Recognition: no theorem link
What can galaxy clustering really tell us about the galaxy-halo connections?
Pith reviewed 2026-05-10 18:59 UTC · model grok-4.3
The pith
A new CS-SHAM method shows galaxy clustering data constrain the galaxy-halo connection mainly for massive halos.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Within the CS-SHAM framework, central and satellite galaxies are independently matched to main and satellite subhalos, allowing three free parameters to characterize the satellite fraction f_sat as a function of stellar mass or magnitude. This approach reliably reproduces galaxy clustering whether M_peak or V_peak is used as the subhalo mass proxy. The models recover the HOD and CLF/CSMF accurately. Galaxy clustering constrains the HOD and CLF/CSMF primarily for relatively massive halos; because the halo bias is nearly constant for low-mass halos, clustering is generally not very sensitive to the satellite population residing in these low-mass systems.
What carries the argument
The CS-SHAM framework of independent central-to-main and satellite-to-satellite subhalo matching, controlled by three free parameters for the satellite fraction f_sat(M).
If this is right
- Galaxy clustering is reproduced accurately with CS-SHAM using either M_peak or V_peak as the subhalo mass proxy.
- The HOD and CLF/CSMF are recovered accurately inside the CS-SHAM framework.
- Clustering measurements constrain the HOD and CLF/CSMF only for relatively massive halos.
- Satellite populations inside low-mass halos remain poorly constrained by clustering alone.
Where Pith is reading between the lines
- Additional observables such as galaxy-galaxy lensing or direct group catalogs would be needed to constrain the occupation of low-mass halos.
- The independent-matching assumption may allow more flexible tests of different galaxy formation models than traditional SHAM.
- Small-scale clustering measurements could be used to refine the satellite fraction specifically in massive systems.
Load-bearing premise
Central and satellite galaxies can be matched independently to main and satellite subhalos without introducing new physical inconsistencies, and three parameters for f_sat capture the necessary flexibility across mass proxies and galaxy formation models.
What would settle it
If CS-SHAM applied to a new mock catalog from a different hydrodynamic simulation fails to recover the input HOD for halos above 10^13 solar masses while still fitting the clustering, the claim that clustering constrains the HOD primarily for massive halos would be falsified.
read the original abstract
Subhalo abundance matching (SHAM) is a commonly used framework for modeling the galaxy-halo connection. Yet, its standard implementation has difficulty reproducing the observed galaxy clustering with high accuracy (e.g., $\chi^2/\mathrm{dof} \approx 1$). To overcome this issue, we propose a novel CS-SHAM framework, in which central and satellite galaxies are independently matched to main and satellite subhalos in simulations. Within this scheme, we introduce three free parameters to explicitly characterize the satellite fraction, $f_{\mathrm{sat}}$, as a function of stellar mass or absolute magnitude. To evaluate the performance of CS-SHAM, we apply it to two sets of mock galaxy catalogs built with the conventional SHAM method but using different subhalo mass proxies, $M_{\mathrm{peak}}$ and $V_{\mathrm{peak}}$, as well as two additional galaxy samples generated from a SAM and from TNG-300. We demonstrate that CS-SHAM reliably reproduces galaxy clustering whether $M_{\mathrm{peak}}$ or $V_{\mathrm{peak}}$ is used as the subhalo mass proxy. We also find that the models are unable to place robust constraints on $f_{\mathrm{sat}}$ if different mass proxies are employed. Indeed, within the CS-SHAM framework the halo occupation distribution (HOD) and conditional luminosity or stellar mass function (CLF/CSMF) are accurately recovered. Furthermore, we demonstrate for the first time that galaxy clustering constrains the HOD and CLF/CSMF primarily for relatively massive halos. Because the halo bias is nearly constant for low-mass halos, galaxy clustering is generally not very sensitive to the satellite population residing in these low-mass systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes a CS-SHAM framework in which central and satellite galaxies are matched independently to main and satellite subhalos, with three free parameters introduced to characterize the satellite fraction f_sat as a function of stellar mass or absolute magnitude. Tested on SHAM mocks using M_peak and V_peak subhalo proxies plus SAM and TNG-300 galaxy samples, the work claims that CS-SHAM reliably reproduces galaxy clustering, accurately recovers the HOD and CLF/CSMF, and that clustering constrains these quantities primarily for massive halos because halo bias is nearly constant at low masses.
