bolometric flux $F_{\textrm{BOL}}$
unavailable in most existing catalogues, we approximated it as the optical
flux of the star, $F_{\textrm{V}}$. We used the formula from Maccacaro et al.
(1988) to write

$\log\left(\frac{F_{\textrm{X}}}{F_{\textrm{V}}}\right)=\log{F_{\textrm{X}}}+\frac{m_{\textrm{V}}}{2.5}+5.37<-2.2\textrm{,}$
(1)

where $m_{\textrm{V}}$ is the optical magnitude of the studied object. In
cases where $m_{\textrm{V}}$ is not listed, we used the G-band magnitude
$m_{\textrm{G}}$ instead, which typically has the same value at the V-band
magnitude for bright enough sources. From GaiaDR2, we used the listed G-band
magnitude values `Gmag`, and for Tycho2 we used the VT-magnitude measurements
`VTmag`. This resulted in 2 257 entries flagged as stars from GaiaDR2; and 96
entries from Tycho2.

ULXs optical counterparts are typically very faint in the optical, presenting
$m_{\textrm{V}}\gtrsim 21$ (Kaaret et al. 2017), which leads to
$\log(F_{\textrm{X}}/F_{\textrm{V}})\gtrsim 0$ for a ULX candidate with
$L_{\textrm{X}}=10^{39}$ erg s-1 at a distance of 20 Mpc. Therefore, we are
safe from accidentally disregarding genuine ULX candidates as stellar objects.
Objects with $0>\log(F_{\textrm{X}}/F_{\textrm{V}})>-2.2$ were considered in
an upcoming section of the pipeline.

Identification of background QSOs. From the 2-10 keV $\log{N}-\log{S}$
distribution above a flux of $10^{-14}$ erg cm-2 s-1 presented in Mateos et
al. (2008), we expect a background source density of 300 deg-2 in the field.
With an accumulated galaxy sky area outside of the local group ($D>1$ Mpc) of
2.25 deg2, this implies that around 670 background contaminants lie in the
line of sight of galaxies where we expect to find most of our ULX candidates.
This clearly motivates the need to identify such objects in currently
available catalogues. Therefore, all remaining unflagged objects found to
overlap within three times their positional uncertainties during the cross-
match with the SDSS-DR14 and VéronQSO were flagged as QSOs. The first query
was performed with SDSS-DR14 and highlighted 140 entries, while the second one
highlighted 135 more.

Identification of miscellaneous objects in SIMBAD. We cross-matched our
remaining unflagged objects with the SIMBAD database with the query tool
provided by CDS, Strasbourg. In this step of the pipeline, sources were
treated differently depending on the nature of the matched objects, indicated
as $\verb|main_type|$ in the SIMBAD
catalogue888http://simbad.u-strasbg.fr/simbad/sim-display?data=otypes. Every
object overlapping within three times their positional uncertainty and with
either $\verb|main_type|=$“Star” or with their `main_type` containing the “*”
symbol, and that whose optical magnitude `Vmag` holds equation (1) was flagged
as a stellar contaminant. Objects with $\verb|main_type|=$“AGN”,
“AGN$\\_$Candidate”, “QSO”, “QSO$\\_$Candidate” or “SN” were also flagged,
regardless of their optical magnitude. Supernovae in particular were
highlighted as their emission is dominated from the expanding envelope. This
step highlighted 5 745 entries of the catalogue.

Identification of miscellaneous optical objects. As previously stated, ULXs
optical counterparts typically present $m_{\textrm{V}}\gtrsim 21$ (Kaaret et
al. 2017), which leads to $\log(F_{\textrm{X}}/F_{\textrm{V}})\gtrsim 0$ for a
ULX candidate with $L_{\textrm{X}}=10^{39}$ erg s-1 at a distance of 20 Mpc.
Objects with larger optical luminosity are usually AGNs or foreground stars.
Sources with $\log(F_{\textrm{X}}/F_{\textrm{VOL}})<-2.2$ were already
labelled as stars in a previous section of the pipeline. In this section, we
looked for optically bright objects of extragalactic origin. We performed a
cross-match with the query tool provided by CDS, Strasbourg, with the
PanSTARRS1 catalogue to find all objects that overlapped within a radius of
three times their positional uncertainties and that held

$\log\left(\frac{F_{\textrm{X}}}{F_{\textrm{V}}}\right)=\log{F_{\textrm{X}}}+\frac{m_{\textrm{V}}}{2.5}+5.37<0\textrm{,}$
(2)

to highlight all sources brighter in the optical than in the X-rays, using the
G-band magnitude `gmag` as an estimate for $m_{\textrm{V}}$. Up to 3 151
entries were flagged as PanSTARRS1 extragalactic objects.

Figure 2: TOPCAT astrometric map of M51 showcasing all 4XMM-DR9 sources within
its isophotal ellipse. The interlopers are composed by PanSTARRS1 and SIMBAD
objects, and all of them but one stay below the ULX luminosity regime if
assumed at the same distance as the others.

###  3.2 Identification of ULX candidates

To identify ULX candidates, we computed individual detection and average
source luminosities from the X-ray fluxes as listed in 4XMM-DR9 (Section 2.1)
and from the distances as listed in HECATE (Section 2.2), along with the
corresponding error propagation. These are listed as `Luminosity` and
`LuminosistyErr` for detections and `SC_Luminosity` and `SC_LuminosistyErr`
for sources. In our catalogue, we consider any X-ray source to be within the
ULX luminosity regime if:

  * •

1) It has at least one detection with luminosity above the Eddington limit
within the uncertainty ($\verb|Luminosity|+\verb|LuminosityErr|>10^{39}$ erg
s-1) in at least one of the potential host galaxies.

This check identified 5 943 entries of diverse nature, corresponding to 3 280
sources in 2 729 galaxies. 2 205 of these objects had been flagged as central,
while only 856 objects in 552 galaxies were clean of counterparts.
Furthermore, some of them present large uncertainties in their data.
Therefore, we imposed additional quality conditions for a source to qualify as
a ULX candidate:

  * •

2) It has no identified counterpart.

  * •

3) It holds $\verb|SC_Luminosity|>\verb|SC_LuminosityErr|$, as otherwise it
would indicate that its source parameters are unreliable.

  * •

4) It has $\verb|SC_SUM_FLAG|<1$. This way, we only considered sources for
which none of the individual detections was flagged as probably spurious.

  * •

5) It has a single potential host galaxy ($\verb|n_Galaxies|=1$), as otherwise
their distances and luminosities are unreliable.

This left originally 730 ULX candidates in 490 galaxies. Additionally, we
created a subcategory of bright ULX candidates, which follows that
$\verb|Luminosity|+\verb|LuminosityErr|>5\times 10^{40}$ erg s-1, into which
only 130 in 122 galaxies were included. We also found 7 sources that, despite
having more than one potential host galaxy ($\verb|n_Galaxies|>1$), their
distances were differing only by a small fraction without affecting their ULX
status. These sources were included to the ULX count, one of them being a
bright candidate.

Sources that were below the ULX luminosity regime in all of their detections
but still hold conditions 2) to 5) are simply referred to as quality sources.
Many of these sources and low-luminosity interlopers identified in GaiaDR2,
PanSTARRS1 and SIMBAD are found in nearby galaxies, as showcased by Figure 2.

###  3.3 Manual inspection of candidates and contaminants

Our procedure revealed 50 ULX candidates with $L_{\textrm{X}}>10^{41}$ erg
s-1, well within the AGN luminosity regime (Brandt & Alexander 2015). However,
we had to consider the possibility of some of them being contaminants in
disguise that survived the filtering pipeline. With the aim to identify
potential counterparts, we inspected the available optical and X-ray images
from the PanSTARRS1 and XMM-Newton surveys for 112 bright ULX candidates. Here
we discuss some of the relevant results:

Figure 3: PanSTARRS1 optical image of the location of source
SRCID=200559905010001. The X-ray source (not shown here) is a blend of the two
cores of the interacting pair. Figure 4: PanSTARRS1 optical image of the
location of source SRCID=201389514010019. The source lies very clearly on top
of a background galaxy.

Source $\verb|SRCID|=200559905010001$ (Figure 3), with detections of
$L_{\textrm{X}}=(4.3\pm 1.3)\times 10^{42}$ erg s-1 and
$L_{\textrm{X}}=(8.2\pm 2.3)\times 10^{42}$ erg s-1, is most likely a blend of
the two AGNs in the interacting galaxy pair NGC 5256.

Source $\verb|SRCID|=206701401010002$ presents $\langle
L_{\textrm{X}}\rangle=(4.5\pm 0.9)\times 10^{41}$ erg s-1 and is found in the
interacting galaxy pair II Zw. This pair has been thoroughly studied in the
X-rays (Inami et al. 2010; Iwasawa et al. 2011), and the XMM-Newton source is
most likely a blend of sources A, C and D from Goldader et al. (1997).

Source $\verb|SRCID|=201389514010019$ (Figure 4) clearly points towards a
background galaxy, despite it being listed as belonging to galaxy NGC 2528.
According to the NASA/IPAC Extragalactic
Database999https://ned.ipac.caltech.edu/ (NED), this galaxy is at $z=0.13$.

A further amount of 8 sources were revealed as spurious detections around an
over-saturated region on the X-ray image. All of the mentioned sources were
flagged accordingly.

Unfortunately, 19 of the sources did not have PanSTARRS1 images available, so
we inspected their positions on NED to look for possible counterparts. This is
particularly relevant for objects with ecliptic latitude below $-30^{\circ}$,
as this area is not entirely covered by the PanSTARRS1 survey. 15 additional
sources showed PanSTARRS1 counterparts of unclear nature, so we double checked
them with NED too. The results were the following:

Sources $\verb|SRCID|=200936502010001$ and $\verb|SRCID|=200029702010002$ were
found to be the central sources of their host galaxies, NGC 5128 and UGC
01841, due to a discrepancy in the value for the central coordinates with
HECATE. Source $\verb|SRCID|=201241101010001$ was identified as the QSO MR
0205. Finally, four remaining sources were also identified with distant
background galaxies.

There is another side to the manual inspection of sources. As ULXs are more
commonly associated with star-forming regions (Kaaret et al. 2017), some
genuine ULXs are located within or next to optically bright H II regions of
spiral and irregular galaxies. This caused confusion in the filtering step
involving PanSTARRS1, and otherwise good candidates were flagged as
interlopers. Therefore, we performed manual inspection for 128 PanSTARRS1
associations that held $\verb|SC_SUM_FLAG|\leq 1$,
$\verb|SC_Luminosity|+\verb|SC_LuminosityErr|\geq 10^{39}$ erg s-1 and
$\verb|SC_Luminosity|>\verb|SC_LuminosityErr|$ with the intention of
recovering genuine ULX candidates.

Figure 5: PanSTARRS1 optical image of the location of source
SRCID=205562803010007 (green marker) in galaxy NGC 2903. The red marker
indicates the position of the PanSTARRS1 counterpart identified by the
pipeline. The optical counterpart constitutes an H II region.

