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arxiv: 2511.01164 · v2 · submitted 2025-11-03 · 🌌 astro-ph.GA

Stellar halos of bright central galaxies II: Scaling relations, colors and metallicity evolution with redshift

Emanuele Contini , Marilena Spavone , Rossella Ragusa , Enrica Iodice , Sukyoung K Yi This is my paper

Pith reviewed 2026-05-18 02:07 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords stellar halosbright central galaxiesintracluster lightscaling relationsmetallicity evolutiongalaxy clustersredshift evolutionsemianalytic models
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0 comments X p. Extension

The pith

Stellar halos act as transition regions between bright central galaxies and intracluster light.

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

This paper applies the semianalytic model FEGA25 to dark matter merger trees to follow the formation and evolution of stellar halos around bright central galaxies from high redshift to the present. It establishes that stellar halo masses correlate tightly with both bright central galaxy masses and intracluster light masses, that colors match those of the intracluster light at every epoch, and that metallicity differences with the central galaxy shrink from roughly 0.4 dex at redshift 2 to 0.1 dex today. A sympathetic reader would care because these results position stellar halos as chemically and dynamically linked bridges in galaxy clusters, showing how stars from varied progenitors assemble into larger structures. Model colors align with ultra-deep imaging from surveys such as VEGAS and FDS, while lower observed metallicities suggest greater contributions from disrupted dwarf galaxies than currently modeled. The work points to upcoming wide-field surveys as direct tests through maps of structure, metallicity, and kinematics.

Core claim

Stellar halos emerge as transition regions between bright central galaxies and the intracluster light. They are dynamically and chemically coupled to both, with their properties depending on halo concentration, intracluster light formation efficiency, and the progenitor mass spectrum. The stellar halo mass correlates strongly with both the bright central galaxy and intracluster light masses, with tighter scatter in the stellar halo-intracluster light relation. Stellar halos and intracluster light exhibit nearly identical color distributions that redden toward the present day, while the metallicity gap between stellar halos and bright central galaxies narrows over cosmic time.

What carries the argument

The transition radius, defined as the stellar component boundary linked to dark matter halo concentration, which separates stellar halos from intracluster light and tracks their coupled mass, color, and metallicity evolution in the updated FEGA25 model.

If this is right

  • Stellar halo mass scales more tightly with intracluster light mass than with bright central galaxy mass.
  • Stellar halos and intracluster light maintain nearly identical colors across all redshifts and both redden toward the present.
  • The metallicity difference between stellar halos and bright central galaxies decreases from about 0.4 dex at redshift 2 to 0.1 dex at redshift 0.
  • Transition radii typically peak near 30 to 40 kpc but can extend to hundreds of kiloparsecs in the most massive halos after redshift 0.5.

Where Pith is reading between the lines

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

  • Stellar halo observations could indirectly constrain dark matter halo concentrations in clusters through the transition radius relation.
  • The metallicity mismatch implies that future models need stronger contributions from low-mass disrupted galaxies to match data.
  • Kinematic mapping in large samples could test whether the dynamical coupling between stellar halos, central galaxies, and intracluster light holds in real clusters.

Load-bearing premise

Stellar halos are correctly identified as the stellar material inside a transition radius set by halo concentration, and the model's updated treatment of intracluster light formation captures the dominant physical processes.

What would settle it

Deep imaging and spectroscopy of many galaxy clusters that show stellar halo colors or metallicities diverging strongly from those of the intracluster light, or that find no link between transition radius and halo concentration.

Figures

Figures reproduced from arXiv: 2511.01164 by Emanuele Contini, Enrica Iodice, Marilena Spavone, Rossella Ragusa, Sukyoung K Yi.

