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arxiv: 1906.11161 · v1 · pith:D25VX2KNnew · submitted 2019-06-26 · 🌌 astro-ph.GA

The quantity of dark matter in early-type galaxies and its relation to the environment

Pith reviewed 2026-05-25 15:38 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords early-type galaxiesdark mattergalaxy environmentdynamical massstellar massinitial mass functionSloan Digital Sky Survey
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The pith

Early-type galaxies in the densest environments contain dark matter amounting to less than 55 to 75 percent of their dynamical mass.

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

The paper compares dynamical mass to stellar mass inside the effective radius of early-type galaxies drawn from large Sloan Digital Sky Survey samples, treating the difference as dark matter or IMF effects. It reports that this dark matter fraction varies systematically with local galaxy density, reaching lower values and a narrower range in the densest regions. The upper bound holds after testing Newtonian dynamics, multiple surface-brightness profiles, and different initial mass functions across redshifts 0.0024 to 0.35. A sympathetic reader would care because the finding ties the internal mass budget of galaxies directly to their large-scale surroundings.

Core claim

The amount of dark matter inside early-type galaxies depends on environment. Galaxies in low-density regions span a wider dark matter range than those in dense regions. In the densest environments the dark matter fraction inside the effective radius is less than approximately 55 to 75 percent of dynamical mass, with the exact upper limit depending on how the initial mass function affects stellar-mass estimates. For a nearly complete sample with log(M_Virial/M_Sun) greater than 10.5 at 0.04 less than or equal to z less than or equal to 0.08 the bound tightens to 60 to 65 percent.

What carries the argument

The difference between dynamical mass (calculated from Newtonian dynamics and surface-brightness profiles within the effective radius) and stellar mass (computed under varying initial mass functions), interpreted as the dark matter fraction and examined as a function of local environmental density.

If this is right

  • Early-type galaxies in low-density environments exhibit a broader range of dark matter fractions inside their effective radii.
  • The upper limit on the dark matter fraction in the densest environments remains below 55 to 75 percent of dynamical mass, tightening to 60 to 65 percent in mass-complete subsamples.
  • The environmental dependence of the dark matter fraction persists across the full redshift interval 0.0024 to 0.35.
  • The reported bounds rely on the assumption that mass discrepancies trace dark matter or IMF variations rather than other dynamical effects.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the result holds, dense environments may preferentially remove or suppress central dark matter through tidal interactions or gas stripping.
  • Galaxy-formation simulations should be checked to see whether they produce systematically lower central dark matter fractions for early-type galaxies specifically in high-density regions.
  • Tighter observational constraints on the initial mass function could shrink the allowed 55 to 75 percent window and strengthen or weaken the environmental signal.
  • Extending the same mass-difference analysis to late-type galaxies would test whether the density dependence is restricted to early-type systems.

Load-bearing premise

Any difference between dynamical mass and stellar mass arises only from dark matter or from a non-universal initial mass function.

What would settle it

A large sample of early-type galaxies in the highest-density environments where the dynamical-to-stellar mass ratio consistently implies a dark matter fraction above 75 percent of dynamical mass, even after switching among different initial mass function assumptions.

Figures

Figures reproduced from arXiv: 1906.11161 by A. Nigoche-Netro, A. Ruelas-Mayorga, E. De la Fuente, G. Ramos-Larios, J. Mendez-Abreu, P. Lagos, R. J. Diaz, S. N. Kemp.

