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A young galaxy cluster in the old Universe

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A massive local galaxy cluster has a blue star-forming fraction of 0.57, far above what galaxy formation models predict.

desk verdict A plausible, transparent report of an unusual local cluster, but the LCDM tension is overstated by an unvalidated model boundary and an uncorrected significance. read the letter →

arxiv 1908.01666 v1 pith:6SOPUE4T submitted 2019-08-05 astro-ph.GA

Tetsuya Hashimoto , Tomotsugu Goto , Rieko Momose , Chien-Chang Ho , Ryu Makiya , Chia-Ying Chiang , Seong Jin Kim This is my paper · ORCID
classification astro-ph.GA
keywords galaxyclustersbluefractionstarformationquenchingmainsequencesemi-analyticmodelscoldgasstreamsButcher-Oemlereffect
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 reports a nearby galaxy cluster that should be red but is not. At redshift $z=0.061$, the cluster SDSS-C4 3028 contains 12 blue star-forming galaxies among 21 members, giving a blue fraction of $0.57\pm0.06$, even though it is hosted by a massive dark-matter halo of roughly $2.0\times10^{14}\,M_\odot$. The same selection and analysis applied to other SDSS clusters puts this value $4.0\sigma$ above their median, and standard semi-analytic galaxy formation models lag more than $4.7\sigma$ behind. The authors argue that the cluster is dynamically relaxed and not unusually rich in low-mass galaxies, so its high star-forming fraction points to missing physics, most plausibly filamentary cold gas streams that can still feed massive halos in the local Universe. If this holds, it is a concrete local counterexample to the standard picture in which massive clusters quench their galaxies early.

What carries the argument

The analysis is carried by a two-part classification machine. First, galaxies are separated into blue star-forming and red quiescent using the stellar mass--star-formation-rate plane: a boundary line is fitted through the local minima of the SFR distribution in five stellar-mass bins of SDSS data, and the same division is applied to the semi-analytic and hydrodynamical model galaxies using a $3\sigma$ offset below the blue main sequence, a choice the authors validate only on SDSS. Second, clusters are identified in a volume-limited, magnitude-limited SDSS sample by a friends-of-friends algorithm with a 0.75 Mpc projected linking length and $\pm1000\ \mathrm{km\,s^{-1}}$ velocity window, starting from galaxies in environments overdense by $8\sigma$, and the same pipeline is run on model galaxy catalogues with matched magnitude and density limits. Halo masses come from the projected velocity dispersion and projected virial radius via a standard virial estimator, calibrated against the true halo masses available in the simulations, and the Dressler--Shectman statistic is used to show the blue cluster is dynamically relaxed.

What would settle it

Re-compute the model blue fractions using the same local-minimum boundary definition applied to the full star-formation-rate distributions of the simulated galaxies rather than a 3-sigma offset below the star-forming relation; if the resulting model blue fractions approach 0.57, the reported >4.7-sigma discrepancy would disappear.

Watch

Extended reading notes

Core claim

The central discovery is a "blue cluster" at $z=0.061$: SDSS-C4 3028, with 21 spectroscopically confirmed member galaxies, a projected velocity dispersion of $510\ \mathrm{km\,s^{-1}}$, a projected virial radius of $0.64$ Mpc, and an implied halo mass of $2.0^{+1.9}_{-1.0}\times10^{14}\,M_\odot$. Its blue fraction, defined by a boundary between the blue star-forming main sequence and the red quiescent population in the stellar mass--SFR plane, is $0.57\pm0.06$, compared with typical blue fractions below about 0.2 for local massive clusters. The cluster is $4.0\sigma$ above the median of 100 SDSS clusters selected by the same friends-of-friends algorithm, $4.7\sigma$ and $5.7\sigma$ above the median predictions of two semi-analytic models, and above the highest blue fraction found in a hydrodynamical simulation box. A Dressler--Shectman test gives $\Delta/N_{\rm member}=0.89$, indicating a virialised system, and the galaxy stellar mass function of the cluster is statistically indistinguishable from the other SDSS clusters, so the excess star formation is not a consequence of an unusual number of low-mass galaxies. The authors conclude that the cluster is an extreme, rare object whose existence challenges current $\Lambda$CDM-based frameworks of galaxy formation and evolution.

Load-bearing premise

The load-bearing assumption is that the line separating blue from red galaxies used for the SDSS data, placed at the local minimum of the star-formation-rate distribution, matches the line used for the simulations, placed 3 standard deviations below the star-forming relation; this equivalence was checked only on the SDSS data, because the simulated galaxies do not show the same clear two-peaked separation.

Editorial extensions

If this is right

  • The Butcher--Oemler pattern is not universal: a massive, dynamically old-looking local halo can host a high blue fraction, so environment alone does not guarantee early quenching.
  • Semi-analytic and hydrodynamical models of galaxy formation underproduce the blue fractions of massive clusters at $z\approx0$; reproducing this object would require gas accretion physics that keeps cold streams alive in deep potential wells.
  • Such local blue clusters can serve as nearby analogues of the $z\sim0.4$--$0.5$ cluster population, where the global transition from star formation to quiescence is happening, and can be studied in much greater detail than their distant counterparts.
  • Mpc-scale filamentary galaxy structures coincident with high blue fractions around the cluster suggest that searches for cold gas streams around other local clusters are a promising observational route.

