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The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The first SRG/eROSITA all-sky morphological catalogue, covering more than twelve thousand optically confirmed galaxy clusters, argues that X-ray selection strongly favors concentrated, likely relaxed clusters, with log concentrations…

desk verdict Solid eRASS1 morphology catalogue with genuinely new forward-modelled parameters; the headline 0.3 dex concentration offset is directionally right but quantitatively depends on AGN-free simulations that the authors honestly flag. read the letter →

arxiv 2502.02239 v1 pith:3EVGGUFT submitted 2025-02-04 astro-ph.CO

J. S. Sanders , Y. E. Bahar , E. Bulbul , V. Ghirardini , A. Liu , N. Clerc , M. E. Ramos-Ceja , T. H. Reiprich
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F. Balzer J. Comparat M. Kluge F. Pacaud X. Zhang
This is my paper · ORCID
classification astro-ph.CO
keywords galaxyclustersintraclustermediumX-raysurveyseROSITAclustermorphologysurfacebrightnessconcentrationselectionfunctioncoolcores
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper publishes a catalogue of 29 morphological parameters for more than twelve thousand clusters detected in the first eROSITA all-sky survey, and argues that the survey's cluster sample is strongly shaped by how concentrated each cluster's X-ray surface brightness is. It introduces two forward-modelled parameters, slosh and multipole magnitudes, that fold the telescope point-spread function into the fit, and uses simulations to show that several older image-based parameters such as power ratios, centroid shift, Gini coefficient, and photon asymmetry are badly biased by noise and PSF blurring. The central empirical claim is that eRASS1 clusters have log concentrations roughly 0.3 dex higher than SPT-, Planck ESZ-, or eFEDS-selected samples, meaning the ratio of flux inside $0.1\,R_{500}$ to flux inside $R_{500}$ is about twice as large, and that cuts on exposure, counts, detection likelihood, or extension likelihood do not remove the offset. If correct, X-ray selection in eRASS1 preferentially finds concentrated, likely relaxed clusters at low redshift while missing the most extreme cool cores at high redshift. The paper also constructs two combined disturbance scores from a two-component Gaussian mixture model and classifies roughly a quarter of bright clusters as disturbed.

What carries the argument

The load-bearing tool is MBProj2D forward modelling of the X-ray surface brightness: the cluster image is compared with a model that includes the eROSITA PSF, background, point sources and neighbouring clusters, using a density profile with a weak inner-slope prior. Concentration is measured from the model, not the image, as $\log$ of the ratio of integrated surface brightness in apertures $0.1\,R_{500}$ and $R_{500}$ ($c_{500}$), or 80 and 800 kpc ($c_{80-800}$). Two new parameters are introduced: slosh, which transforms the radius as $S'(r,\theta) = A(H)\,S(r\,[1 + H\cos(\theta+\theta_0)])$ with $A(H)=(1-H^2)^{3/2}$ to keep the total brightness fixed, and multipole magnitudes $M_m$, which multiply the profile by $[1 + M_m\sin(m\theta+\theta_0)]\,S(r)$. Simulated clusters placed on eROSITA sky maps and run through the eSASS detection pipeline provide the selection functions and bias curves, and a two-component Gaussian mixture model fit to the bright, high-count clusters defines the $D_{\rm shape}$ and $D_{\rm comb}$ disturbance scores.

What would settle it

Take the eRASS1 clusters that fall in the deeper eRASS:4 footprint, re-measure $c_{500}$ and $c_{80-800}$ with detected point sources subtracted, and compare the median concentration with the eRASS1 values: if the median drops by roughly 0.3 dex, the claimed selection effect is not sufficient to explain the offset and the central claim fails.