Significance. If the central results hold, the work would demonstrate that standard SHAM's clustering shortcomings stem from not separating centrals and satellites, while showing limited constraining power of clustering on low-mass satellite populations. The multi-mock testing (SHAM variants, SAM, TNG-300) provides a strength in assessing robustness across proxies and models, though the tuned f_sat parameters reduce the framework's predictive power for galaxy-halo connections.
major comments (3)
- [Abstract] Abstract: the claim that CS-SHAM 'reliably reproduces galaxy clustering' lacks any quantitative support such as reported chi^2/dof values, clustering amplitude residuals, or error bars on the fits, in contrast to the explicit chi^2/dof ≈1 cited for standard SHAM. This omission makes it impossible to judge the magnitude of improvement or rule out overfitting from the three f_sat parameters.
- [§2–3 (f_sat parameterization and fitting)] The section introducing and fitting the three f_sat parameters (likely §2–3): these parameters are explicitly tuned to match clustering statistics, so reproduction on the training mocks is expected by construction; while cross-tests on independent mocks (M_peak vs V_peak, SAM, TNG) are performed, the manuscript does not demonstrate that the recovered HOD/CLF remains consistent when f_sat is held fixed or when parameters are chosen without reference to the clustering data being fit.
- [§5 (HOD and CLF/CSMF recovery)] The HOD/CLF recovery and low-mass halo discussion (likely §5): the assertion that clustering is insensitive to satellites in low-mass halos (M_star < 10^10 M_sun) because bias is flat is plausible, but the independent central/satellite matching plus flexible f_sat could absorb proxy-dependent differences or one-halo term mismatches into the parameters rather than reflecting true galaxy-halo physics; explicit tests showing that the one-halo term and low-mass CLF/CSMF are recovered without bias when f_sat is not refit would be required to support the claim.
minor comments (2)
- [§2] Clarify the exact functional form (e.g., polynomial, broken power-law) chosen for f_sat(M*) or f_sat(M_r) and whether it is the same across all tested proxies and models.
- [Figures/Tables] Add quantitative fit statistics (chi^2, residuals, or posterior widths) to any tables or figures that compare clustering or HOD between CS-SHAM and input mocks.
Simulated Author's Rebuttal
We thank the referee for their thoughtful and constructive comments, which have helped us identify areas where the manuscript can be clarified and strengthened. We address each major comment below and describe the revisions we will implement.
read point-by-point responses
-
Referee: [Abstract] Abstract: the claim that CS-SHAM 'reliably reproduces galaxy clustering' lacks any quantitative support such as reported chi^2/dof values, clustering amplitude residuals, or error bars on the fits, in contrast to the explicit chi^2/dof ≈1 cited for standard SHAM. This omission makes it impossible to judge the magnitude of improvement or rule out overfitting from the three f_sat parameters.
Authors: We agree that the abstract would benefit from explicit quantitative metrics to allow readers to assess the improvement. In the revised manuscript we will update the abstract to reference the chi^2/dof values achieved by CS-SHAM (which are reported in Section 4 and are substantially closer to unity than those of standard SHAM) along with a brief statement on the typical residuals in the projected correlation function. This will make the magnitude of the improvement clear without lengthening the abstract excessively. revision: yes
-
Referee: [§2–3 (f_sat parameterization and fitting)] The section introducing and fitting the three f_sat parameters (likely §2–3): these parameters are explicitly tuned to match clustering statistics, so reproduction on the training mocks is expected by construction; while cross-tests on independent mocks (M_peak vs V_peak, SAM, TNG) are performed, the manuscript does not demonstrate that the recovered HOD/CLF remains consistent when f_sat is held fixed or when parameters are chosen without reference to the clustering data being fit.
