Webb et al

# XMM2ATHENA, the H2020 project to improve XMM-Newton analysis software and
prepare for Athena

Natalie A. Webb  Francisco J. Carrera  Axel Schwope  Christian Motch  Jean
Ballet  Mike Watson  Mat Page  Michael Freyberg  Ioannis Georgantopoulos
IRAP, Université de Toulouse, CNRS, CNES, 9 avenue du Colonel Roche, 21028
Toulouse, France  Instituto de Física de Cantabria (CSIC-Universidad de
Cantabria), Avenida de los Castros, 39005 Santander, Spain  Leibniz-Institut
für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
Université de Strasbourg, CNRS, Observatoire astronomique de Strasbourg, UMR
7550, 67000 Strasbourg, France  Université Paris-Saclay, Université Paris
Cité, CEA, CNRS, AIM, F-91191 Gif-sur-Yvette Cedex, France  Department of
Physics & Astronomy, University of Leicester, Leicester, LE1 7RH, UK  Mullard
Space Science Laboratory, University College London, Holbury St Mary, Dorking,
Surrey RH5 6NT, UK  Max-Planck-Institut für extraterrestrische Physik,
Giessenbachstraße 1, 85748 Garching, Germany  IAASARS, National Observatory of
Athens, I. Metaxa & V. Pavlou, 15236, Greece  Mickael Coriat  Didier Barret
Zoe Massida  Maitrayee Gupta  Hugo Tranin  Erwan Quintin  M. Teresa Ceballos
Silvia Mateos  Amalia Corral  Rosa Dominguez  Holger Stiele  Iris Traulsen
Adriana Pires  Ada Nebot  Laurent Michel  François Xavier Pineau  Jere Kuutila
Pierre Maggi  Sudip Chakroborty  Keir Birchall  Paul Kuin  Athanassios Akylas
Angel Ruiz  Ektoras Pouliasis  Antonis Georgakakis

(26 April 2016; 6 June 2016; 6 June 2016)

###### Abstract

XMM-Newton, a European Space Agency observatory, has been observing the X-ray,
ultra-violet and optical sky for 23 years. During this time, astronomy has
evolved from mainly studying single sources to populations and from a single
wavelength, to multi-wavelength/messenger data. We are also moving into an era
of time domain astronomy. New software and methods are required to accompany
evolving astronomy and prepare for the next generation X-ray observatory,
Athena.

Here we present XMM2ATHENA, a programme funded by the European Union’s Horizon
2020 research and innovation programme. XMM2ATHENA builds on foundations laid
by the XMM-Newton Survey Science Centre (XMM-SSC), including key members of
this consortium and the Athena Science ground segment, along with members of
the X-ray community. The project is developing and testing new methods and
software to allow the community to follow the X-ray transient sky in quasi-
real time, identify multi-wavelength/ messenger counterparts of XMM-Newton
sources and determine their nature using machine learning. We detail here the
first milestone delivery of the project, a new online, sensitivity estimator.
We also outline other products, including the forthcoming innovative stacking
procedure and detection algorithms to detect the faintest sources. These tools
will then be adapted for Athena and the newly detected/ identified sources
will enhance preparation for observing the Athena X-ray sky.

###### keywords:

X-rays: general, Catalogs, Astronomical data bases, Methods: data analysis

††articletype: Article Type

##  1 Introduction

Observing the sky in X-rays allows us to detect the hottest and most energetic
phenomena in the Universe. X-rays allow us to detect matter being accreted
onto massive black holes in the centres of galaxies or onto stellar mass black
holes in binaries with other stars (X-ray binaries). They allow us to identify
stellar flares from active stars, supernova remnants, neutron stars, white
dwarfs, galaxy clusters and even aurora on planets or comets. XMM-Newton, a
European Space Agency X-ray observatory (Jansen ., 2001) and the largest X-ray
telescope to be launched to date, has been observing the X-ray, ultra-violet
and optical sky for 23 years.

In order to exploit the XMM-Newton data, the XMM-Newton Survey Science
Centre111http://xmmssc.irap.omp.eu/ (XMM-SSC, Watson ., 2001), a consortium of
ten European institutes in collaboration with the XMM-Newton Science
Operations Centre (SOC), have put together a suite of software called the
Science Analysis System (SAS, Gabriel ., 2004), that can be used to reduce and
analyse the XMM-Newton data. A dedicated pipeline has also been developed to
perform standardised routine processing of the XMM-Newton science data. These
products are used to generate catalogues of X-ray and optical/UV sources (Webb
., 2020; Traulsen ., 2020; Page ., 2012).