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Extended reading notes

Core claim

Measuring 29 morphological parameters for the 12,075 clusters with usable data from the eRASS1 catalogue, the authors find that the population is systematically more concentrated than clusters selected by the South Pole Telescope, by Planck, or in the deeper eFEDS field: the median log concentration is about 0.3 dex higher, a factor of two in the inner-to-total surface brightness ratio. They show from simulations that concentration controls whether a cluster is detected, with flat-core low-luminosity objects missed at low redshift and very peaked cool cores lost at high redshift because they look point-like; and they demonstrate that the concentration excess survives cuts in exposure time, counts, detection likelihood, and extension likelihood. For the same clusters, concentrations and central densities agree with Chandra and XMM-Newton measurements, and a matched re-analysis of SPT clusters in deeper eRASS:4 data agrees with Chandra at the one-to-one level. Image-based parameters such as power ratios, centroid shift, Gini coefficient, and photon asymmetry are strongly biased by noise and PSF, while the forward-modelled parameters are not. Finally, a two-component Gaussian mixture model applied to the shape parameters and concentration produces a disturbance score; about one quarter of bright clusters are classified as disturbed, and the paper attributes the high relaxed fraction to the survey's sensitivity to concentrated systems.

Load-bearing premise

The quantitative bias curves, reliability thresholds, and selection functions are computed from simulations that contain no AGN, so if unresolved active galactic nuclei are common in eRASS1 clusters the claimed sizes of the concentration bias and high-redshift selection effect could be wrong.

Editorial extensions

If this is right

  • Cosmological analyses using eRASS1 cluster counts will need to include a concentration-dependent selection function, since detection efficiency varies strongly with both redshift and luminosity at fixed concentration.
  • The published catalogue gives bias and uncertainty tables as a function of redshift and count number, so users can correct or discard image-based morphological parameters rather than comparing them blindly with other surveys.
  • At low redshift the sample misses low-luminosity groups and clusters with flat surface brightness profiles; at $z \gtrsim 0.4$ it misses the most concentrated cool cores, including objects like the Phoenix cluster.
  • The $\sim$0.3 dex offset between eRASS1 and SPT/Planck/eFEDS concentrations indicates that X-ray-selected samples contain roughly twice the inner flux fraction of SZ-selected samples, so statements about cool-core fractions and relaxed fractions must be survey-selection aware.
  • Around a quarter of bright clusters are classified as disturbed by the combined score, while shape-only and concentration-inclusive scores can disagree for individual clusters; the two scores are provided so users can separate shape disturbance from peaked-core status.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The selection function implies that eRASS1-based scaling relations, for example luminosity or $Y_X$ versus mass, will inherit a redshift- and luminosity-dependent bias because concentrated clusters are preferentially detected at the faint end; the authors do not work out this corollary here.
  • If the concentration excess is truly selection rather than measurement, then the deeper eRASS:4 survey should show a lower median concentration for the same clusters, an effect that could be checked already with the matched-sample machinery in the paper.
  • The missing high-redshift extreme cool cores could be recovered by wavelet-based or photon-based detection algorithms, which the authors note may find more flat-profile objects; this is a testable prediction of their selection model.
  • Because unresolved AGN were absent from the simulations, adding a realistic AGN population would probably shift the quantitative bias curves at low luminosity and high redshift; the direction of the selection effect would likely remain but its amplitude could change.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This paper measures 29 morphological parameters for 12,075 clusters in the eRASS1 catalogue, introducing two forward-modelled PSF-aware parameters (slosh and multipole magnitudes). It uses MCMC and bootstrap uncertainty propagation, simulations without AGN to quantify parameter biases and the selection function, and matched subsamples against XMM-Newton, Chandra, and eFEDS. The central empirical claim is that eRASS1 clusters are systematically more concentrated than SPT, Planck ESZ, and eFEDS samples, with median log c500 around 0.3 dex higher, and that the concentration distribution depends on redshift and luminosity because the X-ray selection preferentially detects concentrated clusters.

Significance. If correct, this is an important reference catalogue and a clear demonstration of how X-ray selection shapes morphological distributions. The paper's strengths are the scale of the catalogue, the explicit treatment of the PSF through forward-modelled parameters, the detailed uncertainty propagation, and the external validation using matched samples, including 1:1 agreement between eRASS:4 and Chandra when the analysis procedures are matched. However, the quantitative central claim depends on simulations that contain no AGN, a limitation stated in Section 6.11, and on a residual 0.1 dex offset between eRASS1 and XMM/Chandra for the same clusters. These issues make the quantitative selection and bias curves insecure, although the qualitative direction of the selection effect is plausible.