Authors: The referee correctly identifies that the f_sat parameters are optimized against the clustering data of each training mock. The existing cross-tests on SAM and TNG-300 already apply the framework to galaxy populations whose clustering was never used in the fit, and the recovered HOD/CLF in those cases remains accurate. To further address the concern, we will add a dedicated test in the revised Section 3 in which the three f_sat parameters derived from the M_peak SHAM mock are held fixed and applied to the V_peak mock (and vice versa) without any re-optimization; we will show that the resulting HOD and CLF/CSMF remain consistent with the true values within the uncertainties. revision: yes
-
Referee: [§5 (HOD and CLF/CSMF recovery)] The HOD/CLF recovery and low-mass halo discussion (likely §5): the assertion that clustering is insensitive to satellites in low-mass halos (M_star < 10^10 M_sun) because bias is flat is plausible, but the independent central/satellite matching plus flexible f_sat could absorb proxy-dependent differences or one-halo term mismatches into the parameters rather than reflecting true galaxy-halo physics; explicit tests showing that the one-halo term and low-mass CLF/CSMF are recovered without bias when f_sat is not refit would be required to support the claim.
Authors: We appreciate the referee’s caution regarding possible parameter absorption. Our current results already demonstrate that the one-halo term is reproduced to high accuracy once f_sat is allowed to vary (Figure 5), and the low-mass CLF/CSMF is recovered as a consequence. To directly test the referee’s concern, we will add in the revised Section 5 an explicit exercise in which the f_sat parameters are fixed from the primary fit and then applied to the alternate proxies and to the SAM/TNG samples; we will verify that the one-halo term and the low-mass end of the CLF/CSMF remain unbiased. This will strengthen the interpretation that the limited constraining power at low mass is driven by the flatness of halo bias rather than by degeneracy with the f_sat parameters. revision: yes
Circularity Check
CS-SHAM introduces explicit free parameters for f_sat and validates clustering reproduction plus HOD recovery on independent mocks without reducing claims to input by construction.
full rationale
The paper proposes CS-SHAM by adding three free parameters to model f_sat(M) and independently matching centrals/satellites, then applies the framework to mocks generated via standard SHAM (M_peak and V_peak) plus SAM and TNG-300 catalogs. It demonstrates reproduction of clustering and accurate recovery of HOD/CLF/CSMF after fitting those parameters, while noting that f_sat constraints are not robust across proxies and that clustering is insensitive to low-mass satellites due to flat halo bias. No derivation step equates a claimed prediction or first-principles result to the fitted inputs by construction; the parameter fitting is explicit, the recovery is a validation test against known mock truths, and the bias-insensitivity statement rests on standard halo bias properties rather than self-referential fitting. No self-citations or ansatzes are invoked as load-bearing justifications in the provided text.
Axiom & Free-Parameter Ledger
free parameters (1)
- three parameters characterizing f_sat(M*) or f_sat(M_r)
axioms (1)
- domain assumption Subhalos can be ranked and matched to galaxies independently for centrals and satellites
Reference graph
Works this paper leans on
-
[1]
SCIENCE CHINA Physics, Mechanics & Astronomy January 2026 V ol. XX No. X: 000000 https://doi.org/?? ©Science China Press and Springer-Verlag GmbH Germany , part of Springer Nature 2020 phys.scichina.com link.springer.com . Article . SPECIAL TOPIC: What can galaxy clustering really tell us about the galaxy-halo connections? Xiaoju Xu1,2*, Xiaohu Yang2,3*, ...
work page internal anchor Pith review Pith/arXiv arXiv 2026
-
[2]
Xu X, et al. Sci. China-Phys. Mech. Astron.January (2026) V ol. XX No. X000000-4 Table 1Galaxy samples and the corresponding simulations used. galaxy sample nameN-body simulation model or code of sample producing CS-SHAM model applied Jiutian catalogs Jiutian (lightcone) basic one-parameter SHAM fiducial four-parameter ELUCID SAM ELUCID simulation L-Galax...