Figure 2
Figure 2. Figure 2: Frequency distribution of the logarithmic difference between dynamical and stellar mass inside re for the Total-SDSS sample (Kroupa–IMF stellar mass) at different redshifts. Each color represents a different redshift range as follows: orange 0.00 6 z 6 0.04, red 0.04 6 z 6 0.08, indigo 0.08 6 z 6 0.12, cyan 0.12 6 z 6 0.16, turquoise 0.16 6 z 6 0.20, green 0.20 6 z 6 0.24 and magenta 0.24 6 z 6 0.28. The m… view at source ↗
Figure 3
Figure 3. Figure 3: Frequency distribution of the dark matter fraction (fDM) inside re of the 55 massive lens ETGs from Posacki et al. (2015). tion of mass (Tortora et al. 2012b; Cappellari et al. 2013; Nigoche-Netro et al. 2016) and considering that the samples are affected by Malmquist bias (samples at greater redshift contain only massive galaxies), the maximum of this differ￾ence between masses is shifted at higher redshi… view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the logarithmic difference between dynamical and stellar mass inside re as function of density of galaxies for different samples of ETGs considering a Kroupa-IMF stellar mass. Rows 1-2, 3-4 and 5-6 correspond to the total, morphological, and homogeneous SDSS samples, respectively. Rows 1, 3, and 5 are data from the photometric sample. Rows 2, 4 and 6 are data from the spectroscopic sample. … view at source ↗
Figure 5
Figure 5. Figure 5: Density of galaxies (red) and frequency distribution (black) of the logarithmic difference between dynamical and stel￾lar mass inside re for the spectroscopic–homogeneous–SDSS sam￾ple (Kroupa–IMF stellar mass) considering the tenth nearest neighbour. We calculate the mean values of the logarithmic mass dif￾ference for ΣN < 1 Mpc−2 (field), ΣN > 1 Mpc−2 (loose groups) and 9.9 Mpc−2 < ΣN < 10.1 Mpc−2 (compac… view at source ↗
Figure 7
Figure 7. Figure 7: Intrinsic dispersion of the logarithmic difference be￾tween dynamical and stellar mass (black dots) inside re as function of density of galaxies (quasi-constant density) for the spectroscopic–homogeneous–SDSS sample (Kroupa–IMF stellar mass) considering the tenth nearest neighbour. The red contin￾uous line is a first degree polynomial least square fit to the data. tenth nearest neighbour. The term quasi-co… view at source ↗
read the original abstract

We study the behavior of the dynamical and stellar mass inside the effective radius of early-type galaxies (ETGs) as a function of environment considering Newtonian dynamics, different surface--brightness profiles, different initial mass functions (IMF) and different redshift ranges. We use several samples of ETGs --ranging from 19,000 to 98,000 objects-- from the ninth data release of the Sloan Digital Sky Survey. We assume that any difference between the dynamical and stellar mass is due to dark matter and/or a non-universal IMF. The main results, considering samples in the redshift range 0.0024 $\leq\;z\;\leq$ 0.35 are: (i) the amount of dark matter inside ETGs depends on the environment; (ii) ETGs in low density environments span a wider dark matter range than ETGs in dense environments; (iii) the amount of dark matter inside ETGs in the most dense environments will be less than approximately 55\%--75\% of the dynamical mass; (iv) the accurate value of this upper limit depends on the impact of the IMF on the stellar mass estimation; (v) in the case of an ETGs sample which is approximately complete for log$({\bf M_{Virial}}/{\bf M_{Sun}}) > 10.5$ and in the redshift range 0.04 $\leq\;z\;\leq$ 0.08 we find that the amount of dark matter in the most dense environments will be less than approximately 60\%--65\% of the dynamical mass.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that the dark matter fraction within the effective radius of early-type galaxies, inferred by subtracting stellar mass (under varying IMF assumptions) from dynamical mass, depends on local environment. Using SDSS DR9 samples of 19k–98k ETGs, it reports that ETGs in low-density environments show a wider DM range, while those in the densest environments have DM contributing less than ~55–75% of M_dyn (or ~60–65% in a volume-limited subsample at 0.04≤z≤0.08), with the exact upper limit depending on IMF choice. The analysis assumes Newtonian dynamics, considers different surface-brightness profiles and redshift ranges, and explicitly assumes any M_dyn − M_star residual is due to DM or non-universal IMF.

Significance. If the key assumption holds and systematics are controlled, the result would provide a large-sample observational constraint on environmental trends in the DM content of ETGs inside R_e, with potential implications for galaxy formation and the role of environment in mass assembly. The sample sizes are a clear strength for statistical power, but the work is a direct observational comparison without parameter-free derivations or machine-checked elements.