major comments (4)
  1. [§4, §6.11, Figs. 12 and 18] The simulations used to set the quantitative selection functions and bias corrections contain no AGN, and Section 6.11 concedes that adding a point-source population would likely add bias, especially for low-luminosity, high-redshift objects. Since an unresolved central AGN adds flux inside the 0.1 R500 aperture and eROSITA's roughly 30 arcsec survey PSF cannot resolve faint core point sources, the measured eRASS1 c500 values themselves may be inflated. This matters because the claimed 0.3 dex population offset is the sum of roughly a 0.1 dex measurement offset seen in Section 7.1 and a roughly 0.2 dex selection effect; if unresolved AGN contribute to the measurement offset, the quantitative population comparison and the selection curves in Fig. 12 are not yet secure. Please quantify this by adding point-source populations to the simulations or by testing c500 against known central AGN indicators in the eRASS1 sample.
  2. [§7.1 and §7.2] The matched-sample comparisons show a median eRASS1 concentration about 0.1 dex higher than XMM-Newton or Chandra for the same clusters, while the matched eRASS:4 analysis gives a 1:1 relation. This residual offset is not explained, and Section 7.1 lists unresolved point sources as a possible cause. The conclusion that cuts on exposure, counts, detection likelihood, and extension likelihood do not remove the 0.3 dex offset is based on observed distributions that retain this offset; the Abstract's statement that the population difference is a selection effect is therefore stronger than the current evidence supports. The authors should either correct the eRASS1 values for the inferred measurement offset before comparing populations, or demonstrate explicitly that the offset is not AGN-related.
  3. [§8 and Table 5] The Gaussian mixture model is trained on the 175 brightest clusters and then applied to the full sample, including those same 175 objects, to produce the reported roughly 25% disturbed fractions. This training-on-test overlap, without cross-validation, makes the Dshape and Dcomb fractions and the two-component amplitudes in Table 5 difficult to interpret. A cross-validated or independent-sample test is needed before the disturbance classification is used as a catalogue product.
  4. [§4 and Fig. 12] The simulated detection pipeline is image-based rather than the photon-based pipeline used for the real catalogue, and the authors note it is less sensitive near the detection threshold. This could shift the high-redshift selection thresholds in Fig. 12, in particular the claimed loss of clusters with c80−800 of about −0.2 at z = 0.4, and should be quantified so that the quoted threshold values are not taken as final.
minor comments (4)
  1. [§1] In the Introduction, 'parameters are are affected by the signal to noise' contains a duplicated word.
  2. [§8] The text uses 'GGM' in several places, for example 'the GGM model does not show strong evidence', where 'GMM' is intended.
  3. [§7.2] The phrase 'For eFEDS1, we show distributions' appears to be a typo for 'For eFEDS'.
  4. [Table 1] The Type column codes (M, I, V, F, P) are not defined in the table itself; adding a footnote would substantially improve readability for catalogue users.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central results are externally benchmarked, and the AGN-omission in simulations is a stated accuracy limitation rather than a circular reduction.

full rationale

The paper's derivation chain is self-contained and benchmarked against external data. The morphological measurements (concentration, central density, inner slope, shape parameters) come from MBProj2D fits to eRASS1 images and are compared, for matched clusters, with independent Chandra and XMM-Newton analyses (Section 7.1), where the matched eRASS:4/Chandra procedure gives a relation consistent with 1:1 for c500. The population offset of ~0.3 dex in concentration relative to SPT, Planck ESZ, and eFEDS is an empirical comparison of measured distributions, not a fitted restatement of the claim. The selection-function and bias simulations (Section 4; Figs. 12 and 18) are forward models run through the eSASS detection pipeline; they use the same parametrization as the data analysis, but they are used to characterize PSF, noise, and selection effects, not to define the measured concentrations. The paper explicitly concedes that the simulations omit AGN (Section 6.11), which is a completeness and accuracy limitation, not a circular reduction of the result to its inputs. The GMM disturbance scores (Section 8) are fitted to a bright subsample and applied to the same clusters, but this is descriptive clustering/classification rather than a prediction, and the paper checks robustness to the count threshold and number of components. Self-citations to B24 and Ghirardini et al. (2022) supply the cluster catalogue, R500 values, redshifts, and comparison values; these are published data products and external inputs, not the target result, so the citations are not load-bearing in a circular sense. No equation is defined in terms of the quantity it is claimed to predict, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The paper is an empirical catalogue, so the ledger mainly records analysis choices and modeling assumptions rather than physical free parameters. The most important entries are the beta = 2/3 prior, the circularized single-energy PSF, the AGN-free simulations, and the GMM training choices; all are acknowledged in the text.