2026
-
[3]
for fixedf sat parameters, and then randomly clas- sify it as central or satellite. This partitions theM z (or stellar mass) lists into distinct central and satellite lists; (3) The subhalo mass indicators andM z (or stellar mass) are each sorted independently in descending order, after which central and satellite galaxies are assigned to central and sate...
2026
-
[4]
It produces quasi-uniform samples in high-dimensional spaces via binary radical inversion, making it particularly well suited for MCMC applications
The Sobol sequence is widely used to sample param- eter spaces in cosmological simulation suites [75,101-103]. It produces quasi-uniform samples in high-dimensional spaces via binary radical inversion, making it particularly well suited for MCMC applications. Xu X, et al. Sci. China-Phys. Mech. Astron.January (2026) V ol. XX No. X000000-7 100 101 r/h□1Mpc...
2026
-
[5]
We make use of two of them and refer to them as theM peak mock andV peak mock, respectively
consist of three catalogs, each con- structed using basic SHAM with a fixedσ logL based on sub- halo mass indicatorsM peak,V max, andV peak. We make use of two of them and refer to them as theM peak mock andV peak mock, respectively. We apply CS-SHAM using these two mass indicators, which we refer to as theM peak model and Vpeak model in the following. Fo...
2026
-
[6]
To reproduce thew p measured from this mock, theM peak model requires a much largerf 1 (close to the upper bound of our prior), implying a higher satellite fraction
For theV peak mock, theV peak model pro- vides a more accurate recovery of the true parameters within 2σ. To reproduce thew p measured from this mock, theM peak model requires a much largerf 1 (close to the upper bound of our prior), implying a higher satellite fraction. Since the parameters can be degenerate, it is more informa- tive to examine thef sat ...
2026
-
[7]
For illustra- tion, we present three representativeM z bins corresponding to bright (M z =[-23, -22]), intermediate (M z =[-21, -20]), and faint (M z =[-19, -18]) galaxies
Using the best-fit parameters, we can easily predict the HOD for any bin within the consideredM z interval. For illustra- tion, we present three representativeM z bins corresponding to bright (M z =[-23, -22]), intermediate (M z =[-21, -20]), and faint (M z =[-19, -18]) galaxies. As expected, when CS- SHAM employs the same subhalo mass proxy that was used...
2026
-
[8]
In the lower panels, we present results for theV peak Jiutian Xu X, et al. Sci. China-Phys. Mech. Astron.January (2026) V ol. XX No. X000000-13 Mz=[-23,-22] □0.5 0.0 0.5 Mz=[-21,-20] central satellite □0.5 0.0 0.5 10□2 10□1 100 101 102 N (Mh) Mz=[-19,-18] Mpeak model Vpeak model Mpeak mock □0.5 0.0 0.5 log Res. Mz=[-23,-22] 11 12 13 14 15 logMh Mz=[-21,-2...
2026
-
[9]
For satellite galaxies, both models successfully reproduce the ELUCID SAM CSMF in halos with logM h ≳12.9
TheM peak andV peak models yield very similar central CSMFs across all halo mass bins, and both are slightly narrower than those from the ELUCID SAM. For satellite galaxies, both models successfully reproduce the ELUCID SAM CSMF in halos with logM h ≳12.9. Even in the lowest halo mass bin, the discrepancy between the models and the ELUCID SAM remains smal...
2026
-
[10]
introduce a generalized SHAM framework that treatsf sat as a free parameter to describe ELG Xu X, et al. Sci. China-Phys. Mech. Astron.January (2026) V ol. XX No. X000000-17 clustering in the DESI One Percent Survey. They demon- strate thatf sat plays a key role in reproducing the quadrupole of DESI ELGs. Similarly, Favole et al. (2025) employ anf sat par...
2026
-
[11]
the Fundamental Research Funds for the Cen- tral Universities
model the auto- and cross-correlation of DESI ELGs using an SHMR-based approach with parameters that encode the probability that a halo of a given mass hosts an ELG. By introducing extra parameters to capture color or type segregation, our CS-SHAM framework can be straight- forwardly generalized to describe the stellar-mass-dependent clustering of ELGs an...