Reading between the lines

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

  • [Editorial inference] The $4.7\sigma$ gap with models rests on the untested equivalence between the local-minimum boundary in SDSS and the $3\sigma$ main-sequence offset in simulations; if the models' lack of bimodality biases their blue fractions downward, the discrepancy could be smaller than reported.
  • [Editorial inference] Deep CO or HI observations of the 12 blue member galaxies would directly test the inferred cold gas reservoirs and the Kennicutt--Schmidt gas mass fractions, which are currently derived indirectly.
  • [Editorial inference] If cold streams really feed the cluster, its hot intracluster medium should be underluminous in X-rays and weak in the Sunyaev--Zel'dovich effect relative to the halo-mass scaling relations; shallow existing data are consistent with both, so a deep X-ray observation can discriminate.
  • [Editorial inference] Applying the same friends-of-friends pipeline to upcoming wide-area spectroscopic surveys could estimate how many such blue clusters exist, turning a one-object challenge into a statistical one.
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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

3 major / 5 minor

Summary. The paper reports the discovery of a galaxy cluster at z=0.061, identified as SDSS-C4 3028, in which 12 of 21 spectroscopically confirmed members are classified as blue star-forming galaxies, giving a blue fraction of 0.57±0.06. The cluster has a velocity dispersion of 510 km/s, implying a halo mass of about 2.0e14 Msun. The authors compare this cluster with 100 clusters selected with the same friends-of-friends algorithm in SDSS DR7 and find the blue fraction to be 4.0 sigma above the median, and 4.7-5.7 sigma above the blue fractions of cluster populations in the GALACTICUS and nu2GC semi-analytic models and in an EAGLE simulation. They report a 0.003% probability of finding such a blue fraction in one cluster, with a corrected probability of 0.32% after accounting for the 100 comparison clusters. The paper proposes that cold gas streams accreted from large-scale filaments may explain the high star-forming fraction, despite theoretical expectations that such streams do not survive in massive local halos.

Significance. If the main claim is robust, this paper identifies a rare and physically interesting outlier that challenges current prescriptions for galaxy quenching in dense environments. The analysis has notable strengths: the cluster member catalog is provided, the same cluster-selection and blue-fraction definitions are applied to the SDSS data and to the model catalogs, magnitude-matched and density-matched model samples are both considered, and a 100,000-iteration shuffle test is used to estimate the chance probability, including a multiple-testing correction. The empirical blue fraction itself, 12/21, is a straightforward and falsifiable measurement. The principal weakness is the calibration of the blue/quiescent boundary in the models and the overstatement of the uncorrected probability in the abstract; both issues are fixable and do not invalidate the observational discovery.

major comments (3)
  1. [Section 3.5, Figure 5] The claimed >4.7 sigma and >5.7 sigma deviations from the semi-analytic models and EAGLE depend on the assumption that a boundary at 3 sigma below the model main-sequence line is equivalent to the local-minimum boundary used for SDSS. This equivalence is checked only on SDSS (Fig. 4), where the two boundaries are almost identical, and is not tested in the models, whose SFR distributions lack the clear bimodality needed to define a local minimum. If a model main sequence is narrower than the SDSS one, the 3 sigma threshold would fall closer to the ridge and would classify intermediate-SFR galaxies as red, systematically lowering the model blue fraction. Because the central theoretical challenge rests on this model comparison, the authors should provide a sensitivity test, such as varying the sigma threshold or using a fixed absolute SFR cut, to demonstrate that the model discrepancy is not an artifact of the boundary definition.
  2. [Abstract and Section 4] The abstract and the concluding section quote the uncorrected probability of 0.003% and the 4.0 sigma deviation, while Section 4 itself derives the corrected probability of approximately 0.32% after accounting for the 100 comparison clusters (100 x 0.0032%). The corrected probability corresponds to roughly a 3 sigma effect, not 4 sigma. Using only the uncorrected figure in the abstract overstates the statistical significance of the result. The corrected probability should be quoted in the abstract and the text should be adjusted to reflect that the significance is lower after accounting for the look-elsewhere effect.
  3. [Section 5.5 and Abstract] The claim that 'filamentary cold gas streams can exist in massive halos even in the local Universe' is presented as a conclusion, but the supporting evidence is only the projected distribution of galaxies around the cluster (Fig. 8a), not a direct detection of cold gas streams. The paper itself notes in Section 5.5 that analytic arguments predict no cold streams in halos of this mass at z=0.061. The high blue fraction is a direct observational result, but the cold-stream interpretation is speculative and should be clearly framed as a hypothesis rather than a conclusion of the paper.
minor comments (5)
  1. [Section 3.4, Figure 4] The blue/quiescent boundary is fitted over stellar-mass bins from log M*=10.2 to 10.8, but the cluster members in Table B1 include objects at log M* around 9.86 and above 11.1. Please state explicitly how the boundary is extrapolated beyond the fitted range and whether the classification of the most and least massive members is robust to that extrapolation.
  2. [Figure 3 caption] The caption contains a typo: 'redsfhit' should be 'redshift'.
  3. [Section 3.6] The text contains a typo: 'tje' should be 'the' in the sentence describing the nu2GC halo mass definition.
  4. [Section 4, first paragraph] The phrase 'unusually large faction' should be 'unusually large fraction'.
  5. [Section 5.4] The gas-mass estimates from the Kennicutt-Schmidt and extended Schmidt laws require assumed galaxy radii, but the values of r used in the calculation are not stated in the text or in the figure captions.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the blue fraction is a measured quantity and the model comparison relies on external, pre-existing simulation products; the nu2GC self-citation is not load-bearing.