free parameters (6)
  • Outer slope beta prior = 2/3 (truncated normal, width 1/3)
    Default beta-model slope assumed for all clusters in MBProj2D fits; can bias density and concentration for objects with flatter outer profiles.
  • Gaussian smoothing scale = 24 arcsec
    Used to find X-ray peak and compute Gini; affects peak positions, centroid shift and Gini values.
  • GMM component count = 2
    Chosen to split regular and disturbed clusters; third and fourth components have low amplitude, but this choice changes D-score fractions.
  • GMM training threshold = 800 counts
    Only bright clusters are used to fit the disturbance GMM; using 400 counts lowers the disturbed fraction to about 20 percent.
  • PSF energy used for convolution = 1.486 keV survey-averaged
    A single circularized PSF is used for all sources, ignoring energy and off-axis dependence.
  • Photon asymmetry annuli = 0.05, 0.12, 0.2, 0.3 and 1.0 R500
    Chosen annuli for Aphot; the parameter is sensitive to this choice.
assumptions (6)
  • domain assumption Clusters are isothermal with 0.3 solar metallicity in the fitted emissivity model.
    Invoked in Section 3.2 to fix temperature from B24 chains and metallicity; Section 6.10 says this matters mainly for cool groups with line-dominated spectra.
  • ad hoc to paper The simplified density profile (beta model with inner powerlaw, Eq. 22) represents all clusters including disturbed ones.
    Section 3.2 uses this form with outer slope beta restricted around 2/3; Section 2.4 notes disturbed objects may not be well fit and concentrations may be biased.
  • ad hoc to paper Simulated images without AGN are representative enough to quantify parameter biases and selection.
    Section 4 omits AGN; Section 6.11 states including them would likely add bias.
  • domain assumption Catalogue R500 values are accurate enough for scaled morphological parameters.
    Section 6.3 shows a 10 percent R500 error changes c500 by 0.035, ns,0 by -0.022 and alpha by 0.038.
  • domain assumption Image-based eSASS detection in simulations approximates photon-based real detection.
    Section 4 notes that image-based detection uses an average PSF and should be less sensitive close to the detection threshold.
  • domain assumption Baryon fraction fB = 0.175 and mean mass per electron are adopted from external cosmology and plasma tables.
    Used to define ns,0 in Eq. 1; different assumptions shift the density scale but are external inputs.

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Cite this review

Pith. "Pith review of The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters." pith.science (2026). https://pith.science/paper/3EVGGUFT

@misc{pith2026250202239,
  author       = {Pith},
  title        = {Pith review of: The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies I: A catalogue of morphological parameters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3EVGGUFT}},
  note         = {Machine review of arXiv:2502.02239}
}
read the original abstract

The first SRG/eROSITA all-sky X-ray survey, eRASS1, resulted in a catalogue of over twelve thousand optically-confirmed galaxy groups and clusters in the western Galactic hemisphere. Using the eROSITA images of these objects, we measure and study their morphological properties, including their concentration, central density and slope, ellipticity, power ratios, photon asymmetry, centroid shift and Gini coefficient. We also introduce new forward-modelled parameters which take account of the instrument point spread function (PSF), which are slosh, which measures how asymmetric the surface brightness distribution is, and multipole magnitudes, which are analogues to power ratios. Using simulations, we find some non forward-modelled parameters are strongly biased due to PSF and data quality. For the same clusters, we find similar values of concentration and central density compared to results by ourselves using Chandra and previous results from XMM-Newton. The population as a whole has log concentrations which are typically around 0.3 dex larger than South Pole Telescope or Planck-selected samples and the deeper eFEDS sample. The exposure time, detection likelihood threshold, extension likelihood threshold and number of counts affect the concentration distribution, but generally not enough to reduce the concentration to match the other samples. The concentration of clusters in the survey strongly affects whether they are detected as a function of redshift and luminosity. We introduce a combined disturbance score based on a Gaussian mixture model fit to several of the parameters. For brighter clusters, around 1/4 of objects are classified as disturbed using this score, which may be due to our sensitivity to concentrated objects.

