Open Access
Issue
A&A
Volume 671, March 2023
Article Number A149
Number of page(s) 17
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202245236
Published online 21 March 2023

© The Authors 2023

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.

1 Introduction

Since the birth of X-ray astronomy after the discovery of the first extrasolar X-ray source in the early 1960s, thousands of high-energy astrophysical objects were observed and revealed to be of various nature (see the broad review of accreting binaries in Chaty 2022). Of these, high-mass X-ray binaries (HMXBs) are powered by the accretion of material from a massive donor star (M ≥ 8 M) onto a compact object, usually a neutron star (NS) and rarely a black hole (BH). HMXBs are usually divided into subclasses, of which BeHXMBs (see review by Rivinius et al. 2013) host a fast-rotating Be star, and sgHMXBs (see the review by Chaty 2013) host a supergiant companion. Before the launch of INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL), sgHMXBs used to be outnumbered about 1 to 10 compared to BeHMXBs. BeHMXBs transfer matter through the interaction of a compact object with a decretion disk, while sgHMXBs generally transfer mass via an intense stellar wind.

In some rare instances, accretion in sgHMXBs may take place via Roche-lobe overflow, which produces higher X-ray luminosities than wind-accreting systems. This is the case for Cen X-3, and was more recently suggested for IGR J08408-4503 at peri-astron (Ducci et al. 2019). Accretion through a Be disk is much more efficient at transporting angular momentum than via wind. The spin of the compact object spin is therefore correlated to the orbital period in BeHMXBs, but not in sgHMXBs (see e.g. Corbet 1984).

The INTEGRAL satellite (see numbers given in Bird et al. 2016) has a higher sensitivity at high energies than previous generations of hard X-ray observatories. sgHMXBs are therefore no longer a minority. Notably, INTEGRAL allowed the discovery of highly obscured sgHMXBs (Filliatre & Chaty 2004) and supergiant fast X-ray transients (SFXTs; Negueruela et al. 2006b).

The discovery and subsequent unambiguous identification of an HMXB requires several observations at various wavelengths. This is usually performed by independent teams of astronomers, and it can take several years before an HMXB is securely associated with a hard X-ray source. This is mainly due to the difficulty of associating soft X-ray, optical, infrared, and radio counterparts with high-energy detections as the astrometrical precision of hard X-ray observatories, which are physically unable to focus the radiation, is systematically outperformed by at least an order of magnitude compared to focusing observatories.

This leads to a lag in the information available on HMXBs and candidate HMXBs, which is spread within the literature; the more time passes, the more tedious it is to recover valuable parameters characterising the binaries, such as the various counterparts, the spectral type of the companion star, the orbital solution, or the detection of a pulse period. Collecting this information in a single place is necessary for a proper overview of the current observational knowledge on HMXBs, and catalogues dedicated to these peculiar sources have therefore been assembled in the past.

The first edition of such a catalogue was compiled by Bradt & McClintock(1983). Following this, van Paradijs (1995) proposed a second edition, which was then further improved by a third (Liu et al. 2000). Eventually, Liu et al. (2006) compiled the fourth and latest edition to date of the catalogue (although we note that Raguzova & Popov 2005 proposed a similar work immediately before). We hereby present a catalogue of HMXBs in the Galaxy that covers new information brought during the era from INTEGRAL to Gaia (2006–2022).

We can identify various arguments towards the necessity of building an updated catalogue of HMXBs. Firstly, the aforementioned catalogues are still being used today, even though they have not been updated for more than 15 yr. However, the absence of any recent update pushed us to begin compiling recent information on HMXBs in Fortin et al. (2022b) to infer natal kick properties not only on individual systems, but on the population of BeHMXBs and sgHMXBs. We are still missing crucial parameters on many binaries, however, which narrows the number of systems available for population studies. This shows the need for such catalogues to identify good HMXB candidates to follow up and which information to look for in order to complete our knowledge on these sources. Secondly, with the arrival of the new generation of observing facilities dedicated to high-energy and/or transient astronomy such as the Space Variable Objects Monitor (SVOM), the Large Synoptic Survey Telescope (LSST), or eROSITA (for the latter, see e.g. Maitra et al. 2023 for a study of a new BeHMXB in the Large Magellanic Cloud, LMC) and the nascent gravitational astronomy with LIGO1, Virgo, KAGRA2, and LISA3, having a contemporaneous view of the current HMXB landscape would be interesting in the scope of population studies. Catalogues of HMXBs have already been used to constrain their properties as a population (see e.g. Coleiro et al. 2013 and Fortin et al. 2022a). HMXBs are also representatives of a source category that is directly related to supernova explosions as well as to compact binaries that finally merge as gravitational wave sources (see a recent review by van den Heuvel 2019). Comparing the current population of HMXBs with the population of gravitational mergers that is going to build up in the years to come may yield insightful results on stellar evolution in general.

We therefore suggest that for an evolutionary snapshot of the current population of HMXBs, it is necessary to compile measurements on intrinsic binary parameters (orbital period, eccentricity, and systemic radial velocity) as well as measurements of the individual components such as the mass of the compact object (Mx) and its spin period, and of the mass of the optical companion (Mo), its spectral type, and its luminosity class. The latest data release of the Gaia satellite (Gaia Collaboration 2022) has made the distances to Galactic binaries now widely available, giving access to their 3D spatial distribution and therefore their place in the Galactic ecology.

We note that many HMXBs are known in the Magellanic Clouds (MCs) and that previous catalogues (Liu et al. 2005) may also benefit from an update. As stated in Liu et al. (2006), the sheer number of new data justifies splitting these works, especially in our case, where Gaia plays an essential role in the Galaxy (distance determination) that is not applicable to the MCs. The data-mining strategy to recover information about MC HMXBs should also be adapted. Lastly, the population of MC HMXBs is known to be quite different from the Galactic population and therefore deserves a dedicated discussion and paper.

In this paper, we build an updated catalogue of HMXBs and candidate HMXBs in the Galaxy. We also include systems identified as high-mass gamma-ray binaries (HMGB), which are thought to be powered by the spin-down of a pulsar and not by direct accretion onto it (see e.g. the review in Dubus 2013). Since the publication of the last HMXB catalogues, high-energy observations (e.g. INTEGRAL, Chandra, XMM-Newton, Swift, the Monitor of All-sky X-ray Image MAXI, the Nuclear Spec-troscopic Telescope Array NuSTAR, Suzaku, or Fermi) and optical/near-infrared (nIR) follow-ups allowed astronomers to discover new HMXBs. Many of the parameters mentioned above, such as spectral type, period, or eccentricity, were accurately determined. While the catalogue contents proposed here will remain fixed (last updated in September 2022), we also host a dynamic version of the catalogue online that is regularly updated when new observations are performed on HMXBs to add new systems or complete the list of known parameters. We strive to find the original references for each measurement we present, and not just reference previous catalogues. In Sect. 2 we describe how the catalogue is built and how we attempted to automatise the search for the multi-wavelength counterparts to HMXBs. We briefly discuss the resulting catalogue and its uses in Sect. 3 before we conclude in Sect. 4.

2 Building the catalogue

We describe in this section the steps we took in order to build the catalogue. We first used existing catalogues dedicated to HMXBs and cross-matched them with more recent catalogues of hard X-ray sources. We used the services of the Centre de Données Astronomiques de Strasbourg (CDS), namely Sim-bad (Wenger et al. 2000) and Vizier (Ochsenbein et al. 2000), to search for updated content on the sources and searched for missing HMXBs. We semi-automatically searched for known counterparts from hard X-rays to the near-infrared. To complete this, we manually compiled all the known parameters available on the HMXBs that we were able to find in the literature and list the proper reference to the original papers.

The following Sect. 2.1 is quite similar to what is described in a previous work (Fortin et al. 2022b), in which we built what can be seen as a precursor to this catalogue. We provide a summary of what has been done and focus on the additions brought in the present work.

2.1 Reference catalogues

Liu et al. (2006) is the most commonly referred catalogue of HMXBs, listing 114 systems in the Galaxy (including candidates). To build a working base, we added the sources seen by INTEGRAL as of 2016 to this catalogue (Bird et al. 2016). Many of the 939 hard X-ray sources presented in this catalogue are already identified, and nearly 40% are active galactic nuclei.

We thus only added the sources labelled HMXBs, low-mass X-ray binaries (LMXBs), cataclysmic variables (CVs), or still unidentified. Misidentification in the exact type of X-ray binary is not unheard of, therefore we kept all X-ray binaries in this step, and discarded non-HMXB sources only after reviewing the new results published in the literature since then. We performed a positional cross-match using Topcat (Taylor 2005) to find the bulk of sources common to both catalogues, and we manually confirmed any duplicates or sources that were left out because of poor astrometrical constraints. Identifiers of the sources were especially useful in this task because they are often similar from one catalogue to the next. This produced a working base of 128 HMXBs.

In parallel, we queried the Simbad database for sources of the type (or subtype) labelled HXB, the identification associated with HMXBs in Simbad. We retrieved 1288 sources in this way. Most of them were extragalactic; they are usually bundled in very tight regions of the sky associated with close-by galaxies, forming dense patches of extragalactic HMXBs. A simple way to automatically detect and remove them is to discard sources with neighbours closer than 6′. We verified that even in the Galactic plane, the sources we retrieved from Liu et al. (2006) and Bird et al. (2016) are typically twice as separated (around 15′ ). This left us with 175 sources, several of which are isolated extragalactic HMXBs, which we discarded later. We note that only 109 of the base HMXBs were found in this way in Simbad; the remaining 19 are simply not labelled HXB. We individually investigated the 66 additional Simbad HXBs in order to supplement our catalogue.

In effect, a majority of these 66 Simbad HXBs are actually LMXBs. Their primary type in Simbad is still set to HMXBs, however, even though a spectral type is available many times and clearly corresponds to a cool main-sequence star. We discarded them, but kept the remaining entries even when no precise information on spectral type was available in Simbad because we performed a thorough manual search for this information later. At this point, we had a set of 145 HMXBs and candidate HMXBs.

2.2 Finding an unambiguous chain of counterparts

We considered that a secure identification of an HMXB partly comes from having an unambiguous list of its detections from hard X-rays down to the near-infrared. This ensures that none of them are blended with close-by high-energy sources, and it efficiently removes sources listed as HXMBs in the literature that were detected only once in hard X-rays 40 yr ago and have had no new detection since then.

