REVIEW 4 major objections 5 minor 111 references
The Impact of Splashback Galaxies on Galaxy Assembly Bias
T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash
Pith's one-line read This paper claims that splashback galaxies account for a large share of galaxy assembly bias in simulations, with removal tests cutting the full-sample signal by about a third and the central-only signal by about 60%.
desk verdict A solid, useful empirical study showing splashback galaxies carry a disproportionate share of galaxy assembly bias, with a genuine causal gap that the authors themselves acknowledge and a referee can close with a control sample. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the splashback galaxy, defined by its fly-by merger-tree history: a present-day central galaxy that was previously a satellite of a more massive host halo. The second piece of machinery is the shuffling test for galaxy assembly bias, in which galaxies are randomly reassigned to halos of the same mass, erasing secondary-property dependence, and the ratio of the original correlation function to the shuffled one isolates the assembly-bias signal. Comparing the original, splashback-removed, and splashback-reassigned samples under this test is what carries the argument.
What would settle it
Repeat the removal test using splashbacks identified by a different scheme, such as halos between a massive host's virial radius and its splashback radius, or halos found by a different halo finder; if removing that population does not shrink the shuffled-clustering ratio by roughly 30% for the full sample and 60% for centrals, the claimed role of splashback galaxies is an artifact of the fly-by definition rather than a physical driver.
Extended reading notes
Core claim
The paper argues that splashback galaxies, rather than generic halo age or concentration effects alone, are largely responsible for the low-mass tail of halo occupancy variation seen for highly concentrated halos and for halos in dense environments, and that they account for a substantial fraction of the measured galaxy assembly bias. The load-bearing comparison is between a shuffled sample that destroys assembly bias and samples in which splashbacks are removed or reclassified; the central claim is that removing about 3% of the galaxies reduces the large-scale correlation-function excess by roughly a third, and removing about 4% of centrals reduces it by about 60%. Because reassignment rather than removal has little effect on the amplitude, the paper concludes that the effect does not depend on whether a halo finder labels these objects as centrals or as satellites; what matters is the environmentally truncated growth the splashback label traces. The same pattern appears in a hydrodynamical simulation, so the finding is not tied to the particular semi-analytic model.
Load-bearing premise
The fly-by definition of a splashback galaxy, a present-day central that was once a satellite of a currently more massive host, correctly isolates the physically relevant population, so that the measured reduction in assembly bias comes from these objects rather than from merger-tree errors or from excluding major mergers.
Editorial extensions
If this is right
- If the claim holds, halo occupation models that ignore splashbacks will overestimate the role of halo concentration and environment in the low-mass regime, because much of that signal is carried by a few percent of objects.
- Because reassigning splashbacks does not change the signal amplitude, galaxy assembly bias measurements in simulations do not depend on whether a halo finder classifies these objects as centrals or satellites.
- Removal of splashbacks gives the largest GAB reduction in the paper's lower stellar-mass-threshold samples, so deeper galaxy samples should show an even stronger splashback contribution to assembly bias.
- The residual assembly bias after removal indicates that roughly two-thirds of the full-sample signal, and a measurable part of the central-only excess, comes from other environmental and assembly effects, so splashbacks are a major but not the sole driver.
- The result is qualitatively reproduced in a hydrodynamical simulation, suggesting the splashback contribution is a feature of the underlying halo growth in dense environments rather than a quirk of one galaxy formation model.
Reading between the lines
- A natural extension, not tested here, would be to identify splashbacks by splashback radius rather than fly-by histories; if the same ~30% reduction appears, the mechanism is robust to the definition rather than an artifact of merger-tree tracking.
- The paper's emphasis on removal versus reassignment suggests that HOD-style models could treat splashbacks either way and still reproduce clustering; the testable step is to see whether emulators built on either convention match small-scale clustering equally well.
- Because the splashback contribution grows at lower stellar-mass thresholds, deeper surveys reaching fainter galaxies should expect a stronger assembly-bias contamination; this is an inference from the paper's density dependence, not a result it derives.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper investigates the role of splashback galaxies—defined as present-day central galaxies that were formerly satellites of a more massive host—in galaxy assembly bias (GAB). Using the Guo et al. (2011) semi-analytic model applied to the Millennium Simulation, and a qualitative check with TNG300, the authors construct three stellar-mass selected samples and measure halo occupation variations with concentration and environment, as well as the GAB amplitude from the ratio of the galaxy correlation function to a shuffled sample. They compare the original samples with samples in which splashbacks are removed and samples in which splashbacks are reclassified as satellites of their former hosts. The main findings are that splashbacks preferentially occupy low-mass, highly concentrated halos in dense environments with relatively high stellar-to-halo mass ratios; that they account for the low-mass tails in the occupancy variations for concentrated and dense-environment halos; and that removing splashbacks reduces the GAB signal by roughly 30% for the full sample and about 60% for centrals at n=0.01 h^3 Mpc^-3, while reassigning them leaves the amplitude largely unchanged but shifts the transition scale.