Figures

Figures reproduced from arXiv: 2502.02239 by the authors.

Figure 1
Figure 1. Example models exhibiting the effect of the different shape pa￾rameters. Shown are an elliptical model (top left), a slosh model (top right), an M1 model (centre left), an M2 model (centre right), an M3 model (bottom left) and an M4 model (bottom right). For the models we use an angle of θ = 30 deg. The numeric values show the magnitude of the respective parameter. Vikhlinin 2007). Like ellipticity, we model this wi… view at source ↗
Figure 2
Figure 2. Corner plot of the median MBProj2D shape parameters for Chandra observations of clusters in the SPT cluster sample. The quan￾tities are plotted against each other, while the rightmost panels show the probability density of the values. The Pearson correlation coefficient is shown in each panel [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Example Chandra images from the SPT sample shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Morphological parameters against redshift. The parameters are plotted for the bright cluster subset (≥ 300 counts) with larger markers. The small markers show clusters with at least 25 counts, without plotting uncertainties. The colour scale shows the X-ray luminosity …
Figure 5
Figure 5. Figure 5: Parameters against number of counts in an 800 kpc aperture. These parameters are plotted for the bright cluster subset (≥ 300 counts) with larger markers. The small markers show clusters with at least 25 counts, without plotting uncertainties. The colour scale shows th…
Figure 6
Figure 6. Figure 6: Parameters against X-ray luminosity. These parameters are plotted for the bright cluster subset (≥ 300 counts) with larger markers. The small markers show clusters with at least 25 counts, without plotting uncertainties. The X-ray luminosity is inside an 800 kpc radius…
Figure 7
Figure 7. Figure 7: Corner plot of the median MBProj2D shape parameters for the bright cluster (≥ 300 count) subset. The quantities are plotted against each other, while the rightmost panels show the probability density distribution of each value. inner surface brightness profiles. In the…
Figure 8
Figure 8. Figure 8: Correlation matrix between different parameters for the bright cluster subset. Values were measured for clusters with more than 300 counts to reduce the effect of the statistical errors. Log values were taken for the photon asymmetry, power ratios and centroid shift. W…
Figure 9
Figure 9. Figure 9: Example cluster images and morphological parameters. The clusters chosen are those from the catalogue with between 920 and 1080 counts within 800 kpc radius and an uncertainty on the number of counts of less than 5%. The exposure-corrected images are in the 0.2-2.3 keV…
Figure 10
Figure 10. Figure 10: Relationship between detection likelihood (Ldet), extension likelihood (Lext), counts and morphological parameters. (Top panels) The median value of the concentration (c80−800; left), central scaled density (ns,0; centre) and central slope (α; right), in grid points o…
Figure 11
Figure 11. Figure 11: Cumulative distribution of concentration for different subsamples of the eRASS1 catalogue. The cumulative distributions are shown as a function of exposure time (texp), number of counts in a 800 kpc aperture, detection likelihood (Ldet) and extension likelihood (Lext)…
Figure 13
Figure 13. Figure 13: The median X-ray luminosity of clusters in bins of concentra￾tion. The left panel uses bins of c80−800, while the right panel shows c500. The uncertainties shown are calculated with bootstrap resampling. All eRASS1 clusters are included in this analysis. fit peak c80-…
Figure 12
Figure 12. Figure 12: Selection function of clusters with different redshifts and lu￾minosities, as a function of concentration, calculated from simulated observations of spherical clusters. The panels show the detected frac￾tion of clusters at different redshift. For each redshift, we sho…
Figure 14
Figure 14. Figure 14: Fit-centred and peak-centred (∗) quantities as a function of the distance between the cluster peak and the best fit position, for a cluster subsample with Lext > 6 and Ldet > 20. The median quantities in bins of F are shown with bootstrap resampling uncertainties. The…
Figure 15
Figure 15. Figure 15: Recovery of forward modelled parameters as a function of redshift and luminosity, using maximum likelihood. The plots show the recovered parameter values as a function of redshift, for clusters with an input shape compared to an input spherical cluster. The error bar …
Figure 17
Figure 17. Figure 17: Measured non-forward-modelled morphological parameters for simulated model clusters. Clusters are simulated with different luminosi￾ties and redshifts, after sampling from the mass function. The shaded region shows the 1σ range of recovered parameters of detected clus…
Figure 18
Figure 18. Figure 18: Recovered parameter bias from simulated clusters as a function of the redshift and number of counts inside 800 kpc radius. For the concentration, central density and cuspiness, shown is the difference between the recovered values and the input profile, after deproject…
Figure 19
Figure 19. Figure 19: Recovered statistical uncertainty in measured parameters from simulated clusters. This is the standard deviation on the difference between the input and recovered values, as in [PITH_FULL_IMAGE:figures/full_fig_p024_19.png]
Figure 20
Figure 20. Figure 20: Comparison of concentration and scaled central density measurements for the same cluster samples between eRASS and other measure￾ments. The two left columns compare c500 and its distributions, while the two right columns show ns,0. Median values are plotted as vertica…
Figure 21
Figure 21. Figure 21: Cumulative distribution of parameters for SPT, Planck ESZ and eFEDS clusters compared to eRASS1 for different detection thresholds. For eROSITA the solid lines show the values fixing the cluster position at the peak, while the dotted lines show the distribution for wh…
Figure 22
Figure 22. Figure 22: Parameter values as a function of counts, coloured according to the combined shape and concentration disturbance score, Dcomb. certainties on our values, this threshold would imply 80% of our eRASS1 objects are relaxed, similar to the fraction identified by our GMM. C…
Figure 23
Figure 23. Figure 23: Disturbance scores for the sample, for clusters with greater than 25 and greater than 300 counts. (Left) Histograms of the distributions of Dcomb (including concentration) and the purely 2D shape based Dshape. (Right) Dcomb plotted against Dshape for the two count thr…
Figure 24
Figure 24. Figure 24: Examples of eight relaxed and eight disturbed clusters. Clusters are randomly selected from the sample with more than 300 counts, where Dcomb = 0 (top panels) or Dcomb = 1 (bottom panels). The exposure corrected images in the 0.2–2.3 keV band have been smoothed with a…