full rationale

The paper's central comparison is to three external, pre-existing theoretical products (GALACTICUS/MULTIDARK, the nu2GC semi-analytic catalogue, and EAGLE), none of which is re-fitted to SDSS-C4 3028 or to the authors' blue-fraction measurement. The observed blue fraction (12/21 member galaxies above a boundary defined by local minima in the SDSS SFR distribution) is a measurement, not a fitted parameter, and the model blue fractions are computed by applying the same cluster-finding pipeline to each model's own galaxy catalogue with each model's boundary set by a 3-sigma offset from that model's own main-sequence line. This rule is an externally stated criterion, not a parameter adjusted to reproduce the target cluster. The only author-overlap citation is the nu2GC catalogue (Makiya et al. 2016, co-authored by R. Makiya), but it is a public, pre-computed simulation product, and the result is corroborated by GALACTICUS and EAGLE, so the self-citation is not load-bearing. The validation that the 3-sigma boundary matches the local-minimum boundary is performed on SDSS only, not within the unimodal model distributions; that is a methodological applicability concern about the model blue fractions, not a circular reduction of the prediction to its inputs. The selection of the cluster as an extreme outlier in the same SDSS sample is a post-selection statistical issue that the paper addresses with a shuffling simulation, not a circular derivation. No equation or fitted value is reused as the claimed prediction, so no circularity step meets the evidentiary bar.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim relies less on physical constants than on analysis choices: the FoF linking parameters, the 8 sigma density threshold, the fitted SF/quiescent boundaries, and the assumed equivalence between the SDSS and simulation boundaries. These are all listed above as free parameters or ad hoc assumptions. The paper introduces no new physical entities; the cold gas filaments invoked in Section 5.5 are taken from the existing cold accretion literature.

free parameters (5)
  • FoF linking length and velocity window = 0.75 Mpc, +/-1000 km/s
    Hand-chosen in Section 3.1 from cluster radius literature; directly determines the 21 member galaxies and therefore the blue fraction.
  • Density threshold for cluster seeding = 8 sigma above median projected density
    Hand-chosen in Section 3.1; changes which clusters are found and the extreme-value statistics of the sample.
  • SDSS blue/quiescent boundary line = Linear fit to local minima in five stellar mass bins (log M* 10.2-10.8); coefficients not given
    Defined in Section 3.4; every galaxy is classified as SF or QS relative to this fitted line, so a shift in the boundary changes the blue fraction of the cluster and the comparisons.
  • Model blue/quiescent boundary line = Main-sequence peak fit plus 3 sigma offset in each simulation
    Defined in Section 3.5 for GALACTICUS, nu2GC, and EAGLE; the equivalence to the SDSS local-minimum boundary is assumed after an SDSS-only check.
  • nu2GC halo mass conversion formula = log(M200c/MFoF)=0.628-0.054 log MFoF (Eq A1)
    Fitted to 11,394 halos in the nu2GC simulation in Appendix A; used to calibrate model halo masses for comparison.
assumptions (6)
  • domain assumption Projected friends-of-friends groups with 0.75 Mpc and +/-1000 km/s trace real galaxy clusters.
    Used in Section 3.1 to define members; interlopers would bias velocity dispersion and blue fraction.
  • domain assumption MPA-JHU SFR and stellar mass estimates from SDSS DR7 are sufficiently accurate for bimodal classification.
    Used in Section 3.4; systematic uncertainties in these catalogues are not propagated into the blue fraction significance.
  • ad hoc to paper The local minimum of the SFR distribution at fixed stellar mass is the correct SF/quiescent boundary.
    Chosen in Section 3.4; a different boundary definition would change the blue fraction values.
  • ad hoc to paper The 3 sigma main-sequence boundary in simulations is equivalent to the SDSS local-minimum boundary.
    Section 3.5; validated only on SDSS, while the simulations lack the clear bimodality needed for a local-minimum definition.
  • domain assumption The projected virial mass estimator (Eq 2) gives an unbiased halo mass after simulation calibration.
    Section 3.6 shows a residual scatter of +0.29/-0.32 dex in log halo mass, which feeds into the comparison with massive-halo cold stream thresholds.
  • domain assumption The Planck15 Lambda CDM cosmology is the correct background for all distances and model comparisons.
    Stated in Section 1; all luminosity distances, physical separations, and halo mass estimates depend on these parameters.