72 sources were matched to bright nebulous features in spiral galaxies (see
Figure 5 for an example). NED based inspection of 10 further sources also
suggested H II region nature or having only X-ray counterparts, and therefore
were re-flagged as clean. Source $\verb|SRCID|=201109302010027$ in particular
was not matched to any potential contaminant in NED, but it showed two
possible X-ray counterparts. It was kept as an interloper due to its possibly
blended nature.

Another set of 11 sources were identified with infrared sources also in NED,
while four more sources where found to coincide with background galaxies.
These sources were kept in the group of interlopers.

Finally, we searched for possible extragalactic objects in GaiaDR2. Shu et al.
(2019) draw 3 175 537 AGN candidates from 641 266 363 GaiaDR2 sources, a
fraction of 0.5%, which suggested further possible undetected extragalactic
interlopers in our catalogue. Therefore, we performed a final cross-match of
the remaining unmatched sources with GaiaDR2, this time applying condition (2)
to decide whether to flag the object as a possible interloper. 23 ULX were
matched with a GaiaDR2 source. We inspected all of them manually in the NED
database and found that 14 of them did not have a known counterpart, so they
were re-flagged as clean. In contrast, 7 were found to have infrared
counterparts, $\verb|SRCID|=203049401010029$ was matched to a UV source and
$\verb|SRCID|=207843705010011$ was found to be the central source of NGC 7632,
and therefore they were accordingly confirmed as interlopers.

The final version of the catalogue after the manual inspections yielded 1 452
detections of 779 ULX candidates in 517 galaxies, and 163 detections of 94
bright ULX candidates of quality in 94 galaxies.

###  3.4 Luminosity and complete sub-samples

As low-luminosity sources become increasingly harder to detect with increasing
distance, our catalogue presents a bias towards brighter sources at large
distances, hampering any potential study of the ULX population properties.
Following the methodology of Earnshaw et al. (2019), we created the luminosity
sub-samples to ensure the completeness of the set of sources above a
luminosity threshold $L_{\textrm{min}}$ inside of a radius $D_{\textrm{max}}$.

We established a minimum flux for a source to be detected, $F_{\textrm{min}}$,
from which we compute the maximum distances $D_{\textrm{max}}$ at which
sources with luminosity $L_{\textrm{min}}$ can be seen. Then, we selected all
objects with $D<D_{\textrm{max}}$ in each case to make sure that every source
with $L>L_{\textrm{min}}$ is accounted for, mitigating this way the bias
towards brighter sources in the $L>L_{\textrm{min}}$ regime. From these, we
then selected all quality sources and ULX candidates whose host galaxy’s
isophotal dimensions are known ($\verb|MATCH_FLAG|=0$) and that lie at least
$25^{\circ}$ away from the galactic plane. This last condition ensures minimal
photoelectric absorption from the Milky Way.

Figure 6: Distribution of detection fluxes within 4XMM-DR9, regardless of
their nature. Most detections (77%) have been detected with fluxes higher than
$F_{\textrm{X}}\approx 10^{-14}$erg cm-2 s-1. Figure 7: Distance-luminosity
dispersion of all quality sources within the the 38ss, 39ss and 5x40ss, and
the cULXss and the cbULXss, with an imposed sensitivity of $10^{-14}$ erg cm-2
s-1. The distance cuts correspond to $D_{\textrm{max}}\approx 9$, 29 and 204
Mpc.

The most controversial aspect of this method is the choice of
$F_{\textrm{min}}$, since there is no hard detection threshold for the XMM-
Newton observatory as it depends on exposure time and background intensity. To
work around this, we observed in Figure 6 that the distribution of all
detection fluxes in 4XMM-DR9 peaks early at $F_{\textrm{X}}\approx 10^{-14}$
erg cm-2 s-1mark, with 77% of the detections at higher fluxes, so we adopted
this value as the baseline for XMM-Newton’s sensitivity.

With this in hand, we built the luminosity sub-samples 38ss, 39ss and 5x40ss,
with corresponding $L_{\textrm{min}}=10^{38}$, $10^{39}$ and $5\times 10^{40}$
erg s-1 and $D_{\textrm{max}}\approx 9$, 29 and 204 Mpc. We then selected all
ULX candidates with $\verb|n_Galaxies|=1$ belonging to 39ss to build the
complete ULX sub-sample (cULXss), and we did the same with the bright ULX
candidates and the 5x40ss, building the complete bright ULX sub-sample
(cbULXss).

The effectiveness of this method is well illustrated in Figure 7, where the
bias towards brighter sources has been significantly downplayed by excluding
sources with $D>D_{\textrm{max}}$ from the sub-sample. As shown in Table 1,
these two sub-samples contain a reduced amount of 292 and 69 sources. The sub-
samples are also helpful in an additional way: due to the clustering nature of
ULXs, objects at $D>10$ Mpc are prone to blending due to the limited angular
resolution XMM-Newton. The cut at $D_{\textrm{max}}=29$ Mpc also helps in the
mitigation of this bias.

##  4 Results

###  4.1 Contents of the catalogue

Table 1:  Number of detections, sources, host galaxies, mean logarithm of
luminosity and median distance of each population sample with
$\verb||n_{G}alaxies|=1$. All objects with multiple possible host galaxies,
including seven ULX candidates, have been excluded from this table.

Sample | Dets. | Srcs. | Gals. | $\big{\langle}\log\left(\frac{L_{\textrm{X}}}{\textrm{erg\,s${}^{-1}$}}\right)\big{\rangle}$ | $\left[D\right]$
---|---|---|---|---|---
(Mpc)
Quality | 18 421 | 6 385 | 607 | 36.1 | 0.77
ULX | 1 439 | 772 | 515 | 39.6 | 28.4
B. ULX | 162 | 93 | 93 | 40.8 | 127
38ss | 9 594 | 3 290 | 56 | 34.8 | 0.05
39ss | 10 567 | 3 864 | 209 | 35.3 | 0.05
5x40ss | 10 959 | 4 148 | 452 | 35.7 | 0.06
cULXss | 680 | 292 | 146 | 39.2 | 17.5
cbULXss | 126 | 69 | 69 | 40.8 | 128

Table 2:  List with the number of detections, sources and galaxies with every
kind of ``CONT_FLAG— and $\verb||n_{G}alaxies|=1$. ``CONT_FLAG— is the flag
indicating whether a source is clean (“none”) or the catalogue or step where a
counterpart has been identified.

CONT_FLAG | Dets. | Srcs. | Gals.
---|---|---|---
Any | 49 816 | 23 120 | 2 700
“none” | 23 340 | 7 657 | 607
“none(PanSTARRS1)” | 75 | 63 | 60
“none(NED)” | 36 | 16 | 16
“central” | 3 514 | 2 173 | 2 170
“GaiaDR2” | 13 402 | 9 666 | 88
“Tycho2” | 96 | 11 | 6
“SDSS$\\_$DR14” | 140 | 33 | 24
“VeronQSO” | 135 | 38 | 11
“SIMBAD” | 5 745 | 2 224 | 65
“PanSTARRS1” | 2 980 | 1 119 | 136
“manual(PanSTARRS1)” | 10 | 9 | 3
“manual(NED)” | 38 | 26 | 25

Tables 1 and 2 summarise the content of our final catalogue. It contains 50
446 entries corresponding to 23 262 sources, out of which 23 120 are
associated with a single galaxy ($\verb|n_Galaxies|=1$). X-ray sources with
GaiaDR2 associations form the largest group of contaminants, containing a
41.8% of the total number of sources. This is due to the 8 437 matches with
objects of large optical to X-ray flux ratio, 8 270 of which are located in
the Magellanic Clouds and M31, indicating a possible stellar nature.
Nonetheless, none of them fall within the ULX X-ray luminosity regime so no
further regard is paid to them. Central objects follow it, constituting a 9.7%
of the catalogue. They are followed by a 8.4% of objects directly identified
as AGNs or QSOs in SDSS_DR14, VeronQSO and SIMBAD. A further 7.1% have been
identified as stars in GaiaDR2, Tycho2 or SIMBAD. Only 4.9% of the objects
have been selected as potential interlopers from the PanSTARRS1 survey, and a
residual fraction is constituted by supernovae, infrared sources, background
galaxies and a single ultraviolet source.

A total of 3 274 (14.1%) objects have been detected at least once within the
ULX regime ($L_{\textrm{X}}+\Delta L_{\textrm{X}}>10^{39}$ erg s-1). 2 208 of
these consist of central sources, and only 779 (3.3%) qualify as ULX
candidates according to the thresholds established in Sections 3.2 and 3.3,
and 287 are interlopers of various kind. 516 of the ULX candidates have been
detected at least once within the ULX regime with high certainty
($L_{\textrm{X}}-\Delta L_{\textrm{X}}>10^{39}$ erg s-1), and 666 with high
likelihood ($L_{\textrm{X}}>10^{39}$ erg s-1). 761 have at least a detection
well above the NS Eddington limit ($L_{\textrm{X}}-\Delta
L_{\textrm{X}}>1.8\times 10^{38}$ erg s-1). A sub-set of 94 also qualify as
bright ULX candidates ($L_{\textrm{X}}+\Delta L_{\textrm{X}}>5\times 10^{40}$
erg s-1), and all of them are the only candidate in their host galaxy. Among
some of the candidates we find well-known objects such as the NS-ULXs NGC 7793
P13, NGC 5907 ULX-1, the IMBH candidate NGC 2276-3c, and M51-ULS-1, host of
the extragalactic exoplanet candidate M51-ULS-1b (Di Stefano et al. 2020).
Other known objects such as the IMBH candidates M82 X-1 and ESO 243-49 HLX-1
also appear in the catalogue, but with $\verb|SC_SUM_FLAG|>1$.

Only sources and candidates with $\verb|n_Galaxies|=1$ are considered during
the remainder of this section due to their more reliable luminosities and host
galaxy associations.

###  4.2 ULX distribution and abundances

Figure 8: Distance distribution of all sources of quality, ULX candidates and
bright ULX candidates in our catalogue.

In Figure 8, it can be seen that all ULX candidates are found at $D>1$ Mpc,
while bright candidates are found at $D\gtrsim 20$ Mpc. Figure 8 also
illustrates how at $D\gtrsim 100$ Mpc the bright candidates dominate almost
completely due to the bias in favor of brighter sources as discussed in
Section 3.4 and possibly source blending. This is consistent with the
information in Table 1, which shows that median distances of ULX and bright
ULX candidates are 28.4 Mpc and 127 Mpc. Sources of quality cluster in the
nearest galaxies for the same reason. They constitute a population of low
luminosity X-ray sources the dominate the serendipitous X-ray sky in the local
group of galaxies. For instance, M31, M33 and the Magellanic Clouds alone
already concentrate a 17% of all sources of quality. The X-ray content of the
local group is not directly relevant to the ULX population, as it is mostly
composed of typical X-ray binaries and supernovae remnants and it has already
been thoroughly studied (e.g., Sturm et al. 2013; Pietsch 2008). Therefore,
during the raeminder of this paper we focus most of our attention to sources
hosted by galaxies at $D>1$ Mpc.