2021
-
[12]
2 Springel, V ., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435,
2005
-
[13]
A., Cuesta, A
3 Prada, F., Klypin, A. A., Cuesta, A. J., et al. 2012, MNRAS, 423,
2012
-
[14]
G., Adelman, J., Anderson, Jr., J
4 York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120,
2000
-
[15]
S., Kneib, J.-P., Percival, W
5 Dawson, K. S., Kneib, J.-P., Percival, W. J., et al. 2016, AJ, 151,
2016
-
[16]
The DESI Experiment Part I: Science,Targeting, and Survey Design
6 DESI Collaboration, et al. 2016, arXiv e-prints, arXiv:1611.00036. 7 Jing, Y . P., Mo, H. J., and B ¨orner, G. 1998, ApJ, 494,
work page internal anchor Pith review arXiv 2016
-
[17]
A., and Weinberg, D
8 Berlind, A. A., and Weinberg, D. H. 2002, ApJ, 575,
2002
-
[18]
A., Weinberg, D
9 Zheng, Z., Berlind, A. A., Weinberg, D. H., et al. 2005, ApJ, 633,
2005
-
[19]
10 Zheng, Z., and Weinberg, D. H. 2007, ApJ, 659,
2007
-
[20]
J., and van den Bosch, F
11 Yang, X., Mo, H. J., and van den Bosch, F. C. 2003, MNRAS, 339,
2003
-
[21]
C., Mo, H
12 van den Bosch, F. C., Mo, H. J., and Yang, X. 2003, MNRAS, 345,
2003
-
[22]
J., and van den Bosch, F
13 Yang, X., Mo, H. J., and van den Bosch, F. C. 2008, ApJ, 676,
2008
-
[23]
J., and van den Bosch, F
14 Yang, X., Mo, H. J., and van den Bosch, F. C. 2009, ApJ, 695,
2009
-
[24]
H., et al
15 Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2005, ApJ, 630,
2005
-
[25]
H., et al
16 Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2011, ApJ, 736,
2011
-
[26]
C., More, S., et al
17 Cacciato, M., van den Bosch, F. C., More, S., et al. 2013, MNRAS, 430,
2013
-
[27]
2015, ApJ, 806,
18 More, S., Miyatake, H., Mandelbaum, R., et al. 2015, ApJ, 806,
2015
-
[28]
U., van den Bosch, F
19 Lange, J. U., van den Bosch, F. C., Zentner, A. R., et al. 2019, MN- RAS, 490,
2019
-
[29]
L., Banerjee, A., et al
20 Zhai, Z., Tinker, J. L., Banerjee, A., et al. 2023, ApJ, 948,
2023
-
[30]
21 Zhai, Z., and Percival, W. J. 2024, MNRAS, 535,
2024
-
[31]
22 Wang, Y ., Zhai, Z., Yang, X., and Tinker, J. L. 2025, ApJ, 994, 51 23 Navarro, J. F., Frenk, C. S., and White, S. D. M. 1996, ApJ, 462,
2025
-
[32]
E., Sobral, D., Hickox, R
24 Geach, J. E., Sobral, D., Hickox, R. C., et al. 2012, MNRAS, 426,
2012
-
[33]
A., Kraljic, K., et al
25 Alam, S., Peacock, J. A., Kraljic, K., et al. 2020, MNRAS, 497,
2020
-
[34]
G., et al
26 Avila, S., Gonzalez-Perez, V ., Mohammad, F. G., et al. 2020, MNRAS, 499,
2020
-
[35]
2024, A&A, 690, A221
27 Paviot, R., Rocher, A., Codis, S., et al. 2024, A&A, 690, A221. 28 Croton, D. J., Gao, L., and White, S. D. M. 2007, MNRAS, 374,
2024
-
[36]
2018, ApJ, 853,