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Pith. "Pith review of A young galaxy cluster in the old Universe." pith.science (2026). https://pith.science/paper/6SOPUE4T

@misc{pith2026190801666,
  author       = {Pith},
  title        = {Pith review of: A young galaxy cluster in the old Universe},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6SOPUE4T}},
  note         = {Machine review of arXiv:1908.01666}
}
abstract

Galaxies evolve from a blue star-forming phase into a red quiescent one by quenching their star formation activity. In high density environments, this galaxy evolution proceeds earlier and more efficiently. Therefore, local galaxy clusters are dominated by well-evolved red, elliptical galaxies. The fraction of blue galaxies in clusters monotonically declines with decreasing redshift, i.e., the Butcher-Oemler effect. In the local Universe, observed blue fractions of massive clusters are as small as $\lesssim$ 0.2. Here we report a discovery of a \lq \lq blue cluster\rq \rq, that is a local galaxy cluster with an unprecedentedly high fraction of blue star-forming galaxies yet hosted by a massive dark matter halo. The blue fraction is 0.57, which is 4.0 $\sigma$ higher than those of the other comparison clusters under the same selection and identification criteria. The velocity dispersion of the member galaxies is 510 km s$^{-1}$, which corresponds to a dark matter halo mass of 2.0$^{+1.9}_{-1.0}\times 10^{14}$ M$_{\odot}$. The blue fraction of the cluster is more than 4.7 $\sigma$ beyond the standard theoretical predictions including semi-analytic models of galaxy formation. The probability to find such a high blue fraction in an individual cluster is only 0.003\%, which challenges the current standard frameworks of the galaxy formation and evolution in the $\Lambda$CDM Universe. The spatial distribution of galaxies around the blue cluster suggests that filamentary cold gas streams can exist in massive halos even in the local Universe. However these cold streams have already disappeared in the theoretically simulated local universes.

Figures

Figures reproduced from arXiv: 1908.01666 by the authors.

Figure 1
Figure 1. The selection box for our sample in the SDSS DR7. The grey scale shows the number density of galaxies. Galaxies inside the red box are selected as our volume-limited sample. than 8σ from the median of δ (vertical solid line in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. The spatial distribution of clusters in a redsfhit slice. The central positions of identified galaxy clusters are marked by red circles. The blue cluster is shown by a blue circle. time. Following the literature (Crook et al. 2007), the halo mass of each cluster was calculated as Mhalo = 3π 2 σ 2 P Rp G , (2) where G is the gravitational constant, σP is the projected velocity dispersion σ 2 P = Σi (Vi − Vc) 2 Nmembe… view at source ↗
Figure 4
Figure 4. The boundary between blue star-forming and red quiescent galaxies in the SDSS DR7. The grey scale shows the number density of galaxies on this plot. The local minimum den￾sities of five stellar-mass bins are marked by the green dots. The stellar mass bins are chosen to have a clear local minimum in den￾sity. The green solid line is the boundary adopted in this paper, which is defined by a linear fit to the local min… view at source ↗
Figures from the paper (9 more)
Figure 5
Figure 5. Figure 5: The boundary between blue star-forming and red quiescent galaxies in the semi-analytic models and the EAGLE simulation. The magnitude-matched samples are shown (see Section 3.5 for details). The grey scale shows the number density of galaxies on this plot. The local ma…
Figure 6
Figure 6. Figure 6: Uncertainty of halo mass estimate. The normalised histograms of the differences between the true halo mass and derived halo mass through Eq. 2 for GALACTICUS (red) and ν 2GC (blue) model clusters. Different line styles correspond to magnitude-matched and density-matche…
Figure 7
Figure 7. Figure 7: The blue cluster at z = 0.061 found in the SDSS DR7. The member galaxies classified as the blue star forming are marked by blue circle, while the red circle for the red quiescent galaxies. The estimated virial radius of the blue cluster, 0.64 Mpc (8.7 arcmin on sky), i…
Figure 8
Figure 8. Figure 8: The environment of the blue cluster. (left) The projected number density of galaxies around the blue cluster. Individual galaxies within ± 1000 km s−1 around the redshift of the blue cluster are shown by dots. The density is calculated within the linking length of 0.75…
Figure 9
Figure 9. Figure 9: The fraction of the blue star-forming galaxies as a function of the cluster velocity dispersion. (left) The galaxy clusters identified in our analysis of the SDSS DR7 data. The errors of individual clusters are calculated by the Monte Carlo simulation on the galaxy mai…
Figure 10
Figure 10. Figure 10: The fraction of the blue star-forming galaxies as a function of 1/(tc H0). Same as [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: The galaxy stellar mass functions and blue fraction as a function of stellar mass. (left) The black open histogram is the normalised galaxy stellar mass function of all cluster member galaxies identified in the SDSS DR7. The normalised galaxy stellar mass function of …
Figure 12
Figure 12. Figure 12: The fraction of blue star-forming galaxies as a function of sSFR and gas mass. (left) The blue fraction of the SDSS DR7 clusters as a function of the sSFR (=SFR/M∗). The blue cluster is indicated by the encircled blue star. (middle) Same as left except for the gas mas…
Figure 13
Figure 13. Figure 13: The total SFR in individual clusters divided by the halo mass as a function of the look back time or redshift. Only clusters hosted by massive halos (Mhalo ≥ 1014 M ) are included. The blue cluster is shown with the blue star. Other data are collected from the individ…