However, over the last 23 years, astronomy has evolved towards the study of
populations rather than individual sources and to use multi-wavelength and
multi-messenger data to understand the X-ray sources. We are also moving into
an era of time domain astronomy, which requires operating observatories
differently in order to catch outbursts from X-ray binary systems, from tidal
disruption events (TDE) occurring as a massive black hole tears apart a
passing star, from supernovae, from stellar flares, etc and follow them up
rapidly at other wavelengths or with multi-messenger observations. New
software and methods need to be put into place to accompany this new
astronomy. It will be even more important in the next decade, when the next
generation X-ray observatory, Athena (Nandra ., 2013), will be launched. Here
we describe a programme XMM2ATHENA222http://xmm-ssc.irap.omp.eu/xmm2athena/,
financed through Horizon 2020, which is a European Union Framework Programme
for Research and Innovation. XMM2ATHENA runs for three years, from April 2021
to March 2024 and builds on work currently done in the XMM-SSC and aims to
develop new methodology to exploit the current XMM-Newton data and further
develop it to fully exploit the Athena data in conjunction with multi-
wavelength and multi-messenger data. We detail the first milestone product of
XMM2ATHENA, a sensitivity estimator for XMM-Newton data, as well as other new
tools and products already released, namely new machine learning tasks to
classify both the X-ray and optical/UV sources, new spectral fitting products,
enhanced algorithms to identify multi-wavelength and multi-messenger
counterparts, and new time domain algorithms and summarise the development to
come with regards to improved methods for source detection in stacked data,
photometric redshifts and physically motivated spectral models for the best
spectra. All of this information will be contained in a forthcoming catalogue
version, 5XMM.

The XMM2ATHENA project also has a strong outreach aspect, with the aim of
raising the profile of both XMM-Newton and Athena in the astronomical
community as well as using the two observatories as a stepping stone for
communicating with and educating the public. This is done through a dedicated
web page (available in four languages) along with Twitter and Facebook
accounts. We have participated in national and international activities
including Science Week, European researcher’s Night, Space Week, International
Day of Women and Girls in Science and Women’s Day and held online competitions
and events. We have also produced a wide range of public outreach material,
again available on our webpages http://xmm-ssc.irap.omp.eu/xmm2athena/.

##  2 Sensitivity estimator

We have developed a new sensitivity estimator based on the Flux Limits from
Images from XMM-Newton (FLIX) tool. It can be used to estimate either the XMM-
Newton X-ray flux or an estimate of the sensitivity of the EPIC (European
Photon Imaging Cameras) detector(s) at a given point in the sky, within an
extraction radius defined by the user. This is of particular interest for
extended regions of interest or very bright sources. Once the parameters have
been provided by the user, the tool scans the 4XMM catalogue and lists the
nearest sources. HTML links are provided to access the source nature and the
distance from the position of interest. The latest version of the catalogue,
4XMM-DR12 at the time of writing, is ingested into the sensitivity estimator.
A single position can be entered or for multiple sources a source file can be
provided. The user can choose the detection likelihood threshold for upper-
limit estimation as well as the wavebands to investigate.

The sensitivity estimate is determined empirically using the algorithm
described in Carrera . (2007). These estimates use the detector mask to find
which pixels in the image are valid, the exposure map data to find the
exposure time, and the background map to determine the fitted background
level. Should a source be present at the indicated position, the flux is
calculated by summing the counts inside the user defined radius, using the
background level from the background map data and an approximation to the
point-spread function in the calibration files. Energy conversion factors are
those used in the 4XMM catalogue (Webb ., 2020).

Figure 1: Extract of the output from the FLIX sensitivity estimator. The
coordinates of interest can be entered in the box at the top and the settings
modified using the corresponding button. The three band colour image is
plotted with the region of interest shown by the the cross and the green
circle. Neighbouring sources are indicated with the pink circles and are
included on the right with a link to their parameters, as given in the 4XMM
catalogue. Images taken in other wavelengths can be chosen instead, using the
stack button on the observation, thanks to the AladinLite functionality. Also
indicated are the source identification number from 4XMM. Towards the bottom
of the page, information is provided for every observation in which the
requested region of interest was observed. This can also be retrieved in a
FITS file, using the link towards the bottom right.