Figure 9: Hubble type distributions of the cULXss and cbULXss. In both cases,
there is a spike of ULXs in the earliest galaxies. Figure 10: Luminosity
distribution of the sources of quality and ULX candidates in our catalogue
divided by the morphological types of galaxies. A distance cut of $D>1$ Mpc
has been imposed to avoid contamination from the Local Group, heavily
dominated by low-luminosity sources. Figure 11: Distribution of ULX
frequencies (ULX/galaxy) in the cULXss and cbULXss across the morphological
sub-types of host galaxies. The ratios from the cbULXss have been multiplied
by 100 to aid visibility.

Table 3:  Number of ULX candidates in the cULXss and the cbULXss, number of
galaxies that host them, number of galaxies that could potentially hosted
them, ULX frequencies and fraction of galaxies with candidates, divided
according to morphological groups of their host galaxies. No distinction is
made between bright ULXs and their host galaxies as no more than one candidate
has been found in each.

Sample | cULXss
---|---
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
ULXs | 292 | 49 | 26 | 107 | 92 | 17
Galaxies | 149 | 18 | 16 | 53 | 45 | 13
Pot. gals. | 722 | 151 | 75 | 109 | 114 | 147
ULX / gal. | 0.40 | 0.32 | 0.35 | 0.98 | 0.81 | 0.12
Gal. frac. | 0.21 | 0.12 | 0.21 | 0.49 | 0.37 | 0.09
Sample | cbULXss
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
ULXs | 69 | 10 | 16 | 25 | 10 | 1
Pot. gals. | 12 166 | 2 174 | 1 787 | 2 052 | 1 416 | 850
ULX/gal.(%) | 0.57 | 0.46 | 0.90 | 1.22 | 0.71 | 0.12

Figure 12: Distribution of fraction of host galaxies in the cULXss and cbULXss
across their morphological sub-types. The values from cbULXss are shown in %
to aid visibility.

In Table 3, it can be seen that objects from the cULXss are most commonly
associated with early spiral galaxies (ESp., $0\leq T_{\textrm{H}}<5$) and
late spiral galaxies (LSp., $5\leq T_{\textrm{H}}<9$). However, while 68.1% of
the cULXss candidates are found in spiral galaxies, only for 50.7% of the
objects in cbULXss dataset holds the same trend. In Figure 9 it can be seen
how the overwhelming majority of cULXss objects concentrate in spiral galaxies
in the range of $0\leq T_{\textrm{H}}<10$, with a spike in elliptical galaxies
(El., $T_{\textrm{H}}<-3$). For cbULXss, the distribution is similar, but the
spike in elliptical galaxies is more prominent. In both samples, lenticular
galaxies (Le., $-3\leq T_{\textrm{H}}<0$) and irregular galaxies (Ir.,
$T_{\textrm{H}}\geq 9$) are unpreferred. Figure 10 also showcases that while
ULX candidates of quality are more frequently found in late-type galaxies
(LTG, $T_{\textrm{H}}\geq 0$) than in early-type galaxies (ETG,
$T_{\textrm{H}}<0$), this distinction becomes less clear for the bright ULX
candidates.

Besides looking how ULX candidates distribute themselves among galaxy
morphological types, it is also interesting to investigate the fraction of
galaxies that contain any ULX and general ULX frequencies in them. For this,
we build the set of all galaxies in the field of view that would be included
in the cULXss or cbULXss if hosting a ULX was not a requirement candidate,
with the extra condition of having $R1>3$” to exclude galaxies that can only
contain central sources. In essence, this consists of the baseline of galaxies
that would belong to the complete sub-samples if they host at least one ULX
candidate, including the ones that actually do. This is shown in Table 3 as
the line of potential galaxies, and it is used implicitly in Table 4 to
compute the mean SFR and $M_{*}$ values. In Table 3, it is seen that 21% of
galaxies in the cULXss contains at least a ULX candidate. For elliptical
galaxies, this gets reduced to 12%, for lenticular galaxies it is 21%, for
early spirals 49%, and for late spirals 37%. The general ULX rate is $\sim$0.4
ULX/galaxy, being the highest at $\sim$0.98 ULX/galaxy in early spiral
galaxies. Figures 11 and 12 show this results in more detail for the different
galaxy morphological sub-types, with ULX frequencies peaking at $\sim$1.8
ULX/galaxy for $T_{\textrm{H}}\sim 3$ galaxies, of which $\sim$70% host ULX
candidates. These results are mostly in agreement with earlier works, that
either found a larger fraction of ULXs in late-type galaxies (Swartz et al.
2011) or find higher abundances in late-type galaxies (Earnshaw et al. 2019;
Kovlakas et al. 2020). However, Kovlakas et al. (2020) find that the galaxies
with a higher chance of hosting ULXs are $T_{\textrm{H}}\sim 5$ spirals
instead.

Table 4:  Mean star-formation rate, stellar mass, and derived ULX rates for
all morphological types. Computed from all potential galaxies from which we
have SFR and $M_{*}$ information. As stated in Section 2.2, SFR information is
only taken into account for late-type galaxies.

Sample | cULXss
---|---
Subsample | El. | Le. | ESp. | LSp. | Ir.
$\langle\textrm{SFR}\rangle$ | … | … | 1.99 | 0.80 | 0.02
(M⊙yr-1)
$\langle M_{*}\rangle$ | 2.33 | 2.59 | 4.47 | 1.08 | 0.14
($10^{10}\,\textrm{M}_{\odot}$)
$\langle\textrm{sSFR}\rangle$ | … | … | 4.46 | 7.37 | 12.5
($10^{-11}\textrm{yr}^{-1}$)
$\langle\textrm{ULX / SFR}\rangle$ | … | … | 0.49 | 1.01 | 6.41
(M⊙yr-1)-1
$\langle\textrm{ULX / }M_{*}\rangle$ | 1.39 | 1.34 | 2.20 | 7.48 | 80.2
($10^{-11}\,\textrm{M}_{\odot}^{-1}$)
Sample | cbULXss
Subsample | El. | Le. | ESp. | LSp. | Ir.
$\langle\textrm{SFR}\rangle$ | … | … | 3.25 | 1.29 | 1.14
(M⊙yr-1)
$\langle M_{*}\rangle$ | 5.26 | 5.63 | 5.31 | 1.32 | 2.73
($10^{10}\,\textrm{M}_{\odot}$)
$\langle\textrm{sSFR}\rangle$ | … | … | 6.12 | 9.80 | 4.19
($10^{-11}\textrm{yr}^{-1}$) |  |  |  |  |
$\langle\textrm{ULX / SFR}\rangle$ | … | … | 3.75 | 5.46 | 1.03
$10^{-3}$(M⊙yr-1)-1
$\langle\textrm{ULX / }M_{*}\rangle$ | 0.87 | 1.59 | 2.29 | 5.34 | 0.43
($10^{-13}\,\textrm{M}_{\odot}^{-1}$)

To find an explanation for these distributions, we need to explore the
properties of host galaxies, and more particularly, the relationship between
ULX abundances, host SFR and $M_{*}$. In spiral galaxies with high specific-
SFR ($\textrm{sSFR}=\textrm{SFR}/M_{*}$) values and that are dominated by
young stellar populations, the ULX population is typically associated with
HMXB evolution timescales of $\tau\sim$100 Myr (Wiktorowicz et al. 2017), the
rates of which are expected to scale with the SFR. In Table 4, we show how
spiral galaxies are typically the ones with the highest SFR as given in
HECATE. Early spirals have higher SFR absolute values than late spirals
($\sim$1.99 vs. $\sim$0.80 M⊙yr-1), but as expected they also have lower sSFR
values. Despite ULXs being more abundant in early spirals, the ULX frequency
per SFR is higher in late spirals ($\sim$0.49 vs. $\sim$1.01 /M⊙yr-1). The
same holds for the number of ULXs per unit mass, and the whole picture is
exagerated for irregular galaxies. Figure 13 also shows the distribution of
individual ULX/SFR galaxy rates for early and late spiral galaxies in the
cULXss that contain available SFR information. The distributions peak at
$\sim$0.65 and $\sim$0.55 ULX/M⊙yr-1each. Wiktorowicz et al. (2017) predict
the existence of 400 ULXs in a galaxy with solar metallicity, $M_{*}=6\times
10^{10}$ M⊙ and $\textrm{SFR}=600$ M⊙yr-1 for a period of 100 Myr, which
implies 0.67 ULX/M⊙yr-1, staying well withing the range of values provided by
our sample.

Figure 13: Histogram of the number of ULX per star-formation rate for early
and late spiral galaxies from the cULXss. Extremely large numbers on the right
are due to lucky galaxies with low SFR that host a single ULX have been cut
out of the picture. Figure 14: Hardness ratio dispersion of the cULXss and
cbULXss against the abundance contours of some populations of interlopers.
AGNs and QSO include both confirmed and candidates. Only hardness ratios with
uncertainties lower than 0.2 are accepted for the sake of reliability. Figure
15: Hardness ratio dispersion of the cULXss and cbULXss against the abundance
contours of lower luminosity objects present in the 38ss. Only hardness ratios
with uncertainties lower than 0.2 are accepted for the sake of reliability.

For elliptical and lenticular galaxies, different considerations need to be
applied. As expressed by Wiktorowicz et al. (2017), there is a ULX
subpopulation constituted by LMXBs that reach Roche-lobe overflow at
$\tau\sim$1 Gyr instead. This evolutionary path explains the presence of ULXs
in galaxies with very low star-forming activity. In this case, ULX frequencies
are not expected to depend significantly on their current SFR values.

On the other hand, objects in the cbULXss provide a slightly different
picture. All of them are located only beyond the $D\gtrsim 20$ Mpc mark and
have extremely low abundances, as shown in Table 3. Additionally, all of them
are the only bright ULX candidate in their host galaxies. From Wiktorowicz et
al. (2017) predictions the rate of objects with $L_{\textbf{X}}>10^{40}$ erg
s-1, we estimate a typical number of 0.023 bright ULX/M⊙yr-1 in star-forming
galaxies. However, we only measure 0.0037 bright ULX/M⊙yr-1 and 0.0055 bright
ULX/M⊙yr-1 for early and late-spiral galaxies, which is an order of magnitude
smaller. This can be partially explained by considering that our luminosity
cut of $L_{\textbf{X}}>5\times 10^{40}$ erg s-1 is slightly higher, and that
the ULX luminosity distribution in general decays exponentially in most
studies. Nonetheless, a total number of only 36 objects are involved in these
estimations. A larger sample will need to be used in the future to extract
more reliable conclusions.

###  4.3 Spectral properties of the catalogue

The X-ray spectral properties of the identified sources can provide additional
information regarding their nature (e.g., Earnshaw et al. 2019; Walton et al.
2011). Therefore, we proceed with the inspection of the spectral properties of
the objects within our catalogue. We use the hardness ratios, defined as

$\textrm{HR}_{i}=\frac{R_{i+1}-R_{i}}{R_{i+1}+R_{i}}\textrm{,}$ (3)

where $R_{i}$ and $R_{i+1}$ are the count rates in the consecutive energy
bands $i$ and $i+1$. With this quantity, spectral hardness can be compared
between different sources, with larger values indicating harder sources. The
4XMM-DR9 computes the source $HR_{i}$ values from the count rates in the
0.2–0.5; 0.5–1.0; 1.0–2.0; 2.0–4.5; and 4.5–12.0 keV energy bands, both from
the PN and MOS cameras101010http://xmmssc.irap.omp.eu/, $HR_{1}$ and $HR_{2}$
being the most useful thanks to larger photon counts in lower energy bands.