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Works this paper leans on

70 extracted references · 8 canonical work pages

  1. [1]

    N., et al., 2009, @doi [ ] 10.1088/0067-0049/182/2/543 , http://adsabs.harvard.edu/abs/2009ApJS..182..543A 182, 543

    Abazajian K. N., et al., 2009, @doi [ ] 10.1088/0067-0049/182/2/543 , http://adsabs.harvard.edu/abs/2009ApJS..182..543A 182, 543

    doi:10.1088/0067-0049/182/2/543 2009

  2. [2]

    Ai M., Zhu M., 2018, @doi [ ] 10.3847/1538-4357/aac9b7 , http://adsabs.harvard.edu/abs/2018ApJ...862...48A 862, 48

    work page doi:10.3847/1538-4357/aac9b7 2018

  3. [3]

    H., Rieke M

    Bai L., Rieke G. H., Rieke M. J., Christlein D., Zabludoff A. I., 2009, @doi [ ] 10.1088/0004-637X/693/2/1840 , http://adsabs.harvard.edu/abs/2009ApJ...693.1840B 693, 1840

    doi:10.1088/0004-637x/693/2/1840 2009

  4. [4]

    E., Hernquist L

    Barnes J. E., Hernquist L. E., 1991, @doi [ ] 10.1086/185978 , http://adsabs.harvard.edu/abs/1991ApJ...370L..65B 370, L65

    doi:10.1086/185978 1991

  5. [5]

    J., 2012, @doi [ ] 10.1016/j.newast.2011.07.004 , http://adsabs.harvard.edu/abs/2012NewA...17..175B 17, 175

    Benson A. J., 2012, @doi [ ] 10.1016/j.newast.2011.07.004 , http://adsabs.harvard.edu/abs/2012NewA...17..175B 17, 175

    doi:10.1016/j.newast.2011.07.004 2012

  6. [6]

    A., et al., 2006, @doi [ ] 10.1086/508170 , http://adsabs.harvard.edu/abs/2006ApJS..167....1B 167, 1

    Berlind A. A., et al., 2006, @doi [ ] 10.1086/508170 , http://adsabs.harvard.edu/abs/2006ApJS..167....1B 167, 1

    doi:10.1086/508170 2006

  7. [8]

    R., et al., 2001, @doi [ ] 10.1086/320405 , http://adsabs.harvard.edu/abs/2001AJ....121.2358B 121, 2358

    Blanton M. R., et al., 2001, @doi [ ] 10.1086/320405 , http://adsabs.harvard.edu/abs/2001AJ....121.2358B 121, 2358

    doi:10.1086/320405 2001

  8. [9]

    J., Tr \"u mper J., Haberl F., Voges W., Nandra K., 2016, @doi [ ] 10.1051/0004-6361/201525648 , http://adsabs.harvard.edu/abs/2016A

    Boller T., Freyberg M. J., Tr \"u mper J., Haberl F., Voges W., Nandra K., 2016, @doi [ ] 10.1051/0004-6361/201525648 , http://adsabs.harvard.edu/abs/2016A

    doi:10.1051/0004-6361/201525648 2016

Show all 70 references
  1. [11]

    A., 1984, @doi [ ] 10.1086/162519 , http://adsabs.harvard.edu/abs/1984ApJ...285..426B 285, 426

    Butcher H., Oemler Jr. A., 1984, @doi [ ] 10.1086/162519 , http://adsabs.harvard.edu/abs/1984ApJ...285..426B 285, 426

    1984 doi

  2. [12]

    E., Marinello G., Clowes R

    Campusano L. E., Marinello G., Clowes R. G., Haines C. P., Pereira S., Pizarro D., Hitschfeld-Kahler N., S \"o chting I. K., 2018, @doi [ ] 10.3847/1538-4357/aaeff3 , https://ui.adsabs.harvard.edu/abs/2018ApJ...869..145C 869, 145

    2018 doi

  3. [13]

    X., Hennawi J

    Cantalupo S., Arrigoni-Battaia F., Prochaska J. X., Hennawi J. F., Madau P., 2014, @doi [ ] 10.1038/nature12898 , http://adsabs.harvard.edu/abs/2014Natur.506...63C 506, 63

    2014 doi

  4. [15]

    M., 1996, @doi [ ] 10.1086/176827 , https://ui.adsabs.harvard.edu/abs/1996ApJ...458..435C 458, 435

    Colless M., Dunn A. M., 1996, @doi [ ] 10.1086/176827 , https://ui.adsabs.harvard.edu/abs/1996ApJ...458..435C 458, 435

