Pith. sign in

REVIEW 4 major objections 5 minor 102 references

The 'Einstein Gap' — a ~10–30 arcsec deficit in stacked cluster magnification profiles — is claimed to be a strong-lensing displacement of background source images, not missing mass.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

An observed 10-arcsecond deficit in cluster magnification profiles is reproduced by a full ray-tracing lensing simulation and attributed to strong-lensing source displacement, the 'Einstein Gap'.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Plausible strong-lensing explanation of the 10–30 arcsec deficit, but the quantitative case rests on a single tuned halo mass; worth refereeing. the 4 major comments →

arxiv 2509.02213 v1 pith:KDVCLFWF submitted 2025-09-02 astro-ph.GA

Signal Drop in Magnification Profiles: Combining Lensing Simulations and Observations

classification astro-ph.GA
keywords gravitational lensingmagnification biasgalaxy clusterssubmillimetre galaxiesstrong lensingEinstein Gapsatellite density profileSISSA profile
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

This paper tries to explain a persistent puzzle in magnification-bias measurements: the stacked cross-correlation between galaxy clusters and background submillimetre galaxies dips sharply around 10–30 arcseconds, a feature the authors call the 'Einstein Gap.' They first rule out the mundane explanations: stacking roughly 12 million satellite galaxies shows the mass tracer is smooth across the gap, and the gap's presence tracks lens mass rather than sample size. Then an upgraded lensing simulator, which solves the full lens equation and therefore includes the displacement of background images rather than only their magnification, reproduces the observed deficit at angular scales above 10 arcseconds for an average cluster halo mass near 5×10^14 solar masses. The conclusion: the gap is a strong-lensing artifact carved by sources being shifted away from the Einstein radius, and mass profiles derived from magnification bias must account for this effect. A secondary result is a stellar-to-halo mass relation for the outer regions of clusters, and the finding that lensing at large scales traces satellites more than the smooth dark-matter halo.

Core claim

Central claim: the deficit in the stacked magnification-bias cross-correlation between clusters and background submillimetre galaxies at ~10–30 arcsec, the 'Einstein Gap,' is a strong-lensing effect, not missing mass or noise. Stacked satellite density is smooth across the gap (mass exists); the gap tracks lens mass, not sample size; it survives different estimators and matchings. A simulator solving the full lens equation with a SISSA power-law profile, total halo mass the only free parameter, produces a ring-shaped source-displacement deficit at the observed position for an average halo mass of ~5×10^14 Msun. Magnification-only simulations lack the feature: displaced images near the Einste

What carries the argument

The load-bearing object is the SISSA profile (Lapi et al. 2012): a power-law approximation to surface mass density, Σ(s) = Σ0(s/s0)^(-η), joining NFW dark matter and a Sérsic stellar component, fixed at log Σ0 = 9.5, η = 0.8, n ≈ 4, concentration ≈ 5, with total halo mass the sole free parameter. The simulator solves the full lens equation in Einstein-radius units so image positions, not just magnifications, are computed; displaced background sources near the Einstein radius carve the ring-shaped deficit. The companion observable, the stacked satellite number density Σ_sat around BCGs, is the control: continuous across the gap, it rules out a mass deficit and isolates lensing as the cause.

Load-bearing premise

The decisive step assumes that the SISSA power-law profile with literature parameters (log Σ0 = 9.5, η = 0.8, Sérsic index ≈ 4, concentration ≈ 5) adequately represents the stacked cluster population, so that total halo mass — one single value for all lenses — is the only free parameter.

What would settle it

Measure Einstein radii directly for a sample of the ~9,000 ZOU clusters at z≈0.5: if high-resolution imaging finds typical Einstein radii far from the observed 10–30 arcsec gap for ~5×10^14 solar-mass halos, the displacement explanation fails. Cheaper: rerun the simulator with the SISSA parameters (Σ0, η, concentration) varied within their stated ranges and check whether the gap position and fitted mass shift materially. The paper's own inner-region mismatch — the simulator under-predicts the central signal — already signals the fixed parametrization may be too rigid.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Magnification-bias mass profiles fitted without accounting for strong lensing are unreliable in the central region: the paper notes the central NFW-style fit becomes unstable once the caustic is included, yet removing the caustic would erase the observed excess signal.
  • The stacked ZOU clusters have an average total halo mass near 5×10^14 solar masses, with most of it concentrated in the central region: the outer-region NFW fit yields only ~6×10^13 solar masses, and the simulator-matched mass aligns with the stellar-to-halo mass relation for BIN6 BCGs.
  • Gap visibility is a diagnostic of internal mass structure, not just total mass: in the most massive-bin clusters (BIN7) the gap disappears because a centrally concentrated BCG and a rich satellite population fill in the intermediate scales.
  • At large scales the lensing signal traces satellites more than the smooth dark-matter halo, consistent with the low concentrations derived and with the oscillatory features being the cumulative effect of individual strong-lensing events by massive satellites.
  • The satellite-density control test rules out the two alternative explanations proposed earlier — a real lack of mass at 10–30 arcsec and low lens statistics — leaving strong lensing as the operative mechanism.