Figure 1
Figure 1. Figure 1: Schematic representation of central galaxies and their surround￾ing stellar halos and intracluster stars. The stellar halo is defined as the stellar component within the transition radius Rtrans, marking the inter￾mediate region between the galaxy and the intracluster light. Iodice et al. 2021) and the Fornax Deep Survey (FDS; Iodice et al. 2016). These surveys do not provide metallicity measure￾ments. The… view at source ↗
Figure 2
Figure 2. Figure 2: Left panel: stellar halo mass as a function of the transition radius Rtrans, at different redshifts as indicated in the legend. The two black dashed lines represent the 16th and 84th percentiles of the distribution at z = 0. At fixed Rtrans, the stellar halo mass increases slightly with redshift, since dark matter halos with similar concentrations are more evolved at earlier epochs than at later times. Rig… view at source ↗
Figure 3
Figure 3. Figure 3: Scaling relations between the mass of stellar halos and that of the associated central galaxy (left panel) and intracluster light (right panel). The color bar encodes the dark matter halo concentration, with redder colors corresponding to higher concentrations. The stellar halo mass correlates well with both the BCG and the ICL mass, with a notably smaller scatter in the ICL case. In both relations, halo c… view at source ↗
Figure 4
Figure 4. Figure 4: Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relation between g-r and r-i colors for the BCGs (red), stellar halos (black), ICL (blue) at different redshifts (separate panels), and as observed in VEGAS (green diamonds at z = 0). Overall, the three components exhibit comparable colors, largely independent of redshift, and all progressively redden (shifting towards the right side of the diagrams) as the redshift approaches the present epoch. ±1σ distri… view at source ↗
Figure 6
Figure 6. Figure 6: For the same colors shown in the previous figure, the plots illustrate their distributions for each component at z = 0 (left panel) and z = 2 (right panel). As discussed above, both colors become redder with time, but the distributions reveal notable differences. At z = 0, the BCGs display narrower distributions with a more pronounced peak, while at z = 2 their distributions appear quite different. Overall… view at source ↗
Figure 7
Figure 7. Figure 7: Metallicity profiles (log Z) of the three components as a function of radius (see text for details), shown at different redshifts (separate panels). At high redshift, both stellar halos and the ICL tend to be more metal-poor than the BCGs, although this trend becomes less evident at lower redshifts. The shaded rectangles mark the regions enclosing the 16th to 84th percentiles of the distributions, while gr… view at source ↗
Figure 8
Figure 8. Figure 8: Similarly to [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
read the original abstract

We study the formation and evolution of stellar halos (SHs) around bright central galaxies (BCGs), focusing on their scaling relations, colors, and metallicities across cosmic time, and compare model predictions with ultra--deep imaging data. We use the semianalytic model \textsc{FEGA25}, applied to merger trees from high--resolution dark matter simulations, including an updated treatment of intracluster light (ICL) formation. SHs are defined as the stellar component within the transition radius, linked to halo concentration. Predictions are compared with observations from the VST Early-type GAlaxy Survey (VEGAS) and Fornax Deep Survey (FDS). The SH mass correlates strongly with both BCG and ICL masses, with tighter scatter in the SH--ICL relation. The transition radius peaks at 30--40 kpc nearly independent of redshift, but can reach $\sim400$ kpc in the most massive halos, after z=0.5. SHs and ICL show nearly identical color distributions at all epochs, both reddening toward $z=0$. At $z=2$, SHs and the ICL are $\sim0.4$ dex more metal--poor than BCGs, but the gap shrinks to $\sim0.1$ dex by the present time. Observed colors are consistent with model predictions, while observed metallicities are lower, suggesting a larger contribution from disrupted dwarfs. SHs emerge as transition regions between BCGs and the ICL, dynamically and chemically coupled to both. Their properties depend on halo concentration, ICL formation efficiency, and the progenitor mass spectrum. Upcoming wide--field photometric and spectroscopic surveys (e.g. LSST, WEAVE, 4MOST) will provide crucial tests by mapping structure, metallicity, and kinematics in large galaxy samples.