    1996 doi

  5. [16]

    Colless M., et al., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04902.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.328.1039C 328, 1039

    2001

  6. [17]

    A., Schaye J., van de Voort F., Duffy A

    Correa C. A., Schaye J., van de Voort F., Duffy A. R., Wyithe J. S. B., 2018, @doi [ ] 10.1093/mnras/sty871 , http://adsabs.harvard.edu/abs/2018MNRAS.478..255C 478, 255

    2018 doi

  7. [18]

    Cortese L., Gavazzi G., Boselli A., Iglesias-Paramo J., Carrasco L., 2004, @doi [ ] 10.1051/0004-6361:20040381 , https://ui.adsabs.harvard.edu/abs/2004A

    2004 doi

  8. [19]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , http://adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    Crain R. A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , http://adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    2015 doi

  9. [20]

    C., Huchra J

    Crook A. C., Huchra J. P., Martimbeau N., Masters K. L., Jarrett T., Macri L. M., 2007, @doi [ ] 10.1086/510201 , http://adsabs.harvard.edu/abs/2007ApJ...655..790C 655, 790

    2007 doi

  10. [23]

    Dekel A., Birnboim Y., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10145.x , http://adsabs.harvard.edu/abs/2006MNRAS.368....2D 368, 2

    2006

  11. [24]

    Dekel A., et al., 2009, @doi [ ] 10.1038/nature07648 , http://adsabs.harvard.edu/abs/2009Natur.457..451D 457, 451

    2009 doi

  12. [25]

    A., 1988, @doi [ ] 10.1086/114694 , http://adsabs.harvard.edu/abs/1988AJ.....95..985D 95, 985

    Dressler A., Shectman S. A., 1988, @doi [ ] 10.1086/114694 , http://adsabs.harvard.edu/abs/1988AJ.....95..985D 95, 985

    1988 doi

  13. [26]

    Ettori S., et al., 2019, @doi [ ] 10.1051/0004-6361/201833323 , https://ui.adsabs.harvard.edu/abs/2019A

    2019 doi

  14. [27]

    Faucher-Gigu \`e re C.-A., Kere s D., Ma C.-P., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19457.x , http://adsabs.harvard.edu/abs/2011MNRAS.417.2982F 417, 2982

    2011

  15. [28]

    E., Gott III J

    Gunn J. E., Gott III J. R., 1972, @doi [ ] 10.1086/151605 , http://adsabs.harvard.edu/abs/1972ApJ...176....1G 176, 1

    1972 doi

  16. [29]

    P., et al., 2013, @doi [ ] 10.1088/0004-637X/775/2/126 , http://adsabs.harvard.edu/abs/2013ApJ...775..126H 775, 126

    Haines C. P., et al., 2013, @doi [ ] 10.1088/0004-637X/775/2/126 , http://adsabs.harvard.edu/abs/2013ApJ...775..126H 775, 126

    2013 doi

  17. [30]

    P., et al., 2015, @doi [ ] 10.1088/0004-637X/806/1/101 , https://ui.adsabs.harvard.edu/abs/2015ApJ...806..101H 806, 101

    Haines C. P., et al., 2015, @doi [ ] 10.1088/0004-637X/806/1/101 , https://ui.adsabs.harvard.edu/abs/2015ApJ...806..101H 806, 101

    2015 doi

  18. [31]

    S., Massey R

    Hamana T., Miyazaki S., Kashikawa N., Ellis R. S., Massey R. J., Refregier A., Taylor J. E., 2009, @doi [ ] 10.1093/pasj/61.4.833 , http://adsabs.harvard.edu/abs/2009PASJ...61..833H 61, 833

    2009 doi

  19. [32]

    Hayashi M., Kodama T., Tadaki K.-i., Koyama Y., Tanaka I., 2012, @doi [ ] 10.1088/0004-637X/757/1/15 , http://adsabs.harvard.edu/abs/2012ApJ...757...15H 757, 15

    2012 doi

  20. [33]

    Hayashi M., et al., 2018, @doi [ ] 10.1093/pasj/psx088 , http://adsabs.harvard.edu/abs/2018PASJ...70S..17H 70, S17

    2018 doi

  21. [34]

    Ishiyama T., Enoki M., Kobayashi M. A. R., Makiya R., Nagashima M., Oogi T., 2015, @doi [ ] 10.1093/pasj/psv021 , http://adsabs.harvard.edu/abs/2015PASJ...67...61I 67, 61

    2015 doi

  22. [35]

    Kauffmann G., et al., 2003, @doi [ ] 10.1111/j.1365-2966.2003.07154.x , http://adsabs.harvard.edu/abs/2003MNRAS.346.1055K 346, 1055

    2003

  23. [36]

    Kennicutt Jr. R. C., 1998, @doi [ ] 10.1086/305588 , http://adsabs.harvard.edu/abs/1998ApJ...498..541K 498, 541

    1998 doi

  24. [38]

