Author Archive

The week 1-5 of November 2021, I (virtually) participated to the IAU Symposium 366 on the origin of outflows in evolved stars. I had the opportunity to present our recently submitted work on a photometric machine-learning classifier.

This paper is a result of an attempt that started way back during my PhD thesis actually. back then in early 2010’s we started investigating a way to automate the spectral classification of Be X-ray binaries. The problem with these sources is that due to the strong emission in the Balmer lines they cannot be used as characteristic features for their corresponding classes. Thus, a different automated approach is needed (based on a classification scheme that we have developed in Maravelias et al. 2014). We started with a rather small sample of well-classified OB stars in the Galaxy and the Small Magellanic Cloud and implemented a Naive Bayesian Classifier, that actually proved to work very well. However, more tests and a larger sample was in need to proceed to a publication. And as time was limited I was postponing the project.

Finally Elias Kyritsis showed up as graduate student willing to deal with this. After a successful undergraduate thesis on spectral classification of BeXBs in the Large Magellanic Cloud Elias moved from the visual inspection to the automated approach. He was successful in many fields: increasing drastically the sample, trying/optimizing/developing a different machine-learning approach, improving the line measurements, and submitting the paper to A&A. His tremendous effort has paid out finally!

I am really excited about this journey and his accomplishment. Without his help this project will at least delayed a loooooot! Thanks Elia!


A new automated tool for the spectral classification of OB stars

E. Kyritsis, G. Maravelias, A. Zezas, P. Bonfini, K. Kovlakas, P. Reig

(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a combination of the Kernel-Density Estimation and the RF algorithm. We train the algorithms on the Equivalent Width (EW) of characteristic absorption lines (features) measured in high-quality spectra from large Galactic (LAMOST,GOSSS) and extragalactic surveys (2dF,VFTS) with available spectral-types and luminosity classes. We find that the overall accuracy score is ∼70% with similar results across all approaches. We show that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We apply our model in other observational data sets providing examples of potential application of our classifier on real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries. In addition, we propose a reduced 10-features scheme that can be applied to large data sets with lower S/N. The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multi-class classification problems, (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work, is applicable to data of different quality and of different format (e.g.,absolute or normalized flux) while our classifier is agnostic to the Luminosity Class of a star and, as much as possible, metallicity independent.

arXiv:2110.10669

Fig. 8.Top left panel shows the confusion matrix of the best RF model applied to the test sample. The right panel shows the confusion matrix of the PRF best model applied to the same data set. The bottom panel shows the confusion matrix of the KDE-RF method. The overall accuracy is the same for all algorithms, 70 %, with the majority of misclassified objects belonging to neighboring classes, indicating the reliability of the algorithms.

During the 15th Hellenic Astronomical Conference I had the opportunity to present advances in two major topics:

1. Applying machine-learning methods to build a photometric classifier for massive stars in nearby galaxies”  (talk)
2. “Bouncing against the Yellow Void – the case of rho Cas” (poster)

I have also contributed to the following works:

by E. Kyritsis on “A new automated tool for the spectral classification of OB stars”
by Stephan de Wit on “Spectral analysis of evolved massive stars in the SMC and LMC”

EAS 2021 poster contributions

Posted June 28, 2021 By grigoris

Three poster contributions during EAS 2021 with the following … statistics: all of them on massive stars,  two within the framework of the ASSESS project, and two on machine-learning applications.

1. Applying machine-learning methods to build a photometric classifier for massive stars in nearby galaxies

Grigoris Maravelias, Alceste Bonanos, Frank Tramper, Stephan de Wit, Ming Yang, Paolo Bonfini