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

2 major / 2 minor

Summary. The paper uses the semianalytic model FEGA25 applied to high-resolution dark matter merger trees, with an updated ICL formation treatment, to study stellar halos (SHs) of bright central galaxies (BCGs). SHs are defined as the stellar component inside a transition radius tied to halo concentration. The work reports strong SH mass correlations with BCG and ICL masses (tighter for SH-ICL), a transition radius peaking at 30-40 kpc independent of redshift (extending to ~400 kpc in massive halos post-z=0.5), nearly identical SH and ICL color distributions that redden toward z=0, and a metallicity gap shrinking from ~0.4 dex at z=2 to ~0.1 dex at z=0. Model colors match VEGAS and FDS observations while metallicities are overpredicted, implying greater dwarf disruption; the authors conclude SHs are dynamically and chemically coupled transition regions whose properties depend on halo concentration, ICL efficiency, and progenitor mass spectrum, with forecasts for LSST, WEAVE, and 4MOST.

Significance. If the central trends hold, the results advance understanding of BCG, SH, and ICL co-evolution by linking observable scaling relations and chemical properties to halo concentration and merger history across cosmic time. The direct comparison to ultra-deep VEGAS and FDS photometry provides a useful observational anchor, especially for colors, while the noted metallicity offset highlights the role of low-mass progenitors. The redshift-independent transition radius and survey predictions constitute falsifiable outputs that can be tested with upcoming wide-field data.

major comments (2)
  1. [§2] §2 (model description): The SH definition as the stellar component within the transition radius, which is itself tied to halo concentration from the SAM, renders the claim of dynamical and chemical coupling between SHs, BCGs, and ICL dependent on the internal partitioning of FEGA25; without explicit cross-validation against hydrodynamical simulations that independently resolve stripping and merger debris, the reported color similarity and shrinking metallicity gap could partly reflect shared model assumptions rather than physical coupling.
  2. [Abstract and results on metallicity evolution] Abstract and results on metallicity evolution: The model predicts SHs and ICL ~0.4 dex more metal-poor than BCGs at z=2 shrinking to 0.1 dex at z=0, yet observed metallicities are lower than predicted; this offset is load-bearing for the stated dependence on the progenitor mass spectrum, but the manuscript does not quantify how varying the ICL formation efficiency parameters would alter the gap or the SH-ICL correlation tightness.
minor comments (2)
  1. Figure captions should explicitly state the redshift bins and sample selection criteria used for the color and metallicity histograms to allow direct reproduction of the reported agreement with VEGAS/FDS.
  2. The manuscript would benefit from a short table summarizing the free parameters in the updated ICL formation prescription and their adopted values.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and the recommendation for minor revision. The comments raise valid points about model dependence and parameter sensitivity that we address below. We will incorporate targeted revisions to strengthen the manuscript without altering its core conclusions.

read point-by-point responses

  1. Referee: §2 (model description): The SH definition as the stellar component within the transition radius, which is itself tied to halo concentration from the SAM, renders the claim of dynamical and chemical coupling between SHs, BCGs, and ICL dependent on the internal partitioning of FEGA25; without explicit cross-validation against hydrodynamical simulations that independently resolve stripping and merger debris, the reported color similarity and shrinking metallicity gap could partly reflect shared model assumptions rather than physical coupling.

    Authors: We agree that the SH definition relies on the transition radius computed from halo concentration within FEGA25, making the reported coupling inherently tied to the model's partitioning scheme. This choice is physically motivated by the expected transition between the central galaxy and ICL regimes and is consistent with observational surface-brightness-based definitions. While direct cross-validation against hydrodynamical simulations is not included here, the close match between predicted and observed colors from VEGAS and FDS provides empirical support for the physical relevance of the results. The metallicity evolution follows from the shared merger history and progenitor mass spectrum in the underlying dark-matter trees. In the revised manuscript we will expand the discussion in §2 to explicitly note these model assumptions, their potential influence on the reported similarities, and the value of future hydrodynamical comparisons. revision: partial

  2. Referee: Abstract and results on metallicity evolution: The model predicts SHs and ICL ~0.4 dex more metal-poor than BCGs at z=2 shrinking to 0.1 dex at z=0, yet observed metallicities are lower than predicted; this offset is load-bearing for the stated dependence on the progenitor mass spectrum, but the manuscript does not quantify how varying the ICL formation efficiency parameters would alter the gap or the SH-ICL correlation tightness.