major comments (3)
  1. [Abstract] Abstract (and the assumption stated therein): the central upper limits on DM fraction (points iii and v) rest on the untested premise that 'any difference between the dynamical and stellar mass is due to dark matter and/or a non-universal IMF.' No section quantifies or rules out environment-correlated biases in SDSS velocity-dispersion or photometric measurements (e.g., fiber collisions or background subtraction in dense fields) that could systematically inflate M_dyn or deflate M_star preferentially in high-density regions, thereby artificially lowering the inferred DM fraction.
  2. [Results (samples 19k-98k)] Results for the 19k–98k galaxy samples: the reported DM upper limits lack error bars, confidence intervals, or robustness checks against selection functions, completeness variations, or redshift-dependent systematics, making it impossible to assess whether the claimed environmental trend exceeds measurement uncertainties.
  3. [Abstract (main results i–iii)] The headline claim that DM inside ETGs 'depends on the environment' is obtained solely by subtracting two mass estimators; without explicit tests showing that environment does not correlate with biases in either estimator, the observed trend in M_star/M_dyn cannot be unambiguously attributed to DM content.
minor comments (2)
  1. [Abstract] Abstract contains minor LaTeX formatting artifacts (e.g., '$z$' with extra spacing) that should be cleaned for readability.
  2. [Methods] The paper should clarify in the methods how the effective radius and velocity dispersion are measured consistently across environments to allow reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting important caveats regarding our assumptions and the robustness of the results. We address each major comment below and indicate the changes planned for the revised version.

read point-by-point responses
  1. Referee: [Abstract] Abstract (and the assumption stated therein): the central upper limits on DM fraction (points iii and v) rest on the untested premise that 'any difference between the dynamical and stellar mass is due to dark matter and/or a non-universal IMF.' No section quantifies or rules out environment-correlated biases in SDSS velocity-dispersion or photometric measurements (e.g., fiber collisions or background subtraction in dense fields) that could systematically inflate M_dyn or deflate M_star preferentially in high-density regions, thereby artificially lowering the inferred DM fraction.

    Authors: The assumption that residuals between dynamical and stellar mass are attributable to dark matter or IMF variations is stated explicitly in the abstract and Section 2. We have not performed dedicated tests to quantify or exclude environment-dependent biases in SDSS measurements (e.g., fiber collisions or background subtraction). We agree this represents a limitation of the current analysis. In the revised manuscript we will add a new subsection discussing these potential systematics, their possible correlation with local density, and how they could affect the reported trends. revision: yes

  2. Referee: [Results (samples 19k-98k)] Results for the 19k–98k galaxy samples: the reported DM upper limits lack error bars, confidence intervals, or robustness checks against selection functions, completeness variations, or redshift-dependent systematics, making it impossible to assess whether the claimed environmental trend exceeds measurement uncertainties.

    Authors: The quoted upper limits (55–75 % and 60–65 %) are conservative bounds derived from the highest stellar-to-dynamical mass ratios found in the densest environments; they are not presented as statistical measurements with formal uncertainties. We acknowledge that the manuscript would benefit from explicit robustness tests. In the revision we will add checks against variations in sample selection, completeness, and redshift range, together with a qualitative discussion of how these factors influence the upper limits. revision: partial

  3. Referee: [Abstract (main results i–iii)] The headline claim that DM inside ETGs 'depends on the environment' is obtained solely by subtracting two mass estimators; without explicit tests showing that environment does not correlate with biases in either estimator, the observed trend in M_star/M_dyn cannot be unambiguously attributed to DM content.

    Authors: The primary result is an observed environmental dependence of the stellar-to-dynamical mass ratio inside R_e. Under the Newtonian assumption and the stated interpretation of residuals, we attribute this to dark-matter content. We agree that, absent explicit tests for correlated biases, the attribution cannot be considered unambiguous. We will revise the abstract and the discussion of results i–iii to foreground the mass-ratio trend itself and to include stronger caveats regarding possible systematics in the mass estimators. revision: yes

Circularity Check

0 steps flagged

No circularity; direct observational subtraction of independently estimated masses

full rationale

The paper computes M_dyn from velocity dispersions and sizes and M_star from photometry under varied IMFs, then reports the residual fraction in different environments. The central claim follows from this subtraction under an explicit assumption stated in the abstract; no equation reduces the reported DM upper limits to a fitted parameter or self-citation by construction. The derivation remains self-contained against external mass-estimation benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on the domain assumption that mass discrepancy equals dark matter or IMF variation; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Any difference between dynamical and stellar mass is due to dark matter and/or a non-universal IMF
    Explicitly stated as the interpretive premise for all results.

pith-pipeline@v0.9.0 · 5860 in / 1067 out tokens · 24168 ms · 2026-05-25T15:38:31.483786+00:00 · methodology

discussion (0)

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Works this paper leans on

121 extracted references · 121 canonical work pages

  1. [1]

    Abazajian et al., 2003, AJ, 126, 2081

  2. [2]

    Abazajian et al., 2009, ApJS, 182, 543

  3. [3]

    Adelman-McCarthy, J. K. et al., 2008, ApJS, 175, 297

  4. [4]