Significance. If the causal attribution holds, this is a valuable result: it identifies a small, individually identifiable galaxy population that accounts for a substantial fraction of GAB in simulations, with direct implications for HOD modeling and for interpreting observations of assembly bias. The study is methodologically transparent, uses publicly available simulations, contains no fitted parameters, and includes a cross-check with a hydrodynamical simulation. These are genuine strengths. However, the central quantitative claim—that splashbacks are largely responsible for the measured reduction—is not yet secured, because the splashback selection shares the very secondary properties (low mass, high concentration, dense environment) that generate the GAB signal. The manuscript's own Section 5 caveat that splashbacks may be tracers rather than the origin of GAB is an appropriate framing, but the abstract and Section 4.3 currently state the stronger causal claim without a placebo control. The recommendation below is therefore major revision rather than acceptance.
major comments (4)
- [Section 4.3, Figure 4] The headline claim that removing about 3-4% of galaxies reduces GAB by roughly 30% for the full sample and 60% for centrals is an attribution claim, but the experiment has no placebo arm. Splashbacks are removed because they preferentially occupy low-mass, high-concentration halos in dense environments (Section 4.1, Figure 2), which are exactly the properties that generate the GAB signal. Removing any comparably selected population of non-splashback centrals, matched in halo mass, concentration, environment, and stellar mass, could produce a similar reduction even if fly-by history were irrelevant. The only reported control, raising the stellar-mass threshold to match the reduced number density (Section 4.3), checks the overall density dependence but not the selective removal of low-mass, dense-environment, high-concentration centrals. The authors should add a matched placebo removal, or otherwise demonstrate that the reduction is specifically associated with the splashback fly-by history rather than with the shared environmental and structural properties.
- [Section 4.3, Figure 4] The percentage reductions (roughly 30% and 60%) are central to the abstract and conclusions, but they are quoted without propagated uncertainties. The shaded regions in Figure 4 reflect scatter among shuffled samples, and jackknife errors are stated to be subdominant without being shown or quantified. The authors should report uncertainties on the large-scale ratio and propagate them to the reduction percentages. The TNG300 confirmation in Appendix A is explicitly qualitative and, given its smaller volume and larger uncertainties, should not be used to support the specific percentages.
- [Section 3.1 and Section 4.3] The reassignment experiment does not close the attribution gap. Since galaxy positions are unchanged, reassignment changes only the shuffling procedure, not the physical selection of the splashback population, as the paper itself notes in Section 3.2. The green lines in Figure 4 therefore demonstrate insensitivity to halo classification, not that fly-by history is the causal ingredient. The manuscript's Section 5 statement that 'there may be no fundamental difference between true splashbacks and galaxies that remained in the outskirts' and that splashbacks are 'tracers rather than the origin' points directly to this unresolved issue. Unless the placebo test in the first comment is added, the causal language should be softened to the tracer interpretation throughout the abstract and results.
- [Section 4.2, Figure 3] The claim that removing or reassigning splashbacks 'eliminates' the low-mass tail of the occupancy variation for concentrated halos and dense environments is presented without statistical significance. The effects are described as 'subtle but yet distinct', but no error bars or significance estimates are shown for the halo occupation functions in Figure 3. Since this claim is load-bearing for the conclusion that splashbacks are largely responsible for the occupancy-variation tails, the authors should quantify the significance of the tail reduction, for example with bootstrap resampling of halos or repeated shuffling.
minor comments (5)
- [Abstract and captions] There are several typos: 'splasback' in the abstract, 'Millenium' in the caption of Figure 3, and 'splasbacks' in the caption of Figure 4; these should be corrected.
- [Appendix A and Figure 5] The text of Appendix A states that the TNG300 shaded regions come from 20 randomly shuffled samples, while the caption of Figure 5 says 10; this inconsistency should be reconciled.
- [Table 1] The footnote that galaxy numbers are quoted in units of thousands is easy to misread; consider writing the column header explicitly as 'Ntot (10^3 galaxies)' and similarly for the other counts.
- [Section 4.2] In Figure 3, the dashed lines are labeled 'Reassigned splashbacks', but the text also uses this figure to infer the removal case by reading the low-mass central occupation. A sentence clarifying which curves correspond to the removal scenario would improve readability.