    Authors: We concur that a quantitative exploration of how ICL formation efficiency parameters affect the metallicity gap and the tightness of the SH–ICL relation would better substantiate the link to the progenitor mass spectrum. The current work presents results for the fiducial model calibrated to observations. We will add a short sensitivity analysis (new subsection or appendix) that varies the ICL efficiency within the range explored during model calibration and shows the resulting changes to the gap and correlation scatter. This will demonstrate that the main evolutionary trends remain robust. revision: yes

Circularity Check

0 steps flagged

No significant circularity: predictions derived from external merger trees and SAM

full rationale

The paper applies the FEGA25 semianalytic model to merger trees extracted from high-resolution dark matter simulations, defines SHs as the stellar component inside a transition radius tied to halo concentration, and generates predictions for scaling relations, colors, and metallicities that are compared against independent observational datasets (VEGAS, FDS). No quoted step shows a prediction reducing by construction to a fitted input, a self-citation chain, or a definitional tautology; the central claims about dynamical/chemical coupling emerge as model outputs rather than inputs. This is the normal self-contained case for simulation-based work.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the semianalytic model's internal prescriptions for stellar mass assembly and ICL formation, which are drawn from prior literature and not re-derived here.

free parameters (1)
  • ICL formation efficiency parameters
    Updated treatment of intracluster light formation is invoked but specific parameter values are not listed in the abstract.
axioms (2)
  • domain assumption Merger trees from high-resolution dark matter simulations accurately represent the assembly history of halos.
    The model is applied directly to these trees without independent verification shown in the abstract.
  • domain assumption Stellar halos are the stellar component inside the transition radius tied to halo concentration.
    This definition is stated as the basis for all reported scaling relations.

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Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages

  1. [1]

    Amorisco, N. C. 2017, MNRAS, 464, 2882 Bahé, Y . M., Barnes, D. J., Dalla Vecchia, C., et al. 2017, MNRAS, 470, 4186

    work page 2017

  2. [2]

    J., Kay, S

    Barnes, D. J., Kay, S. T., Bahé, Y . M., et al. 2017, MNRAS, 471, 1088

    work page 2017

  3. [3]

    C., Carollo, D., Ivezi´c, Ž., et al

    Beers, T. C., Carollo, D., Ivezi´c, Ž., et al. 2012, ApJ, 746, 34

    work page 2012

  4. [4]

    W., Koposov, S

    Belokurov, V ., Erkal, D., Evans, N. W., Koposov, S. E., & Deason, A. J. 2018, MNRAS, 478, 611

    work page 2018

  5. [5]

    L., Fattahi, A., et al

    Belokurov, V ., Sanders, J. L., Fattahi, A., et al. 2020, MNRAS, 494, 3880

    work page 2020

  6. [6]

    2024, A&A, 690, A115

    Beltrand, C., Monachesi, A., D’Souza, R., et al. 2024, A&A, 690, A115

    work page 2024

  7. [7]

    Bullock, J. S. & Johnston, K. V . 2005, ApJ, 635, 931

    work page 2005

  8. [8]

    2015, A&A, 581, A10

    Capaccioli, M., Spavone, M., Grado, A., et al. 2015, A&A, 581, A10

    work page 2015

  9. [9]

    C., Chiba, M., et al

    Carollo, D., Beers, T. C., Chiba, M., et al. 2010, ApJ, 712, 692

    work page 2010

  10. [10]

    C., Lee, Y

    Carollo, D., Beers, T. C., Lee, Y . S., et al. 2007, Nature, 450, 1020

    work page 2007

  11. [11]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

    work page 2003

  12. [12]

    L., Habib, S., Heitmann, K., et al

    Child, H. L., Habib, S., Heitmann, K., et al. 2018, ApJ, 859, 55

    work page 2018

  13. [13]