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Reference graph

Works this paper leans on

53 extracted references · 25 canonical work pages · cited by 1 Pith paper

  1. [1]

    G., van den Bergh, S., & Nair, P

    Abraham, R. G., van den Bergh, S., & Nair, P. 2003, ApJ, 588, 218

    2003

  2. [2]

    J., & Scott, P

    Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

    2009

  3. [3]

    2016, ApJ, 827, 112 Bîrzan, L., Rafferty, D

    Biffi, V ., Borgani, S., Murante, G., et al. 2016, ApJ, 827, 112 Bîrzan, L., Rafferty, D. A., McNamara, B. R., Wise, M. W., & Nulsen, P. E. J. 2004, ApJ, 607, 800

    work page 2016

  4. [4]

    E., Stalder, B., de Haan, T., et al

    Bleem, L. E., Stalder, B., de Haan, T., et al. 2015, ApJS, 216, 27 Böhringer, H., Pratt, G. W., Arnaud, M., et al. 2010, A&A, 514, A32 Böhringer, H., Schuecker, P., Pratt, G. W., et al. 2007, A&A, 469, 363 Böhringer, H. & Werner, N. 2010, A&A Rev., 18, 127

    work page 2015

  5. [5]

    2022, A&A, 661, A1

    Brunner, H., Liu, T., Lamer, G., et al. 2022, A&A, 661, A1

    2022

  6. [6]

    2024, A&A, 685, A106

    Bulbul, E., Liu, A., Kluge, M., et al. 2024, A&A, 685, A106

    2024

  7. [7]

    2022, A&A, 661, A10

    Bulbul, E., Liu, A., Pasini, T., et al. 2022, A&A, 661, A10

    2022

  8. [8]

    Buote, D. A. & Tsai, J. C. 1995, ApJ, 452, 522

    1995

Show all 53 references
  1. [9]