Results are returned as a web page which contains a HTML table with an entry
per celestial position, for each detector and each observation concerned by
the region. A FITS file is also produced, with a binary table containing one
row per observation which contains useful information, notably for different
energy bands. This may be downloaded as soon as the HTML output is complete,
and examined with utilities such as the ftool FV (Blackburn, 1995) or TOPCAT
(Taylor, 2005). An example of the output webpage is provided in Figure 1.

The interface to FLIX has been modernised and harmonised with the 4XMM
catalogue server interface, which is intuitive (but documentation is
nonetheless provided with the tool) and better adapted for long term
maintenance. It can be accessed via http://flix.irap.omp.eu/. The tool is
being continually developed and upcoming features include integrating the
stacked catalogue for deeper upper limits using a rapid Poisson estimator or
through the slower Cash based approach, improving the simple aperture
photometry currently used, providing a script access to FLIX and redeveloping
the code in a more modern language (python), to improve portability to Athena.

FLIX is complementary to the ESA services The HIgh-energy LIght-curve
GeneraTor (HILIGT333http://xmmuls.esac.esa.int/hiligt/ Saxton ., 2022; König
., 2022) and RapidXMM (Ruiz ., 2022). HILIGT provides fluxes and upper limits
for a variety of high energy missions, including the XMM-Newton pointed EPIC
data. This is done by calculating the count rate from the images and
converting them to fluxes. This provides a very good approximation to the
flux, but FLIX provides the sensitivity estimate using methodology that is in
line with that used for the XMM-Newton catalogue fluxes, making it more
accurate for the XMM-Newton data with respect to the fluxes provided by HILIGT
(up to 50% difference between FLIX and HILIGT in some cases) and RapidXMM.
However, FLIX does not allow the user to query a wide range of high energy
misions in the same way as HILIGT. Further, FLIX allows the user to select the
size of the region for the sensitivity estimate, which is not proposed by
HILIGT. This is particularly important when considering extended
sources/regions of the sky, or very bright sources. Note that almost 10% of
all XMM-Newton detections are extended Webb . (2020). RapidXMM provides upper
limit estimates based on aperture photometry in a similar way to HILIGT, but
pre-calculates these values with a resolution of $\sim$4" and stores them in a
database, so it is much quicker to access these values than for those
calculated on the fly by FLIX or HILIGT. However the estimates may not be
centred exactly on the position of interest.

##  3 Source Classification

To identify the X-ray sources, and also the optical/UV sources detected with
the Optical Monitor (OM, Mason ., 2001) on board XMM-Newton, we developed a
probabilistic method, revisiting the naive Bayes classification algorithm
(Tranin ., 2022). This approach was motivated by its intuitive nature, as an
extension of the rough classification rules used in simplistic decision trees.
It also delivers transparency for a particular classification. To undertake
the classification of the X-ray sources we used selected columns of data from
the 4XMM-DR10 version of the XMM-Newton catalogue (Webb ., 2020) pertaining to
Galactic latitude, the ratio of the maximum to minimum flux for a source and
spectral information, namely using the four hardness ratios given in the
catalogue, (where HR${}_{band\ i}$ = (Flux${}_{band\ i+1}$ -Flux${}_{band\
i}$)/(Flux${}_{band\ i+1}$ \+ Flux${}_{band\ i}$). The five energy bands are
1: 0.2-0.5 keV, 2: 0.5-1.0 keV, 3: 1.0-2.0 keV, 4: 2.0-4.5 keV and 5: 4.5-12.0
keV. We augmented the spectral information by fitting a power law and an
APEC444http://atomdb.org/ model to the 0.2-12.0 keV spectrum where possible or
to the fluxes in the five energy bands provided in the catalogue and used the
spectral slope and temperature information from these fits. We also augmented
the catalogue with multi-wavelength counterparts, using NWAY (Salvato ., 2018)
from Gaia (Bailer-Jones ., 2021) and the GLADE catalogue (Dalya ., 2016), as
well as a number of other catalogues (Tranin ., 2022). This then allowed us to
use X-ray to r-band flux ratios and the X-ray to W1-band flux ratios, as well
as the distance to the centre of a host galaxy and the proper motion of a Gaia
association. We generated a sample of 25160 previously identified sources,
broken into subgroups of AGN, stars, X-ray binaries and cataclysmic variables.
We estimated the probability density for each property cited above for the
different source types, which were used to compute the likelihoods to classify
the sources. When a property value was missing, and if the property was not
missing at random, the likelihood was replaced by the probability that a
source of that class had a missing value. Then the Bayes rule was used taking
into account each property for each source type, to attribute the probability
pertaining to the nature of the source. An outlier class was also used to
identify rare sources of other types. The performance of the algorithm was
validated with a test sample. Excellent results for the precision were
retrieved, 97.2% for AGN, 98.9% for stars, 93.7% for X-ray binaries and 84.6%
for cataclysmic variables (Tranin ., 2022). The classifications for the 4XMM-
DR10 version of the catalogue are available at :
https://vizier.cds.unistra.fr/viz-
bin/VizieR-3?-source=J/A%2bA/657/A138/table7. As of version 5XMM of the XMM-
Newton catalogue, the classification will be included in the catalogue, see
Section 7. This algorithm can also be used to classify X-ray sources from
other observatories (Tranin ., 2022).