Where Pith is reading between the lines

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

  • If the gap is source displacement, its angular position should scale with the Einstein radius, so binning lenses by richness or redshift should move the gap in a predictable way — a cheap statistical check the paper does not run.
  • The gap depth should also depend on the background source redshift distribution; splitting the SMG sample by redshift would test the geometric prediction and could sharpen the mass estimate.
  • Treating the gap as a stacked estimator of a typical Einstein radius would convert a nuisance for magnification bias into a statistical strong-lensing probe, complementary to and far cheaper than individual Einstein-radius measurements.
  • Because the fixed SISSA parameters (Σ0, η, n, c) are taken from the literature rather than fitted, the 5×10^14 solar-mass value carries the uncertainty of that prior; fitting these parameters jointly — or letting concentration vary with mass — is the natural next step and would either confirm or shift the inferred mass scale.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper combines stacked satellite number-density profiles with stacked magnification-bias cross-correlation functions (CCFs) for galaxy clusters from the Zou et al. (2021, 2022) catalogue, using WISE-positioned submillimetre galaxies as background sources. The authors show that the satellite distribution follows an NFW profile at large radii but exhibits an inner excess, and that it shows no dip at the ~10–30 arcsec scales where the lensing CCF displays a marked deficit. They argue that this rules out missing mass and poor statistics as explanations, and instead attribute the deficit—termed the 'Einstein Gap'—to strong-lensing displacement of background sources. The central quantitative support is a lensing simulator using a SISSA mass profile with fixed parameters (log Sigma0 = 9.5, eta = 0.8, Sersic index 4, concentration ~5), with total halo mass M_h as the only free parameter; the authors report that M_h ~ 5e14 Msun reproduces the observed deficit at theta >~10 arcsec. They also derive NFW masses and concentrations for seven BCG stellar-mass bins and compare the resulting stellar-to-halo mass relation with literature results.

Significance. If the central claim holds, the paper resolves a long-standing anomaly in magnification-bias CCF measurements: the apparent signal deficit at intermediate angular scales is not a mass deficit or a statistical fluke but a strong-lensing effect on source positions. This would have direct implications for how mass-density profiles are interpreted from stacked CCF measurements, and the paper's identification of a new observable feature ('Einstein Gap') is potentially useful. The work also carries out careful observational cross-checks that are genuinely valuable: Appendix C demonstrates robustness of the dip to cross-matching strategy and estimator, and the satellite-profile comparison in Figs. 3–5 convincingly rules out missing baryonic mass or sample-size effects. The use of WISE positions to improve the astrometric accuracy is an important strength. However, the decisive simulator match is underconstrained, as detailed in the major comments, so the strongest claim—that strong lensing displacement causes the gap—is plausible but not yet established to the standard claimed in the conclusions.