    Knebe A., et al., 2018, @doi [ ] 10.1093/mnras/stx2662 , http://adsabs.harvard.edu/abs/2018MNRAS.474.5206K 474, 5206

    2018 doi

  25. [39]

    Kneib J.-P., et al., 2003, @doi [ ] 10.1086/378633 , http://adsabs.harvard.edu/abs/2003ApJ...598..804K 598, 804

    2003 doi

  26. [40]

    L., Smail I., Bower R

    Kodama T., Balogh M. L., Smail I., Bower R. G., Nakata F., 2004, @doi [ ] 10.1111/j.1365-2966.2004.08271.x , http://adsabs.harvard.edu/abs/2004MNRAS.354.1103K 354, 1103

    2004

  27. [41]

    Koyama Y., Kodama T., Shimasaku K., Hayashi M., Okamura S., Tanaka I., Tokoku C., 2010, @doi [ ] 10.1111/j.1365-2966.2009.16226.x , http://adsabs.harvard.edu/abs/2010MNRAS.403.1611K 403, 1611

    2010

  28. [42]

    Kroupa P., 2002, @doi [Science] 10.1126/science.1067524 , http://adsabs.harvard.edu/abs/2002Sci...295...82K 295, 82

    2002 doi

  29. [43]

    Lintott C., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.17432.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.410..166L 410, 166

    2011

  30. [44]

    S., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06033.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.338..197M 338, 197

    Madgwick D. S., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06033.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.338..197M 338, 197

    2003

  31. [45]

    Makiya R., et al., 2016, @doi [ ] 10.1093/pasj/psw005 , http://adsabs.harvard.edu/abs/2016PASJ...68...25M 68, 25

    2016 doi

  32. [46]

    A., Cava A., Biviano A., Moretti A., Poggianti B., Bettoni D., 2019, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv190106393M

    Mamon G. A., Cava A., Biviano A., Moretti A., Poggianti B., Bettoni D., 2019, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv190106393M

    2019

  33. [47]

    L., Shapley A

    Martin C. L., Shapley A. E., Coil A. L., Kornei K. A., Bundy K., Weiner B. J., Noeske K. G., Schiminovich D., 2012, @doi [ ] 10.1088/0004-637X/760/2/127 , http://adsabs.harvard.edu/abs/2012ApJ...760..127M 760, 127

    2012 doi

  34. [48]

    C., Matuszewski M., Morrissey P., Neill J

    Martin D. C., Matuszewski M., Morrissey P., Neill J. D., Moore A., Cantalupo S., Prochaska J. X., Chang D., 2015, @doi [ ] 10.1038/nature14616 , http://adsabs.harvard.edu/abs/2015Natur.524..192M 524, 192

    2015 doi

  35. [49]

    H., Guo Y., Hertzberg J., Katz N., Mo H

    McIntosh D. H., Guo Y., Hertzberg J., Katz N., Mo H. J., van den Bosch F. C., Yang X., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13531.x , http://adsabs.harvard.edu/abs/2008MNRAS.388.1537M 388, 1537

    2008

  36. [50]

    Merritt D., 1987, @doi [ ] 10.1086/164953 , https://ui.adsabs.harvard.edu/abs/1987ApJ...313..121M 313, 121

    1987 doi

  37. [51]

    J., et al., 2005, @doi [ ] 10.1086/431357 , http://adsabs.harvard.edu/abs/2005AJ....130..968M 130, 968

    Miller C. J., et al., 2005, @doi [ ] 10.1086/431357 , http://adsabs.harvard.edu/abs/2005AJ....130..968M 130, 968

    2005 doi

  38. [52]

    V., Dalal N., Gottl \"o ber S., 2011, @doi [ ] 10.1088/0067-0049/195/1/4 , https://ui.adsabs.harvard.edu/abs/2011ApJS..195....4M 195, 4

    More S., Kravtsov A. V., Dalal N., Gottl \"o ber S., 2011, @doi [ ] 10.1088/0067-0049/195/1/4 , https://ui.adsabs.harvard.edu/abs/2011ApJS..195....4M 195, 4

    2011 doi

  39. [53]

    A., 1974, @doi [ ] 10.1086/153216 , https://ui.adsabs.harvard.edu/abs/1974ApJ...194....1O 194, 1

    Oemler Jr. A., 1974, @doi [ ] 10.1086/153216 , https://ui.adsabs.harvard.edu/abs/1974ApJ...194....1O 194, 1

    1974 doi

  40. [54]

    Oguri M., et al., 2018, @doi [Publications of the Astronomical Society of Japan] 10.1093/pasj/psx042 , https://ui.adsabs.harvard.edu/\#abs/2018PASJ...70S..20O 70, S20

    2018 doi

  41. [55]

    Peng Y., Maiolino R., Cochrane R., 2015, @doi [ ] 10.1038/nature14439 , http://adsabs.harvard.edu/abs/2015Natur.521..192P 521, 192

    2015 doi

  42. [56]

    Planck Collaboration et al., 2016a, @doi [ ] 10.1051/0004-6361/201525830 , https://ui.adsabs.harvard.edu/\#abs/2016A&A...594A..13P 594, A13

    doi

  43. [57]