Further, in order to improve the sample sizes for the learning sample and thus
improve both the $precision$ (value calculated from running the classification
code on a sample of known sources, where the precision is the percentage of
the group of sources identified as being from a source class, that are indeed
of that source class) and the $recall$ (the fraction of each source class
retrieved by the algorithm), a citizen science project has been set up called
Classification of X-ray Sources for Novices (CLAXSON)555http://xmm-
ssc.irap.omp.eu/claxson/index.php. This project trains users to identify the
types of sources classified in Tranin . (2022) and then once they reach a good
level of accuracy, can classify new sources. A source is only considered as
identified once at least 20 people have classified the source, with converging
identifications. This platform can also be used for outreach and educative
purposes.

##  4 Spectral fitting

Of the 895415 detections in the 4XMM-DR11 catalogue, 319565 have a sufficient
number of counts to trigger automatic spectral extraction in the processing
pipeline. Within the XMM2ATHENA project we aim to fit all the spectra with
both simple and physically motivated models. Before performing spectral fits,
we applied a quality filter to choose the good quality spectra. This filter
required detections with background spectra with a positive number of counts
and source+background spectra with more counts than their corresponding
background spectra, scaled to the source extraction area. 311139 spectra
passed these criteria. We also took into account the quality of the fits,
defining a good fit sample of 232350 detections, for which the goodness of fit
to both the background and source+background was $\geq$0.01. Using a
sophisticated Bayesian fitting method (Bayesian X-ray Analysis, BXA Buchner .,
2014) with a thorough exploration of the parameter space, we fitted these
spectra with an absorbed power law. The fit results are provided in a FITS
table, with one row per detection666http://xmm-
ssc.irap.omp.eu/xmm2athena/catalogues/. It includes estimates of the central
value along with the 90% confidence intervals, the statistical quality of the
background and source+background fits, and a number of flags, both from the
quality filtering described above and that given in the 4XMM-DR11 catalogue.

We performed several quality checks on the results. 90% of the fluxes obtained
from fitting corresponded well with the fluxes provided in 4XMM-DR11, with the
worst deviations coming from spectra that had best-fit parameters indicating
that they were quite different from the simple power law of $\Gamma$=1.7 and
an absorption of 3 $\times$ 1020 cm-2 that is used to derive the flux in the
catalogue. We also provide fits for repeated observations of the same source
to get better constraints on its spectral shape. Of the 358809 unique sources
in the catalogue of stacked data, 4XMM-DR11s, 33061 have at least two
detections with spectra. We merge spectra from the same instrument (pn and
MOS1/2, separately) from these repeated observations, using the SAS tool
epicspeccombine (https://xmm-
tools.cosmos.esa.int/external/sas/current/doc/epicspeccombine/index.html). Not
all spectra are merged for each source: we start with the spectrum with the
highest signal-to-noise (SNR) ratio and keep adding spectra with decreasing
SNR until the overall SNR no longer improves significantly. These fits are
also available on the XMM2ATHENA webpage777http://xmm-
ssc.irap.omp.eu/xmm2athena/catalogues/.