Table 5:  Number of objects in every contaminant population and their mean
hardness ratios. Contaminants found in the PanSTARRS1 survey and extragalactic
objects from PanSTARRS1 are not included due to the diverse nature of the
matched objects.

OBJ$\\_$TYPE | # | $\langle\textrm{HR}_{\textrm{1}}\rangle$ | $\langle\textrm{HR}_{\textrm{2}}\rangle$ | $\langle\textrm{HR}_{\textrm{3}}\rangle$ | $\langle\textrm{HR}_{\textrm{4}}\rangle$
---|---|---|---|---|---
“central” | 2 246 | 0.52 |  $-$0.10 |  $-$0.34 |  $-$0.18
Stellar objs. | 1 652 | 0.45 |  $-$0.05 |  $-$0.47 |  $-$0.10
“AGN” | 320 | 0.61 | 0.43 |  $-$0.21 |  $-$0.26
“AGN$\\_$Cand.” | 1 416 | 0.59 | 0.52 |  $-$0.20 |  $-$0.23
“QSO” | 126 | 0.47 | 0.24 |  $-$0.26 |  $-$0.29
“QSO$\\_$Cand.” | 81 | 0.55 | 0.31 |  $-$0.30 |  $-$0.25
“SN” | 27 | 0.66 | 0.07 |  $-$0.28 |  $-$0.28
“IrS” | 17 | 0.42 | 0.13 |  $-$0.53 |  $-$0.20
“back. galaxy” | 9 | 0.41 |  $-$0.08 |  $-$0.01 |  $-$0.32

Table 6:  Mean hardness ratios of the cULXss and cbULXss populations, broken down according to the morphological type of host galaxies. Population | Size | $\langle\textrm{HR}_{1}\rangle$ | $\langle\textrm{HR}_{2}\rangle$ | $\langle\textrm{HR}_{3}\rangle$ | $\langle\textrm{HR}_{4}\rangle$
---|---|---|---|---|---
cULXss | 292 | 0.51 | 0.24 |  $-$0.25 |  $-$0.50
El.-hosted | 49 | 0.42 | 0.09 |  $-$0.27 |  $-$0.46
Le.-hosted | 27 | 0.36 | 0.13 |  $-$0.24 |  $-$0.40
ESp.-hosted | 107 | 0.49 | 0.24 |  $-$0.25 |  $-$0.52
LSp.-hosted | 92 | 0.61 | 0.36 |  $-$0.24 |  $-$0.53
Ir.-hosted | 17 | 0.53 | 0.21 |  $-$0.35 |  $-$0.52
cbULXss | 69 | 0.43 | 0.19 |  $-$0.32 |  $-$0.23
El.-hosted | 10 | 0.41 | 0.06 |  $-$0.49 |  $-$0.39
Le.-hosted | 16 | 0.43 | 0.10 |  $-$0.33 |  $-$0.24
ESp.-hosted | 25 | 0.47 | 0.23 |  $-$0.24 |  $-$0.19
LSp.-hosted | 10 | 0.43 | 0.35 |  $-$0.21 |  $-$0.22
Ir.-hosted | 1 | 0.98 | 0.29 |  $-$0.30 | 0.34

From Table 5, it is apparent how the contaminant populations have distinct
average spectral properties from each other. Supernovae, AGNs and QSOs tend to
be the objects with the hardest spectra. Comparing the interloper hardness
ratios with the ULX hardness ratios shown in Table 6 also shows that, as
expected, the typical spectra of the ULX population is also distinct from
those of stellar objects, supernovae, infrared sources and nuclear sources,
while bearing the closest spectral resemblance with the AGN and QSO
population. These properties are made more apparent by looking at Figure 14,
where it can be seen that AGNs and QSOs cluster around slightly harder spectra
than the cULXss and cbULXss samples, and that there is even less overlap of
the ULX population with the supernovae and the stellar populations. Figure 15
shows that the ULX population has the largest resemblance with the population
of X-ray objects just below the ULX luminosity threshold
($10^{38}<L_{\textrm{X}}<10^{39}$ erg s-1).

There are also noteworthy spectral differences within the ULX sample itself.
Table 6 showcases a division between ULX spectral properties in relation to
their host galaxies for the cULXss. ULXs in lenticular and elliptical galaxies
present the softest spectra in all cases both for faint and bright ULXs. The
hardest spectra are found in late spiral galaxies. This notion is clearer in
Figure 16, where the hardness ratio distributions of ULXs in late-type and
early-type galaxies are seen to cluster around slightly different values.
However, Figure 17 illustrates that the data comes with a large dispersion,
despite the increasing trends in the values of $HR_{1}$ and $HR_{2}$ with the
morphological sub-class of each galaxy up until the irregulars ones. This
information points towards photoelectric absorption being higher in spiral
galaxies than in elliptical and irregular ones, in agreement with what has
been reported in previous XMM-Newton-based works (Walton et al. 2011; Earnshaw
et al. 2019), but also towards unaccounted factors being more determinant for
the individual properties of ULXs.

Figure 16: Hardness ratio versus luminosity dispersion of objects in the
cULXss (squares) and cbULXss (diamonds), distinguishing between objects hosted
in late type galaxies ($T_{\textrm{H}}\geq 0$) and early type galaxies
($T_{\textrm{H}}<0$). Density contours have been drawn to help the eye. Only
hardness ratios with uncertainties lower than 0.2 are accepted for the sake of
reliability. Figure 17: $HR_{1}$ and $HR_{2}$ across morphological sub-types
of host galaxies from cULXss sources. The error bars show the $1\sigma$
dispersion of the values, and the data points have been slightly moved to the
left ($HR_{1}$) and to the right ($HR_{2}$) to ease visibility.

Looking back at Table 6, a division between the cULXss and cbULXss is seen. On
average, low-luminosity ULXs present harder spectra than the bright ones. It
is unlikely that the host galaxies of bright ULX candidates contain less
neutral gas in general, and perhaps their generally softer spectra is an
indication of bright candidates having a different physical nature than the
rest of the candidates. Their emission could also result from the blending of
lower-luminosity sources as suggested in Section 5.1.3, perhaps with the
diffuse emission of the stellar population in the host galaxies. Within the
cbULXss itself, it also appears that candidates in late-type galaxies cluster
around slightly harder spectra than those hosted by early-type galaxies, as
showcased in Figure 16.

Table 7: Top: mean value of $\verb||VAR_{P}ROB|$ from 4XMM-DR9s according to
morphology groups. Middle: occupancy of variability of cULXss and cbULXss
variability bins (in % and as defined in Section 4.4), also split according to
the morphological type of their host galaxy. Bottom: mean max to min flux
ratio of sources in each variability bin and morphology group.

Sample |  cULXss-DR9s
---|---
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
Size | 147 | 28 | 15 | 51 | 49 | 4
$\langle\verb|VAR_PROB|\rangle$ | 0.18 | 0.23 | 0.26 | 0.20 | 0.10 | 0.22
1$\sigma$ constant | 8.8 | 3.6 | 20.0 | 13.7 | 4.1 | 0.0
uncertain | 15.0 | 28.5 | 13.4 | 9.9 | 8.2 | 50
1$\sigma$ variable | 15.6 | 21.4 | 13.3 | 19.6 | 12.2 | 0.0
2$\sigma$ variable | 15.0 | 17.9 | 20.0 | 13.7 | 14.3 | 0.0
3$\sigma$ variable | 45.6 | 28.6 | 33.3 | 43.1 | 61.2 | 50
$\langle\verb|FRATIO|\rangle$ | 72.8 | 6.02 | 4.10 | 26.4 | 186.1 | 2.42
1$\sigma$ constant | 1.57 | 1.31 | 1.69 | 1.57 | 1.53 | …
1$\sigma$ variable | 11.4 | 2.04 | 2.50 | 22.82 | 3.51 | …
2$\sigma$ variable | 10.0 | 4.39 | 6.24 | 3.04 | 2.61 | …
3$\sigma$ variable | 151.6 | 14.5 | 5.87 | 48.8 | 297.7 | 3.70
Sample |  cbULXss-DR9s
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
Size | 31 | 3 | 8 | 12 | 7 | 0
$\langle\verb|VAR_PROB|\rangle$ | 0.42 | 0.41 | 0.35 | 0.39 | 0.49 | …
1$\sigma$ constant | 22.6 | 0.0 | 12.5 | 25.0 | 28.6 | …
uncertain | 35.4 | 66.7 | 37.5 | 41.7 | 42.8 | …
1$\sigma$ variable | 19.4 | 33.3 | 25.0 | 25.0 | 0.0 | …
2$\sigma$ variable | 3.2 | 0.0 | 0.0 | 8.3 | 0.0 | …
3$\sigma$ variable | 19.4 | 0.0 | 25.0 | 16.7 | 28.6 | …
$\langle\verb|FRATIO|\rangle$ | 11.6 | 2.90 | 3.29 | 2.40 | 42.0 | …
1$\sigma$ constant | 1.95 | … | 5.55 | 1.53 | 1.09 | …
1$\sigma$ variable | 1.85 | 1.90 | 1.42 | 2.12 | … | …
2$\sigma$ variable | 4.31 | … | 4.31 | … | … | …
3$\sigma$ variable | 49.92 | … | 4.57 | 3.67 | 141.5 | …

###  4.4 ULX variability

The differences between ULX candidates also extend to their variability
properties. We find 147 cULXss counterparts within three times their
positional uncertainty in the 4XMM-DR9s objects with `N_CONTRIB`$\geq 2$. This
number goes down to 31 for the cbULXss sample. For all these sources, we have
at our disposal variability information drawn from their multiple overlapping
observations (Traulsen et al. 2019).

We pay attention to `VAR_PROB`, which is the probability of a source not
showcasing variability between observations, in essence a long-term
variability measure. For our analysis, we put the ULX candidates in five bins
of variability probability. We consider objects with
$\verb|VAR_PROB|>1\sigma$, where $1\sigma=0.6827$, to be most likely constant,
and we collect them in the $1\sigma$ constant bin. Thereafter, we collect the
objects with $1-1\sigma>\verb|VAR_PROB|>1-2\sigma$,
$1-2\sigma>\verb|VAR_PROB|>1-3\sigma$ and $\verb|VAR_PROB|<1-3\sigma$ into the
$1\sigma$ variable, $2\sigma$ variable and $3\sigma$ variable bins, being
$2\sigma=0.9545$ and $2\sigma=0.9973$, while the remainder are left in the
uncertain variability bin. We also register the `FRATIO` values of the matched
objects, which corresponds to the ratio between the highest and the lowest
detected flux from a source, and compute the average value not only for every
population time, but also for every `VAR_PROB` bin.