- [Section 3.2] The shuffling procedure is described only verbally; specifying the exact halo-mass binning (width and range) and the treatment of halos with no assigned galaxies would aid reproducibility, even though the authors state that results are insensitive to binning.
Circularity Check
Partial circularity: the dense-environment, low-mass occupancy tail is essentially a restatement of the fly-by splashback selection, though the GAB reduction itself is an empirical measurement with independent content.
-
self definitional
[Section 3.1 (definition) and Sections 4.1-4.2 (occupancy-variation results)]
"a practical definition of splashback galaxies is present-day central galaxies that were previously satellites of a currently more massive halo. ... The 'tail' of galaxies in low-mass halos is mostly eliminated when the splashback galaxies are reassigned to their former host halos ... These behaviors are expected, as we have demonstrated in Fig. 2 that splashback galaxies predominantly occupy low-mass halos in dense environments."
The fly-by definition already requires that each splashback galaxy has a currently more massive former host halo. By construction, such an object is located in the vicinity of a massive halo, which is a dense environment, and its present halo is less massive than that former host. The paper's finding that splashbacks preferentially occupy low-mass halos in dense environments, and that removing them eliminates the low-mass dense-environment occupancy tail, is therefore largely a restatement of the selection criterion rather than an independent physical discovery.
full rationale
Most of the quantitative derivation is self-contained and empirical. Splashback galaxies are identified from merger trees using an explicit fly-by definition; the GAB signal is obtained by comparing the galaxy correlation function to a shuffled sample following Croton et al. (2007); the number-density control demonstrates that the reduction is not merely a trivial density effect; and the results are reproduced with TNG300. No parameter is fitted to the GAB signal and no prediction is produced from a fitted value, so there is no fitted-input-called-prediction circularity. The main partial circularity is in the occupancy-variation attribution: because the splashback definition requires a currently more massive former host, the resulting preference for low-mass halos in dense environments is largely built into the selection. Removing these objects and finding that the dense-environment low-mass tail disappears is therefore an expected consequence of the definition rather than an independent validation of splashback-specific physics. The paper itself acknowledges this limitation when it states that 'splashbacks are not necessarily the origin of GAB, but rather tracers of it.' The central quantitative claims about the 30-60% reduction of GAB retain independent empirical content, but the causal attribution to splashback-specific evolution is weakened by the self-definitional selection. Score 4 reflects partial circularity with substantial independent measurement.
Assumptions & free parameters
assumptions (4)
- domain assumption The Guo et al. (2011) semi-analytic model applied to the Millennium Simulation accurately represents the galaxy-halo connection, including satellite and central classification, at the stellar mass thresholds used.
- domain assumption The fly-by definition of splashback galaxies, based on merger trees, correctly identifies the physically relevant splashback population.
- domain assumption The shuffling procedure removes assembly bias while preserving the one-halo term, so the ratio of original to shuffled correlation functions measures GAB.
- domain assumption Results are insensitive to cosmology and galaxy formation model; TNG300 confirms qualitatively.
Cite this review
Pith. "Pith review of The Impact of Splashback Galaxies on Galaxy Assembly Bias." pith.science (2026). https://pith.science/paper/GSLCY7HK
@misc{pith2026260807791,
author = {Pith},
title = {Pith review of: The Impact of Splashback Galaxies on Galaxy Assembly Bias},
year = {2026},
howpublished = {\url{https://pith.science/paper/GSLCY7HK}},
note = {Machine review of arXiv:2608.07791}
}
read the original abstract
The clustering of galaxies is affected by the assembly history of their underlying dark matter halos. This complex phenomenon, known as galaxy assembly bias, has been extensively studied, but the exact physical origin remains unclear. Splashback halos, typically low-mass halos that have traversed larger neighboring halos, have been suggested to be associated with halo assembly bias. Using a semi-analytic galaxy-formation model applied to the Millennium simulation, we explicitly explore the role that splasback galaxies play in galaxy assembly bias. We identify splashbacks as present-day central galaxies that were formerly satellites of a more massive host, and construct stellar-mass selected galaxy samples with the splashbacks either removed or reclassified as satellites of their former host halo. We find that splashbacks tend to reside in low-mass, highly concentrated halos and in dense environments, and that they have relatively high stellar-to-halo mass ratios. Splashbacks appear to be largely responsible for the low-mass tail of the occupancy variation for highly concentrated halos and for halos in dense environments. Finally, when computing the impact of assembly bias on galaxy clustering, we find that while removing the splashbacks significantly reduces the signal, reassigning them has little effect on its amplitude but shifts the transition scale. We repeat the analysis with the hydrodynamical simulation TNG300, confirming the robustness of our results. Our results provide insight into assembly bias and have potential implications for modeling the galaxy-halo connection.