Hence, we verified each of the HMXBs in the present catalogue for their counterparts at various wavelengths. In increasing typical astrometric precision, we cross-matched the available position of HMXBs with the catalogues listed in Table 1. Independently of the origin of the positional data that were retrieved, we first queried each catalogue in a cone whose angular size varied depending on (1) the typical astrometrical accuracy of the queried catalogue and (2) the accuracy of the initial positional data. If the positional data were more accurate than the queried catalogue, the cone size was set to the radii given in Table1, which are about twice of the worst astrometric performance in the corresponding catalogue. If the astrometric precision of the queried catalogue was more accurate than the positional data, the cone size was set to the error available in the positional data.

Then, after reviewing the counterparts found at high energies, we performed a recursive search, from poorly accurate counterparts to the most accurate catalogues (2MASS and Gaia). This allowed us to recover the chain of detection from high energies down to the optical/nIR wavelengths, as well as the soft X-ray detections whose astrometrical accuracies (particularly from Chandra and XMM-Newton) can rival optical telescopes.

There is a limit in this process because this automatic query can generate false counterparts because the typical astrometrical accuracies that we used are based on the worst performance of each facility, so that any systematic errors in the astrometric calibration between catalogues could be taken into account. Systematic errors in astrometry appear to be especially large in older catalogues (Uhuru, the High Energy Astronomy Observatory HEAO, or Ariel V) because we often find that the historical detections of high-energy sources are not exactly compatible with more recent detections (e.g. INTEGRAL or Swift) when considering their 90% positional uncertainty. We also note that for observing facilities with astrometrical accuracies of about 1″ or lower (Swift, XMM-Newton, Chandra, 2MASS, and Gaia), we added 0.5″ to the positional uncertainty when validating the chain of counterparts. For instance, some XMM-Newton, Chandra, 2MASS, and Gaia detections of the same source can be so precise that they are not technically compatible with one another; for Galactic sources, even when we look towards the Galactic plane in crowded regions, it is unlikely that two separate sources lie closer than 0.5″. Using this value of systematic error was already successful in Fortin et al. (2022b), who searched for unambiguous Gaia counterparts to 2MASS sources.

We verified each individual result of this automatic counterpart search. We manually removed false detections of counterparts, and searched for actual counterparts in the literature when necessary. When we manually input coordinates from specific publications, we added a reference towards it in the online catalogue; they usually come from Astronomer’s Telegrams4 and are therefore not necessarily present in the queried catalogues.

Table 1

Queried catalogues for the counterpart search.

2.3 Retrieving binary parameters and new HMXBs

We made extensive use of NASA’s Astrophysics Data System5 (ADS) to recover the parameters and their corresponding references. Some papers greatly facilitated the process as they already listed information on some HMXBs in our catalogue. Orbital periods, spin periods, and spectral types are found in Belczynski & Ziolkowski (2009), spin periods of pulsars are reported in Annala & Poutanen (2010), spectroscopic information on Ae/Be stars is given in Fairlamb et al. (2015), tabled data on BeHMXBs is presented in Tsygankov et al. (2017) and Reig et al. (2017), HMXBs detected by INTEGRAL are reported in Sidoli & Paizis (2018), an overview of SFXT candidates is given in Sguera et al. (2020), much information on radio pulsars is collected in van den Eijnden et al. (2021), XMM-Newton and Swift observations of sgHMXBs are reported in Ferrigno et al. (2022), and HMXBs seen by Fermi are presented in Harvey et al. (2022).

For each information we compiled (spectral type, systemic radial velocity, masses, orbital period, spin period, and eccentricity), we provide the reference to the paper that first reported the measurements. While the articles listed above greatly sped up the process, we still manually checked each and every listed source in ADS and Simbad to search for any missing measurement and/or reference. This step is crucial not only to gather the most complete set of data on HMXBs in one place, but also to ensure that we do not cite papers in which no actual measurement was made. This facilitates determining the original source.

Furthermore, we also searched for papers announcing the detection of new HMXBs between 2016 and 2022, and added any new entry to the catalogue after performing the same precautionary steps described in this section. We mention for instance HD 96670, which was recently identified as new BH HMXB candidate in Gomez & Grindlay (2021).

2.4 Contents of the catalogue

In Table A.1 we provide a single identifier that is either the historical name of the HMXBs, the most commonly used (e.g. for INTEGRAL sources), or the main identifier as queried in Simbad. This service can be used to retrieve other identifiers available for the HMXBs. The “Spectype” column refers to the spectral type of the donor star in the binary. We also provide an indication of the subclass of the HMXBs: Be, supergiant (sg), supergiant fast X-ray transient (SFXT), and a few peculiar subclasses such as sgB[e] or Wolf-Rayet (WR). Most of the subclass information comes from the spectral type of the companion; if no spectral type is provided, a reference may be available to motivate the choice of subclass. The sky coordinates of the most accurate counterpart we found are listed alongside their 90% positional error. We also include distance inferences from Bailer-Jones et al. (2021) when a Gaia DR3 counterpart is available. These distances are based on Gaia EDR3, and as a result, they cannot be directly retrieved using the Gaia DR3 identifiers we provide in the full catalogue; instead, we retrieved the Gaia EDR3 identifiers first using a cone sky match, and then queried the distances in Bailer-Jones et al. (2021). Finally, Table A.1 provides a variability flag (“Var”) that summarises whether the HMXBs were flagged as variable sources in the INTEGRAL, 4XMM DR11, or Chandra catalogues, or if the ratio of the peak to mean flux in the Swift 2SXPS catalogue is greater than 5. The detailed information about individual variability flags is given in the online version of the catalogue.

In Table A.2 we introduce the orbital characteristics of the catalogued HMXBs. We have separated this information from Table A.1 for readability, but the full online catalogue contains information from both tables together. First are given indications on the mass of the compact object (Mx) and the companion star (Mo). Companion masses that were broadly inferred from the spectral type by us are labelled with a dagger; we used the atlas of Be stars from Porter (1996) and the stellar parameters for O stars available in Martins et al. (2005). The orbital period, eccentricity, spin period, and radial systemic velocity are given as available in the literature.

In addition to all the information in Tables A.1 and A.2, the online version of the catalogue provides a list of the multi-wavelength counterparts to each HMXB. For each counterpart, we provide the right ascension and declination in J2000, the 90% positional error, and the identifier as listed in the queried catalogues. This can facilitate any further cross match because sky matches can produce false associations, and identifiers help to identify any mistake in this matter.

The full catalogue content is available on Vizier in a fixed version. We also host it in a dynamic version that can be browsed online6, and which will be updated upon the request of users. New versions will be regularly published on the website and will be available for download in various formats.

thumbnail Fig. 1

Edge-on view of the 152 HMXBs in the Galaxy. Galactic latitudes are indicated in degrees. Background image credits: ESA/Gaia/DPAC.

3 Results, discussions, and byproducts

In this catalogue, we present 152 HMXBs and candidates in the Galaxy. This is a 33% increase from Liu et al. (2006) for the whole sample. We can also compare the increase in securely identified HMXBs because the 2006 catalogue mentions that only 63 were confirmed systems, the remaining 51 were candidates at that time. In the current catalogue, if we consider HMXBs for which we have a spectral type indicative of a massive star as confirmed, then we count 126 confirmed HMXBs. If we add to this those with a detected orbital period and spin period, this pushes the number of confirmed HMXBs to 134, more than twice the number of Liu et al. (2006). The Galactic sky map of HMXBs is shown in Fig. 1. We note that 111 of the HMXBs have a Gaia DR3 counterpart, of which 4 do not have a parallax estimation. We show the face-on Galactic distribution of HMXBs seen by Gaia in Fig. 2, which indicates a positional correlation between Galactic spiral arms and HMXBs that was recently explored in Fortin et al. (2022a) along with Galactic stellar clusters to retrieve the possible birthplaces and age of the binaries.

We find that the current number of BeHMXBs in the Galaxy is 74; there are 52 sgHMXBs, of which 21 are SFXT candidates, and 5 are sgB[e] systems. Two HMXBs have a Wolf-Rayet companion. We also note that the spectral type of the companion is poorly constrained in 28 HMXBs, which indicates that optical/nIR identification campaigns are still very necessary. Liu et al. (2006) listed 50 BeHMXBs and 16 sgHMXBs according to the listed spectral types. This means that we improve the census of these subclasses by 50% and more than 200%, respectively. The dramatic increase in known sgHMXBs over the past 15 yr is associated with the performances of the INTEGRAL satellite at high energies, which has at least in part lifted the observational bias we had towards BeHMXBs.

This catalogue was compiled to facilitate the retrieval of information on HMXBs and to allow for considerations to be made not only on individual systems, but on Galactic HMXBs as a population. We provide two examples of how this catalogue can be used for this purpose.

As a first example of how the data may be used is to build a Corbet diagram, shown in Fig. 3 with the 38 HMXBs in Liu et al. (2006; top panel) and the 75 HMXBs from the current catalogue (bottom panel), for which orbital period and spin period are determined. It is a great tool for visualising the effect of mass transfer in wind accretion versus decretion disk. Because the angular momentum is transferred very efficiently when an NS interacts with a decretion disk, BeHMXBs present a strong correlation (PspinPorb2${P_{{\rm{spin}}}} \propto P_{{\rm{orb}}}^2$, Corbet 1984); on the other hand, sgHMXBs do not show a significant correlation as wind accretion is inefficient at angular momentum transfer.

As expected, the updated Corbet diagram shows a dichotomy between sgHMXBs, which tend to have shorter orbital periods and host more slowly spinning NSs, and BeHMXBs with longer orbital periods but slightly faster-spinning NSs. Even with the greatly improved census on sgHMXBs, they do not show any particular correlation in the Corbet diagram, as opposed to BeHMXBs (see e.g. Cheng et al. 2014), whose orbital period generally increases with spin period. A few remarkable systems can be quickly identified: the two millisecond pulsars SAXJ0635.2+0533 and PSRB1259-63 (the latter orbiting its companion in more than 1000 d), and at the opposite end, the very slowly rotating 1A 0114+650, IGR J19140+0951, 1H 1249-637, and 4U 1954+31. The last system is also peculiar because it is the only HMXB in the Galaxy with a confirmed MI massive supergiant donor star (Hinkle et al. 2020).