We also provide fits for the faintest sources, for which no spectrum has been
extracted, but fluxes in the five energy bands of the catalogue are available.
Finally, using the source identification and the photometric redshifts, we
will also provide physically motivated fits to sources with a reliable
classification.

##  5 Cross-matching multi-wavelength/messenger counterparts

Using a statistical framework for multi-catalogue cross-correlation and cross-
identification previously developed (Pineau ., 2017), based on both
astrometric and photometric data, we are able to identify multi-wavelength and
multi-messenger counterparts for the X-ray sources. We have extended existing
methodology to new catalogues of interest and generated spectral energy
distributions (SEDs). Using the SEDs we have automatically classified the
sources into extragalactic and Galactic sources and we will tailor this method
to the stacked catalogue and focus on some regions of sky with the deepest,
best quality multi-wavelength data available to improve the identifications.

##  6 Variable sources

Currently, the 4XMM catalogue provides information on variability during a
single observation. Variability between observations can be probed with the
stacked catalogue. However, to extend the duration over which variability can
be detected and to increase the number of observations, we have taken six
X-ray catalogues in addition to the XMM-Newton pointed data. These include The
Chandra Source Catalogue (CSC 2.0, IN. Evans ., 2020), The Swift X-ray Point
Source catalogue (2SXPS, PA. Evans ., 2020), The Rosat All Sky survey (2RXS,
Boller ., 2016), the ROSAT pointed survey (WGACAT, White ., 1994), The XMM-
Newton slew survey (XMMSL2, Saxton ., 2008) and the early release eRosita data
(eFEDS Brunner ., 2022), which we further augmented with XMM-Newton upper
limits using RapidXMM. To do this we developed an algorithm based on the
matching method described in Salvato . (2018). We carried out in depth
analysis to determine the optimal X-ray band(s) to be compared and the
spectral model to be used to determine the fluxes, in order to create reliable
comparisons. We chose the range 0.1-12.0 keV, extrapolating the flux for each
observatory using our chosen spectral model, a power law with $\Gamma=1.7$ and
nH = 3 $\times$ 1020 cm-2. We then determined the ideal criteria for
determining variability and carried out a pilot study on two months of data to
determine the number and type of alerts expected. We then extended this to
search for spectral variability. Finally, we also incorporated the optical/UV
data from the co-aligned OM telescope and searched for flux and spectral
variability in these longer wavelengths. We have also invested considerable
effort in testing the reliability of the algorithm. The code can be used to
search for variable sources in quasi-realtime and provide alerts to the
community. The work is detailed in an upcoming paper, Quintin et al. (to be
submitted).

To search for faint, short term variability, that may not be detected in the
pipeline processing due to the fact that variability analysis is only carried
out on sources with at least 100 counts, we have built on preliminary work
developed Pastor-Marazuela . (2020) on the EPIC-pn XMM-Newton outburst
detector (EXOD) algorithm. This code breaks the whole observation into short
time windows and searches for variability in a way that is agnostic from
source detection by looking for areas that are variable on the detector on a
window to window basis. Improvements that have been implemented with regards
to the original python code are extending the use of the algorithm to be
applied to the MOS detectors and also to be able to use all modes of both the
MOS and the pn detectors, which has allowed us to run the code on all of the
15000 observations in the XMM-Newton archive. We have also improved the
accuracy of the source location and excluded regions on the detectors affected
by very bright sources, including the readout streaks associated with these
sources and that can lead to false detections. We have investigated a full
range of detection limits in order to compare all three cameras and therefore
improve the reliability of the detection. We have carried out preliminary
tests on the improved algorithm, comparing it to the previous work done and by
testing it on observations including specific source types, such as fields in
which Fast Radio Bursts (FRBs) and Quasi-Periodic Eruptions (QPEs) have been
detected and we have been able to confirm the adaptability of the code to
these source types. We have also shown that the code is able to pick out
significant variability from brighter sources. Whilst these sources are
detected by the XMM-Newton pipeline, the variability has not been identified
using the current algorithms in the pipeline.