    Planck Collaboration et al., 2016b, @doi [ ] 10.1051/0004-6361/201525823 , http://adsabs.harvard.edu/abs/2016A

    doi

  44. [58]

    Popesso P., et al., 2012, @doi [ ] 10.1051/0004-6361/201117973 , http://adsabs.harvard.edu/abs/2012A

    2012 doi

  45. [59]

    H., Schechter P., 1974, @doi [ ] 10.1086/152650 , http://adsabs.harvard.edu/abs/1974ApJ...187..425P 187, 425

    Press W. H., Schechter P., 1974, @doi [ ] 10.1086/152650 , http://adsabs.harvard.edu/abs/1974ApJ...187..425P 187, 425

    1974 doi

  46. [60]

    J., Ostriker J

    Rees M. J., Ostriker J. P., 1977, @doi [ ] 10.1093/mnras/179.4.541 , http://adsabs.harvard.edu/abs/1977MNRAS.179..541R 179, 541

    1977 doi

  47. [61]

    Rubin K. H. R., Prochaska J. X., Koo D. C., Phillips A. C., 2012, @doi [ ] 10.1088/2041-8205/747/2/L26 , http://adsabs.harvard.edu/abs/2012ApJ...747L..26R 747, L26

    2012 doi

  48. [62]

    Salim S., et al., 2007, @doi [ ] 10.1086/519218 , http://adsabs.harvard.edu/abs/2007ApJS..173..267S 173, 267

    2007 doi

  49. [63]

    B., Soifer B

    Sanders D. B., Soifer B. T., Elias J. H., Madore B. F., Matthews K., Neugebauer G., Scoville N. Z., 1988, @doi [ ] 10.1086/165983 , http://adsabs.harvard.edu/abs/1988ApJ...325...74S 325, 74

    1988 doi

  50. [64]

    Schaye J., et al., 2015, @doi [ ] 10.1093/mnras/stu2058 , http://adsabs.harvard.edu/abs/2015MNRAS.446..521S 446, 521

    2015 doi

  51. [65]

    Shi Y., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa3e6 , http://adsabs.harvard.edu/abs/2018ApJ...853..149S 853, 149

    2018 doi

  52. [66]

    A., Zeldovich Y

    Sunyaev R. A., Zeldovich Y. B., 1970, @doi [ ] 10.1007/BF00653471 , https://ui.adsabs.harvard.edu/\#abs/1970Ap&SS...7....3S 7, 3

    1970 doi

  53. [67]

    Tago E., Saar E., Tempel E., Einasto J., Einasto M., Nurmi P., Hein \"a m \"a ki P., 2010, @doi [ ] 10.1051/0004-6361/200913687 , http://adsabs.harvard.edu/abs/2010A

    2010 doi

  54. [68]

    S., Mart \' nez V

    Tempel E., Stoica R. S., Mart \' nez V. J., Liivam \"a gi L. J., Castellan G., Saar E., 2014, @doi [ ] 10.1093/mnras/stt2454 , http://adsabs.harvard.edu/abs/2014MNRAS.438.3465T 438, 3465

    2014 doi

  55. [69]

    W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , http://adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    Trayford J. W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , http://adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    2015 doi

  56. [70]

    A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    Tremonti C. A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    2004 doi

  57. [71]

    M., 2010, @doi [ ] 10.1088/0004-637X/714/2/1470 , http://adsabs.harvard.edu/abs/2010ApJ...714.1470U 714, 1470

    Umetsu K., Medezinski E., Broadhurst T., Zitrin A., Okabe N., Hsieh B.-C., Molnar S. M., 2010, @doi [ ] 10.1088/0004-637X/714/2/1470 , http://adsabs.harvard.edu/abs/2010ApJ...714.1470U 714, 1470

    2010 doi

  58. [72]

    Voges W., et al., 1999, , http://adsabs.harvard.edu/abs/1999A

    1999

  59. [73]

    Wang L., et al., 2014, @doi [ ] 10.1093/mnras/stt2481 , http://adsabs.harvard.edu/abs/2014MNRAS.439..611W 439, 611

    2014 doi

  60. [74]

    Webb T. M. A., et al., 2013, @doi [ ] 10.1088/0004-6256/146/4/84 , http://adsabs.harvard.edu/abs/2013AJ....146...84W 146, 84

    2013 doi

  61. [75]

    B., Sunyaev R

    Zeldovich Y. B., Sunyaev R. A., 1969, @doi [ ] 10.1007/BF00661821 , https://ui.adsabs.harvard.edu/\#abs/1969Ap&SS...4..301Z 4, 301

    1969 doi

  62. [76]

    G., Franx M., Fabricant D., Kelson D

    van Dokkum P. G., Franx M., Fabricant D., Kelson D. D., Illingworth G. D., 1999, @doi [ ] 10.1086/312154 , http://adsabs.harvard.edu/abs/1999ApJ...520L..95V 520, L95

    1999 doi

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.