As a second example, we built a distribution of soft X-ray luminosities of HMXBs in the Galaxy. The current catalogue does not list the common high-energy information such as X-ray fluxes, hardness ratio, or hydrogen column density. HMXBs can be variable sources or be obscured, and the modelling of their high-energy emission requires a case-by-case approach, which is why we do not provide such high-level information. However, with the provided list of their counterparts in soft and hard X-ray catalogues, users can easily find this information. First, we use the distances in Table A.l, which were queried in Bailer-Jones et al. (2021) using the Gaia DR3 positions. Then, we query the Swift 2SXPS catalogue using their available Swift identifiers, and fetch the value of the unabsorbed flux in the 0.3−10 keV band (column apee_flux_b). In Fig. 4 we present the distribution of X-ray luminosities that can be derived from Swift and Gaia data. We note that the source showing extreme X-ray luminosity at >1040 erg s−1 is IGR J16318-4848, the prime example of an absorbed sgB[e]HMXBs (see Fortin et al. 2020 for recent broadband observations of this binary). This luminosity should clearly be considered with caution because of the uncertainty on the very high absorption in the line of sight and on the distance to the source. This is but a very crude example, as the users might wish to consider other X-ray bands or hardness ratios coming from their preferred observatories, or might consider the exact models used to infer fluxes (de-reddened or not, power law vs. black body, etc). There are many other possibilities of use for this catalogue depending on the user's goal.

thumbnail Fig. 2

Face-on view of the 107 Galactic HMXBs with Gaia parallaxes. Bars indicate the 68% confidence interval in distance. Background image credits: NASA/JPL-Caltech/R. Hurt (SSC/Caltech).

thumbnail Fig. 3

Corbet diagram of the 38 HMXBs in the Liu et al. (2006) catalogue (top panel) and the 75 HMXBs in the current catalogue (bottom panel). BeHMXBs are shown as blue dots, sgHMXBs (SFXTs) are shown as green triangles (squares), HMGB are shown as pink squares, and the remaining HMXBs with a peculiar and/or unclear spectral type are shown as red crosses “Indef”).

thumbnail Fig. 4

Distribution of soft X-ray luminosities of the Galactic HMXBs seen by Swift and Gaia (N = 89).

4 Conclusion

After more than 15 yr of multi-wavelength observation campaigns, the landscape of Galactic HMXBs has changed significantly. Much information about them is available throughout the literature. We present an updated catalogue of HMXBs in the Milky Way containing not only basic information such as identifiers, subclasses, and positions, but also multi-wavelength counterparts and orbital binary parameters. These are available from an in-depth both automatised and manual survey performed across published papers and catalogues of high-energy sources.

Compared to the last published catalogue of HMXBs by Liu et al. (2006), the total number of HMXBs known in the Galaxy has increased by roughly 33% (see Fig. 5), by a factor of two when considering confirmed systems, and by a factor of three in the particular case of sgHMXBs. The latter most definitely benefited from the capabilities of INTEGRAL and HMXBs in general, through many focused optical/nIR identification campaigns, as well as multiple follow-up efforts in the soft X-ray band, which are essential in the process of constraining the exact position of hard X-ray sources in the sky. In addition, the data collected by the Gaia satellite since 2015 offer unrivalled estimates of positions and velocities, including distances to HMXBs across two-thirds of the catalogue, which it was not possible to achieve at this scale before.

The search for new X-ray binaries and information on them is still active, and the arrival of new observing facilities will ensure continued interest in this field. The eROSITA, SVOM, and LSST observatories will not only contribute to studying currently known or new persistent systems, but will also provide much more information on transient sources and therefore provide insight into other stages of binary evolution such as supernova explosions or merger events. The addition of the gravitational messenger by the LIGO/Virgo/KAGRA observatories will work in synergy with electromagnetic transient sky facilities to constrain the endpoint of binary evolution; we will soon, if we do not already, have access to observational data on phases spanning the entire life of massive binary stars. The coming years will thus provide many opportunities for studying the evolution of massive stars in binaries, which contribute to the Galactic ecology by their X-ray emission, heavy nucleus formation, and possible retro-action on the interstellar medium.

thumbnail Fig. 5

Evolution of the number and nature of HMXBs in the Galaxy with an identified spectral type from 2006 (left) to 2022 (right).

Acknowledgements

We thank the anonymous Referee for their insightful remarks that helped us improving both this paper and the online catalogue. The authors were supported by the LabEx UnivEarthS: Interface project 110 “Binary rEvolution: from binary evolution towards merging of compact objects”. SC is grateful to the CNES (Centre National d’Études Spatiales) for the funding of MINE (Multi-wavelength INTEGRAL Network). FG is CONICET researcher. FG acknowledges support by PIP 0113 (CONICET), PICT-2017-2865 (ANPCyT) and PIBAA 1275 (CONICET). This work made use of NASA’s Astrophysics Data System (ADS) web services, and of the services associated to the Centre de Données Astronomiques de Strasbourg (CDS) Simbad and Vizier. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. Software: Topcat (Taylor 2005), MATPLOTLIB (Hunter 2007), NUMPY (van der Walt et al. 2011), SCIPY (Virtanen et al. 2020) and PYTHON from http://python.org

Appendix A Catalogue of Galactic HMXBs

Table A.1

Catalogue of Galactic HMXBs: General information.

Table A.2

Catalogue of Galactic HMXBs: Orbital data.