    2014, MNRAS, 437, 3787

    Contini, E., De Lucia, G., Villalobos, Á., & Borgani, S. 2014, MNRAS, 437, 3787

    work page 2014

  14. [14]

    Contini, E. & Gu, Q. 2020, ApJ, 901, 128

    work page 2020

  15. [15]

    Contini, E., Jeon, S., Rhee, J., Han, S., & Yi, S. K. 2023, ApJ, 958, 72

    work page 2023

  16. [16]

    K., & Jeon, S

    Contini, E., Yi, S. K., & Jeon, S. 2024c, arXiv e-prints, arXiv:2404.01560

    work page arXiv

  17. [17]

    K., & Kang, X

    Contini, E., Yi, S. K., & Kang, X. 2018, MNRAS, 479, 932

    work page 2018

  18. [18]

    K., & Kang, X

    Contini, E., Yi, S. K., & Kang, X. 2019, ApJ, 871, 24

    work page 2019

  19. [19]

    2024, A&A, 683, A59

    Contreras-Santos, A., Knebe, A., Cui, W., et al. 2024, A&A, 683, A59

    work page 2024

  20. [20]

    P., Cole, S., Frenk, C

    Cooper, A. P., Cole, S., Frenk, C. S., et al. 2010, MNRAS, 406, 744

    work page 2010

  21. [21]

    P., Martínez-Delgado, D., Helly, J., et al

    Cooper, A. P., Martínez-Delgado, D., Helly, J., et al. 2011, ApJ, 743, L21

    work page 2011

  22. [22]

    P., Parry, O

    Cooper, A. P., Parry, O. H., Lowing, B., Cole, S., & Frenk, C. 2015, MNRAS, 454, 3185

    work page 2015

  23. [23]

    A., Wyithe, J

    Correa, C. A., Wyithe, J. S. B., Schaye, J., & Duffy, A. R. 2015, MNRAS, 452, 1217

    work page 2015

  24. [24]

    A., Schaye, J., Bower, R

    Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 1937

    work page 2015

  25. [25]

    2018, MNRAS, 480, 2898 de Jong, R

    Cui, W., Knebe, A., Yepes, G., et al. 2018, MNRAS, 480, 2898 de Jong, R. S., Agertz, O., Berbel, A. A., et al. 2019, The Messenger, 175, 3 de Vaucouleurs, G. 1959, Handbuch der Physik, 53, 275

    work page 2018

  26. [26]

    J., Belokurov, V ., & Evans, N

    Deason, A. J., Belokurov, V ., & Evans, N. W. 2013, ApJ, 763, 113

    work page 2013

  27. [27]

    J., Belokurov, V ., Koposov, S

    Deason, A. J., Belokurov, V ., Koposov, S. E., & Rockosi, C. M. 2014, ApJ, 787, 30

    work page 2014

  28. [28]

    J., Mao, Y .-Y ., & Wechsler, R

    Deason, A. J., Mao, Y .-Y ., & Wechsler, R. H. 2016, ApJ, 821, 5

    work page 2016

  29. [29]

    J., Gregg, M

    Drinkwater, M. J., Gregg, M. D., & Colless, M. 2001, ApJ, 548, L139 D’Souza, R. & Bell, E. F. 2018, MNRAS, 474, 5300

    work page 2001

  30. [30]

    2015, MNRAS, 446, 120

    Duc, P.-A., Cuillandre, J.-C., Karabal, E., et al. 2015, MNRAS, 446, 120

    work page 2015

  31. [31]

    1979, The Annals of Statistics, 7, 1

    Efron, B. 1979, The Annals of Statistics, 7, 1

    work page 1979

  32. [32]

    M., Sales, L

    Elias, L. M., Sales, L. V ., Helmi, A., & Hernquist, L. 2020, MNRAS, 495, 29

    work page 2020

  33. [33]

    S., McCarthy, I

    Font, A. S., McCarthy, I. G., Crain, R. A., et al. 2011, MNRAS, 416, 2802

    work page 2011

  34. [34]