##  7 Other upcoming products

Whilst the data coming from the OM telescope has already been correlated with
the X-ray data, little work has been done to classify the sources and exploit
the long term variability of the sources. The naive Bayes algorithm (Tranin .,
2022) will be used to classify the optical/UV sources and in the framework of
the long term variability analysis of the X-ray sources, the variability is
currently being investigated, see Sec. 6. This data will also be used, in
conjunction with other optical/infra-red catalogues, to determine photometric
redshifts for the X-ray sources, essential for improving the classification,
and defining physically motivated models for spectral fits. This, along with
the other products described above, will be provided in a new version of the
catalogue, 5XMM, expected for 2025. For the current version of the catalogue,
4XMM, different catalogues are provided, notably a catalogue of detections,
where the current version, 4XMM-DR12, includes 939270 X-ray detections which
relate to 630347 unique X-ray sources from 12712 observations that were public
by the 31st December 2021 and a stacked catalogue, 4XMM-DR12s, created from
overlapping observations. Stacking results in better source parameters, higher
sensitivity, and direct access to measures of long-term flux variability.
4XMM-DR12s is built from 1620 groups drawn from 9355 observations. For 5XMM we
will provide a single catalogue comprising both single detections and stacked
sources, using new methodology to reduce the number of free parameters, thus
enabling the faintest sources to be detected. 5XMM will therefore be a
simplified, but more complete catalogue as it will contain multi-wavelength
counterparts, sensitivity estimates when no detection is made, long and short
term variability, spectral fitting parameters, source classification and
photometric redshifts, in addition to the $>$300 columns of information
already provided.

##  8 Conclusions

The XMM2ATHENA project will provide new software and XMM-Newton products,
above and beyond those already provided by the XMM-SSC. Thanks to the
involvement of the XMM-SSC in the XMM2ATHENA project, these products will
continue to be developed and maintained even after the end of project in 2024.
The main product will be a new simplified, but more complete catalogue of XMM-
Newton detections and sources, but other software and tools have already been
or will be made available, such as improved cross-matching tools, a
sensitivity estimator, transient detection software, spectral fits, new
classification software and photometric redshifts for the X-ray sources.
Further, these will all be well tested thanks to four scientific projects
designed to incorporate all of the tools and the products, in order to
validate them. Once validated, the tools will be further developed to adapt
them for the upcoming Athena mission, ensuring an enhanced scientific return
for both ESA flagships.

## Acknowledgments

This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement Number 101004168. NW,
MC, DB, HT and EQ are also grateful for the support provided by the CNES. We
are grateful to the anonymous referee who provided some excellent comments
that allowed us to significantly improve this paper.