Figures
Figures from the paper (2 more)
Reference graph
Works this paper leans on
-
[1]
Adhikari, S., Dalal, N., & Chamberlain, R. T. 2014, JCAP, 2014, 019, doi: 10.1088/1475-7516/2014/11/019
-
[2]
C., Zehavi, I., Contreras, S., & Norberg, P
Artale, M. C., Zehavi, I., Contreras, S., & Norberg, P. 2018, MNRAS, 480, 3978, doi: 10.1093/mnras/sty2110
-
[3]
Ayromlou, M., Kauffmann, G., Anand, A., & White, S. D. M. 2023, MNRAS, 519, 1913, doi: 10.1093/mnras/stac3637
-
[4]
Balogh, M. L., Navarro, J. F., & Morris, S. L. 2000, ApJ, 540, 113, doi: 10.1086/309323
doi:10.1086/309323 2000
-
[5]
Bardeen, J. M., Bond, J. R., Kaiser, N., & Szalay, A. S. 1986, ApJ, 304, 15, doi: 10.1086/164143
doi:10.1086/164143 1986
-
[6]
Behroozi, P. S., Wechsler, R. H., & Wu, H.-Y. 2013, ApJ, 762, 109, doi: 10.1088/0004-637X/762/2/109
-
[7]
Benson, A. J., Bower, R. G., Frenk, C. S., et al. 2003, ApJ, 599, 38, doi: 10.1086/379160
doi:10.1086/379160 2003
-
[9]
Blanton, M. R., & Berlind, A. A. 2007, ApJ, 664, 791, doi: 10.1086/512478
doi:10.1086/512478 2007
Show all 111 references
-
[10]
R., Cole, S., Efstathiou, G., & Kaiser, N
Bond, J. R., Cole, S., Efstathiou, G., & Kaiser, N. 1991, ApJ, 379, 440, doi: 10.1086/170520
1991 doi
-
[11]
A., & Smith, A
Borrow, J., Vogelsberger, M., O’Neil, S., McDonald, M. A., & Smith, A. 2023, MNRAS, 520, 649, doi: 10.1093/mnras/stad045
2023 doi
-
[12]
2017, MNRAS, 469, 594, doi: 10.1093/mnras/stx873
Garaldi, E. 2017, MNRAS, 469, 594, doi: 10.1093/mnras/stx873
2017 doi
-
[13]
J., Hernquist, L., et al
Bose, S., Eisenstein, D. J., Hernquist, L., et al. 2019, MNRAS, 490, 5693, doi: 10.1093/mnras/stz2546
2019 doi
-
[15]
S., Dekel, A., Kolatt, T
Bullock, J. S., Dekel, A., Kolatt, T. S., et al. 2001, ApJ, 555, 240, doi: 10.1086/321477
2001 doi
-
[16]
Busch, P., & White, S. D. M. 2017, MNRAS, 470, 4767, doi: 10.1093/mnras/stx1584
2017 doi
-
[17]
C., Hearin, A., et al
Campbell, D., van den Bosch, F. C., Hearin, A., et al. 2015, MNRAS, 452, 444, doi: 10.1093/mnras/stv1091 15
2015 doi
-
[18]
E., Schaye, J., et al
Chaves-Montero, J., Angulo, R. E., Schaye, J., et al. 2016, MNRAS, 460, 3100, doi: 10.1093/mnras/stw1225
2016 doi
- [19]
-
[20]
E., & Zennaro, M
Contreras, S., Angulo, R. E., & Zennaro, M. 2021a, MNRAS, 504, 5205, doi: 10.1093/mnras/stab1170
-
[21]
M., Norberg, P., & Padilla, N
Contreras, S., Baugh, C. M., Norberg, P., & Padilla, N. 2013, MNRAS, 432, 2717, doi: 10.1093/mnras/stt629
2013 doi
-
[22]
Contreras, S., Chaves-Montero, J., & Angulo, R. E. 2023, MNRAS, 525, 3149, doi: 10.1093/mnras/stad2434
2023 doi
-
[23]
Contreras, S., Chaves-Montero, J., Zennaro, M., & Angulo, R. E. 2021b, MNRAS, 507, 3412, doi: 10.1093/mnras/stab2367
-
[24]
2019, MNRAS, 484, 1133, doi: 10.1093/mnras/stz018
Contreras, S., Zehavi, I., Padilla, N., et al. 2019, MNRAS, 484, 1133, doi: 10.1093/mnras/stz018
2019 doi
-
[26]
J., Springel, V., White, S
Croton, D. J., Springel, V., White, S. D. M., et al. 2006, MNRAS, 365, 11, doi: 10.1111/j.1365-2966.2005.09675.x