The results are shown in Table 7, where it is seen that ULX candidates from
the cULXss hosted by late spiral galaxies are the ones with the highest
chances of showcasing variability between different observations, as they
present the lowest `VAR_PROB` average value, with 61.2% of them belonging to
the $3\sigma$ variable bin, and only 4.1% staying in the $1\sigma$ constant
bin. In contrast, ULX candidates in lenticular galaxies present the highest
`VAR_PROB` average value, falling 20% of them in the $1\sigma$ constant bin.
However, the ULX population with the lowest fraction of candidates in the
$3\sigma$ variable bin is that of candidates hosted by elliptical galaxies.
ULX candidates hosted by early spiral galaxies present in-between variability
properties.

Regarding the measured variation of fluxes between different observations,
Table 7 also shows that for ULX candidates hosted by late-spiral galaxies the
`FRATIO` values span two orders of magnitude on average, both for the entire
set and for those in the $3\sigma$ variable bin. ULX candidates in lenticular
galaxies showcase the lowest `FRATIO` values, being followed by those in
elliptical galaxies. In these cases, the inter-observation luminosity
variability does not go beyond one order of magnitude. ULXs in early spiral
galaxies repeat their role as a bridge between late spiral-hosted and
lenticular-hosted candidates. Not surprisingly, candidates in the $1\sigma$
constant present average values of `FRATIO` of the order of 1 regardless of
their host galaxy, and while some exceptions are seen, in general `FRATIO`
increases with decreasing `VAR_PROB`.

Some trends are similar for the cbULXss. As shown in the lower half of Table
7, bright candidates hosted by late spiral galaxies are the ones with the
highest fraction of ULX candidates in the $3\sigma$ variable bin, and they
once again have the highest `FRATIO` average values. However, they are
typically less variable than their cULXss counterparts, as seen from the
higher `VAR_PROB` and lower `FRATIO` average values for the entire set of
candidates regardless of the morphological type of the host galaxy. The
difference between early spiral-hosted and elliptical and lenticular-hosted
ULXs is also blurrier, most likely due to the small size of the available
sample.

It should be noted that despite these general trends, the dispersion in the
`VAR_PROB` and `FRATIO` values is too large to find a reliable dependence on
other quantities. It is tempting to propose a relationship between variability
and the star-formation activity in galaxies, as coincidentally late spiral
galaxies also present the highest sSFR -excluding irregular galaxies from the
analysis-, while both elliptical and lenticular galaxies present the lowest
sSFR values. Despite these hints towards a correlation between variability and
galaxy morphology, a further investigation of the variability properties in
the context of the sSFR of the galaxies does not show and clear correlation.

Sutton et al. (2013) make a thorough study of the variability of several ULXs,
stating that an increase of X-ray brightness usually goes in hand with a
softening of the source due to accretion winds narrowing their funnel shape
and obscuring the inner regions of the disc. We do not find such relationship
for many cases either. For instance, NGC 5204 X-1, one of their notable
examples, which appears in our cULXss with $\langle
L_{\textbf{X}}\rangle=(6.0\pm 0.6)\times 10^{39}$ erg s-1, presents the
complete opposite behaviour within XMM-Newton observations, ranging from
$HR_{1}\approx 0.33$ and $HR_{2}\approx-0.02$ at $L_{\textbf{X}}\approx
5\times 10^{39}$ erg s-1 to $HR_{1}\approx 0.42$ and $HR_{2}\approx 0.02$ at
$L_{\textbf{X}}\approx 8\times 10^{39}$ erg s-1. Nonetheless, the methodology
and sample data in both of our studies diverge greatly, and in particular our
methodology is rather limited when making statements about individual sources.

##  5 Discussion

###  5.1 Limitations of our study

####  5.1.1 Limitations of the catalogue

With 779 identified candidates, our catalogue provides the largest ULX sample
built up to date, only followed by the 629 candidates provided by the Chandra-
based catalogue from Kovlakas et al. (2020), the 470 candidates from Walton et
al. (2011) and the 394 candidates from Earnshaw et al. (2019). Out of these,
94 are bright candidates with at least one detection with
$L_{\textrm{X}}>5\times 10^{40}$ erg s-1. However, to avoid biases towards
bright sources and blending, we have only considered the 292 candidates from
the cULXss and 62 from the cbULXss for the population study. While these
ensures the veracity of our results for the whole cULXss, the small size of
the cbULXss hampers or study severely. In addition, as suggested in Section
5.2.2, the bright population is more prone to contamination by unidentified
interlopers and source blending, hampering the study even further.

Aside from the manual inspection of sources during the filtering process, our
study is also strictly limited to collective properties of our samples. Both
the interloper and the ULX populations are very heterogeneous samples at the
astrophysical level, and while some trends are identified, individual objects
can differ greatly from each other. For instance, we have identified in
Section 4.3 that ULX candidates in late-type galaxies tend to present harder
spectra than those hosted by early-type ones. However, it is postulated that
ULXs present hard or soft states depending on the viewing angle and the
accretion rate (Sutton et al. 2013), and therefore it is most likely that the
spectrum of an individual ULX is determined by these factors rather than the
morphological type of their host galaxy.

From a population study such as the one presented in this work we cannot draw
any conclusions on the nature of individual sources. However, we can make
connections between their collective properties (spectra, variability,
frequency) and the nature of the stellar populations in their host galaxy. For
example we see a trend for ULXs in late type spirals to exhibit harder
colours, indicative of more significant photoelectric absorption. However, we
do not see any trend of variability indicators with the sSFR. The variability
and spectral trends can be better addressed with systematic observations of
individual objects (e.g., Sutton et al. 2013; Kaaret et al. 2017).

####  5.1.2 Selection biases

Figure 18: Hubble Type distributions of galaxies with listed values in HECATE,
within 4XMM-DR9 XMM-Newton observation, and within our catalogue. Portions
have been normalized to sum 1 in all of the three cases due to the large
differences in size between the sets.

The main limitation of 4XMM-DR9 is that it has been constructed from
observations of galaxies or clusters that were already of interest to
astronomers. As such, the characteristics of our ULX sample may deviate from
those of the true ULX population. In Figure 18 it is seen how galaxies
included in 4XMM-DR9 present a slight bias towards earlier types with respect
to the full content of HECATE. This bias only gets amplified further down in
the construction of our catalogue.

Choosing to use 4XMM-DR9 as the only X-ray catalogue of reference also limits
the size of the X-ray sample available to us, as it covers only 2.7% of the
sky area and 8.6% of all the galaxies present in HECATE. Fortunately, other
recent works such as Kovlakas et al. (2020) already take into account non-
overlapping parts of the sky, despite suffering the same bias as our own
individually.

The most straightforward way to tackle these issues is studying the ULX
content of blind all-sky surveys. A good opportunity for this is presented by
the still on-going eROSITA All Sky Survey (eRASS, Merloni et al. 2012; Predehl
et al. 2021). By observing the entirety of the sky instead of selected
patches, and accounting for the average eROSITA’s sensitivity at the end of
the eRASS, we assess that around $\sim$1 000 new ULX candidates will be
discovered in HECATE galaxies of at least 20″ in size, eROSITA’s FWHM angular
resolution.

Further biases in our catalogue stem from our own design choices, particularly
from focusing our studies on source parameters. Occasionally, quality flags
and some measures of source parameters pick the worst possible value among all
the values assigned to individual detections. This means that if a legitimate
source has one detection with $\verb|SUM_FLAG|>1$, this bad value percolates
into `SC_SUM_FLAG` and renders the source non-elegible for being a ULX
candidate under our criteria. Ignoring the `SC_SUM_FLAG` value, the total
number of ULX candidates of quality raises to 847. This choice was made
because a bad detection often leads to unreliable source parameters, but the
true extent of this effect would need to be checked individually for every
source. Nonetheless, all of these sources are still available to the user of
the catalogue.

Another bias we introduced was the selection of point-like sources with high
detection likelihood, which excluded 1 632 sources within HECATE’s isophotal
ellipses, in contrast to the 23 262 accepted ones in the final catalogue.
However, we do think that the exclusion of extended sources is justified, as
ULXs are point-like sources.

####  5.1.3 XMM-Newton’s angular resolution

XMM-Newton’s angular resolution is a limiting factor due to the clustered
nature of star-forming regions, where ULXs tend to be found (Anastasopoulou et
al. 2016; Kaaret et al. 2017). H II regions, both Galactic and extragalactic,
present sizes of the order of $\sim$10 pc, at most $\sim$100 pc (Tremblin et
al. 2014; Hunt & Hirashita 2009, plus references within). This implies that we
should expect contamination from neighbouring X-ray sources in the ULX
candidate parameters at $D\gtrsim 10$ Mpc, or even blending of sources into
spurious ULX candidates. The cULXss deals with this issue by establishing the
cut at $D_{\textrm{max}}\approx 29$ Mpc, so we expect little contamination to
our ULX population study. However, it is very telling how at
$D_{\textrm{max}}\gtrsim 100$ Mpc the bright ULX candidates completely
overtake over the normal ULX candidates. In fact, it was explicitly revealed
in Section 3.3 that some of the manually investigated objects suffer from this
problem.

Other catalogues built with higher resolution telescopes such as Chandra are
able to partially alleviate this problem, but the issue reappears at $D\gtrsim
40$ Mpc as stated in Kovlakas et al. (2020). Individual follow up of
interesting sources is once again essential to untangle the nature of these
objects.

###  5.2 Discussion of contents

####  5.2.1 Candidates at the ULX luminosity threshold

As expressed in Section 3.2, our criterion for the selection of ULX candidates
consists on whether a source has at least one detection whose luminosity is
above this established threshold within its 1$\sigma$ uncertainty. This rather
lax condition allows for typically low-luminosity sources to be considered ULX
candidates. However, it manages to take into account that some variable
objects may behave as ULXs with intermittence, and also NS-ULXs close to the
Eddington limit. It is noteworthy that, while in this work we have used the
common definition for the ULX luminosity threshold in the literature
($L_{\textrm{X}}>10^{39}$ erg s-1, Kaaret et al. 2017), this is but an
approximation, as the Eddington limit lies at $1.8\times 10^{38}$ erg s-1 for
a 1.4 M⊙ NS, and at $1.3\times 10^{39}$ erg s-1 for a 10 M⊙ StBH.

As exposed in Section 4.1, only 516 of our ULX candidates have at least one
detection with luminosity above the ULX threshold with more than 1$\sigma$
significance ($L_{\textrm{X}}-\Delta L_{\textrm{X}}>10^{39}$ erg s-1) and 150
other are also likely to be ULXs within their 1$\sigma$ uncertainty
($L_{\textrm{X}}>10^{39}$ erg s-1). This leaves the catalogue with 106 objects
that qualify as ULXs only within the uncertainty of their brightest detection
($L_{\textrm{X}}<10^{39}$ erg s-1), and are therefore unlikely to be ULXs
according to the common definition. However, from this sub-set, a vast
majority of 95 have been observed with luminosity well above that of the
Eddington limit for a 1.4 M⊙ accreting NS ($L_{\textrm{X}}-\Delta
L_{\textrm{X}}>1.8\times 10^{39}$ erg s-1), and therefore still deserve a
place in this catalogue in consideration of a more comprehensive ULX
luminosity threshold.