    Aguerri, J. A. L., M\'endez-Abreu, J., & Corsini, E. M., 2009, A&A, 495, 491

  5. [5]

    W., Treu, T., Bolton, A

    Auger, M. W., Treu, T., Bolton, A. S., Gavazzi, R., Koopmanz, L. V., E., Marshall, P. J., Moustakas, L. A., & Burles, S., 2010b, ApJ, 724, 511

  6. [6]

    Alabi, A., B., et al., 2016, MNRAS, 460, 3838

  7. [7]

    \'Avila-Reese, V., Firmani, C., & Hern\'andez, X., 1998, ApJ, 505, 37

  8. [8]

    \'Avila-Reese, V., & Firmani, C., 2000, Rev. Mex. A&A, 36, 23

  9. [9]

    K., Balogh, M

    Baldry, I. K., Balogh, M. L., Bower., R. G., et al. 2006, MNRAS, 373, 469

  10. [10]

    L., Baldry, I

    Balogh, M. L., Baldry, I. K., Nichol, R. et al. 2004, ApJ, 615, L101

  11. [11]

    M., Cole, S., & Frenk, C

    Baugh, C. M., Cole, S., & Frenk, C. S., 1996, MNRAS, 283, 1361

  12. [12]

    Beifiori, A., et al., 2014, ApJ, 789, 92

  13. [13]

    F., McIntosh, D

    Bell, E. F., McIntosh, D. H., Katz N., & Weinberg M. D. 2003, ApJS, 149, 289

  14. [14]

    J., Cole, S., Frenk, C

    Benson, A. J., Cole, S., Frenk, C. S., Baugh, C. M., & Lacey, C. G., 2000, MNRAS, 311, 793

  15. [15]

    2003b, AJ, 125, 1849

    Bernardi, M., et al. 2003b, AJ, 125, 1849

  16. [16]

    M., Sheth, R

    Bernardi. M., Sheth, R. K., Fischer, J.L., Meert, A., Chae, K.H., Dom\'inguez-S\'anchez, H., Huertas-Company, V., Shankar, F. & Vikram, V. 2018, MNRAS, 475, 757

  17. [17]

    R., et al., 2003, ApJ, 594, 186

    Blanton, M. R., et al., 2003, ApJ, 594, 186

  18. [18]

    Charlot, S., White,S

    Brinchmann, J., S. Charlot, S., White,S. D. M., Tremonti, C., Kauffmann, G.. Heckman, T. & Brinkmann, J. 2004, MNRAS, 351, 1151

  19. [19]

    S., Kolatt, T

    Bullock, J. S., Kolatt, T. S., Sigad, Y., Sommerville, R. S., Kravtsov, A. V., Kyplin, A. A., Primack, J. R., & Dekel, A., 2001, MNRAS, 321, 559

  20. [20]

    & Puddu, E

    Busarello, G., Capaccioli, M., Capozziello, S., Longo, G. & Puddu, E. 1997, A&A, 320, 415

  21. [21]

    Campbell, D. J. R., Frenk, C. S., Jenkins, A., Eke, V. R., Navarro, J. F., Sawala, T., Schaller M., Fattahi A., Oman K. A. & Theuns T. 2017, MNRAS, 469, 2335

  22. [22]

    & van de Ven, G

    Cappellari, M., Bacon, R., Bureau, R., Damen, M.C., Davies, R.L., de Zeeuw, P.T., Emsellem, E., Falc\'on-Barroso, J., Krajnovi\'c, D., Kuntscher, H., McDermid, R.M., Peletier, R.F., Sarzi, M., van den Bosch, R.C.E. & van de Ven, G. 2006, MNRAS, 366, 1126

  23. [23]

    Cappellari, M., McDermid, R. M. Alatalo, K et al., G. 2012, Nature, 484, 485

  24. [24]

    2013, MNRAS, 432, 1709

    Cappellari, M., Scott, N., Alatalo, K., et al. 2013, MNRAS, 432, 1709

  25. [25]

    F., & Tortora, C., 2010, MNRAS, 409, 1570

    Cardone, V. F., & Tortora, C., 2010, MNRAS, 409, 1570

  26. [26]

    F., Tortora, C., Molinaro, R., Salzano, V., 2009, A&A 504, 769

    Cardone, V. F., Tortora, C., Molinaro, R., Salzano, V., 2009, A&A 504, 769

  27. [27]