    J., Brook, C

    Gallart, C., Bernard, E. J., Brook, C. B., et al. 2019, Nature Astronomy, 3, 932

    work page 2019

  35. [35]

    & Martel, H

    Gendron, V . & Martel, H. 2025, MNRAS, 541, 2513

    work page 2025

  36. [36]

    M., Kalirai, J

    Gilbert, K. M., Kalirai, J. S., Guhathakurta, P., et al. 2014, ApJ, 796, 76

    work page 2014

  37. [37]

    2022, ApJ, 932, 44

    Gilhuly, C., Merritt, A., Abraham, R., et al. 2022, ApJ, 932, 44

    work page 2022

  38. [38]

    Grand, R. J. J., Gómez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179

    work page 2017

  39. [39]

    F., et al

    Harmsen, B., Monachesi, A., Bell, E. F., et al. 2017, MNRAS, 466, 1491

    work page 2017

  40. [40]

    2008, A&A Rev., 15, 145

    Helmi, A. 2008, A&A Rev., 15, 145

    work page 2008

  41. [41]

    2020, ARA&A, 58, 205

    Helmi, A. 2020, ARA&A, 58, 205

    work page 2020

  42. [42]

    H., et al

    Helmi, A., Babusiaux, C., Koppelman, H. H., et al. 2018, Nature, 563, 85

    work page 2018

  43. [43]

    F., Kereš, D., Oñorbe, J., et al

    Hopkins, P. F., Kereš, D., Oñorbe, J., et al. 2014, MNRAS, 445, 581

    work page 2014

  44. [44]

    A., Lewis, G

    Ibata, R. A., Lewis, G. F., McConnachie, A. W., et al. 2014, ApJ, 780, 128

    work page 2014

  45. [45]

    2016, ApJ, 820, 42

    Iodice, E., Capaccioli, M., Grado, A., et al. 2016, ApJ, 820, 42

    work page 2016

  46. [46]

    2017, ApJ, 851, 75

    Iodice, E., Spavone, M., Cantiello, M., et al. 2017, ApJ, 851, 75

    work page 2017

  47. [47]

    2019, A&A, 623, A1

    Iodice, E., Spavone, M., Capaccioli, M., et al. 2019, A&A, 623, A1

    work page 2019

  48. [48]

    2021, The Messenger, 183, 25 Ivezi´c, Ž., Kahn, S

    Iodice, E., Spavone, M., Capaccioli, M., et al. 2021, The Messenger, 183, 25 Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

    work page 2021

  49. [49]

    C., Dalton, G

    Jin, S., Trager, S. C., Dalton, G. B., et al. 2024, MNRAS, 530, 2688

    work page 2024

  50. [50]

    J., Kim, J., et al

    Joo, H., Jee, M. J., Kim, J., et al. 2025, ApJ, 990, 96

    work page 2025

  51. [51]

    S., Gilbert, K

    Kalirai, J. S., Gilbert, K. M., Guhathakurta, P., et al. 2006, ApJ, 648, 389 Krajnovi´c, D., Emsellem, E., den Brok, M., et al. 2018, MNRAS, 477, 5327

    work page 2006

  52. [52]

    Lane, J. M. M., Bovy, J., & Mackereth, J. T. 2022, MNRAS, 510, 5119

    work page 2022

  53. [53]

    Lee, J. & Yi, S. K. 2017, ApJ, 836, 161

    work page 2017

  54. [54]

    2016, ApJ, 830, 62

    Merritt, A., van Dokkum, P., Abraham, R., & Zhang, J. 2016, ApJ, 830, 62

    work page 2016

  55. [55]

    C., Harding, P., Feldmeier, J

    Mihos, J. C., Harding, P., Feldmeier, J. J., et al. 2017, ApJ, 834, 16

    work page 2017

  56. [56]

    A., Grand, R

    Monachesi, A., Gómez, F. A., Grand, R. J. J., et al. 2016, MNRAS, 459, L46

    work page 2016

  57. [57]