2006
-
[27]
R., & Shirokov, A
Dalal, N., White, M., Bond, J. R., & Shirokov, A. 2008, ApJ, 687, 12, doi: 10.1086/591512
2008 doi
-
[29]
2021, ApJ, 909, 112, doi: 10.3847/1538-4357/abd947
Diemer, B. 2021, ApJ, 909, 112, doi: 10.3847/1538-4357/abd947
2021 doi
-
[30]
Carvalho, R. R. 2023, MNRAS, 519, 4884, doi: 10.1093/mnras/stad001
2023 doi
-
[31]
Gao, L., Springel, V., & White, S. D. M. 2005, MNRAS, 363, L66, doi: 10.1111/j.1745-3933.2005.00084.x
2005
-
[32]
Gao, L., & White, S. D. M. 2007, MNRAS, 377, L5, doi: 10.1111/j.1745-3933.2007.00292.x
2007
-
[33]
2014, MNRAS, 445, 175, doi: 10.1093/mnras/stu1654
Genel, S., Vogelsberger, M., Springel, V., et al. 2014, MNRAS, 445, 175, doi: 10.1093/mnras/stu1654
2014 doi
-
[35]
F., et al
Giocoli, C., Palmucci, L., Lesci, G. F., et al. 2024, A&A, 687, A79, doi: 10.1051/0004-6361/202449561
2024 doi
-
[36]
E., et al
Guo, Q., White, S., Angulo, R. E., et al. 2013, MNRAS, 428, 1351, doi: 10.1093/mnras/sts115
2013 doi
-
[38]
S., et al
Hadzhiyska, B., Liu, S., Somerville, R. S., et al. 2021, MNRAS, 508, 698, doi: 10.1093/mnras/stab2564
2021 doi
-
[39]
2023, MNRAS, 524, 2507, doi: 10.1093/mnras/stad731
Hadzhiyska, B., Eisenstein, D., Hernquist, L., et al. 2023, MNRAS, 524, 2507, doi: 10.1093/mnras/stad731
2023 doi
-
[41]
P., Watson, D
Hearin, A. P., Watson, D. F., & van den Bosch, F. C. 2015, MNRAS, 452, 1958, doi: 10.1093/mnras/stv1358
2015 doi
-
[42]
Henriques, B. M. B., Yates, R. M., Fu, J., et al. 2020, MNRAS, 491, 5795, doi: 10.1093/mnras/stz3233
2020 doi
-
[44]
2018, MNRAS, 475, 1177, doi: 10.1093/mnras/stx3253
Gonzalez-Perez, V. 2018, MNRAS, 475, 1177, doi: 10.1093/mnras/stx3253
2018 doi
-
[46]
2025, A&A, 703, A247, doi: 10.1051/0004-6361/202555329
Lacerna, I., Padilla, N., & Palma, D. 2025, A&A, 703, A247, doi: 10.1051/0004-6361/202555329
2025 doi
-
[47]
D., et al
Lacerna, I., Rodriguez, F., Montero-Dorta, A. D., et al. 2022, MNRAS, 513, 2271, doi: 10.1093/mnras/stac1020
2022 doi
-
[48]
1999, MNRAS, 302, 111, doi: 10.1046/j.1365-8711.1999.02090.x
Lemson, G., & Kauffmann, G. 1999, MNRAS, 302, 111, doi: 10.1046/j.1365-8711.1999.02090.x
1999
- [49]
-
[51]
2016, ApJ, 819, 119, doi: 10.3847/0004-637X/819/2/119
Lin, Y.-T., Mandelbaum, R., Huang, Y.-H., et al. 2016, ApJ, 819, 119, doi: 10.3847/0004-637X/819/2/119
2016 doi
-
[52]
2026, PhRvD, 113, 023525, doi: 10.1103/c5w1-qtx4
Liu, Z., Miyatake, H., Schaye, J., et al. 2026, PhRvD, 113, 023525, doi: 10.1103/c5w1-qtx4
2026 doi
-
[53]
D., & Porciani, C
Ludlow, A. D., & Porciani, C. 2011, MNRAS, 413, 1961, doi: 10.1111/j.1365-2966.2011.18282.x
2011
-
[54]
Mansfield, P., & Kravtsov, A. V. 2020, MNRAS, 493, 4763, doi: 10.1093/mnras/staa430 Mart ´ ın-Navarro, I., Pillepich, A., Nelson, D., et al. 2021, Nature, 594, 187, doi: 10.1038/s41586-021-03545-9
2020 doi
-
[55]
A., et al
Matthee, J., Schaye, J., Crain, R. A., et al. 2017, MNRAS, 465, 2381, doi: 10.1093/mnras/stw2884
2017 doi
-
[56]