    W., C\^ot\'e, P., West, A

    Chen, C. W., C\^ot\'e, P., West, A. A., Peng, E. W. & Ferrarese, L. 2010, ApJS, 191, 1

  28. [28]

    & Renzini, A

    Ciotti, L., Lanzoni, B. & Renzini, A. 1996, MNRAS, 282, 1

  29. [29]

    G., Baugh, C

    Cole, S., Lacey, C. G., Baugh, C. M., & Frenk, C. S., 2000, MNRAS, 319, 168

  30. [30]

    H., 2009, ApJ, 696, 620

    Conroy, C., & Wechsler, R. H., 2009, ApJ, 696, 620

  31. [31]

    M., Wegner, G

    Corsini, E. M., Wegner, G. A., Thomas, J., Saglia, R. P., & Bender R. 2017, MNRAS, 466, 974

  32. [32]

    A., Graves, G

    Conroy, C., Dutton, A. A., Graves, G. J., Mendel, J. T., & Pieter G. van Dokkum, P. G. 2013, ApJL, 776, 26

  33. [33]

    J., et al., 2005, MNRAS, 356, 1155

    Croton, D. J., et al., 2005, MNRAS, 356, 1155

  34. [34]

    & Davis, M., 1987, ApJ, 696, 620

    Djorgovski, S. & Davis, M., 1987, ApJ, 696, 620

  35. [35]

    J., Zandivarez, A

    Dom\'inguez, M. J., Zandivarez, A. A., Mart\'inez, H. J., Merch\'an, M. E., Muriel, H., & Lambas, D. G., 2002, MNRAS, 335,825

  36. [36]

    Rev., 50, 447

    D'Onofrio, M., Valentinuzzi, T., Secco, L., Caimmi, R., Bindoni, D., 2006, New Astron. Rev., 50, 447

  37. [37]

    Dressler, A., 1980, ApJ, 236, 351

  38. [38]

    L., Faber, S

    Dressler, A., Lynden-Bell, D., Burstein, D., Davies, R. L., Faber, S. M., Terlevich, R., & Wegner, G., 1987, ApJ, 313, 42

  39. [39]

    A., Macci\'o, A

    Dutton, A. A., Macci\'o, A. V., Mendel, J. T., Simard, L., & de Lorenzi, F. 2013, MNRAS, 432, 2496

  40. [40]

    F., McDermid, R

    Emsellem, E., Cappellari, M., Peletier, R. F., McDermid, R. M., Bacon, R., Bureau, M., Copin, Y., Davies, R. L., Krajnovic, Davor., Kuntschner, H., et al. 2004, MNRAS, 352, 721

  41. [41]

    M., & Jackson, R

    Faber, S. M., & Jackson, R. E., 1976, ApJ, 204, 668

  42. [42]

    Faure, C., et al., 2011, A&A, 529, 72

  43. [43]

    Ferrera, I, Saha, P., & Williams, L. L. R., 2005, ApJ, 623, L5

  44. [44]

    Firmani, C.& Avila-Reese, V., MNRAS, 315, 457

  45. [45]

    A., Lasky, P., Graham, A

    Forbes, D. A., Lasky, P., Graham, A. W., & Spitler, L., 2008, MNRAS, 389, 1924

  46. [46]

    Gott, J. R. III, 1977, ARAA, 15, 235

  47. [47]

    Graham, A., Colless, M., 1997, MNRAS, 287, 221

  48. [48]

    Grillo, C., 2010, ApJ, 722, 779

  49. [49]

    Grillo, C., Gobat, R., Lombardi, M., & Rosati, P., 2009, A&A, 501, 461

  50. [50]

    Grillo, C., & Gobat, R., 2010, MNRAS,402, L67

  51. [51]

    G\'omez et al., 2003, ApJ, 584, 210

  52. [52]

    J., 2009, PhD thesis, Univ

    Graves, G. J., 2009, PhD thesis, Univ. California

  53. [53]

    W., Driver, S

    Graham, A. W., Driver, S. P., Petrosian, V., Conselice, C. J., Bershady, M. A., Crawford, S. M., and Goto, T. 2005, AJ, 130, 1535

  54. [54]

    L., 2003, MNRAS, 346, 601

    Goto, T., Yamauchi, Ch, Fujita, Y., Okamura, S., Sekiguchi, M., Smail, I., Bernardi, M., & G\'omez, P. L., 2003, MNRAS, 346, 601

  55. [55]

    Granato et al., 2001, MNRAS, 324, 757

  56. [56]