####  5.2.2 The most luminous ULXs

Table 8: The number of ULX candidates according to their host galaxies, the number of background (AGNs, QSOs and background galaxies) and foreground (stellar objects) that would otherwise qualify as ULX candidates, and the total fraction that they would constitute if added to the total. Sample | ULX candidates of quality
---|---
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
Candidates | 779 | 141 | 129 | 261 | 195 | 28
Background | 74 | 15 | 13 | 25 | 12 | 3
Foreground | 27 | 6 | 3 | 13 | 1 | 1
Total (%) | 11.5 | 13.0 | 11.0 | 22.8 | 6.25 | 12.5
Sample | Bright ULX candidates of quality
Subsample | All | El. | Le. | ESp. | LSp. | Ir.
Candidates | 94 | 15 | 20 | 33 | 14 | 2
Background | 29 | 7 | 7 | 9 | 3 | 1
Foreground | 5 | 2 | 0 | 2 | 0 | 0
Total (%) | 26.6 | 37.8 | 26.9 | 25.0 | 17.6 | 33.3

Our catalogue contains 94 ULX candidates of quality with at least one
detection with luminosity $L_{\textrm{X}}>5\times 10^{40}$ erg s-1. This
sample extends to 109 sources in total if we ignore the `SC_SUM_FLAG` column.
It has been exposed in sections 4.3 and 4.4 that, while the cbULXss follows
the same trends as the cULXss, in the sense that candidates hosted by late-
type galaxies tend to present harder spectra and higher variability, it is
also true that the bright candidates have generally softer spectra and more
subdued variability than their cULXss cousins.

A tempting way to explain this difference is to invoke different physical
origins of the sources. Hyperluminous X-ray sources (HLXs) are ULXs with
$L_{\textbf{X}}>10^{41}$ erg s-1, and albeit the community has recently
shifted towards explaining them as typical X-ray binaries at the high end of
the luminosity distribution due to very large mass transfer rates (Bachetti
2016; Wiktorowicz et al. 2017), they are still the best objects where to look
for BHs of large masses or even IMBH (Kaaret et al. 2017). This search is
further motivated by first direct confirmation ever of an IMBH in the
gravitational wave event GW190521 (Abbott et al. 2020a, b), which is proof
that black holes of these masses do exist and are to be considered as
potential explanation for the brightest ULXs. Indeed, a handful of ULXs such
as ESO 243-49 HLX-1 (Kong et al. 2007; Servillat et al. 2011; Pasham et al.
2014) or NGC 2276-3c (Mezcua et al. 2015) constitute promising candidates of
being such kind of objects, the three of which are included in our catalogue.

In our catalogue, ESO 243-49 HLX-1 corresponds to source
$\verb|SRCID|=202045402010003$, and has an average luminosity of $\langle
L_{\textrm{X}}\rangle=(1.7\pm 0.4)\times 10^{41}$ erg s-1 and a maximum
luminosity of $L_{\textrm{X}}=(7\pm 2)\times 10^{41}$ erg s-1. Unfortunately,
one of its 6 detections has $\verb|SUM_FLAG|=3$, so it has not been included
among our list of ULX candidates of quality. M82 X-1 also has been recovered
as source $\verb|SRCID|=201122902010001$, but this source is actually a blend
of M82 X-1 and the NS-ULX M82 X-2 (Bachetti et al. 2014), as they are only
0.52” apart and are therefore undistinguishable by XMM-Newton. Furthermore,
this source also has one detection with $\verb|SUM_FLAG|=3$. NGC 2276-3c
($\verb|SRCID|=200223402010005$) is the only one of these three that appears
as bright ULX of quality. However, it is also constituted by three blended
sources in XMM-Newton, one of them being a genuine IMBH candidate (Mezcua et
al. 2015).

Nonetheless, it is known that not all bright ULX candidates need to explained
with the presence of accreting massive BHs. For example, another object
classified as a bright ULX candidate in our catalogue is NGC 5907 ULX-1, a NS-
ULX that has been observed with a luminosity of $L_{\textbf{X}}>10^{41}$ erg
s-1 (Israel et al. 2017).

This implies that there must be other explanations to their different
properties. Many times, the reason may not even be astrophysical. As seen in
Figure 8, almost all of them are located at $D\gtrsim 20$ Mpc, which makes
them prone to be the result of blending of several low-luminosity sources. The
fact that they are always found alone in their host galaxies is also a strong
indication of source blending. This is reinforced by the explicit detection of
confused sources among manually inspected objects in Section 3.3. It is also
possible that there is significant contribution of unidentified nuclear
sources to our cbULXss. Table 5.2.3 showcases well that indeed a much larger
share of background interlopers was identified for the bright ULX candidates
than in the general sample, implying that the unidentified contaminants may
also constitute a larger fraction.

Nonetheless, from the forementioned objects, only NGC 2276-3c has $\langle
L_{\textrm{X}}\rangle>10^{41}$ erg s-1. In fact, only 25 belong to the HLX
class within their uncertainty, 33 if `SC_SUM_FLAG` is ignored. From the ones
with $\verb|SC_SUM_FLAG|\leq 1$, only three match with the brightest objects
from Earnshaw et al. (2019), the study closest to ours in terms of sampling
and methodology. NGC 4077, IC 4596 are included in Earnshaw et al. (2019) as
potential IMBH candidates. IC 4320 also appears in Earnshaw et al. (2019) and
Walton et al. (2011), and has already been investigated by Sutton et al.
(2012). The remaining 22 do not seem to appear in previous literature as far
as we are aware. If extended to other objects qualified as contaminants, we
also find a match for the HLXs in IC 4252 and UGC 6697. The former is
considered a central source in our catalogue, while the later holds
$\verb|n_Galaxies|=2$, $\verb|SC_SUM_FLAG|=2$ and has been identified with a
PanSTARRS1 counterpart, and therefore they do not meet the requirements for
being considered sources of quality. Others also included in Earnshaw et al.
(2019), such as UGC 1934 and NGC 2276 belong to our bright ULX candidates, but
do not qualify as HLXs.

####  5.2.3 Effectiveness of the filtering pipeline

In Table 5, it is seen that a total number of 1 943 sources are matched with
AGNs, QSOs or background galaxies, and 1 652 with stellar objects.
Additionally, in Table 8 it is shown that 74 and 27 objects in each of this
group would have been classified as ULX candidates if it were not for their
association. In total, they would constitute a 11.5% of the ULX candidates of
quality. It is therefore natural to ask whether the exclusion of these objects
is accurate or, in the opposite end, sufficient at all.

In the case of stellar objects, we have applied extra conditions besides
positional coincidences based on the X-ray flux and optical magnitude of the
sources. This way, an X-ray source was only classified as a stellar
contaminant if the stellar counterpart was bright enough to explain the X-ray
emission or, in the least, to contaminate it significantly. And in any case,
most of the objects classified as stellar contaminants concentrate in nearby
galaxies devoid of ULX candidates. Nonetheless, the best way to attest the
efficacy of the pipeline is comparing the spectral values of ULXs to those of
stellar contaminants, shown in Tables 6 and 5, and seeing that the set of
cULXss and the cbULXss have distinguishable spectral properties with respect
to the bulk of stellar objects.

On the other hand, the direct exclusion of any X-ray source coincident with a
known QSO, AGN or SDSS-DR14 source may rise more concerns. To inspect whether
this method has overshot or has been insufficient, we can compare the number
of expected background sources to the identified ones. In Section 3.1 it is
mentioned that around 670 background contaminants are expected in the set of
galaxies at $D>1$ Mpc from the $\log{N}-\log{S}$ presented in Mateos et al.
(2008), which is equal to ${\sim}12$% of sources at that distance if it were
to be true. In our catalogue, 538 sources at $D>1$ Mpc (${\sim}10$%) were
actually classified as possible QSOs or AGNs. Therefore, it can be concluded
that our filtering pipeline does a good job on identifying most of the
background contaminants, but also that ${\sim}2$% of our sources may still be
unidentified background sources, including ${\sim}15$ ULX candidates.

###  5.3 Comparison with other works

####  5.3.1 Previous serendipitous XMM-Newton ULX catalogues

Earnshaw et al. (2019) followed much of the methodology presented in Walton et
al. (2011). As Walton et al. (2011) build their sample from 2XMM-
DR1111111http://xmmssc.irap.omp.eu and Earnshaw et al. (2019) from 3XMM-DR4,
their work can be seen as an update on the ULX content of the XMM-Newton
survey. Likewise, our work can also be seen as an update on the ULX content of
XMM-Newton with respect to Earnshaw et al. (2019). Therefore, we expect to
recover most of the ULX candidates presented their work and Walton et al.
(2011).

A source positional cross-match recovers 1 008 sources from the total of the 1
314 listed in Earnshaw et al. (2019). As Earnshaw et al. (2019) do not
preserve sources classified as interlopers, 80% of the matches correspond to
clean sources in our side. The remaining sources consist of 11 central
sources, 30 stars, one QSO, 16 SIMBAD objects, 21 objects from GaiaDR2, 32
extragalactic objects from PanSTARRS1 and 1 background galaxy manually
identified in NED.

Figure 19: Dispersion of the HECATE to RC3 semi-major and semi-minor axes
ratios for galaxies appearing in both this work and Earnshaw et al. (2019),
respectively. Figure 20: TOPCAT astrometric maps of sources found in galaxies
NGC 4649 (large ellipses at the center) and NGC 4647 (smaller ellipses at the
upper right). The isophotal ellipses from RC3 and HECATE are also shown.

The missing 306 sources are explained mostly by the HECATE updates to the
isophotal radii of galaxies with respect to RC3 (de Vaucouleurs et al. 1991;
Corwin et al. 1994) used in Earnshaw et al. (2019). Figure 19 showcases how
most of the newly updated galaxy dimensions often differ slightly from the
older values, both towards larger and smaller values. This leads both to the
loss and new inclusion of some peripheral sources if the new sizes are smaller
or larger, respectively. This phenomenon is well illustrated in Figure 20,
where it is seen how smaller isophotal ellipses lead to the exclusion of seven
sources from Earnshaw et al. (2019). For the same reason, we only recover 271
galaxies as sometimes all the sources present in one galaxy are lost. As a
rough estimate for the loss of sources, we take 154 galaxies present in both
Earnshaw et al. (2019) and our catalogue and that are smaller in HECATE than
RC3 and compute that, on average, a 26% of the area sky area is lost. On the
other hand, for 112 galaxies that are larger, hold $\verb|n_Galaxies|=1$, and
excluding a source that we found to be matched in different galaxies for both
catalogues, the mean sky area increment is of a 39%. This gives an intuition
into the 23% of missing sources from Earnshaw et al. (2019), but it also
indicates that some extra sources have been included. Nonetheless, the newer
values considered more accurate due to the increase in photometric data
(Makarov et al. 2014).

In addition to this, the discrepancies can be further explained by the
improvement of detection algorithms in the 4XMM editions of the serendipitous
XMM-Newton catalogues with respect to the 3XMM editions. As such, a fraction
of the spurious detections present in 3XMM-DR4 are expected to be properly
flagged or not included in 4XMM-DR9.