Mass loss is a key parameter in the evolution of massive stars. Despite the recent progress in the theoretical understanding of how stars lose mass, discrepancies between theory and observations still hold. Even worse, episodic mass loss in evolved massive stars is not included in the models while the importance of its role in the evolution os massive stars is currently undetermined. A major hindrance to determining the role of episodic mass loss is the lack of large samples of classified stars. Given the recent availability of extensive photometric catalogs from various surveys spanning a range of metallicity environments, we aim to remedy the situation by applying machine learning techniques to these catalogs.We compiled a large catalog of known massive stars in M31 and M33, using IR (Spitzer) and optical (Pan-STARRS) photometry, as well as Gaia astrometric information. We grouped them in 7 classes (Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and outliers, e.g. QSO’s and background galaxies). Using this catalog as a training set, we built an ensemble classifier utilizing color indices as features. The probabilities from three machine-learning algorithms (Support Vector Classification, Random Forests, Neural Networks) are combined to obtain the final classifications. The overall performance of the classifier is ~87%. Highly populated (Red/Blue/Yellow Supergiants) and well-defined classes (B[e] Supergiants) have a high recovery rate between ~98-74%. On the contrary, Wolf-Rayet sources are detected at ~20% while Luminous Blue Variables are almost non-existent. The is mainly due to the small sample sizes of these classes, although M31 and M33 have spectral classifications for several massive stars (about 2500). In addition, the mixing of spectral types, as there are no strict boundaries in the features space (color indexes) between those classes, complicates the classification. In an independent application of the classifier to other galaxies (IC 1613, WLM, Sextans A) we obtained an overall accuracy of ~71% despite the missing values on their features (which we replace with averaged values from the training sample). This approach results only in a few percent difference, with the remaining discrepancy attributed to the different metallicity environments of their host galaxies. The classifier’s prediction capability is only limited by the available number of sources per class, reflecting the rarity of these objects and the possible physical links between these massive star phases. Our methodology is also efficient in correctly classifying sources with missing data and at lower metallicities, making it an excellent tool for spotting interesting objects and prioritizing targets for observations. Future spectroscopic observations will offer a test-bed of its actual performance along with opportunities for improvement.

For more see this k-poster (submitted for SS32: Machine Learning and Visualisation in Data Intensive Era ).

2. A new automated tool for the spectral classification of OB stars

E. Kyritsis, G. Maravelias, A. Zezas, P. Bonfini, K. Kovlakas, P. Reig

As more and more large spectroscopic surveys become available, an automated approach in spectral classification becomes necessary. Due to the importance of the massive stars it is of paramount importance to identify the phenomenological parameters of these stars (e.g., the spectral type ) which can be used as proxies to their physical parameters (e.g mass, temperature).
In this work, we use the Random Forest (RF) algorithm to develop a tool for automated spectral classification of the OB-type stars into their sub-types. We use the regular RF algorithm, the Probabilistic RF (PRF) which is an extension of RF that incorporates uncertainties, and we introduce the KDE – RF method which is a combination of the Kernel-Density Estimation and the RF algorithm. We train the algorithms on the Equivalent Width (EW) of characteristic absorption lines measured in the spectra from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral-type classification. By following an adaptive binning approach we group the labels of these data on 11 sub-types within the range O3-B9. We examined which of the characteristic spectral lines (features) are more important to use based on a number of feature selection methods and we searched for the optimal hyper-parameters of the classifiers, to achieve the best performance.
From the feature screening process, we find 13 spectral lines as the optimal number of features. We find that the overall accuracy score is ~ 76 % with similar results across all approaches, with our KDE – RF being slightly lower at ~ 73 %. In addition, we show that our optimized RF model can reach an overall accuracy score of ~ 85 % in the ideal case of robust measurement of the weakest characteristic spectral lines. We apply our model in other observational data sets providing examples of potential application of our classifier on real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray Binaries, especially for data with S/N in the range 50-300. Furthermore, we present an alternative model for lower quality data S/N < 25 based on a reduced feature-set classification scheme, including only the strongest spectral lines.
The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when used for spectral classification of early-type stars. This is strengthened also by the fact that we are working with real-world data and not with simulations. In addition, the approach presented in this work is very fast and applicable to products from different surveys in terms of quality (e.g different resolutions) and of different formats (e.g., absolute or normalized flux).