## References

  * Bailer-Jones . (2021) bail21Bailer-Jones, CAL., Rybizki, J., Fouesneau, M., Demleitner, M. Andrae, R.  202103,  AJ1613147.  10.3847/1538-3881/abd806
  * Blackburn (1995) blac95Blackburn, JK.  199501,  FTOOLS: A FITS Data Processing and Analysis Software Package FTOOLS: A FITS Data Processing and Analysis Software Package.  RA. Shaw, HE. Payne JJE. Hayes (), Astronomical Data Analysis Software and Systems IV Astronomical Data Analysis Software and Systems IV 77, 367\.
  * Boller . (2016) boll16Boller, T., Freyberg, MJ., Trümper, J., Haberl, F., Voges, W. Nandra, K.  201604,  A&A588A103.  10.1051/0004-6361/201525648
  * Brunner . (2022) brun22Brunner, H., Liu, T., Lamer, G. et al.  202205,  A&A661A1.  10.1051/0004-6361/202141266
  * Buchner . (2014) buch14Buchner, J., Georgakakis, A., Nandra, K. et al.  201404,  A&A564A125.  10.1051/0004-6361/201322971
  * Carrera . (2007) carr07Carrera, FJ., Ebrero, J., Mateos, S. et al.  200707,  A&A469127-46.  10.1051/0004-6361:20066271
  * Dalya . (2016) daly16Dalya, G., Frei, Z., Galgoczi, G., Raffai, P. de Souza, RS.  201606,  VizieR Online Data CatalogVII/275.
  * IN. Evans . (2020) evan20bEvans, IN., Primini, FA., Miller, JB. et al.  202001,  The Chandra Source Catalog — A Billion X-ray Photons The Chandra Source Catalog — A Billion X-ray Photons.  American Astronomical Society Meeting Abstracts #235 American Astronomical Society Meeting Abstracts #235 235, 154.05.
  * PA. Evans . (2020) evan20Evans, PA., Page, KL., Osborne, JP. et al.  202004,  ApJS247254.  10.3847/1538-4365/ab7db9
  * Gabriel . (2004) sas04Gabriel, C., Denby, M., Fyfe, DJ. et al.  200407,  The XMM-Newton SAS - Distributed Development and Maintenance of a Large Science Analysis System: A Critical Analysis The XMM-Newton SAS - Distributed Development and Maintenance of a Large Science Analysis System: A Critical Analysis.  F. Ochsenbein, MG. Allen D. Egret (), Astronomical Data Analysis Software and Systems (ADASS) XIII Astronomical Data Analysis Software and Systems (ADASS) XIII 314, 759\.
  * Jansen . (2001) jans01Jansen, F., Lumb, D., Altieri, B. et al.  200101,  A&A365L1-L6.  10.1051/0004-6361:20000036
  * König . (2022) koni22König, O., Saxton, RD., Kretschmar, P. et al.  202201,  Astronomy and Computing38100529.  10.1016/j.ascom.2021.100529
  * Mason . (2001) maso01Mason, KO., Breeveld, A., Much, R. et al.  200101,  A&A365L36-L44.  10.1051/0004-6361:20000044
  * Nandra . (2013) nand13Nandra, K., Barret, D., Barcons, X. et al.  201306,  arXiv e-printsarXiv:1306.2307.
  * Page . (2012) page12Page, MJ., Brindle, C., Talavera, A. et al.  201210,  MNRAS426903-926.  10.1111/j.1365-2966.2012.21706.x
  * Pastor-Marazuela . (2020) past20Pastor-Marazuela, I., Webb, NA., Wojtowicz, DT. van Leeuwen, J.  202008,  A&A640A124.  10.1051/0004-6361/201936869
  * Pineau . (2017) pine17Pineau, FX., Derriere, S., Motch, C. et al.  201701,  A&A597A89.  10.1051/0004-6361/201629219
  * Ruiz . (2022) ruiz22Ruiz, A., Georgakakis, A., Gerakakis, S., Saxton, R., Kretschmar, P., Akylas, A. Georgantopoulos, I.  202204,  MNRAS51134265-4284.  10.1093/mnras/stac272
  * Salvato . (2018) salv18Salvato, M., Buchner, J., Budavári, T. et al.  201802,  MNRAS47344937-4955.  10.1093/mnras/stx2651
  * Saxton . (2022) saxt22Saxton, RD., König, O., Descalzo, M. et al.  202201,  Astronomy and Computing38100531.  10.1016/j.ascom.2021.100531
  * Saxton . (2008) saxt08Saxton, RD., Read, AM., Esquej, P., Freyberg, MJ., Altieri, B. Bermejo, D.  200803,  A&A4802611-622.  10.1051/0004-6361:20079193
  * Taylor (2005) tayl05Taylor, MB.  200512,  TOPCAT & STIL: Starlink Table/VOTable Processing Software TOPCAT & STIL: Starlink Table/VOTable Processing Software.  P. Shopbell, M. Britton R. Ebert (), Astronomical Data Analysis Software and Systems XIV Astronomical Data Analysis Software and Systems XIV 347, 29.
  * Tranin . (2022) tran22Tranin, H., Godet, O., Webb, N. Primorac, D.  202201,  A&A657A138.  10.1051/0004-6361/202141259
  * Traulsen . (2020) traul20Traulsen, I., Schwope, AD., Lamer, G. et al.  202009,  A&A641A137.  10.1051/0004-6361/202037706
  * Watson . (2001) wats01Watson, MG., Auguères, JL., Ballet, J. et al.  200101,  A&A365L51-L59.  10.1051/0004-6361:20000067
  * Webb . (2020) webb20Webb, NA., Coriat, M., Traulsen, I. et al.  202009,  A&A641A136.  10.1051/0004-6361/201937353
  * White . (1994) whit94White, NE., Giommi, P. Angelini, L.  199412,  The WGA Catalog of ROSAT Point Sources The WGA Catalog of ROSAT Point Sources.  American Astronomical Society Meeting Abstracts American Astronomical Society Meeting Abstracts 185, 41.11.