References

  1. Abdollahi, S., Acero, F., Baldini, L., et al. 2022, ApJS, 260, 53 [NASA ADS] [CrossRef] [Google Scholar]
  2. Abubekerov, M. K., Antokhina, É. A., & Cherepashchuk, A. M. 2004, Astron. Rep., 48, 89 [NASA ADS] [CrossRef] [Google Scholar]
  3. Adams, C. B., Benbow, W., Brill, A., et al. 2021, ApJ, 923, 241 [NASA ADS] [CrossRef] [Google Scholar]
  4. Annala, M., & Poutanen, J. 2010, A&A, 520, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Antokhin, I. I., Cherepashchuk, A. M., Antokhina, E. A., & Tatarnikov, A. M. 2022, ApJ, 926, 123 [NASA ADS] [CrossRef] [Google Scholar]
  6. Aragona, C., McSwain, M. V., Grundstrom, E. D., et al. 2009, ApJ, 698, 514 [Google Scholar]
  7. Aragona, C., McSwain, M. V., & De Becker, M. 2010, ApJ, 724, 306 [Google Scholar]
  8. Aret, A., Kraus, M., & Šlechta, M. 2016, MNRAS, 456, 1424 [NASA ADS] [CrossRef] [Google Scholar]
  9. Ash, T. D. C., Reynolds, A. P., Roche, P., et al. 1999, MNRAS, 307, 357 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147 [Google Scholar]
  11. Bamba, A., Yokogawa, J., Ueno, M., Koyama, K., & Yamauchi, S. 2001, PASJ, 53, 1179Barnstedt, J., Staubert, R., Santangelo, A., et al. 2008, A&A, 486, 293 [NASA ADS] [Google Scholar]
  12. Barsukova, E. A., Borisov, N. V., Burenkov, A. N., et al. 2006, ASP Conf. Ser., 355, 305 [NASA ADS] [Google Scholar]
  13. Baykal, A., Inam, S. Ç., Stark, M. J., et al. 2007, MNRAS, 374, 1108 [NASA ADS] [CrossRef] [Google Scholar]
  14. Baykal, A., Gögüş, E., Çagdaş Inam, S., & Belloni, T. 2010, ApJ, 711, 1306 [NASA ADS] [CrossRef] [Google Scholar]
  15. Belczynski, K., & Ziolkowski, J. 2009, ApJ, 707, 870 [NASA ADS] [CrossRef] [Google Scholar]
  16. Belloni, T., Hasinger, G., Pietsch, W., et al. 1993, A&A, 271, 487 [NASA ADS] [Google Scholar]
  17. Bhargava, Y., Rao, A. R., Singh, K. P., et al. 2017, ApJ, 849, 141 [NASA ADS] [CrossRef] [Google Scholar]
  18. Bikmaev, I. F., Nikolaeva, E. A., Shimansky, V. V., et al. 2017, Astron. Lett., 43, 664 [NASA ADS] [CrossRef] [Google Scholar]
  19. Bildsten, L., Chakrabarty, D., Chiu, J., et al. 1997, ApJS, 113, 367 [CrossRef] [Google Scholar]
  20. Bird, A. J., Bazzano, A., Malizia, A., et al. 2016, ApJS, 223, 15 [Google Scholar]
  21. Bissinger, M. 2016, PhD thesis, Friedrich Alexander University of Erlangen-Nuremberg, Germany [Google Scholar]
  22. Blair, D. G., & Candy, B. N. 1985, MNRAS, 212, 219 [NASA ADS] [CrossRef] [Google Scholar]
  23. Blay, P., Negueruela, I., Reig, P., et al. 2006, A&A, 446, 1095 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Blundell, K. M., Bowler, M. G., & Schmidtobreick, L. 2007, A&A, 474, 903 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Bodaghee, A., Tomsick, J. A., & Rodriguez, J. 2012, ApJ, 753, 3 [NASA ADS] [CrossRef] [Google Scholar]
  26. Bonnet-Bidaud, J. M., & Mouchet, M. 1998, A&A, 332, L9 [NASA ADS] [Google Scholar]
  27. Bradt, H. V. D., & McClintock, J. E. 1983, ARA&A, 21, 13 [NASA ADS] [CrossRef] [Google Scholar]
  28. Brodskaya, E. S. 1960, Izvestiya Ordena Trudovogo Krasnogo Znameni Krymskoj Astrofizicheskoj Observatorii, 24, 160 [NASA ADS] [Google Scholar]
  29. Butler, S. C., Tomsick, J. A., Chaty, S., et al. 2009, ApJ, 698, 502 [NASA ADS] [CrossRef] [Google Scholar]
  30. Capitanio, F., Bird, A. J., Fiocchi, M., Scaringi, S., & Ubertini, P. 2011, ApJS, 195, 9 [NASA ADS] [CrossRef] [Google Scholar]
  31. Casares, J., Ribas, I., Paredes, J. M., Martí, J., & Allende Prieto, C. 2005a, MNRAS, 360, 1105 [NASA ADS] [CrossRef] [Google Scholar]
  32. Casares, J., Ribó, M., Ribas, I., et al. 2005b, MNRAS, 364, 899 [Google Scholar]
  33. Casares, J., Corral-Santana, J. M., Herrero, A., et al. 2011, Astrophys. Space Sci. Proc., 21, 559 [NASA ADS] [CrossRef] [Google Scholar]
  34. Casares, J., Negueruela, I., Ribó, M., et al. 2014, Nature, 505, 378 [Google Scholar]
  35. Chakrabarty, D., Koh, T., Bildsten, L., et al. 1995, ApJ, 446, 826 [NASA ADS] [CrossRef] [Google Scholar]
  36. Chaty, S. 2013, Adv. Space Res., 52, 2132 [NASA ADS] [CrossRef] [Google Scholar]
  37. Chaty, S. 2022, Accreting Binaries; Nature, Formation, and Evolution, AAS-IOP Astronomy (Bristol: Institute of Physics Publishing) [Google Scholar]
  38. Chaty, S., Rahoui, F., Foellmi, C., et al. 2008, A&A, 484, 783 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Chen, J. C., Davis, J. E., Doe, S. M., et al. 2019, VizieR Online Data Catalog: IX/57 [Google Scholar]
  40. Cheng, Z. Q., Shao, Y., & Li, X. D. 2014, ApJ, 786, 128 [NASA ADS] [CrossRef] [Google Scholar]
  41. Cherepashchuk, A. M., Belinski, A. A., Dodin, A. V., & Postnov, K. A. 2021, MNRAS, 507, L19 [NASA ADS] [CrossRef] [Google Scholar]
  42. Chernyakova, M., Lutovinov, A., Rodriguez, J., & Revnivtsev, M. 2005, MNRAS, 364, 455 [NASA ADS] [CrossRef] [Google Scholar]
  43. Chojnowski, S. D., Wisniewski, J. P., Whelan, D. G., et al. 2017, AJ, 153, 174 [NASA ADS] [CrossRef] [Google Scholar]
  44. Coe, M. J., Roche, P., Everall, C., et al. 1994, MNRAS, 270, L57 [NASA ADS] [Google Scholar]
  45. Coe, M. J., Fabregat, J., Negueruela, I., Roche, P., & Steele, I. A. 1996, MNRAS, 281, 333 [NASA ADS] [CrossRef] [Google Scholar]
  46. Coe, M. J., Bird, A. J., Hill, A. B., et al. 2007, MNRAS, 378, 1427 [NASA ADS] [CrossRef] [Google Scholar]
  47. Coleiro, A., Chaty, S., Zurita Heras, J. A., Rahoui, F., & Tomsick, J. A. 2013, A&A, 560, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Coley, J. B., Corbet, R. H. D., Mukai, K., & Pottschmidt, K. 2014, ApJ, 793, 77 [NASA ADS] [CrossRef] [Google Scholar]
  49. Coley, J. B., Corbet, R. H. D., Fürst, F., et al. 2019, ApJ, 879, 34 [NASA ADS] [CrossRef] [Google Scholar]
  50. Cominsky, L., Li, F., Bradt, H., et al. 1978, IAU Circ., 3163, 1 [NASA ADS] [Google Scholar]
  51. Cook, M. C., & Warwick, R. S. 1987, MNRAS, 225, 369 [NASA ADS] [CrossRef] [Google Scholar]
  52. Corbet, R. H. D. 1984, A&A, 141, 91 [NASA ADS] [Google Scholar]
  53. Corbet, R. H. D., & Krimm, H. A. 2009, ATel, 2008, 1 [Google Scholar]
  54. Corbet, R. H. D., & Krimm, H. A. 2010, ATel, 3079, 1 [NASA ADS] [Google Scholar]
  55. Corbet, R. H. D., & Krimm, H. A. 2013, ApJ, 778, 45 [NASA ADS] [CrossRef] [Google Scholar]
  56. Corbet, R. H. D., & Remillard, R. 2005, ATel, 377, 1 [NASA ADS] [Google Scholar]
  57. Corbet, R. H. D., Marshall, F. E., Peele, A. G., & Takeshima, T. 1999, ApJ, 517, 956 [NASA ADS] [CrossRef] [Google Scholar]
  58. Corbet, R. H. D., Hannikainen, D. C., & Remillard, R. 2004, ATel, 269, 1 [NASA ADS] [Google Scholar]
  59. Corbet, R. H. D., Barbier, L., Barthelmy, S., et al. 2005, ATel, 649, 1 [NASA ADS] [Google Scholar]
  60. Corbet, R. H. D., Barbier, L., Barthelmy, S., et al. 2006, ATel, 779, 1 [NASA ADS] [Google Scholar]
  61. Corbet, R. H. D., Coley, J. B., & Krimm, H. A. 2016, ATel, 9823, 1 [NASA ADS] [Google Scholar]
  62. Corbet, R. H. D., Coley, J. B., & Krimm, H. A. 2017, ApJ, 846, 161 [NASA ADS] [CrossRef] [Google Scholar]
  63. Corbet, R. H. D., Chomiuk, L., Coe, M. J., et al. 2019, ApJ, 884, 93 [Google Scholar]
  64. Corbet, R. H. D., Coley, J. B., Krimm, H. A., Pottschmidt, K., & Roche, P. 2021, ApJ, 906, 13 [Google Scholar]
  65. Corbet, R. H. D., Coley, J. B., Gendreau, K. C., et al. 2022, ATel, 15614, 1 [NASA ADS] [Google Scholar]
  66. Crampton, D., Hutchings, J. B., & Cowley, A. P. 1985, ApJ, 299, 839 [NASA ADS] [CrossRef] [Google Scholar]
  67. Cusumano, G., Maccarone, M. C., Nicastro, L., Sacco, B., & Kaaret, P. 2000, ApJ, 528, L25 [NASA ADS] [CrossRef] [Google Scholar]
  68. Cusumano, G., Segreto, A., La Parola, V., et al. 2013, ApJ, 775, L25 [NASA ADS] [CrossRef] [Google Scholar]
  69. Cusumano, G., Segreto, A., La Parola, V., et al. 2015, MNRAS, 446, 1041 [CrossRef] [Google Scholar]
  70. Cusumano, G., D’Aì, A., Segreto, A., La Parola, V., & Del Santo, M. 2020, MNRAS, 498, 2750 [NASA ADS] [CrossRef] [Google Scholar]
  71. Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, VizieR Online Data Catalog: II/246 [Google Scholar]
  72. D’Aì, A., Cusumano, G., La Parola, V., & Segreto, A. 2015, MNRAS, 451, 2835 [CrossRef] [Google Scholar]
  73. Delgado-Marti, H., Levine, A. M., Pfahl, E., & Rappaport, S. A. 2001, ApJ, 546, 455 [CrossRef] [Google Scholar]
  74. Densham, R. H., & Charles, P. A. 1982, MNRAS, 201, 171 [NASA ADS] [CrossRef] [Google Scholar]
  75. Dönmez, Ç. K., Serim, M. M., Inam, S. Ç., et al. 2020, MNRAS, 496, 1768 [CrossRef] [Google Scholar]
  76. Doroshenko, V., Tsygankov, S., & Santangelo, A. 2018, A&A, 613, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Doroshenko, V., Santangelo, A., Tsygankov, S. S., & Ji, L. 2021, A&A, 647, A165 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Drave, S. P., Bird, A. J., Townsend, L. J., et al. 2012, A&A, 539, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  79. Dubus, G. 2013, A&A Rev., 21, 64 [NASA ADS] [CrossRef] [Google Scholar]
  80. Ducci, L., Romano, P., Ji, L., & Santangelo, A. 2019, A&A, 631, A135 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Evans, P. A., Page, K. L., Osborne, J. P., et al. 2020, ApJS, 247, 54 [Google Scholar]
  82. Fabrika, S. N. 1997, Ap&SS, 252, 439 [NASA ADS] [CrossRef] [Google Scholar]
  83. Fairlamb, J. R., Oudmaijer, R. D., Mendigutia, I., Ilee, J. D., & van den Ancker, M.E. 2015, MNRAS, 453, 976 [NASA ADS] [CrossRef] [Google Scholar]
  84. Falanga, M., Bozzo, E., Lutovinov, A., et al. 2015, A&A, 577, A130 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Ferrigno, C., Farinelli, R., Bozzo, E., et al. 2013, A&A, 553, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Ferrigno, C., Bozzo, E., & Romano, P. 2022, A&A, 664, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  87. Filliatre, P., & Chaty, S. 2004, ApJ, 616, 469 [NASA ADS] [CrossRef] [Google Scholar]
  88. Finger, M. H., Wilson, R. B., & Chakrabarty, D. 1996a, A&AS, 120, 209 [NASA ADS] [Google Scholar]
  89. Finger, M. H., Wilson, R. B., & Harmon, B. A. 1996b, ApJ, 459, 288 [NASA ADS] [CrossRef] [Google Scholar]
  90. Finger, M. H., Bildsten, L., Chakrabarty, D., et al. 1999, ApJ, 517, 449 [NASA ADS] [CrossRef] [Google Scholar]
  91. Finley, J. P., Belloni, T., & Cassinelli, J. P. 1992, A&A, 262, L25 [NASA ADS] [Google Scholar]
  92. Fiocchi, M., Bazzano, A., Bird, A. J., et al. 2013, ApJ, 762, 19 [NASA ADS] [CrossRef] [Google Scholar]