    G., Ettori, S., Lovisari, L., et al

    Campitiello, M. G., Ettori, S., Lovisari, L., et al. 2022, A&A, 665, A117

    2022

  2. [10]

    & Fusco-Femiano, R

    Cavaliere, A. & Fusco-Femiano, R. 1978, A&A, 70, 677

    1978

  3. [11]

    H., et al

    Clowe, D., Bradaˇc, M., Gonzalez, A. H., et al. 2006, ApJ, 648, L109

    2006

  4. [12]

    2020, The Open Journal of As- trophysics, 3, 13

    Comparat, J., Eckert, D., Finoguenov, A., et al. 2020, The Open Journal of As- trophysics, 3, 13

    2020

  5. [13]

    2011, A&A, 526, A79

    Eckert, D., Molendi, S., & Paltani, S. 2011, A&A, 526, A79

    2011

  6. [14]

    Edge, A. C. & Stewart, G. C. 1991, MNRAS, 252, 414

    1991

  7. [15]

    Fabian, A. C. 2012, ARA&A, 50, 455

    2012

  8. [16]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306

    2013

  9. [17]

    E., Bulbul, E., et al

    Ghirardini, V ., Bahar, Y . E., Bulbul, E., et al. 2022, A&A, 661, A12

    2022

  10. [18]

    2023, MNRAS, 518, 4238

    Gianfagna, G., Rasia, E., Cui, W., et al. 2023, MNRAS, 518, 4238

    2023

  11. [19]

    S., Mittal, R., Reiprich, T

    Hudson, D. S., Mittal, R., Reiprich, T. H., et al. 2010, A&A, 513, A37

    2010

  12. [20]

    J., & Finoguenov, A

    Johnson, R., Ponman, T. J., & Finoguenov, A. 2009, MNRAS, 395, 1287 Käfer, F., Finoguenov, A., Eckert, D., et al. 2020, A&A, 634, A8 Käfer, F., Finoguenov, A., Eckert, D., et al. 2019, A&A, 628, A43

    2009

  13. [21]

    2024, A&A, 688, A210

    Kluge, M., Comparat, J., Liu, A., et al. 2024, A&A, 688, A210

    2024

  14. [22]

    T., Kravtsov, A

    Lau, E. T., Kravtsov, A. V ., & Nagai, D. 2009, ApJ, 705, 1129

    2009

  15. [23]

    T., Nagai, D., Kravtsov, A

    Lau, E. T., Nagai, D., Kravtsov, A. V ., Vikhlinin, A., & Zentner, A. R. 2012, ApJ, 755, 116

    2012

  16. [24]

    2024, A&A, 688, A186

    Liu, A., Bulbul, E., Shin, T., et al. 2024, A&A, 688, A186

    2024

  17. [25]

    M., Primack, J., & Madau, P

    Lotz, J. M., Primack, J., & Madau, P. 2004, AJ, 128, 163

    2004

  18. [26]

    R., Jones, C., et al

    Lovisari, L., Forman, W. R., Jones, C., et al. 2017, ApJ, 846, 51

    2017

  19. [27]

    Mann, A. W. & Ebeling, H. 2012, MNRAS, 420, 2120

    2012

  20. [28]

    & Vikhlinin, A

    Markevitch, M. & Vikhlinin, A. 2007, Phys. Rep., 443, 1

    2007

  21. [29]

    W., Bayliss, M., et al

    McDonald, M., Allen, S. W., Bayliss, M., et al. 2017, ApJ, 843, 28

    2017

  22. [30]

    A., et al

    McDonald, M., Bayliss, M., Benson, B. A., et al. 2012, Nature, 488, 349

    2012

  23. [31]

    A., Vikhlinin, A., et al

    McDonald, M., Benson, B. A., Vikhlinin, A., et al. 2013, ApJ, 774, 23

    2013

  24. [32]

    McNamara, B. R. & Nulsen, P. E. J. 2012, New Journal of Physics, 14, 055023

    2012

  25. [33]

    & Goulding, A

    Melchior, P. & Goulding, A. D. 2018, Astronomy and Computing, 25, 183

    2018

  26. [34]