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

3. Evolved massive stars in the Magellanic Clouds

Ming Yang, Alceste Bonanos, Biwei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Man I Lam, Shu Wang, Xiaodian Chen, Yi Ren, Frank Tramper, Zoi Spetsieri

We present two clean, magnitude-limited (IRAC1 or WISE1≤15.0 mag) multiwavelength source catalogs for the Large and Small Magellanic Cloud (LMC and SMC). The catalogs were built by crossmatching (1”) and deblending (3”) between the source list of Spitzer Enhanced Imaging Products (SEIP) and Gaia Data Release 2 (DR2), with strict constraints on the Gaia astrometric solution in order to remove the foreground contamination. It is estimated that about 99.5% of the targets in our catalog are most likely genuine members of the LMC and SMC. The LMC catalog contains 197,004 targets in 52 different bands, while SMC catalog including contains 45,466 targets in 50 different bands, ranging from the ultraviolet to the far-infrared. Additional information about radial velocities and spectral and photometric classifications were collected from the literature. For the LMC, we compare our sample with the sample from Gaia Collaboration et al. (2018), indicating that the bright end of our sample is mostly comprised of blue helium-burning stars (BHeBs) and red HeBs with inevitable contamination of main sequence stars at the blue end. For the SMC, by using the evolutionary tracks and synthetic photometry from MESA Isochrones & Stellar Tracks and the theoretical J-Ks color cuts, we identified and ranked 1,405 red supergiant (RSG), 217 yellow supergiant (YSG), and 1,369 blue supergiant (BSG) candidates in the SMC in five different color-magnitude diagrams (CMDs), where attention should also be paid to the incompleteness of our sample. For the LMC, due to the problems with models, we applied modified magnitude and color cuts based on previous studies, and identified and ranked 2,974 RSG, 508 YSG, and 4,786 BSG candidates in the LMC in six CMDs. The comparison between the CMDs from the two catalogs of the LMC SMC indicates that the most distinct difference appears at the bright red end of the optical and near-infrared CMDs, where the cool evolved stars (e.g., RSGs, asymptotic giant branch stars, and red giant stars) are located, which is likely due to the effect of metallicity and star formation history. A further quantitative comparison of colors of massive star candidates in equal absolute magnitude bins suggests that there is essentially no difference for the BSG candidates, but a large discrepancy for the RSG candidates since LMC targets are redder than the SMC ones, which may be due to the combined effect of metallicity on both spectral type and mass-loss rate as well as the age effect. The effective temperatures (Teff) of massive star populations are also derived from reddening-free color of (J-Ks). The Teff ranges are 3500≤Teff≤5000 K for an RSG population, 5000≤Teff≤8000 K for a YSG population, and Teff≥8000 K for a BSG population, with larger uncertainties toward the hotter stars.

For more see this k-poster (submitted for S16: Massive stars: birth, rotation, and chemical evolution).

Evolved Massive Stars at Low-metallicity IV.Using 1.6μm “H-bump” to identify red supergiant stars:a case study of NGC 6822

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Man I Lam, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri

Abstract

We present a case study of using a novel method to identify red supergiant (RSG) candidates in NGC 6822, based on their 1.6 μm “H-bump”. We collected 32 bands of photometric data for NGC 6822 ranging from optical to MIR. By using the theoretical spectra from MARCS, we demonstrate that there is a prominent difference around 1.6 μm (“H-bump”) between low-surface-gravity (LSG) and high-surface-gravity (HSG) targets. Taking advantage of this feature, we identify efficient color-color diagrams (CCDs) of rzH and rzK to separate HSG and LSG targets from crossmatching of optical and NIR data. Moreover, synthetic photometry from ATLAS9 also give similar results. Further separating RSG candidates from the rest of the LSG candidates is done by using semi-empirical criteria on NIR CMDs and resulted in 323 RSG candidates. Meanwhile, the simulation of foreground stars from Besançon models also indicates that our selection criteria is largely free from the contamination of Galactic giants. In addition to the “H-bump” method, we also use the traditional BVR method as a comparison and/or supplement, by applying a slightly aggressive cut to select as much as possible RSG candidates (358 targets). Furthermore, the Gaia astrometric solution is used to constrain the sample, where 181 and 193 targets were selected from the “H-bump” and BVR method, respectively. The percentages of selected targets in both methods are similar as 60\%, indicating the comparable accuracy of the two methods. In total, there are 234 RSG candidates after combining targets from both methods with 140 (60\%) of them in common. The final RSG candidates are in the expected locations on the MIR CMDs, while the spatial distribution is also coincident with the FUV-selected star formation regions, suggesting the selection is reasonable and reliable.
arXiv.org: 2101.08689