E., & Weinberg, D
McEwen, J. E., & Weinberg, D. H. 2018, MNRAS, 477, 4348, doi: 10.1093/mnras/sty882
2018 doi
-
[57]
2025, A&A, 695, A159, doi: 10.1051/0004-6361/202452709
Rodriguez, F., & Favole, G. 2025, A&A, 695, A159, doi: 10.1051/0004-6361/202452709
2025 doi
-
[58]
D., & Rodriguez, F
Montero-Dorta, A. D., & Rodriguez, F. 2024, MNRAS, 531, 290, doi: 10.1093/mnras/stae796
2024 doi
-
[59]
More, S., Diemer, B., & Kravtsov, A. V. 2015, ApJ, 810, 36, doi: 10.1088/0004-637X/810/1/36
2015 doi
-
[60]
F., Frenk, C
Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493, doi: 10.1086/304888
1997 doi
-
[61]
2019, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x 16
Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x 16
2019 doi
-
[62]
J., & Dalal, N
Obuljen, A., Percival, W. J., & Dalal, N. 2020, JCAP, 2020, 058, doi: 10.1088/1475-7516/2020/10/058
2020 doi
-
[63]
2025, A&A, 697, A226, doi: 10.1051/0004-6361/202453086 Oyarz´ un, G
Chaves-Montero, J. 2025, A&A, 697, A226, doi: 10.1051/0004-6361/202453086 Oyarz´ un, G. A., Tinker, J. L., Bundy, K., Xhakaj, E., &
2025 doi
-
[64]
Wyithe, J. S. B. 2024, ApJ, 974, 29, doi: 10.3847/1538-4357/ad6de1
2024 doi
-
[65]
C., et al
Palma, D., Lacerna, I., Artale, M. C., et al. 2025, A&A, 693, A67, doi: 10.1051/0004-6361/202450976
2025 doi
-
[66]
G., & Pahwa, I
Paranjape, A., Kovaˇ c, K., Hartley, W. G., & Pahwa, I. 2015, MNRAS, 454, 3030, doi: 10.1093/mnras/stv2137
2015 doi
-
[67]
N., Zentner, A
Pearl, A. N., Zentner, A. R., Newman, J. A., et al. 2024, ApJ, 963, 116, doi: 10.3847/1538-4357/ad1ffd
2024 doi
-
[68]
2018a, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656
Pillepich, A., Springel, V., Nelson, D., et al. 2018a, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656
-
[69]
2018b, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112
Pillepich, A., Nelson, D., Hernquist, L., et al. 2018b, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112
-
[70]
Pimbblet, K. A. 2011, MNRAS, 411, 2637, doi: 10.1111/j.1365-2966.2010.17869.x
2011
-
[71]
Ramakrishnan, S., Paranjape, A., Hahn, O., & Sheth, R. K. 2019, MNRAS, 489, 2977, doi: 10.1093/mnras/stz2344
2019 doi
-
[72]
2023, MNRAS, 522, 4181, doi: 10.1093/mnras/stad1239
Rana, D., More, S., Miyatake, H., et al. 2023, MNRAS, 522, 4181, doi: 10.1093/mnras/stad1239
2023 doi
-
[73]
2023, JCAP, 2023, 016, doi: 10.1088/1475-7516/2023/10/016
Rocher, A., Ruhlmann-Kleider, V., Burtin, E., et al. 2023, JCAP, 2023, 016, doi: 10.1088/1475-7516/2023/10/016
2023 doi
-
[74]
Rodriguez, F., & Montero-Dorta, A. D. 2026, A&A, 707, A34, doi: 10.1051/0004-6361/202558239
2026 doi
-
[75]
N., Mart ´ ınez, H
Ruiz, A. N., Mart ´ ınez, H. J., Coenda, V., et al. 2023, MNRAS, 525, 3048, doi: 10.1093/mnras/stad2267
2023 doi
-
[76]
N., Maller, A
Salcedo, A. N., Maller, A. H., Berlind, A. A., et al. 2018, MNRAS, 475, 4411, doi: 10.1093/mnras/sty109
2018 doi
-
[77]
N., Zu, Y., Zhang, Y., et al