29 Zehavi, I., Contreras, S., Padilla, N., et al. 2018, ApJ, 853,
2018
-
[37]
2019, MNRAS, 484,
30 Contreras, S., Zehavi, I., Padilla, N., et al. 2019, MNRAS, 484,
2019
-
[38]
2020, MNRAS, 493,
31 Hadzhiyska, B., Bose, S., Eisenstein, D., et al. 2020, MNRAS, 493,
2020
-
[39]
2021, MNRAS, 502,
32 Xu, X., Zehavi, I., and Contreras, S. 2021, MNRAS, 502,
2021
-
[40]
E., and Weinberg, D
33 McEwen, J. E., and Weinberg, D. H. 2018, MNRAS, 477,
2018
-
[41]
J., and Leauthaud, A
34 Yuan, S., Eisenstein, D. J., and Leauthaud, A. 2020, MNRAS, 493,
2020
-
[42]
N., Zu, Y ., Zhang, Y ., et al
35 Salcedo, A. N., Zu, Y ., Zhang, Y ., et al. 2022, Science China Physics, Mechanics, and Astronomy, 65, 109811. 36 Kravtsov, A. V ., Berlind, A. A., Wechsler, R. H., et al. 2004, ApJ, 609,
2022
-
[43]
H., and Kravtsov, A
37 Conroy, C., Wechsler, R. H., and Kravtsov, A. V . 2006, ApJ, 647,
2006
-
[44]
38 Vale, A., and Ostriker, J. P. 2006, MNRAS, 371,
2006
-
[45]
S., Conroy, C., and Wechsler, R
39 Behroozi, P. S., Conroy, C., and Wechsler, R. H. 2010, ApJ, 717,
2010
-
[46]
M., Wechsler, R
40 Reddick, R. M., Wechsler, R. H., Tinker, J. L., et al. 2013, ApJ, 771,
2013
-
[47]
S., Wechsler, R
41 Behroozi, P. S., Wechsler, R. H., and Wu, H.-Y . 2013, ApJ, 762,
2013
-
[48]
P., Somerville, R
42 Moster, B. P., Somerville, R. S., Maulbetsch, C., et al. 2010, ApJ, 710,
2010
-
[49]
P., Naab, T., and White, S
43 Moster, B. P., Naab, T., and White, S. D. M. 2013, MNRAS, 428,
2013
-
[50]
J., van den Bosch, F
44 Yang, X., Mo, H. J., van den Bosch, F. C., et al. 2012, ApJ, 752,
2012
-
[51]
S., et al
45 Guo, H., Zheng, Z., Behroozi, P. S., et al. 2016, MNRAS, 459,
2016
-
[52]
C., Padmanabhan, N., et al
46 Campbell, D., van den Bosch, F. C., Padmanabhan, N., et al. 2018, MNRAS, 477,
2018
-
[53]
E., Schaye, J., et al
47 Chaves-Montero, J., Angulo, R. E., Schaye, J., et al. 2016, MNRAS, 460,
2016
-
[54]
A., Chuang, C.-H., Prada, F., et al
48 Rodr’ıguez-Torres, S. A., Chuang, C.-H., Prada, F., et al. 2016, MN- RAS, 460,
2016
-
[55]
49 Stiskalek, R., Desmond, H., Holvey, T., and Jones, M. G. 2021, MN- RAS, 506,
2021
-
[56]
50 Tonnesen, S., and Ostriker, J. P. 2021, ApJ, 917,
2021
-
[57]
R., and Wechsler, R
51 DeRose, J., Becker, M. R., and Wechsler, R. H. 2022, ApJ, 940,
2022
-
[58]
2023, ApJ, 944,
52 Chuang, C.-Y ., and Lin, Y .-T. 2023, ApJ, 944,
2023
-
[59]
2023, MNRAS, 523,
53 Masaki, S., Kashino, D., Ishikawa, S., and Lin, Y .-T. 2023, MNRAS, 523,
2023
-
[60]
2007, MNRAS, 375,
54 De Lucia, G., and Blaizot, J. 2007, MNRAS, 375,
2007
-
[61]
2011, MNRAS, 413,
55 Guo, Q., White, S., Boylan-Kolchin, M., et al. 2011, MNRAS, 413,
2011
-
[62]
2014, MNRAS, 437,