Evolved Massive Stars at Low-metallicity III. A Source Catalog for the Large Magellanic Cloud

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Man I Lam, Yi Ren, Frank Tramper, Zoi T. Spetsieri

We present a clean, magnitude-limited (IRAC1 or WISE115.0 mag) multiwavelength source catalog for the LMC. The catalog was built upon crossmatching (1′′) and deblending (3′′) between the SEIP source list and Gaia DR2, with strict constraints on the Gaia astrometric solution to remove the foreground contamination. The catalog contains 197,004 targets in 52 different bands including 2 ultraviolet, 21 optical, and 29 infrared bands. Additional information about radial velocities and spectral/photometric classifications were collected from the literature. The bright end of our sample is mostly comprised of blue helium-burning stars (BHeBs) and red HeBs with inevitable contamination of main sequence stars at the blue end. After applying modified magnitude and color cuts based on previous studies, we identify and rank 2,974 RSG, 508 YSG, and 4,786 BSG candidates in the LMC in six CMDs. The comparison between the CMDs of the LMC and SMC indicates that the most distinct difference appears at the bright red end of the optical and near-infrared CMDs, where the cool evolved stars (e.g., RSGs, AGB, and RGs) are located, which is likely due to the effect of metallicity and SFH. Further quantitative comparison of colors of massive star candidates in equal absolute magnitude bins suggests that, there is basically no difference for the BSG candidates, but large discrepancy for the RSG candidates as LMC targets are redder than the SMC ones, which may be due to the combined effect of metallicity on both spectral type and mass-loss rate, and also the age effect. The Teff of massive star populations are also derived from reddening-free color of (JKS)0. The Teff ranges are 3500<Teff<5000 K for RSG population, 5000<Teff<8000 K for YSG population, and Teff>8000 K for BSG population, with larger uncertainties towards the hotter stars.
arXiv.org: 2012.08623

Creating OSIRIS mosaic images

Posted September 15, 2020 By grigoris

To properly process data (either imaging or multi-slit spectra) from the OSIRIS instrument at GTC you need first to convert the original data to single images. The raw fits data contain two extensions corresponding to the two CCD sensors. To combine these into a single 2110 X 2051 pixels image (including the gap between the sensors) they provide with a convenient IRAF script called Mosaic Tool. This takes into account more than simply adding the extensions. As written in the ReadmeFirst.txt file:

(i) corrects the input OSIRIS pre-image for overscan to provide a uniform background, (ii) executes the mosaic assembly according to the rotation/traslation parameters obtained from the MD Polynomial Manual (current version: V5; see contents of the attached MD_polynomials.pdf file) on a new created image, (iii) copies the zero extension of the input image in the new one, and (iv) updates the WCS keywords. Such updating consists in switch the input reference pixel (CRPIX1, CRPIX2) to the center of the mosaic (see MD_polynomials.pdf, p.12) and calculate the celestial coordinates (CRVAL1, CRVAL2) corresponding to the new reference, based on the original WCS parameters. Hence, the correction of the astrometric solution is the same of the input image.

So it does quite a work! To run it you have to start IRAF, define the task and apply it to an raw image:

-->task $mosaic_2x2=mosaic_2x2_v2.cl
-->mosaic_2x2 0002604740-20200628-OSIRIS-OsirisBroadBandImage.fits

(the name for the task, in this case mosaic_2x2 is not really important). When I tried that (using PyRAF v2.1.5 with IRAF 2.16.1 on a Debian 10 machine) I got an error:

Traceback (innermost last):
File "", line 1, in
File "", line 16, in mosaic
iraf.noao.PY()
AttributeError: Parameter PY not found

which I couldn’t understand. Even after contacting the support the mystery remained. Back in early August we had only a couple of images from our pre-imaging run (and not time to waste…) and the support team was kind enough to do it for us. But now that we have the actual spectroscopic data the images are way too more to ask again for this. Therefore, I tried to search a bit deeper than the first time.