Salcedo, A. N., Zu, Y., Zhang, Y., et al. 2022, Science China Physics, Mechanics, and Astronomy, 65, 109811, doi: 10.1007/s11433-022-1955-7
2022 doi
-
[78]
B., M¨ oller, O., Lee, J., & White, S
Sandvik, H. B., M¨ oller, O., Lee, J., & White, S. D. M. 2007, MNRAS, 377, 234, doi: 10.1111/j.1365-2966.2007.11595.x
2007
-
[79]
2019, MNRAS, 487, 1570, doi: 10.1093/mnras/stz1338
Prada, F., & Klypin, A. 2019, MNRAS, 487, 1570, doi: 10.1093/mnras/stz1338
2019 doi
-
[80]
N., et al
Shao, Z., Zu, Y., Salcedo, A. N., et al. 2025, arXiv e-prints, arXiv:2510.20896, doi: 10.48550/arXiv.2510.20896
2025 doi
-
[81]
K., & Tormen, G
Sheth, R. K., & Tormen, G. 2004, MNRAS, 350, 1385, doi: 10.1111/j.1365-2966.2004.07733.x
2004
-
[82]
Sin, L. P. T., Lilly, S. J., & Henriques, B. M. B. 2017, MNRAS, 471, 1192, doi: 10.1093/mnras/stx1674
2017 doi
-
[83]
2012, ApJ, 751, 17, doi: 10.1088/0004-637X/751/1/17
Sinha, M., & Holley-Bockelmann, K. 2012, ApJ, 751, 17, doi: 10.1088/0004-637X/751/1/17
2012 doi
-
[84]
2016, ApJ, 833, 109, doi: 10.3847/1538-4357/833/1/109
Smith, R., Choi, H., Lee, J., et al. 2016, ApJ, 833, 109, doi: 10.3847/1538-4357/833/1/109
2016 doi
-
[85]
J., Berlind, A
Smith, W. J., Berlind, A. A., & Sinha, M. 2024, MNRAS, 535, 1426, doi: 10.1093/mnras/stae2339
2024 doi
-
[86]
Springel, V., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726, doi: 10.1046/j.1365-8711.2001.04912.x
2001
-
[87]
Springel, V., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629, doi: 10.1038/nature03597
2005 doi
-
[88]
2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304
Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304
2018 doi
-
[89]
Stephenson, H. M. O., Stott, J. P., Butler, J., Webster, M., & Head, J. 2025, MNRAS, 537, 1542, doi: 10.1093/mnras/staf120
2025 doi
-
[90]
2016, MNRAS, 458, 1510, doi: 10.1093/mnras/stw332
Leauthaud, A. 2016, MNRAS, 458, 1510, doi: 10.1093/mnras/stw332
2016 doi
-
[91]
2019, MNRAS, 490, 4945, doi: 10.1093/mnras/stz2832
Sunayama, T., & More, S. 2019, MNRAS, 490, 4945, doi: 10.1093/mnras/stz2832
2019 doi
-
[92]
Sato-Polito, G., & Artale, M. C. 2021, MNRAS, 500, 2777, doi: 10.1093/mnras/staa3319
2021 doi
-
[93]
2014a, Nature, 509, 177 —
Vogelsberger, M., Genel, S., Springel, V., et al. 2014a, Nature, 509, 177 —. 2014b, MNRAS, 444, 1518, doi: 10.1093/mnras/stu1536
-
[94]
2019, MNRAS, 488, 470, doi: 10.1093/mnras/stz1351
Walsh, K., & Tinker, J. 2019, MNRAS, 488, 470, doi: 10.1093/mnras/stz1351
2019 doi
-
[95]
J., & Jing, Y
Wang, H., Mo, H. J., & Jing, Y. P. 2009, MNRAS, 396, 2249, doi: 10.1111/j.1365-2966.2009.14884.x
2009
-
[96]
Y., Mo, H
Wang, H. Y., Mo, H. J., & Jing, Y. P. 2007, MNRAS, 375, 633, doi: 10.1111/j.1365-2966.2006.11316.x
2007
-
[97]
2023, MNRAS, 523, 1268, doi: 10.1093/mnras/stad1169
Wang, K., Peng, Y., & Chen, Y. 2023, MNRAS, 523, 1268, doi: 10.1093/mnras/stad1169
2023 doi
-
[98]
2026, MNRAS, 546, stag110, doi: 10.1093/mnras/stag110
Wang, K., Schaye, J., Ben ´ ıtez-Llambay, A., et al. 2026, MNRAS, 546, stag110, doi: 10.1093/mnras/stag110