    A., Grand, R

    Monachesi, A., Gómez, F. A., Grand, R. J. J., et al. 2019, MNRAS, 485, 2589

    work page 2019

  58. [58]

    2023, MN- RAS, 521, 800

    Montenegro-Taborda, D., Rodriguez-Gomez, V ., Pillepich, A., et al. 2023, MN- RAS, 521, 800

    work page 2023

  59. [59]

    & Trujillo, I

    Montes, M. & Trujillo, I. 2018, MNRAS, 474, 917

    work page 2018

  60. [60]

    C., Rich, R

    Mouhcine, M., Ferguson, H. C., Rich, R. M., Brown, T. M., & Smith, T. E. 2005, ApJ, 633, 810

    work page 2005

  61. [61]

    P., Conroy, C., Bonaca, A., et al

    Naidu, R. P., Conroy, C., Bonaca, A., et al. 2020, ApJ, 901, 48

    work page 2020

  62. [62]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493

    work page 1997

  63. [63]

    2024, A&A, 686, A157

    Nelson, D., Pillepich, A., Ayromlou, M., et al. 2024, A&A, 686, A157

    work page 2024

  64. [64]

    2018, MNRAS, 475, 624

    Nelson, D., Pillepich, A., Springel, V ., et al. 2018, MNRAS, 475, 624

    work page 2018

  65. [65]

    2019, Computational Astrophysics and Cosmology, 6, 2

    Nelson, D., Springel, V ., Pillepich, A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2

    work page 2019

  66. [66]

    2014, MNRAS, 440, 762

    Oliva-Altamirano, P., Brough, S., Lidman, C., et al. 2014, MNRAS, 440, 762

    work page 2014

  67. [67]

    2018, MNRAS, 475, 648

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648

    work page 2018

  68. [68]

    Pinna, F., Walo-Martín, D., Grand, R. J. J., et al. 2024, A&A, 683, A236 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6

    work page 2024

  69. [69]

    A., Cuesta, A

    Prada, F., Klypin, A. A., Cuesta, A. J., Betancort-Rijo, J. E., & Primack, J. 2012, MNRAS, 423, 3018

    work page 2012

  70. [70]

    L., Lagos, C

    Proctor, K. L., Lagos, C. d. P., Ludlow, A. D., & Robotham, A. S. G. 2024, MNRAS, 527, 2624

    work page 2024

  71. [71]

    2023, A&A, 670, L20

    Ragusa, R., Iodice, E., Spavone, M., et al. 2023, A&A, 670, L20

    work page 2023

  72. [72]

    2022, Frontiers in Astronomy and Space Sciences, 9, 852810

    Ragusa, R., Mirabile, M., Spavone, M., et al. 2022, Frontiers in Astronomy and Space Sciences, 9, 852810

    work page 2022

  73. [73]

    2021, A&A, 651, A39

    Ragusa, R., Spavone, M., Iodice, E., et al. 2021, A&A, 651, A39

    work page 2021

  74. [74]

    2018, A&A, 616, A121

    Sarzi, M., Iodice, E., Coccato, L., et al. 2018, A&A, 616, A121

    work page 2018

  75. [75]

    A., Bower, R

    Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521

    work page 2015

  76. [76]

    R., et al

    Spavone, M., Capaccioli, M., Napolitano, N. R., et al. 2017, A&A, 603, A38

    work page 2017

  77. [77]

    2018, ApJ, 864, 149

    Spavone, M., Iodice, E., Capaccioli, M., et al. 2018, ApJ, 864, 149

    work page 2018

  78. [78]

    2022, A&A, 663, A135

    Spavone, M., Iodice, E., D’Ago, G., et al. 2022, A&A, 663, A135

    work page 2022

  79. [79]

    S., et al

    Spavone, M., Iodice, E., Lohmann, F. S., et al. 2024, A&A, 689, A306

    work page 2024

  80. [80]

    2020, A&A, 639, A14

    Spavone, M., Iodice, E., van de Ven, G., et al. 2020, A&A, 639, A14

    work page 2020

Showing first 80 references.