I started by checking the mosaic_2x2_v2.cl file, and I removed all tasks to search where could be the problem. So I edited it to run this:

 
procedure mosaic (fobjeto)

file fobjeto {"",prompt="Object file:"}


begin

string objeto
file   text

objeto=mosaic.fobjeto

noao.
noao.artdata.
noao.imred
noao.imred.bias.
images.imgeom
ctio

#stsdas
#toolbox
#imgtools

end

In this form it only calls the basic packages of IRAF. Still though, when I attempted to run it again I took the same error! So the problem was already at the beginning. Then, I noticed these dots after the packages (e.g. noao, noao.artdata.) where obviously python was trying to get a non-existing attribute. After removing these dots it actually proceeded! Only after this revelation, it came to my mind to actually try the IRAF cl directly (when you have the python supported version there is no need to look back to cl!). However, it made a difference as (obviously) the cl doesn’t care about the cots… So the problem was python specific.

The script continued running only to … stuck again:


Traceback (innermost last):
File "", line 1, in
File "", line 44, in mosaic
iraf.imcreate(image = 'CCD_mosaic.fits', naxis = 2, naxis1 = 2110,naxis2 = 2051,pixtype = 'real')
AttributeError: Undefined IRAF task `imcreate'

However, this is easily interpreted. imcreate is a task in the ctio package, which is external (not part of the original core of IRAF). So you need to find the ctio package and put it under the iraf/extern/. This was actually not easy to spot since the support of IRAF by the STScI has dropped and most links are broken. The package is supported by the iraf-community, but I didn’t try to check exactly how to download (as I didn’t find it under my iraf/ directory). On the other hand, I found an alternative (in the iraf.net/forum) using the mkpattern task, under the artdata package which is already loaded in the file. Then, I just replaced the following line:

imcreate(image="CCD_mosaic.fits",naxis=2,naxis1=2110,naxis2=2051,pixtype="real")

with

mkpattern(input="CCD_mosaic.fits",pixtype="real", ndim=2, ncols=2110, nlines=2051)

and all problems are finally solved! The script runs to the end and provides the dinal mosaic image.

Bottomline, or what to do in three single steps

  • If you are running cl/ecl directly AND have ctio package installed you don’t need to do anything
  • If you are running cl/ecl directly and you don’t have ctio, just replace imcreate with mkpattern
  • If you are running on python then remove the dots when the IRAF packages are called

I would therefore strongly suggest the support team to remove the dots and replace the imcreate task so that it can work in all cases.
(For any potential user here is my modified version: mosaic_2x2_v2_gm.cl.txt – remove .txt)

Preparing for MOS observations with the GTC

Posted August 18, 2020 By grigoris

The pandemic of Covid-19 has obviously affected our observing runs also. ESO is (still) totally shut down while GTC after some off period it seems that it is under some limited operation.

Our accepted observing program with GTC includes Mulit-Object Spectroscopic observations with the OSIRIS instrument. For this we need to prepare masks, i.e. metal plates on which a number of slits is cut to allow the light of specific targets to pass and acquire their spectrum. In order to optimize the positioning of those slits (and make sure that they are placed on the correct coordinates) we have asked for pre-imaging, e.g. the acquisition of short exposures of the FOVs we have requested (for all our targets). With these and the appropriate software (Mask Designer) it becomes easy to create the masks.

Some weeks ago we actually received (quite unexpectedly, given the pandemic status) a couple of images. That allowed us to prepare the corresponding masks (after resolving all technical difficulties and questions of course). An example of these masks is shown below for the galaxy IC 10. When creating the mask we have to avoid slits that result in overlapping spectra (because we will end up with useless data). That’s why we need to prioritize our targets and select the best combination which will allow for the maximum non-colliding number of targets to be observed, respecting all constraints imposed by the program and the instrument involved. Although mask designing with modern software tools can become easy it is still a time-consuming step that needs caution and accuracy.

Mask for multi-slit spectroscopic observations with OSIRIS GTC - Target galaxy IC 10

The image shows the mask designed for the galaxy IC 10. The small white line are the slits with the corresponding spectra visible as vertical thick green lines. Smaller lines correspond to fiducial stars, i.e. stars that help to the alignment of the mask. The yellow box corresponds to the physical limits of the maks. The background image is a short exposure of IC 10 in the r band.