Regarding the ULX content, 260 sources classified by us as ULX candidates in
our catalogue have a counterpart in Earnshaw et al. (2019). This implies the
recovery of 68% of their 384 candidates. This can be explained by the more
strict filtering pipeline used in our analysis, in addition to the reasons
exposed above. We also recover 337 of the 470 objects in Walton et al. (2011),
72% of the total.

Our findings regarding the spectral and abundance properties of the cULXss are
a confirmation of the findings from Walton et al. (2011) and Earnshaw et al.
(2019). Both of them find an overabundance of ULXs in spiral galaxies and a
clear tendency for late-type galaxy hosted candidates to present slightly
harder spectra. In addition, Earnshaw et al. (2019) also find that the
spectral properties of the ULX population resemble mostly that of the X-ray
population in the range of $10^{38}<L_{\textrm{X}}<10^{39}$ erg s-1, while the
AGN population presents a slightly different distribution. Finally, we recover
five of the HLX candidates in their catalogues.

####  5.3.2 Previous serendipitous Chandra ULX catalogues

As of the writing of this paper, Kovlakas et al. (2020) is the most recent and
largest Chandra-based ULX catalogue. Their work goes in parallel to our own,
presenting 629 ULX candidates in 309 galaxies out of 23 043 sources in 2 218
galaxies. Therefore, our catalogues complement each other to a great extent.
Most remarkably, we use the same reference list of galaxies, HECATE, which
leads to a very interesting comparison of results.

If we restrict our comparison to mutually shared galaxies at $D>1$ Mpc, their
numbers decrease to 11 359 sources in 849 galaxies. Kovlakas et al. (2020)
focused on galaxies with $D<40$ Mpc, and selected as ULX candidates all non-
nuclear objects in AGN galaxies, or all objects in non-AGN galaxies, which
have $L_{\textrm{X}}>10^{39}$ erg s-1 and present negative pileup and
unreliability flags in Chandra. Using those conditions, 341 of these sources
consist of ULX candidates in their catalogue. These 849 galaxies contain 3 130
sources in our catalogue, out of which 457 are ULX candidates on our side.
Only 2 091 sources have a direct counterpart on both sides, including 301 ULX
candidates on our side and 144 from Kovlakas et al. (2020). Finally, only 74
of the ULX candidates coincide on both sides.

Two remarkable discrepancies are noticed. Firstly, the larger density of
sources in Kovlakas et al. (2020) in comparison to our own catalogue, and
secondly, the lower yield of ULXs in Kovlakas et al. (2020) for the same sky
area. The first one can be easily explained by the means of Chandra’s sharper
resolution, which allows for the detection of many fainter sources which are
harder to resolve with XMM-Newton. The second point can be explained by the
difference of our ULX filtering criterion. We choose as a ULX candidate those
objects that, aside from having good data and being clean of interlopers, find
themselves within the ULX luminosity regime in at least one of their
detections, including the corresponding uncertainties. This implies the
inclusion of objects at the ULX luminosity threshold into our ULX candidate
set, as discussed in Section 5.2.1. By contrast, Kovlakas et al. (2020) use
only the average source luminosity as their criterion. Using their criterion,
our set of ULX candidates in the same area would get reduced from 457 to 347,
closer to their 341 candidates. Additionally, we are prone to source blending
due to XMM-Newton’s poorer resolution, leading to slight luminosity
overestimates.

Figure 21: Dispersion of source luminosities for coinciding $D>1$ Mpc sources
in our catalogue (horizontal axis) and Kovlakas et al. (2020) (vertical axis).
ULX candidates in our work catalogue are highlighted in blue.

There is an extra, more subtle, factor that contributes to the lack of
coinciding candidates. As illustrated by Figure 21, the estimated luminosity
of matched sources give are in good agreement between the two catalogues, but
with a typical dispersion of around order of magnitude. This alone explains
why there are ULX candidates in our side that are not accounted as such in
(Kovlakas et al. 2020) and vice versa, despite having a positional match on
the sky. The source of the spread can be both intrinsic variability in the
sources and uncertainties arising during the flux measurement. Nonetheless,
these discrepancies add to the mutual completeness of the two catalogues. Even
in overlapping observations, both catalogues are to be considered to have a
complete view of the total ULX content.

Besides quantity, we perform similar qualitative work using the same
parameters as provided by HECATE. Kovlakas et al. (2020) find an overabundance
of ULX in spiral galaxies as well as a spike in elliptical galaxies, in
agreement to our results. The most interesting common result is that the
number of ULX candidates per SFR in early-spiral galaxies is the closest to
the one predicted in Wiktorowicz et al. (2017), of 0.67 ULX/M⊙yr-1. In our
case, we estimate a peak value of 0.65 ULX/M⊙yr-1for early spiral galaxies,
while Kovlakas et al. (2020) present a value of 0.51 ULX/M⊙yr-1. We also agree
with Kovlakas et al. (2020) in that there is ULX population in early-type
galaxies exists despite their low SFR values, indicating that it is composed
by an older population of LMXB. However, regarding the spiral galaxies we see
that in the XMM-Newton sample galaxies with $T_{\textrm{H}}\sim 3$ present the
highest ULX frequencies and fraction of hosts, while Kovlakas et al. (2020)
find it at galaxies with $T_{\textrm{H}}\sim 5$ (Sc galaxies in the original
paper). This difference will most likely get blurred as ULX catalogue sizes
grow in the future.

###  5.4 Future prospects

Our catalogue consists of the largest ULX collection built up to date, and it
is based on the most recent XMM-Newton serendipitous catalogue available at
the time. Nonetheless, the available catalogues keep improving both in size
and quality. For instance, in late 2020, the tenth data release of the XMM-
Newton serendipitous catalogue, 4XMM-
DR10121212http://xmmssc.irap.omp.eu/Catalogue/4XMM-DR10/4XMM˙DR10.html, was
made available to the public, adding 25 034 new sources from 443 new
observations, an increase of almost an extra 5% with respect to 4XMM-DR9. As
the available samples keep increasing, so will do the samples of ULX
candidates available to the community. This will allow for more solid
statements regarding the spectral and variability properties of the bright ULX
population, and to better identify it as a subgroup of the ULX population.

A particularly promising resource that will soon be available is that of the
eRASS survey (Merloni et al. 2012), which has been active for more than a year
as of the writing of this paper (Predehl et al. 2021). Based on the area
observed by eROSITA in the western side of the galactic hemisphere as of April
27, 2020, the preliminary study of Bernadich (2020) finds a total number of
132 ULX candidates, of which only 30 are matched to our catalogue within a 3
times their positional uncertainty. This is to be expected, as sources from
the eRASS in general are spread all across the sky, having the observations
little overlap with the XMM-Newton field. Taking into account the area covered
by eROSITA and its average sensitivity at the end of the survey by 2024, the
discovery of around 1 000 new ULX candidates is expected.

X-ray catalogues are not the only ones of relevance to the search for ULXs.
Information from other wavelength ranges help to uncover investigate the
nature of the observed objects. A lot of work is being put on that side too.
For instance, also in late 2020, the Gaia Early Data Release 3 was made
available to early access, providing positional and apparent brightness
information for $10^{8}$ new sources (Gaia Collaboration 2020), with a full
release planned by 2022131313https://www.cosmos.esa.int/web/gaia/release.
Given our methodology, catalogues like this will be of great aid in
identifying further interlopers in future works.

###  5.5 Conclusions

We have built an expanded ULX catalogue from the latest available XMM-Newton
serendipitous source catalogue, 4XMM-DR9, and the HECATE list of galaxies. A
total number of 23 262 point-like sources of high-detection likelihood have
been included within the isophotal ellipses of HECATE galaxies, out of which 3
274 have at least one detection within the ULX luminosity regime
($L_{\textrm{X}}>10^{39}$ erg s-1). However, most of these sources consist of
contaminating interlopers, 2 208 of them being the nuclear source of their
host galaxy. We have built a filtering pipeline to identify such possible
interlopers from other available catalogues and databases, such as PanSTARRS1,
Tycho2, SDSS-DR14, VéronQSO, SIMBAD, PanSTARRS1 and NED. In the end, 779
objects in 617 galaxies qualify as ULX candidates, with 94 with at least one
detection with luminosity over the $L_{\textrm{X}}>5\times 10^{40}$ erg s-1
threshold. Around 30 of these objects qualify as HLXs, with $\langle
L_{\textrm{X}}\rangle>10^{41}$ erg s-1, many of which require individual
follow-ups to untangle their physical nature.

We show that ULX candidates from our complete sub-sample are preferably found
in late-type galaxies, in agreement to the notion presented in recent ULX
census (Kovlakas et al. 2020; Earnshaw et al. 2019) and reviews (Kaaret et al.
2017, and references therein). In particular, early and late spiral galaxies
present the largest ULX frequencies, with 49% of early spiral galaxies and 37%
late spiral galaxies hosting at least one ULX. These galaxies are also the
ones with the highest SFR per unit of stellar mass according to the HECATE
values, confirming the correlation between ULX and star-formation. We compute
of 0.49 ULX/M⊙yr-1 for early spirals and up to 1.01 ULX/M⊙yr-1 for late
spirals. These numbers range across the values exposed in previous literature
(Wiktorowicz et al. 2017; Kovlakas et al. 2020).

From the hardness ratio measurements provided by 4XMM-DR9, we have shown that
our ULX sample has spectral properties that are distinct from those of the
interloper population. At the same time, we show that ULX candidates hosted by
late-type galaxies tend to have harder spectra, most likely due to higher
photoelectric absorption. These results are overly similar to those already
presented by previous XMM-Newton-based ULX studies, such as Walton et al.
(2011) and Earnshaw et al. (2019). The same trend is seen in the brighter
candidates, but they have softer spectra in general.

From the stacked version 4XMM-DR9s, built from overlapping XMM-Newton
observations, we also see that ULX candidates hosted by late spiral galaxies
are the ones with the highest probability of showcasing inter-observation
variability, with the highest amplitude modulations, while candidates hosted
by elliptical and lenticular galaxies fall on the other end. As far as we are
aware, this is the first study of this kind. Other variability studies such as
Sutton et al. (2013) focus on a more thorough study of individual objects
rather than on the whole sample, and therefore the results are not fully
comparable. Indeed, to fully understand the variability properties of a
population as heterogeneous as ULXs, individual follow-ups are essential.
Again, the bright candidates distinguish themselves by showing milder
variability in general, albeit similar trends attempt to surface among them.

We are not able to make solid statements regarding the spectral and
variability properties of bright candidates due to the small size of the
complete bright sub-sample. The expected growth of available X-ray samples in
the future will mitigate this problem. Of particular promise is the all-sky
blind eRASS survey being performed by the eROSITA observatory (Predehl et al.
2021).

###### Acknowledgements.

This work was supported by the German DLR under project 50OX1901. Konstantinos
Kovlakas and Andreas Zezas acknowledge support from the European Research
Council under the European Union’s Seventh Framework Programme (FP/2007-2013)
/ ERC Grant Agreement n. 617001, and the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie RISE
action, Grant Agreement n. 691164 (ASTROSTAT). The authors would also like to
thank all peers who helped in the construction of the catalogue by offering
their own tools and experience, and the anonymous referee who helped to
improve this paper. This research made use of the cross-match service provided
by CDS, Strasbourg.