    2024, A&A, 682, A34

    Merloni, A., Lamer, G., Liu, T., et al. 2024, A&A, 682, A34

    2024

  27. [35]

    H., & Jaritz, V

    Mittal, R., Hicks, A., Reiprich, T. H., & Jaritz, V . 2011, A&A, 532, A133

    2011

  28. [36]

    J., Evrard, A

    Mohr, J. J., Evrard, A. E., Fabricant, D. G., & Geller, M. J. 1995, ApJ, 447, 8

    1995

  29. [37]

    A., et al

    Nurgaliev, D., McDonald, M., Benson, B. A., et al. 2013, ApJ, 779, 112 O’Hara, T. B., Mohr, J. J., Bialek, J. J., & Evrard, A. E. 2006, ApJ, 639, 64

    2013

  30. [38]

    K., Fabian, A

    Panagoulia, E. K., Fabian, A. C., & Sanders, J. S. 2014, MNRAS, 438, 2341

    2014

  31. [39]

    B., Fardal, M

    Poole, G. B., Fardal, M. A., Babul, A., et al. 2006, MNRAS, 373, 881

    2006

  32. [40]

    W., Croston, J

    Pratt, G. W., Croston, J. H., Arnaud, M., & Böhringer, H. 2009, A&A, 498, 361

    2009

  33. [41]

    2021, A&A, 647, A1

    Predehl, P., Andritschke, R., Arefiev, V ., et al. 2021, A&A, 647, A1

    2021

  34. [42]

    A., McNamara, B

    Rafferty, D. A., McNamara, B. R., Nulsen, P. E. J., & Wise, M. W. 2006, ApJ, 652, 216

    2006

  35. [43]

    2013, The Astronomical Review, 8, 40

    Rasia, E., Meneghetti, M., & Ettori, S. 2013, The Astronomical Review, 8, 40

    2013

  36. [44]

    2017, MNRAS, 468, 1917

    Rossetti, M., Gastaldello, F., Eckert, D., et al. 2017, MNRAS, 468, 1917

    2017

  37. [45]

    S., Fabian, A

    Sanders, J. S., Fabian, A. C., Russell, H. R., & Walker, S. A. 2018, MNRAS, 474, 1065

    2018

  38. [46]

    S., Rosati, P., Tozzi, P., et al

    Santos, J. S., Rosati, P., Tozzi, P., et al. 2008, A&A, 483, 35

    2008

  39. [47]

    2022, A&A, 665, A78

    Seppi, R., Comparat, J., Bulbul, E., et al. 2022, A&A, 665, A78

    2022

  40. [48]

    N., Bean, R., Doré, O., et al

    Spergel, D. N., Bean, R., Doré, O., et al. 2007, ApJS, 170, 377

    2007

  41. [49]

    & Sarazin, C

    Valdarnini, R. & Sarazin, C. L. 2021, MNRAS, 504, 5409

    2021

  42. [50]

    R., et al

    Vikhlinin, A., Burenin, R., Forman, W. R., et al. 2007, in Heating versus Cool- ing in Galaxies and Clusters of Galaxies, ed. H. Böhringer, G. W. Pratt, A. Finoguenov, & P. Schuecker, 48

    2007

  43. [51]

    A., Ebeling, H., et al

    Vikhlinin, A., Burenin, R. A., Ebeling, H., et al. 2009, ApJ, 692, 1033 V oit, G. M. 2005, Reviews of Modern Physics, 77, 207

    2009

  44. [52]

    H., Bullock, J

    Wechsler, R. H., Bullock, J. S., Primack, J. R., Kravtsov, A. V ., & Dekel, A. 2002, ApJ, 568, 52 Weißmann, A., Böhringer, H., Šuhada, R., & Ameglio, S. 2013, A&A, 549, A19

    2002

  45. [53]

    E., Pacaud, F., Reiprich, T

    Xu, W., Ramos-Ceja, M. E., Pacaud, F., Reiprich, T. H., & Erben, T. 2022, A&A, 658, A59 Article number, page 31 of 32 A&A proofs: manuscript no. morph_cat Appendix A: Description of catalogue Table A.1 describes the columns in the morphology catalogue, giving the column name a...

    2022

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