56 Guo, Q., and White, S. 2014, MNRAS, 437,
2014
-
[63]
E., and Zennaro, M
57 Contreras, S., Angulo, R. E., and Zennaro, M. 2021, MNRAS, 508, Xu X, et al. Sci. China-Phys. Mech. Astron.January (2026) V ol. XX No. X000000-18
2021
-
[64]
V ., Mao, Y .-Y ., Becker, M
58 Lehmann, B. V ., Mao, Y .-Y ., Becker, M. R., et al. 2017, ApJ, 834,
2017
-
[65]
E., and Zennaro, M
59 Contreras, S., Angulo, R. E., and Zennaro, M. 2021, MNRAS, 504,
2021
-
[66]
E., Springel V ., White S
60 Contreras S., Angulo R. E., Springel V ., White S. D. M., Hadzhiyska B., Hernquist L., Pakmor R., et al., 2023, MNRAS, 524,
2023
-
[67]
E., Alonso D., Georgiou C., Dvornik A., 2026, MNRAS, 545, staf2143 62 Gao H., Jing Y
61 Mahony C., Contreras S., Angulo R. E., Alonso D., Georgiou C., Dvornik A., 2026, MNRAS, 545, staf2143 62 Gao H., Jing Y . P., Zheng Y ., Xu K., 2022, ApJ, 928,
2026
-
[68]
P., Gui, S., et al
63 Gao, H., Jing, Y . P., Gui, S., et al. 2023, ApJ, 954,
2023
-
[69]
2024, MNRAS, 527,
64 Favole G., Comparat J., Prada F., Yepes G., Jullo E., Niemiec A., Kneib J.-P., et al., 2016, MNRAS, 461, 3421 65 Yu, J., Zhao, C., Gonzalez-Perez, V ., et al. 2024, MNRAS, 527,
2016
-
[70]
2024, MNRAS, 529,
66 Gu, Y ., Yang, X., Han, J., et al. 2024, MNRAS, 529,
2024
-
[71]
E., et al
67 Guo, Q., White, S., Angulo, R. E., et al. 2013, MNRAS, 428,
2013
-
[72]
2016, MNRAS, 458,
68 Luo, Y ., Kang, X., Kauffmann, G., and Fu, J. 2016, MNRAS, 458,
2016
-
[73]
A., Bower, R
69 Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446,
2015
-
[74]
2018, MNRAS, 475,
70 Nelson, D., Pillepich, A., Springel, V ., et al. 2018, MNRAS, 475,
2018
-
[75]
2019, Comput
71 Nelson, D., Springel, V ., Pillepich, A., et al. 2019, Comput. Astrophys. Cosmol., 6,
2019
-
[76]
2025, Science China Physics, Mechan- ics, and Astronomy, 68, 109511
72 Han, J., Li, M., Jiang, W., et al. 2025, Science China Physics, Mechan- ics, and Astronomy, 68, 109511. 73 Gong, Y ., Liu, X., Cao, Y ., et al. 2019, ApJ, 883,
2025
-
[77]
2020, A&A, 641, A6
74 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6. 75 Chen, Z., Yu, Y ., Han, J., and Jing, Y . 2025, Science China Physics, Mechanics, and Astronomy, 68, 289512. 76 Springel, V ., Pakmor, R., Zier, O., and Reinecke, M. 2021, MNRAS, 506,
2020
-
[78]
E., Springel, V ., White, S
77 Angulo, R. E., Springel, V ., White, S. D. M., et al. 2012, MNRAS, 426,
2012
-
[79]
S., and White, S
78 Davis, M., Efstathiou, G., Frenk, C. S., and White, S. D. M. 1985, ApJ, 292,
1985
-
[80]
P., Wang, H., and Wang, W
79 Han, J., Jing, Y . P., Wang, H., and Wang, W. 2012, MNRAS, 427,
2012
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.