So, the masks have been prepared, verified, constructed, … and now we wait for the real spectroscopic observations to be obtained! Fingers crossed!


note: the current article has been written originally for the ASSESS group.

 

Some 2020 Perseids observed …

Posted August 17, 2020 By grigoris

It has been many many years since my last visual observations of meteors (back in 2010…). Last week I found the opportunity to observe a bit the peak of the Perseid shower.

I observed for a couple of hours, just before the moon rise, from my place in the relatively light polluted Heraklion of Crete. I was a bit tired since I have waken up in the morning and didn’t manage to get any sleep so definitely I missed some fainter ones. But there were a few bright Perseids that I saw with a couple of them being kind of spectacular!

Although the total number recorded was low (7 Perseids and 5 Sporadics) I enjoyed the observation. Sitting relaxed in a chair and viewing the nigh sky feels nice, especially when the anticipation on the next bright meteor is added.

You can find the particular observation log as Session ID 80974 at IMO.

eROSITA’s first all-sky image

Posted June 26, 2020 By grigoris

Recently the first all-sky image of the X-ray sky was released by the eROSITA instrument on-board the Spectrum-Roentgen-Gamma” (SRG) mission. It is fascinating that they refer to a million X-ray, which is almost 10 times more than what has been discovered from all X-ray surveys so far, so a bright X-ray future lies ahead! The image is captivating, especially when you try to pinpoint some of the brightest and interesting sources.

The energetic universe as seen with the eROSITA X-ray telescope. The first eROSITA all-sky survey was conducted over a period of six months by letting the telescope rotate continuously, thus providing a uniform exposure of about 150-200 seconds over most of the sky, with the ecliptic poles being visited more deeply. As eROSITA scans the sky, the energy of the collected photons is measured with an accuracy ranging from 2% – 6%. To generate this image, in which the whole sky is projected onto an ellipse (so-called Aitoff projection) with the centre of the Milky Way in the middle and the body of the Galaxy running horizontally, photons have been colour-coded according to their energy (red for energies 0.3-0.6 keV, green for 0.6-1 keV, blue for 1-2.3 keV). The original image, with a resolution of about 10”, and a corresponding dynamic range of more than one billion, is then smoothed (with a 10’ FWHM Gaussian) in order to generate the above picture.The red diffuse glow away from the galactic plane is the emission of the hot gas in the vicinity of the solar system (the Local Bubble). Along the plane itself, dust and gas absorb the lowest energy X-ray photons, so that only high-energy emitting sources can be seen, and their colour appears blue in the image. The hotter gas close to the galactic centre, shown in green and yellow, carries imprinted the history of the most energetic processes in the life of the Milky Way, such as supernova explosions, driving fountains of gas out of the plane, and, possibly, past outburst from the now dormant supermassive black hole in the centre of the galaxy. Piercing through this turbulent, hot diffuse medium, are hundreds of thousands of X-ray sources, which appear mostly white in the image, and uniformly distributed over the sky. Among them, distant active galactic nuclei (including a few emitting at a time when the Universe was less than one tenth of its current age) are visible as point sources, while clusters of galaxies reveal themselves as extended X-ray nebulosities. In total, about one million X-ray sources have been detected in the eROSITA all-sky image, a treasure trove that will keep the teams busy for the coming years.

Credit: Jeremy Sanders, Hermann Brunner and the eSASS team (MPE); Eugene Churazov, Marat Gilfanov (on behalf of IKI)

Source: Max Planck Institute for Extraterrestrial Physics, Our deepest view of the X-ray sky, (accessed 26 June 2020)

 

Annotated version of the eROSITA First All-Sky image. Several prominent X-ray features are marked, ranging from distant galaxy clusters (Coma, Virgo, Fornax, Perseus) to extended sources such as Supernova Remnants (SNRs) and Nebulae to bright point sources, e.g. Sco X-1, the first extrasolar X-ray source to be detected. The Vela SNR is to the right of this image, the Large Magellanic Cloud in the bottom right quadrant, the Shapley supercluster in the upper right (though not easily visible in this projection).

Source: Max Planck Institute for Extraterrestrial Physics, Presskit for the eROSITA First All-Sky Survey (accessed 26 June 2020)