  93. Forman, W., Jones, C., Cominsky, L., et al. 1978, ApJS, 38, 357 [Google Scholar]
  94. Fortin, F., Chaty, S., Coleiro, A., Tomsick, J. A., & Nitschelm, C. H. R. 2018, A&A, 618, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  95. Fortin, F., Chaty, S., & Sander, A. 2020, ApJ, 894, 86 [NASA ADS] [CrossRef] [Google Scholar]
  96. Fortin, F., Garcia, F., & Chaty, S. 2022a, A&A, 665, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  97. Fortin, F., Garcia, F., Chaty, S., Chassande-Mottin, E., & Simaz Bunzel, A. 2022b, A&A, 665, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  98. Gaia Collaboration 2022, VizieR Online Data Catalog: I/355 [Google Scholar]
  99. Galloway, D. K., Morgan, E. H., & Levine, A. M. 2004, ApJ, 613, 1164 [NASA ADS] [CrossRef] [Google Scholar]
  100. Galloway, D. K., Wang, Z., & Morgan, E. H. 2005, ApJ, 635, 1217 [NASA ADS] [CrossRef] [Google Scholar]
  101. Gamen, R., Barbà, R. H., Walborn, N. R., et al. 2015, A&A, 583, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  102. Garcia, B. 1993, ApJS, 87, 197 [NASA ADS] [CrossRef] [Google Scholar]
  103. Garcia, F., Fogantini, F. A., Chaty, S., & Combi, J. A. 2018, A&A, 618, A61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  104. Garrison, R. F., Hiltner, W. A., & Schild, R. E. 1977, ApJS, 35, 111 [Google Scholar]
  105. Gies, D. R., & Bolton, C. T. 1986, ApJS, 61, 419 [Google Scholar]
  106. Gies, D. R., Bolton, C. T., Thomson, J. R., et al. 2003, ApJ, 583, 424 [Google Scholar]
  107. Gögüş, E., Patel, S. K., Wilson, C. A., et al. 2005, ApJ, 632, 1069 [CrossRef] [Google Scholar]
  108. Gomez, S., & Grindlay, J. E. 2021, ApJ, 913, 48 [NASA ADS] [CrossRef] [Google Scholar]
  109. Gontcharov, G. A. 2006, Astron. Lett., 32, 759 [Google Scholar]
  110. González-Galán, A. 2015, ArXiv e-prints [arXiv:15®3.®1®87] [Google Scholar]
  111. González-Galán, A., Negueruela, I., Castro, N., et al. 2014, A&A, 566, A131 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  112. Gotthelf, E. V., Halpern, J. P., Camilo, F., Markwardt, C., & Swank, J. 2008, ATel., 1392, 1 [NASA ADS] [Google Scholar]
  113. Gregory, P. C. 2002, ApJ, 575, 427 [NASA ADS] [CrossRef] [Google Scholar]
  114. Grindlay, J. E., Petro, L. D., & McClintock, J. E. 1984, ApJ, 276, 621 [NASA ADS] [CrossRef] [Google Scholar]
  115. Grundstrom, E. D., Boyajian, T.S., Finch, C., et al. 2007, ApJ, 660, 1398 [NASA ADS] [CrossRef] [Google Scholar]
  116. Grunhut, J. H., Bolton, C. T., & McSwain, M. V. 2014, A&A, 563, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  117. Haberl, F., Angelini, L., Motch, C., & White, N. E. 1998, A&A, 330, 189 [NASA ADS] [Google Scholar]
  118. Hardorp, J., Theile, I., & Voigt, H. H. 1964, Hamburger Sternw. Warner & Swasey Obs., C03, 0 [Google Scholar]
  119. Hare, J., Halpern, J. P., Clavel, M., et al. 2019, ApJ, 878, 15 [NASA ADS] [CrossRef] [Google Scholar]
  120. Harmanec, P., Habuda, P., Štefl, S., et al. 2000, A&A, 364, L85 [NASA ADS] [Google Scholar]
  121. Harris, D. E., Forman, W., Gioia, I. M., et al. 1990, Einstein Observatory Catalog of IPC X-ray Sources, 2 [Google Scholar]
  122. Harvey, M., Rulten, C. B., & Chadwick, P. M. 2022, MNRAS, 512, 1141 [NASA ADS] [CrossRef] [Google Scholar]
  123. Hemphill, P., Coley, J., Fuerst, F., et al. 2019a, ATel, 12556, 1 [NASA ADS] [Google Scholar]
  124. Hemphill, P. B., Rothschild, R. E., Cheatham, D. M., et al. 2019b, ApJ, 873, 62 [NASA ADS] [CrossRef] [Google Scholar]
  125. Hill, A. B., Walter, R., Knigge, C., et al. 2005, A&A, 439, 255 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  126. Hillwig, T.C., Gies, D.R., Huang, W., et al. 2004, ApJ, 615, 422 [NASA ADS] [CrossRef] [Google Scholar]
  127. Hinkle, K. H., Lebzelter, T., Fekel, F. C., et al. 2020, ApJ, 904, 143 [NASA ADS] [CrossRef] [Google Scholar]
  128. Houk, N. 1978, Michigan catalogue of two-dimensional spectral types for the HD stars [Google Scholar]
  129. Hu, C.-P., Chou, Y., Ng, C. Y., Lin, L. C.-C., & Yen, D.C.-C. 2017, ApJ, 844, 16 [NASA ADS] [CrossRef] [Google Scholar]
  130. Hulleman, F., in’t Zand, J.J.M., & Heise, J. 1998, A&A, 337, L25 [NASA ADS] [Google Scholar]
  131. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [Google Scholar]
  132. Hutchings, J. B. 1984, PASP, 96, 312 [NASA ADS] [CrossRef] [Google Scholar]
  133. Hutchings, J. B., Cowley, A. P., Crampton, D., & Williams, G. 1981, PASP, 93, 741 [NASA ADS] [CrossRef] [Google Scholar]
  134. Hutchings, J. B., Crampton, D., Cowley, D., Cowley, A. P., & Bord, D. J. 1982, PASP, 94, 541 [NASA ADS] [CrossRef] [Google Scholar]
  135. Hutchings, J. B., Crampton, D., Cowley, A. P., & Thompson, I. B. 1987, PASP, 99, 420 [NASA ADS] [CrossRef] [Google Scholar]
  136. Hynes, R. I., Clark, J. S., Barsukova, E. A., et al. 2002, A&A, 392, 991 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  137. in’t Zand, J. J. M., & Heise, J. 2004, ATel, 362, 1 [Google Scholar]
  138. in’t Zand, J. J. M., Baykal, A., & Strohmayer, T. E. 1998, ApJ, 496, 386 [CrossRef] [Google Scholar]
  139. in’t Zand, J. J. M., Halpern, J., Eracleous, M., et al. 2000, A&A, 361, 85 [Google Scholar]
  140. in’t Zand, J. J. M., Corbet, R. H. D., & Marshall, F. E. 2001a, ApJ, 553, L165 [CrossRef] [Google Scholar]
  141. in’t Zand, J. J. M., Swank, J., Corbet, R. H. D., & Markwardt, C. B. 2001b, A&A, 380, L26 [CrossRef] [EDP Sciences] [Google Scholar]
  142. Islam, N., & Paul, B. 2016, MNRAS, 461, 816 [NASA ADS] [CrossRef] [Google Scholar]
  143. Islam, N., Maitra, C., Pradhan, P., & Paul, B. 2015, MNRAS, 446, 4148 [NASA ADS] [CrossRef] [Google Scholar]
  144. Israel, G. L., Covino, S., Campana, S., et al. 2000, MNRAS, 314, 87 [NASA ADS] [CrossRef] [Google Scholar]
  145. Israel, G. L., Negueruela, I., Campana, S., et al. 2001, A&A, 371, 1018 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  146. Ives, J. C., Sanford, P. W., & Bell Burnell, S. J. 1975, Nature, 254, 578 [NASA ADS] [CrossRef] [Google Scholar]
  147. Iyer, N., & Paul, B. 2017, MNRAS, 471, 355 [NASA ADS] [CrossRef] [Google Scholar]
  148. Jain, C., Paul, B., & Dutta, A. 2009, MNRAS, 397, L11 [NASA ADS] [CrossRef] [Google Scholar]
  149. Jain, C., Paul, B., & Maitra, C. 2011, ATel, 3785, 1 [NASA ADS] [Google Scholar]
  150. Jaisawal, G. K., Naik, S., Ho, W. C. G., et al. 2020, MNRAS, 498, 4830 [NASA ADS] [CrossRef] [Google Scholar]
  151. Jaisawal, G. K., Naik, S., Gupta, S., et al. 2021, J. Astrophys. Astron., 42, 33 [NASA ADS] [CrossRef] [Google Scholar]
  152. Janot-Pacheco, E., Ilovaisky, S. A., & Chevalier, C. 1981, A&A, 99, 274 [NASA ADS] [Google Scholar]
  153. Jaschek, M., & Egret, D. 1982, IAU Symp., 98, 261 [NASA ADS] [Google Scholar]
  154. Jenke, P. A., & Wilson-Hodge, C. A. 2017, ATel, 10812, 1 [NASA ADS] [Google Scholar]
  155. Jenke, P. A., Finger, M. H., Wilson-Hodge, C. A., & Camero-Arranz, A. 2011, AIP Conf. Ser., 1379, 212 [NASA ADS] [Google Scholar]
  156. Johnston, S., Manchester, R. N., Lyne, A. G., et al. 1992, ApJ, 387, L37 [Google Scholar]
  157. Johnston, S., Manchester, R. N., Lyne, A. G., Nicastro, L., & Spyromilio, J. 1994, MNRAS, 268, 430 [NASA ADS] [Google Scholar]
  158. Jonker, P. G., Nelemans, G., & Bassa, C. G. 2007, MNRAS, 374, 999 [Google Scholar]
  159. Kaaret, P., Piraino, S., Halpern, J., & Eracleous, M. 1999, ApJ, 523, 197 [NASA ADS] [CrossRef] [Google Scholar]
  160. Kaaret, P., Cusumano, G., & Sacco, B. 2000, ApJ, 542, L41 [NASA ADS] [CrossRef] [Google Scholar]
  161. Kabiraj, S., & Paul, B. 2020, MNRAS, 497, 1059 [NASA ADS] [CrossRef] [Google Scholar]
  162. Kaper, L., van der Meer, A., & Najarro, F. 2006, A&A, 457, 595 [CrossRef] [EDP Sciences] [Google Scholar]
  163. Karasev, D. I., Lutovinov, A. A., & Burenin, R. A. 2010, MNRAS, 409, L69 [CrossRef] [Google Scholar]
  164. Karasev, D. I., Lutovinov, A. A., Revnivtsev, M. G., & Krivonos, R. A. 2012, Astron. Lett., 38, 629 [NASA ADS] [CrossRef] [Google Scholar]
  165. Kaur, R., Paul, B., Kumar, B., & Sagar, R. 2008, MNRAS, 386, 2253 [NASA ADS] [CrossRef] [Google Scholar]
  166. Kelley, R. L., Apparao, K. M. V., Doxsey, R. E., et al. 1981, ApJ, 243, 251 [NASA ADS] [CrossRef] [Google Scholar]
  167. Kelley, R. L., Rappaport, S., & Ayasli, S. 1983, ApJ, 274, 765 [NASA ADS] [CrossRef] [Google Scholar]
  168. Kennea, J. A., Curran, P., Krimm, H., et al. 2010, ATel, 3060, 1 [NASA ADS] [Google Scholar]
  169. Kharchenko, N. V., Scholz, R. D., Piskunov, A. E., Röser, S., & Schilbach, E. 2007, Astron. Nachr., 328, 889 [Google Scholar]
  170. Kinugasa, K., Torii, K., Hashimoto, Y., et al. 1998, ApJ, 495, 435 [NASA ADS] [CrossRef] [Google Scholar]
  171. Koenigsberger, G., Canalizo, G., Arrieta, A., Richer, M. G., & Georgiev, L. 2003, Rev. Mexicana Astron. Astrofis., 39, 17 [Google Scholar]
  172. Koenigsberger, G., Georgiev, L., Moreno, E., et al. 2006, A&A, 458, 513 Krtička, J., Kubát, J., & Krtičková, I. 2015, A&A, 579, A111 [Google Scholar]
  173. Lamb, R. C., Markert, T. H., Hartman, R. C., Thompson, D. J., & Bignami, G. F. 1980, ApJ, 239, 651 [NASA ADS] [CrossRef] [Google Scholar]
  174. La Palombara, N., Sidoli, L., Esposito, P., Israel, G. L., & Rodríguez Castillo, G. A. 2021, A&A, 649, A118 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  175. La Parola, V., Cusumano, G., Segreto, A., et al. 2013a, ApJ, 775, L24 [NASA ADS] [CrossRef] [Google Scholar]
  176. La Parola, V., D’Aì, A., Cusumano, G., et al. 2013b, ArXiv e-prints [arXiv:1305.3916] [Google Scholar]
  177. LaSala, J., Charles, P. A., Smith, R. A. D., Balucinska-Church, M., & Church, M. J. 1998, MNRAS, 301, 285 [NASA ADS] [CrossRef] [Google Scholar]
  178. Levenhagen, R. S., & Leister, N. V. 2006, MNRAS, 371, 252 [Google Scholar]
  179. Levine, A. M., Rappaport, S., Remillard, R., & Savcheva, A. 2004, ApJ, 617, 1284 [NASA ADS] [CrossRef] [Google Scholar]
  180. Levine, A. M., Bradt, H. V., Chakrabarty, D., Corbet, R. H. D., & Harris, R. J. 2011, ApJS, 196, 6 [NASA ADS] [CrossRef] [Google Scholar]
  181. Lin, X. B., Church, M. J., Nagase, F., & Bałucińska-Church, M. 2002, MNRAS, 337, 1245 [NASA ADS] [CrossRef] [Google Scholar]
  182. Lindstrøm, C., Griffin, J., Kiss, L. L., et al. 2005, MNRAS, 363, 882 [CrossRef] [Google Scholar]