## References

  * Abbott et al. (2020a) Abbott, R., Abbott, T. D., Abraham, S., et al. 2020a, Phys. Rev. Lett., 125, 101102
  * Abbott et al. (2020b) Abbott, R., Abbott, T. D., Abraham, S., et al. 2020b, ApJ, 900, L13
  * Anastasopoulou et al. (2016) Anastasopoulou, K., Zezas, A., Ballo, L., & Della Ceca, R. 2016, MNRAS, 460, 3570
  * Bachetti (2016) Bachetti, M. 2016, Astron. Nachr., 337, 349
  * Bachetti et al. (2014) Bachetti, M., Harrisson, F. A., Walton, D. J., et al. 2014, Nature, 504, 202
  * Bernadich (2020) Bernadich, M. C. 2020, Master’s Thesis (Uppsala University) [diva2::1438261]
  * Blanton et al. (2017) Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, AJ, 154, 28
  * Brandt & Alexander (2015) Brandt, W. N. & Alexander, D. M. 2015, A&AR, 23, 1
  * Carpano et al. (2018) Carpano, S., Haberl, F., Maitra, C., & Vasilopoulos, G. 2018, MNRAS, 476, L45
  * Chambers et al. (2016) Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016 [arXiv:1612.05560]
  * Colbert & Ptak (2002) Colbert, E. J. M. & Ptak, A. F. 2002, ApJS, 143, 25
  * Corwin et al. (1994) Corwin, H. G. J., Buta, R. J., & de Vaucouleurs, G. 1994, AJ, 108, 2128
  * Cseh et al. (2014) Cseh, D., Webb, N. A., Godet, O., et al. 2014, MNRAS, 2014
  * de Vaucouleurs et al. (1991) de Vaucouleurs, G., , de Vaucouleurs, A., Corwin, H., et al. 1991, Third Reference Catalogue of Bright Galaxies (RC3) (Springer-Verlag: New York)
  * Di Stefano et al. (2020) Di Stefano, R., Berndtsson, J., Urquhart, R., et al. 2020 [arXiv:2009.08987]
  * Earnshaw et al. (2019) Earnshaw, H. P., Roberts, T. P., Middleton, M. J., et al. 2019, MNRAS, 483, 5554
  * Eracleous et al. (2002) Eracleous, M., Shields, J. C., Chartas, G., & Moran, E. C. 2002, ApJ, 565, 108
  * ESA: XMM-Newton SOC (2019) ESA: XMM-Newton SOC. 2019, XMM-Newton Users Handbook, issue 2.18
  * Flewelling et al. (2016) Flewelling, H. A., Magnier, E. A., Chambers, K. C., et al. 2016 [arXiv:1612.05243]
  * Freund et al. (2018) Freund, S., Robrade, J., Schneider, P. C., & Schmitt, J. H. M. M. 2018, A&A, 614, A125
  * Fürst et al. (2016) Fürst, F., Walton, D. J., Harrison, F. A., et al. 2016, ApJ, 831, L14
  * Gaia Collaboration (2018) Gaia Collaboration. 2018, A&A, 616, A1
  * Gaia Collaboration (2020) Gaia Collaboration. 2020 [arXiv:2012.01533]
  * Ghosh et al. (2008) Ghosh, H., Mathur, S., Fiore, F., & Ferrarese, L. 2008, ApJ, 687, 216
  * Gilfanov (2004) Gilfanov, M. 2004, Prog. Theor. Phys. Suppl., 155, 49
  * Goldader et al. (1997) Goldader, J. D., Goldader, D. L., & Joseph, R. D. 1997, AJ, 113, 1569
  * Grimm et al. (2003) Grimm, H. J., Gilfanov, M., & Sunyaev, R. 2003, ChJAA, 3, 257
  * Ho et al. (2001) Ho, L. C., Feigelson, E. D., Townsley, L. K., et al. 2001, ApJ, 549, L51
  * Høg et al. (2000) Høg, E., Fabricius, C., Makarov, V. V., et al. 2000, A&A, 355, L27
  * Hunt & Hirashita (2009) Hunt, L. K. & Hirashita, H. 2009, A&A, 507, 1327
  * Inami et al. (2010) Inami, H., Armus, L., Surace, J. A., et al. 2010, AJ, 140, 163
  * Israel et al. (2017) Israel, G. L., Belfiore, A., Stella, L., et al. 2017, Science, 355, 817
  * Israel et al. (2016) Israel, G. L., Papitto, A., Esposito, P., et al. 2016, MNRAS, 466, L48
  * Iwasawa et al. (2011) Iwasawa, K., Sanders, D. B., Teng, S. H., et al. 2011, A&A, 529, A106
  * Kaaret et al. (2017) Kaaret, P., Feng, H., & Roberts, T. P. 2017, ARA&A, 55, 303
  * Karachentsev et al. (2004) Karachentsev, I. D., Karachentseva, V. E., Huchtmeier, W. K., & Makarov, D. I. 2004, The Astrophysical Journal, 127, 2031
  * Kim & Fabbiano (2004) Kim, D. & Fabbiano, G. 2004, ApJ, 611, 846
  * Kim & Fabbiano (2010) Kim, D. & Fabbiano, G. 2010, ApJ, 721, 1523
  * King (2002) King, A. R. 2002, MNRAS, 335, L13
  * King (2004) King, A. R. 2004, MNRAS, 347, L18
  * Kong et al. (2007) Kong, A. K. H. et al. 2007, ApJ, 671, 349
  * Kovlakas et al. (2020) Kovlakas, K., Zezas, A., Andrews, J. J., et al. 2020, MNRAS, 498, 4790
  * Kovlakas et al. (2021) Kovlakas, K., Zezas, A., Andrews, J. J., et al. 2021, MNRAS, 506, 1896
  * Liu & Bregman (2005) Liu, J.-F. & Bregman, J. N. 2005, ApJS, 157, 59
  * Liu et al. (2006) Liu, J.-F., Bregman, J. N., & Irwin, J. 2006, ApJ, 642, 171
  * Maccacaro et al. (1988) Maccacaro, T., Gioia, I. M., Wolter, A., et al. 1988, ApJ, 326, 680
  * Madhusudhan et al. (2006) Madhusudhan, N., Justham, N. S., Nelson, L., et al. 2006, ApJ, 640, 918
  * Makarov et al. (2014) Makarov, D., Prugniel, P., Terekhova, N., et al. 2014, A&A, 570, A13
  * Mateos et al. (2008) Mateos, S., Warwick, R. S., Carrera, F. J., et al. 2008, A&A, 492, 51
  * Merloni et al. (2012) Merloni, A., Predehl, P., Becker, W., et al. 2012 [arXiv:1209.3114]
  * Mezcua et al. (2015) Mezcua, M., Roberts, T. P., Lobanov, A. P., & Sutton, A. D. 2015, MNRAS, 448, 1893
  * Mineo et al. (2012) Mineo, S., Gilfanov, M., & Sunyaev, R. 2012, MNRAS, 419, 2095
  * Pasham et al. (2014) Pasham, D. R., Strohmayer, T. E., & Mushotzky, R. F. 2014, Nature, 513, 74
  * Paturel et al. (1985) Paturel, G., Fouque, P., Bottinelli, L., & Gougenheim, L. 1985, A&AS, 80, 299
  * Pavlovskii et al. (2017) Pavlovskii, K., Ivanova, N., Belczynski, K., & Van, K. X. 2017, MNRAS, 2016, 2092
  * Pietsch (2008) Pietsch, W. 2008, Astron. Nachr., 329, 170
  * Predehl et al. (2021) Predehl, P., Andritschke, R., Arefiev, V., et al. 2021, A&A, 647, A1
  * Rappaport et al. (2005) Rappaport, S. A., Podsiadlowski, P., & Pfahl, E. 2005, MNRAS, 356, 401
  * Remillard & McClintock (2006) Remillard, R. A. & McClintock, J. E. 2006, ARA&A, 44, 42
  * Roberts & Warwick (2000) Roberts, T. P. & Warwick, R. S. 2000, MNRAS, 315, 98
  * Rosen et al. (2016) Rosen, S. R., Webb, N. A., Watson, M. G., et al. 2016, A&A, 590, A1
  * Servillat et al. (2011) Servillat, M., Farrell, S. A., Lin, D., et al. 2011, ApJ, 743, 6
  * Shu et al. (2019) Shu, Y., Koposov, S. E., Evans, N. W., et al. 2019, MNRAS, 489, 4741
  * Sturm et al. (2013) Sturm, R., Haberl, F., Pietsch, W., et al. 2013, A&A, 558, A3
  * Sutton et al. (2013) Sutton, A. D., Roberts, T. P., Gladstone, J. C., et al. 2013, MNRAS, 435, 1758
  * Sutton et al. (2012) Sutton, A. D., Roberts, T. P., Walton, D. J., et al. 2012, MNRAS, 423, 1154
  * Swartz et al. (2004) Swartz, D. A., Ghosh, K. K., Tennant, A. F., & Wu, K. 2004, ApJS, 154, 159
  * Swartz et al. (2011) Swartz, D. A., Soria, R., Tennant, A. F., & Yukita, M. 2011, ApJ, 741, 49
  * Taylor (2005) Taylor, M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, ed. P. Shopbell, M. Britton, & R. Ebert, 29
  * Traulsen et al. (2019) Traulsen, I., Schwope, A. D., Lamer, G., et al. 2019, A&A, 624, A77
  * Traulsen et al. (2020) Traulsen, I., Schwope, A. D., Lamer, G., et al. 2020, A&A, 641, A137
  * Tremblin et al. (2014) Tremblin, P., Anderson, L. D., & Didelon, P. 2014, A&A, 568, A4
  * Véron-Cetty & Véron (2010) Véron-Cetty, M. P. & Véron, P. 2010, A&A, 518, A10
  * Walton et al. (2011) Walton, D. J., Roberts, T. P., Mateos, S., & Heard, V. 2011, MNRAS, 416, 1844
  * Wang et al. (2016) Wang, S., Qiu, Y., Liu, J., & Bregman, J. N. 2016, ApJ, 829, 20
  * Webb et al. (2020) Webb, N. A., Coriat, M., Traulsen, I., et al. 2020, A&A, 641, A136
  * Webb et al. (2012) Webb, N. A., Cseh, D., Lenc, E., et al. 2012, Science, 337, 554
  * Wegner et al. (2000) Wegner, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9
  * Wiktorowicz et al. (2015) Wiktorowicz, G., Sobolewska, M., Lasota, J.-P., & Belczynski, K. 2015, ApJ, 810, 20
  * Wiktorowicz et al. (2017) Wiktorowicz, G., Sobolewska, M., Lasota, J.-P., & Belczynski, K. 2017, ApJ, 846, 17
  * Zhang et al. (2009) Zhang, W. M., Soria, R., Zhang, S. N., et al. 2009, ApJ, 699