2026 doi
-
[99]
M., De Lucia, G., & Yang, X
Wang, L., Weinmann, S. M., De Lucia, G., & Yang, X. 2013, MNRAS, 433, 515, doi: 10.1093/mnras/stt743
2013 doi
-
[100]
2025a, ApJ, 988, 280, doi: 10.3847/1538-4357/ade98b
Wang, Y., Zehavi, I., Contreras, S., Cole, S., & Norberg, P. 2025a, ApJ, 988, 280, doi: 10.3847/1538-4357/ade98b
-
[101]
Wang, Y., Zhai, Z., Yang, X., & Tinker, J. L. 2025b, ApJ, 994, 51, doi: 10.3847/1538-4357/ae0c10
-
[102]
H., & Tinker, J
Wechsler, R. H., & Tinker, J. L. 2018, ARA&A, 56, 435, doi: 10.1146/annurev-astro-081817-051756
2018 doi
-
[103]
H., Zentner, A
Wechsler, R. H., Zentner, A. R., Bullock, J. S., Kravtsov, A. V., & Allgood, B. 2006, ApJ, 652, 71, doi: 10.1086/507120
2006 doi
-
[104]
R., Tinker, J
Wetzel, A. R., Tinker, J. L., Conroy, C., & van den Bosch, F. C. 2014, MNRAS, 439, 2687, doi: 10.1093/mnras/stu122
2014 doi
-
[105]
White, S. D. M. 1999, Ap&SS, 267, 355, doi: 10.1023/A:1002770429758 17
1999 doi
-
[106]
White, S. D. M., & Rees, M. J. 1978, MNRAS, 183, 341, doi: 10.1093/mnras/183.3.341
1978 doi
-
[107]
2024, ApJ, 971, 157, doi: 10.3847/1538-4357/ad57c7
Xu, W., Shan, H., Li, R., et al. 2024, ApJ, 971, 157, doi: 10.3847/1538-4357/ad57c7
2024 doi
-
[108]
2021a, MNRAS, 507, 4879, doi: 10.1093/mnras/stab2464
Xu, X., Kumar, S., Zehavi, I., & Contreras, S. 2021a, MNRAS, 507, 4879, doi: 10.1093/mnras/stab2464
-
[109]
2021b, MNRAS, 502, 3242, doi: 10.1093/mnras/stab100
Xu, X., Zehavi, I., & Contreras, S. 2021b, MNRAS, 502, 3242, doi: 10.1093/mnras/stab100
-
[110]
2021, MNRAS, 502, 3582, doi: 10.1093/mnras/stab235
Guo, H. 2021, MNRAS, 502, 3582, doi: 10.1093/mnras/stab235
2021 doi
-
[111]
J., et al
Yuan, S., Zhang, H., Ross, A. J., et al. 2024, MNRAS, 530, 947, doi: 10.1093/mnras/stae359
2024 doi
-
[112]
2018, ApJ, 853, 84, doi: 10.3847/1538-4357/aaa54a
Zehavi, I., Contreras, S., Padilla, N., et al. 2018, ApJ, 853, 84, doi: 10.3847/1538-4357/aaa54a
2018 doi
-
[113]
E., Contreras, S., et al
Zehavi, I., Kerby, S. E., Contreras, S., et al. 2019, ApJ, 887, 17, doi: 10.3847/1538-4357/ab4d4d
2019 doi
-
[114]
Zentner, A. R. 2007, International Journal of Modern Physics D, 16, 763, doi: 10.1142/S0218271807010511
2007 doi
-
[115]
R., Hearin, A
Zentner, A. R., Hearin, A. P., & van den Bosch, F. C. 2014, MNRAS, 443, 3044, doi: 10.1093/mnras/stu1383
2014 doi
-
[116]
2014, ApJ, 782, 44, doi: 10.1088/0004-637X/782/1/44
Zhang, J., Ma, C.-P., & Riotto, A. 2014, ApJ, 782, 44, doi: 10.1088/0004-637X/782/1/44
2014 doi
- [117]
- [118]
-
[119]
2016, MNRAS, 457, 4360, doi: 10.1093/mnras/stw221
Zu, Y., & Mandelbaum, R. 2016, MNRAS, 457, 4360, doi: 10.1093/mnras/stw221
2016 doi
-
[120]
Zu, Y., Mandelbaum, R., Simet, M., Rozo, E., & Rykoff, E. S. 2017, MNRAS, 470, 551, doi: 10.1093/mnras/stx1264
2017 doi
-
[121]
Zu, Y., Zheng, Z., Zhu, G., & Jing, Y. P. 2008, ApJ, 686, 41, doi: 10.1086/591071
2008 doi
Reviewed August 11, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.