  183. Liu, Q. Z., van Paradijs, J., & van den Heuvel, E. P. J. 2000, A&AS, 147, 25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  184. Liu, Q. Z., van Paradijs, J., & van den Heuvel, E. P. J. 2005, A&A, 442, 1135 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  185. Liu, Q. Z., van Paradijs, J., & van den Heuvel, E. P. J. 2006, A&A, 455, 1165 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  186. Lopes de Oliveira, R., Motch, C., Haberl, F., Negueruela, I., & Janot-Pacheco, E. 2006, A&A, 454, 265 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  187. Lutovinov, A., Rodriguez, J., Revnivtsev, M., & Shtykovskiy, P. 2005, A&A, 433, L41 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  188. Lutovinov, A. A., Buckley, D. A. H., Townsend, L. J., Tsygankov, S. S., & Kennea, J. 2016, MNRAS, 462, 3823 [NASA ADS] [CrossRef] [Google Scholar]
  189. Lyubimkov, L. S., Rostopchin, S. I., Roche, P., & Tarasov, A. E. 1997, MNRAS, 286, 549 [NASA ADS] [CrossRef] [Google Scholar]
  190. Maitra, C., Kaltenbrunner, D., Haberl, F., et al. 2023, A&A, 669, A30 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  191. Maíz Apellániz, J., Sota, A., Arias, J. I., et al. 2016, ApJS, 224, 4 [CrossRef] [Google Scholar]
  192. Makishima, K., Kawai, N., Koyama, K., et al. 1984, pAsJ, 36, 679 [NASA ADS] [Google Scholar]
  193. Malacaria, C., Kretschmar, P., Madsen, K. K., et al. 2021, ApJ, 909, 153 [NASA ADS] [CrossRef] [Google Scholar]
  194. Malacaria, C., Bhargava, Y., Coley, J. B., et al. 2022, ApJ, 927, 194 [NASA ADS] [CrossRef] [Google Scholar]
  195. Marcu-Cheatham, D. M., Pottschmidt, K., Kühnel, M., et al. 2015, ApJ, 815, 44 [Google Scholar]
  196. Markwardt, C. B., Pereira, D., Ray, P. S., Smith, E., & Swank, J. H. 2008, ATel, 1679, 1 [NASA ADS] [Google Scholar]
  197. Markwardt, C. B., Baumgartner, W. H., Skinner, G. K., & Corbet, R. H. D. 2010, ATel, 2564, 1 [NASA ADS] [Google Scholar]
  198. Marsden, D., Gruber, D. E., Heindl, W. A., Pelling, M. R., & Rothschild, R. E. 1998, ApJ, 502, L129 [NASA ADS] [CrossRef] [Google Scholar]
  199. Marti, J., Luque-Escamilla, P. L., & Muñoz-Arjonilla, Á. J. 2016, A&A, 596, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  200. Martins, F., Schaerer, D., & Hillier, D. J. 2005, A&A, 436, 1049 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  201. Masetti, N., Mason, E., Morelli, L., et al. 2008, A&A, 482, 113 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  202. Masetti, N., Parisi, P., Palazzi, E., et al. 2009, A&A, 495, 121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  203. Masetti, N., Parisi, P., Palazzi, E., et al. 2010, A&A, 519, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  204. Masetti, N., Landi, R., Parisi, P., Bazzano, A., & Bird, A. J. 2012, ATel, 4209, 1 [NASA ADS] [Google Scholar]
  205. Masetti, N., Parisi, P., Palazzi, E., et al. 2013, A&A, 556, A120 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  206. Masetti, N., Ferreira, T. S., Saito, R. K., Kammers, R., & Minniti, D. 2018, ATel, 11992, 1 [NASA ADS] [Google Scholar]
  207. Mason, A. B., Clark, J. S., Norton, A. J., Negueruela, I., & Roche, P. 2009, A&A, 505, 281 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  208. Mason, A. B., Norton, A. J., Clark, J. S., Negueruela, I., & Roche, P. 2010, A&A, 509, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  209. Mason, A. B., Norton, A. J., Clark, J. S., Negueruela, I., & Roche, P. 2011, A&A, 532, A124 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  210. Mason, A. B., Clark, J. S., Norton, A. J., et al. 2012, MNRAS, 422, 199 [NASA ADS] [CrossRef] [Google Scholar]
  211. Massi, M., & Torricelli-Ciamponi, G. 2016, A&A, 585, A123 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  212. Mathew, B., & Subramaniam, A. 2011, Bull. Astron. Soc. India, 39, 517 [NASA ADS] [Google Scholar]
  213. Mattana, F., Götz, D., Falanga, M., et al. 2006, A&A, 460, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  214. McBride, V. A., Wilms, J., Coe, M. J., et al. 2006, A&A, 451, 267 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  215. McBride, V. A., Wilms, J., Kreykenbohm, I., et al. 2007, A&A, 470, 1065 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  216. McClintock, J. E., Rappaport, S., Joss, P. C., et al. 1976, ApJ, 206, L99 [Google Scholar]
  217. McClintock, J. E., Rappaport, S. A., Nugent, J. J., & Li, F. K. 1977, ApJ, 216, L15 [NASA ADS] [CrossRef] [Google Scholar]
  218. McCollum, B., & Laine, S. 2019, ATel, 12560, 1 [NASA ADS] [Google Scholar]
  219. Mereghetti, S., Romano, P., & Sidoli, L. 2008, A&A, 483, 249 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  220. Miller-Jones, J. C. A., Deller, A. T., Shannon, R. M., et al. 2018, MNRAS, 479, 4849 [Google Scholar]
  221. Miller-Jones, J. C. A., Bahramian, A., Orosz, J. A., et al. 2021, Science, 371, 1046 [Google Scholar]
  222. Morel, T., & Grosdidier, Y. 2005, MNRAS, 356, 665 [NASA ADS] [CrossRef] [Google Scholar]
  223. Moritani, Y., Kawano, T., Chimasu, S., et al. 2018, PASJ, 70, 61 [NASA ADS] [Google Scholar]
  224. Motch, C., & Janot-Pacheco, E. 1987, A&A, 182, L55 [NASA ADS] [Google Scholar]
  225. Motch, C., Haberl, F., Dennerl, K., Pakull, M., & Janot-Pacheco, E. 1997, A&A, 323, 853 [NASA ADS] [Google Scholar]
  226. Motch, C., Herent, O., & Guillout, P. 2003, Astron. Nachr., 324, 61 [NASA ADS] [CrossRef] [Google Scholar]
  227. Mukerjee, K., & Antia, H. M. 2021, ApJ, 920, 139 [NASA ADS] [CrossRef] [Google Scholar]
  228. Nabizadeh, A., Tsygankov, S. S., Karasev, D. I., et al. 2019, A&A, 622, A198 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  229. Nabizadeh, A., Tsygankov, S. S., Molkov, S. V., et al. 2022, A&A, 657, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  230. Nazé, Y., Rauw, G., Czesla, S., Smith, M. A., & Robrade, J. 2022, MNRAS, 510, 2286 [CrossRef] [Google Scholar]
  231. Negueruela, I., & Okazaki, A. T. 2001, A&A, 369, 108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  232. Negueruela, I., & Schurch, M. P. E. 2007, A&A, 461, 631 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  233. Negueruela, I., Roche, P., Fabregat, J., & Coe, M. J. 1999, MNRAS, 307, 695 [NASA ADS] [CrossRef] [Google Scholar]
  234. Negueruela, I., Israel, G. L., Marco, A., Norton, A. J., & Speziali, R. 2003, A&A, 397, 739 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  235. Negueruela, I., Smith, D. M., Harrison, T. E., & Torrejón, J. M. 2006a, ApJ, 638, 982 [NASA ADS] [CrossRef] [Google Scholar]
  236. Negueruela, I., Smith, D. M., Reig, P., Chaty, S., & Torrejón, J. M. 2006b, ESA SP, 604, 165 [NASA ADS] [Google Scholar]
  237. Negueruela, I., Casares, J., Verrecchia, F., et al. 2008, ATel, 1876, 1 [Google Scholar]
  238. Negueruela, I., Ribó, M., Herrero, A., et al. 2011, ApJ, 732, L11 [Google Scholar]
  239. Nemravová, J., Harmanec, P., Koubský, P., et al. 2012, A&A, 537, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  240. Nespoli, E., Fabregat, J., & Mennickent, R. E. 2008a, ATel, 1396, 1 [NASA ADS] [Google Scholar]
  241. Nespoli, E., Fabregat, J., & Mennickent, R. E. 2008b, A&A, 486, 911 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  242. Nikolaeva, E. A., Bikmaev, I. F., Melnikov, S. S., et al. 2013, Bull. Crimean Astrophys. Observ., 109, 27 [CrossRef] [Google Scholar]
  243. Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  244. O’Connor, B., Gögüş, E., Huppenkothen, D., et al. 2022, ApJ, 927, 139 [CrossRef] [Google Scholar]
  245. Okazaki, A. T., & Negueruela, I. 2001, A&A, 377, 161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  246. Orosz, J. A., Kuulkers, E., van der Klis, M., et al. 2001, ApJ, 555, 489 [NASA ADS] [CrossRef] [Google Scholar]
  247. Pacheco, E. J., Chevalier, C., & Ilovaisky, S. A. 1982, IaU Symp., 98, 151 [NASA ADS] [Google Scholar]
  248. Pakull, M. W., Motch, C., & Negueruela, I. 2003, ATel, 202, 1 [NASA ADS] [Google Scholar]
  249. Parkes, G. E., Murdin, P. G., & Mason, K. O. 1978, MNRAS, 184, 73P [NASA ADS] [CrossRef] [Google Scholar]
  250. Parkes, G. E., Murdin, P. G., & Mason, K. O. 1980, MNRAS, 190, 537 [Google Scholar]
  251. Paul, B., Agrawal, P. C., Mukerjee, K., et al. 2001, A&A, 370, 529 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  252. Pearlman, A. B., Coley, J. B., Corbet, R. H. D., & Pottschmidt, K. 2019, ApJ, 873, 86 [NASA ADS] [CrossRef] [Google Scholar]
  253. Pellizza, L. J., Chaty, S., & Negueruela, I. 2006, A&A, 455, 653 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  254. Pellizza, L. J., Chaty, S., & Chisari, N. E. 2011, A&A, 526, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  255. Picchi, P., Shore, S. N., Harvey, E. J., & Berdyugin, A. 2020, A&A, 640, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  256. Pike, S. N., & Harrison, F. A. 2020, ATel, 14291, 1 [NASA ADS] [Google Scholar]
  257. Piraino, S., Santangelo, A., Giarrusso, S., et al. 1999, Nucl. Phys. B Proc. Suppl., 69, 220 [Google Scholar]
  258. Polcaro, V. F., Rossi, C., Giovannelli, F., et al. 1990, A&A, 231, 354 [NASA ADS] [Google Scholar]
  259. Popper, D. M. 1950, ApJ, 111, 495 [NASA ADS] [CrossRef] [Google Scholar]
  260. Porter, J. M. 1996, MNRAS, 280, L31 [NASA ADS] [CrossRef] [Google Scholar]
  261. Pradhan, P., Maitra, C., Paul, B., & Paul, B. C. 2013, MNRAS, 436, 945 [NASA ADS] [CrossRef] [Google Scholar]
  262. Raguzova, N. V., & Popov, S. B. 2005, Astron. Astrophys. Transac., 24, 151 [NASA ADS] [CrossRef] [Google Scholar]
  263. Rahoui, F., & Chaty, S. 2008, A&A, 492, 163 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  264. Rahoui, F., Chaty, S., Lagage, P. O., & Pantin, E. 2008, A&A, 484, 801 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  265. Raichur, H., & Paul, B. 2010a, MNRAS, 406, 2663 [NASA ADS] [CrossRef] [Google Scholar]
  266. Raichur, H., & Paul, B. 2010b, MNRAS, 401, 1532 [NASA ADS] [CrossRef] [Google Scholar]
  267. Ratti, E. M., Bassa, C. G., Torres, M. A. P., et al. 2010, MNRAS, 408, 1866 [NASA ADS] [CrossRef] [Google Scholar]
  268. Ray, P. S., & Chakrabarty, D. 2002, ApJ, 581, 1293 [NASA ADS] [CrossRef] [Google Scholar]
  269. Reed, B. C. 2003, AJ, 125, 2531 [NASA ADS] [CrossRef] [Google Scholar]
  270. Reig, P., & Roche, P. 1999, MNRAS, 306, 100 [NASA ADS] [CrossRef] [Google Scholar]