    P., 2013, MNRAS, 429,2924

    Hilz, M., Naab, T., & Ostriker, J. P., 2013, MNRAS, 429,2924

  57. [57]

    W., et al., 2004, ApJ, 601, L29

    Hogg, D. W., et al., 2004, ApJ, 601, L29

  58. [58]

    B., & Bernardi, M

    Hyde, J. B., & Bernardi, M. 2009, MNRAS, 394, 1978

  59. [59]

    1995a, MNRAS, 273, 1097

    J rgensen, I., Franx, M., & K rgaard, p. 1995a, MNRAS, 273, 1097

  60. [60]

    1995b, MNRAS, 276, 1341

    J rgensen, I., Franx, M., & K rgaard, p. 1995b, MNRAS, 276, 1341

  61. [61]

    Kauffmann, G., White, S. D. M., & Guiderdoni, B., 1993, MNRAS, 264, 201

  62. [62]

    M., White, S

    Kauffmann, G., Heckman, T. M., White, S. D. M. 2003, MNRAS, 341, 33

  63. [63]

    M., Menard, B., Brinchmann, J., Charlot, S., Tremonti, C., & Brinkmann J., 2004, MNRAS, 353, 713

    Kauffmann, G., White, S., Heckman, T. M., Menard, B., Brinchmann, J., Charlot, S., Tremonti, C., & Brinkmann J., 2004, MNRAS, 353, 713

  64. [64]

    Koopmans, L.V.E., Treu, T., Bolton, A.S., et al, 2006, ApJ, 649, 599

  65. [65]

    1977, ApJ, 218, 333

    Kormendy, J. 1977, ApJ, 218, 333

  66. [66]

    R., de La Rosa I., G., & Lopes, P

    La Barbera, F., de Carvalho, R. R., de La Rosa I., G., & Lopes, P. A. A., 2010b, MNRAS, 408, 1335

  67. [67]

    et al., 2007, MNRAS, 379, 1599

    Lawrence A. et al., 2007, MNRAS, 379, 1599

  68. [68]

    & Falco, E

    Leir, D., Ferraras, I., Saha, P. & Falco, E. E., 2011, ApJ, 740, 97

  69. [69]

    J., Schawinski, K., Slosar, A., Land, K., Bamford, S,

    Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S,. Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D., Murray, P., & van den Berg J. 2008, MNRAS, 389, 1179

  70. [70]

    V., Dutton, A

    Macci\`o, A. V., Dutton, A. A., & van den Bosch, F. C., 2008, MNRAS, 391, 1940

  71. [71]

    H., 2006, ApJ, 647, 763

    Maller, A., Katz, N., Kere s D., Dav\'e, R., & Weinberg, D. H., 2006, ApJ, 647, 763

  72. [72]

    M., & Brinkman, J., 2006, MNRAS, 368, 715

    Mandelbaum, R., Seljak, U., Kauffmann, G., Hirata, C. M., & Brinkman, J., 2006, MNRAS, 368, 715

  73. [73]

    J., ApJ, 569, 101

    Marinoni, C., & Hudson, M. J., ApJ, 569, 101

  74. [74]

    Maulbetsch, C., Avila-Reese, V., Col\'in, P., Gottl\"obber, S, Khalatyan, A, & Steinmetz, M., 2007, Ap,J, 654, 53

  75. [75]

    P., Sommerville, R., S., Maulbetsch, C., van den Bosch, F

    Moster, B. P., Sommerville, R., S., Maulbetsch, C., van den Bosch, F. C., Macci\`o, A. V., Naab, T., & Oser, L., 2010, ApJ, 710, 903

  76. [76]

    F., Barden, M., H\"au ler, B., 2011, ApJ, 734, 69

    More, A., Jahnke, K., More, S., Gallazzi, A., Bell, E. F., Barden, M., H\"au ler, B., 2011, ApJ, 734, 69

  77. [77]

    Murali, C., Katz, N., Hernquist, L., Weinberg, D. H. & Dav\'e, R., 2002, ApJ, 571,1

  78. [78]

    et al., 2005, MNRAS, 357, 691

    Napolitano, N., R. et al., 2005, MNRAS, 357, 691

  79. [79]

    Napolitano, N., R., Romanowsky, A., J., &Tortora, C., 2010, MNRAS, 405, 2351

  80. [80]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., White, S. D. M., 1996, ApJ, 462,563

Showing first 80 references.