  271. Reig, P., & Zezas, A. 2018, A&A, 613, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  272. Reig, P., Negueruela, I., Buckley, D. A. H., et al. 2001, A&A, 367, 266 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  273. Reig, P., Negueruela, I., Fabregat, J., et al. 2004, A&A, 421, 673 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  274. Reig, P., Negueruela, I., Fabregat, J., Chato, R., & Coe, M. J. 2005a, A&A, 440, 1079 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  275. Reig, P., Negueruela, I., Papamastorakis, G., Manousakis, A., & Kougentakis, T. 2005b, A&A, 440, 637 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  276. Reig, P., Zezas, A., & Gkouvelis, L. 2010, A&A, 522, A107 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  277. Reig, P., Nespoli, E., Fabregat, J., & Mennickent, R. E. 2011, A&A, 533, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  278. Reig, P., Blay, P., & Blinov, D. 2017, A&A, 598, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  279. Reig, P., Fabregat, J., & Alfonso-Garzón, J. 2020, A&A, 640, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  280. Rho, J., Moon, D. S., Gotthelf, E., Pannuti, T., & Corbet, R. 2004, AAS Astrophys. Div., 8, 17.30 [Google Scholar]
  281. Rivinius, T., Carciofi, A. C., & Martayan, C. 2013, A&A Rev., 21, 69 [NASA ADS] [CrossRef] [Google Scholar]
  282. Rodes-Roca, J. J., Torrejón, J. M., Martínez-Núñez, S., Bernabéu, G., & Magazzú, A. 2013, A&A, 555, A115 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  283. Rodes-Roca, J. J., Bernabeu, G., Magazzù, A., Torrejón, J. M., & Solano, E. 2018, MNRAS, 476, 2110 [NASA ADS] [Google Scholar]
  284. Romano, P., Sidoli, L., Ducci, L., et al. 2010, MNRAS, 401, 1564 [NASA ADS] [CrossRef] [Google Scholar]
  285. Roy, J., Agrawal, P. C., Singari, B., & Misra, R. 2020, Res. Astron. Astrophys., 20, 155 [Google Scholar]
  286. Safi-Harb, S., Ribó, M., Butt, Y., et al. 2007, ApJ, 659, 407 [NASA ADS] [CrossRef] [Google Scholar]
  287. Salganik, A., Tsygankov, S. S., Djupvik, A. A., et al. 2022, MNRAS, 509, 5955 [Google Scholar]
  288. Saraswat, P., & Apparao, K. M. V. 1992, ApJ, 401, 678 [NASA ADS] [CrossRef] [Google Scholar]
  289. Segreto, A., La Parola, V., Cusumano, G., et al. 2013, A&A, 558, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  290. Sguera, V., Hill, A. B., Bird, A. J., et al. 2007, A&A, 467, 249 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  291. Sguera, V., Drave, S. R., Bird, A. J., et al. 2011, MNRAS, 417, 573 [NASA ADS] [CrossRef] [Google Scholar]
  292. Sguera, V., Drave, S. R., Sidoli, L., et al. 2013, A&A, 556, A27 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  293. Sguera, V., Sidoli, L., Bird, A. J., Paizis, A., & Bazzano, A. 2020, MNRAS, 491, 4543 [NASA ADS] [CrossRef] [Google Scholar]
  294. Sharma, P., Sharma, R., Jain, C., & Dutta, A. 2022, MNRAS, 509, 5747 [Google Scholar]
  295. Shaw, S. E., Hill, A. B., Kuulkers, E., et al. 2009, MNRAS, 393, 419 [NASA ADS] [CrossRef] [Google Scholar]
  296. Shenavrin, V. I., Taranova, O. G., & Nadzhip, A. E. 2011, Astron. Rep., 55, 31 [NASA ADS] [CrossRef] [Google Scholar]
  297. Shirke, P., Bala, S., Roy, J., & Bhattacharya, D. 2021, J. Astrophys. Astron., 42, 58 [NASA ADS] [CrossRef] [Google Scholar]
  298. Sidoli, L., & Paizis, A. 2018, MNRAS, 481, 2779 [NASA ADS] [CrossRef] [Google Scholar]
  299. Sidoli, L., Esposito, P., Motta, S. E., Israel, G. L., & Rodríguez Castillo, G. A. 2016, MNRAS, 460, 3637 [NASA ADS] [CrossRef] [Google Scholar]
  300. Sidoli, L., Israel, G. L., Esposito, P., Rodríguez Castillo, G. A., & Postnov, K. 2017, MNRAS, 469, 3056 [CrossRef] [Google Scholar]
  301. Sidoli, L., Postnov, K., Tiengo, A., et al. 2020, A&A, 638, A71 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  302. Sidoli, L., Sguera, V., Esposito, P., Oskinova, L., & Polletta, M. 2022, MNRAS, 512, 2929 [NASA ADS] [CrossRef] [Google Scholar]
  303. Smith, D. M., Heindl, W. A., & Swank, J. H. 2002, ApJ, 578, L129 [NASA ADS] [CrossRef] [Google Scholar]
  304. Smith, D. M., Hazelton, B., Coburn, W., et al. 2005, ATel, 557, 1 [NASA ADS] [Google Scholar]
  305. Smith, M. A., Lopes de Oliveira, R., & Motch, C. 2012, ApJ, 755, 64 [NASA ADS] [CrossRef] [Google Scholar]
  306. Sota, A., Maíz Apellániz, J., Walborn, N. R., et al. 2011, ApJS, 193, 24 [Google Scholar]
  307. Sota, A., Maíz Apellániz, J., Morrell, N. I., et al. 2014, ApJS, 211, 10 [Google Scholar]
  308. Stecchini, P. E., Castro, M., Jablonski, F., D’Amico, F., & Braga, J. 2017, ApJ, 843, L10 [NASA ADS] [CrossRef] [Google Scholar]
  309. Stecchini, P. E., D’Amico, F., Jablonski, F., Castro, M., & Braga, J. 2020, MNRAS, 493, 2694 [NASA ADS] [CrossRef] [Google Scholar]
  310. Stella, L., White, N. E., Davelaar, J., et al. 1985, ApJ, 288, L45 [NASA ADS] [CrossRef] [Google Scholar]
  311. Stickland, D., Lloyd, C., & Radziun-Woodham, A. 1997, MNRAS, 286, L21 [NASA ADS] [CrossRef] [Google Scholar]
  312. Stollberg, M. T., Finger, M. H., Wilson, R. B., et al. 1993, IAU Circ., 5836, 1 [NASA ADS] [Google Scholar]
  313. Stoyanov, K. A., Zamanov, R. K., Latev, G. Y., Abedin, A. Y., & Tomov, N. A. 2014, Astron. Nachr., 335, 1060 [NASA ADS] [CrossRef] [Google Scholar]
  314. Strader, J., Chomiuk, L., Cheung, C. C., Salinas, R., & Peacock, M. 2015, ApJ, 813, L26 [NASA ADS] [CrossRef] [Google Scholar]
  315. Strohmayer, T., Rodriquez, J., Markwardt, C., et al. 2009, ATel, 2002, 1 [Google Scholar]
  316. Taylor, M. B. 2005, ASP Conf. Ser., 347, 29 [Google Scholar]
  317. Thompson, T. W. J., Tomsick, J. A., in’t Zand, J. J. M., Rothschild, R. E., & Walter, R. 2007, ApJ, 661, 447 [NASA ADS] [CrossRef] [Google Scholar]
  318. Torii, K., Kinugasa, K., Katayama, K., et al. 1998, ApJ, 508, 854 [NASA ADS] [CrossRef] [Google Scholar]
  319. Torii, K., Sugizaki, M., Kohmura, T., Endo, T., & Nagase, F. 1999, ApJ, 523, L65 [NASA ADS] [CrossRef] [Google Scholar]
  320. Torrejón, J. M., & Orr, A. 2001, A&A, 377, 148 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  321. Torrejón, J. M., Negueruela, I., Smith, D. M., & Harrison, T. E. 2010, A&A, 510, A61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  322. Townsend, L. J., Coe, M. J., Corbet, R. H. D., & Hill, A. B. 2011, MNRAS, 416, 1556 [NASA ADS] [CrossRef] [Google Scholar]
  323. Tsygankov, S. S., Wijnands, R., Lutovinov, A. A., Degenaar, N., & Poutanen, J. 2017, MNRAS, 470, 126 [Google Scholar]
  324. Tsygankov, S. S., Lutovinov, A. A., Molkov, S. V., et al. 2021, ApJ, 909, 154 [NASA ADS] [CrossRef] [Google Scholar]
  325. Uchida, N., Takahashi, H., Fukazawa, Y., & Makishima, K. 2021, PASJ, 73, 1389 [NASA ADS] [CrossRef] [Google Scholar]
  326. van den Eijnden, J., Degenaar, N., Russell, T. D., et al. 2021, MNRAS, 507, 3899 [NASA ADS] [CrossRef] [Google Scholar]
  327. van den Heuvel, E. P. J. 2019, IAU Symp., 346, 1 [NASA ADS] [Google Scholar]
  328. van der Meer, A., Kaper, L., van Kerkwijk, M. H., Heemskerk, M. H. M., & van den Heuvel, E. P. J. 2007, A&A, 473, 523 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  329. van der Walt, S., Colbert, C., & Varoquaux, G. 2011, Comput. Sci. Eng., 13, 2 [Google Scholar]
  330. van Kerkwijk, M. H., Geballe, T. R., King, D. L., van der Klis, M., & van Paradijs, J. 1996, A&A, 314, 521 [Google Scholar]
  331. van Paradijs, J. 1995, in X-ray Binaries (Cambridge: Cambridge University Press), 536 [Google Scholar]
  332. van Soelen, B., Mc Keague, S., Malyshev, D., et al. 2022, MNRAS, 515, 1078 [NASA ADS] [CrossRef] [Google Scholar]
  333. Verrecchia, F., Israel, G. L., Negueruela, I., et al. 2002, A&A, 393, 983 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  334. Vieira, S. L. A., Corradi, W. J. B., Alencar, S. H. P., et al. 2003, AJ, 126, 2971 [Google Scholar]
  335. Vijapurkar, J., & Drilling, J. S. 1993, ApJS, 89, 293 [NASA ADS] [CrossRef] [Google Scholar]
  336. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261 [CrossRef] [Google Scholar]
  337. Waisberg, I. R., & Romani, R. W. 2015, ApJ, 805, 18 [NASA ADS] [CrossRef] [Google Scholar]
  338. Walter, R., Zurita Heras, J., Bassani, L., et al. 2006, A&A, 453, 133 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  339. Walter, R., Lutovinov, A. A., Bozzo, E., & Tsygankov, S. S. 2015, A&A Rev., 23, 2 [NASA ADS] [CrossRef] [Google Scholar]
  340. Wang, Z. X., & Gies, D. R. 1998, PASP, 110, 1310 [Google Scholar]
  341. Warwick, R. S., Marshall, N., Fraser, G. W., et al. 1981, MNRAS, 197, 865 [NASA ADS] [CrossRef] [Google Scholar]
  342. Webb, N. A., Coriat, M., Traulsen, I., et al. 2020, A&A, 641, A136 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  343. Wen, L., Levine, A. M., Corbet, R. H. D., & Bradt, H. V. 2006, ApJS, 163, 372 [NASA ADS] [CrossRef] [Google Scholar]
  344. Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  345. White, N. E., Mason, K. O., Huckle, H. E., Charles, P. A., & Sanford, P. W. 1976, ApJ, 209, L119 [NASA ADS] [CrossRef] [Google Scholar]
  346. White, N. E., Giommi, P., & Angelini, L. 2000, VizieR Online Data Catalog: IX/31 [Google Scholar]
  347. Williams, S. J., Gies, D. R., Matson, R. A., et al. 2010, ApJ, 723, L93 [NASA ADS] [CrossRef] [Google Scholar]
  348. Wilson, C. A., Finger, M. H., Harmon, B. A., et al. 1997, ApJ, 479, 388 [NASA ADS] [CrossRef] [Google Scholar]
  349. Wilson, C. A., Finger, M. H., Harmon, B. A., Chakrabarty, D., & Strohmayer, T. 1998, ApJ, 499, 820 [NASA ADS] [CrossRef] [Google Scholar]
  350. Wilson, C. A., Finger, M. H., Coe, M. J., Laycock, S., & Fabregat, J. 2002, ApJ, 570, 287 [NASA ADS] [CrossRef] [Google Scholar]
  351. Wolff, M. T., Ray, P. S., Ng, M., et al. 2022, ATel, 15556, 1 [NASA ADS] [Google Scholar]
  352. Wood, K. S., Meekins, J. F., Yentis, D. J., et al. 1984, ApJS, 56, 507 [Google Scholar]
  353. Zamanov, R., Stoyanov, K. A., Wolter, U., Marchev, D., & Petrov, N. I. 2019, A&A, 622, A173 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  354. Zhao, Y., Heinke, C. O., Tsygankov, S. S., et al. 2019, MNRAS, 488, 4427 [CrossRef] [Google Scholar]
  355. Zorec, J., Frémat, Y., & Cidale, L. 2005, A&A, 441, 235 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  356. Zurita Heras, J. A., & Chaty, S. 2008, A&A, 489, 657 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  357. Zurita Heras, J. A., De Cesare, G., Walter, R., et al. 2006, A&A, 448, 261 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

1

Laser Interferometer Gravitational-Wave Observatory.

2

Kamioka Gravitational Wave Detector.

3

Laser Interferometer Space Antenna.

All Tables

Table 1

Queried catalogues for the counterpart search.

Table A.1

Catalogue of Galactic HMXBs: General information.

Table A.2

Catalogue of Galactic HMXBs: Orbital data.

All Figures

thumbnail Fig. 1

Edge-on view of the 152 HMXBs in the Galaxy. Galactic latitudes are indicated in degrees. Background image credits: ESA/Gaia/DPAC.

In the text
thumbnail Fig. 2

Face-on view of the 107 Galactic HMXBs with Gaia parallaxes. Bars indicate the 68% confidence interval in distance. Background image credits: NASA/JPL-Caltech/R. Hurt (SSC/Caltech).

In the text
thumbnail Fig. 3

Corbet diagram of the 38 HMXBs in the Liu et al. (2006) catalogue (top panel) and the 75 HMXBs in the current catalogue (bottom panel). BeHMXBs are shown as blue dots, sgHMXBs (SFXTs) are shown as green triangles (squares), HMGB are shown as pink squares, and the remaining HMXBs with a peculiar and/or unclear spectral type are shown as red crosses “Indef”).

In the text
thumbnail Fig. 4

Distribution of soft X-ray luminosities of the Galactic HMXBs seen by Swift and Gaia (N = 89).

In the text
thumbnail Fig. 5

Evolution of the number and nature of HMXBs in the Galaxy with an identified spectral type from 2006 (left) to 2022 (right).

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.