major comments (4)
  1. [Sec. 5.1, Eq. (A19), Fig. 9] The central simulator comparison is a one-parameter fit: M_h is adjusted while log Sigma0 = 9.5, eta = 0.8, Sersic n = 4, and c ~ 5 are fixed from Lapi et al. (2012). The Einstein radius in Eq. (A19) depends directly on Sigma0 and eta, so the gap position is degenerate with these fixed parameters, and the quoted M_h ~ 5e14 Msun has no attached uncertainty. The text itself notes that varying M_h, Sigma0 and eta is left for future work. As it stands, the 'predicted position' of the gap is not a prediction; it is a fitted quantity. Please vary Sigma0 and eta within the range quoted by Lapi et al. (2012) (eta in particular is stated as 0.8–0.9) and show how the gap position and depth change, or otherwise justify that the result is robust to these choices.
  2. [Sec. 5.1, Table 1] All lenses in the simulation are assigned the same total mass M_h, despite the ZOU sample spanning a wide mass range (Table 1 gives NFW masses from ~1e13 to ~1e14 Msun for the same BCG bins, and the outer-region fit for BIN6 is ~6e13 Msun). Lensing is nonlinear in mass, so the stacked Einstein radius of a population is not the Einstein radius of the mean mass. A delta-function 5e14 Msun halo can produce a deep, narrow gap that may be washed out or shifted by a realistic mass distribution. Please rerun the simulator with a mass distribution consistent with the catalogue (e.g., using the SHMR or the NFW masses in Table 1) and demonstrate that the gap remains at theta >~10 arcsec. Without this test, the central inference is vulnerable to a single-halo artifact.
  3. [Sec. 5.1, Fig. 9] The 'excellent agreement' between simulation and observed CCF is assessed visually. No goodness-of-fit statistic, no uncertainty band from multiple simulator realizations, and no comparison of different M_h values are shown, and the inner-region mismatch is acknowledged but not quantified. Please add a quantitative comparison (e.g., chi-square over the fitted range, or a likelihood) and show that the 10–30 arcsec deficit is reproduced at a statistically acceptable level. This is particularly important because the paper's conclusion rests on the match of a single model curve to one observed profile.
  4. [Sec. 5.1 and Sec. 4.4, Fig. 7] The claim that the simulator-derived M_h 'aligns perfectly with the SHMR relation' is presented as a confirmation, but it is not an independent validation: M_h was chosen to place the gap at the observed angular scale, and the SHMR comparison uses the same stellar mass (BIN6) that was already identified as representative of the ZOU sample in Sec. 4.3. The blue triangle in Fig. 7 therefore represents a consistency check, not a prediction. Please rephrase the text accordingly, and if possible provide a second, independent estimate of M_h from the full shape of the CCF (rather than only the gap position) to break the degeneracy.
minor comments (5)
  1. [Abstract] Line 'distribution.This study' is missing a space; also the abstract is a single dense paragraph that would be clearer if broken into sentences with explicit hypotheses.
  2. [Sec. 3.1] Typo: 'nknife method' should be 'jackknife method'.
  3. [References] The reference 'Carollo C.M. Ferguson H.C. W. R. e., 1999' is malformed and appears incomplete; 'van Der Burg' is inconsistently capitalized. Please recheck the reference list against the journal style.
  4. [Sec. 4.1] The normalization of the satellite profile to the lensing profile is described as using an MCMC approach, but no prior or likelihood is specified. A brief sentence on the normalization procedure (e.g., which data points are used and what uncertainty is assumed) would improve reproducibility.
  5. [Data Availability] The statement 'No new data were generated' is technically true for observations, but the new simulator is a key product of the work. Making the code public would strengthen the paper's reproducibility.

Circularity Check

1 steps flagged

The central gap-reproduction claim in Sec. 5.1 is a one-parameter fit: M_h is chosen so that the SISSA Einstein radius matches the observed 10–30 arcsec deficit, then reported as a 'predicted position'.

specific steps
  1. fitted input called prediction [Section 5.1 (Magnification bias simulator), Eq. A19, Fig. 9]
    "Consequently, the total halo mass remained the sole free parameter in this study. ... The angular position of this ring depends primarily on the total mass used in the simulation (with logΣ0 and η fixed). ... we found excellent agreement between the external lensing measurements (>10 arcseconds) and the predicted position of the signal deficit for an average cluster halo mass of approximately 5·10^14 M⊙."

    Eq. A19 gives theta_E = theta0 [2/(2-eta) Sigma0/Sigma_c]^(1/eta), so with fixed SISSA parameters (logSigma0=9.5, eta=0.8) the Einstein-ring scale, and hence the simulated gap position, is a monotonic function of the only free parameter M_h. The paper scans M_h, finds the value that puts the gap at the observed ~10–30 arcsec deficit, and then calls this the 'predicted position' of the signal deficit. This is fitting the parameter to the measured feature and reading the same feature back off the model; the agreement for theta > 10 arcsec is forced by the choice of M_h, not an independent prediction. The strong-lensing displacement mechanism itself is not tautological, and the later SHMR comparison (Fig. 7) is an external sanity check, so the circularity is partial rather than total.

full rationale

Most of the paper is independent observational work: stacking the ZOU cluster catalogue to measure the magnification-bias CCF, constructing satellite number-density profiles, fitting NFW profiles, and deriving a stellar-to-halo mass relation. These parts do not reduce to prior claims; the reality of the signal drop is re-measured here and checked in Appendix C against alternative estimators and cross-matching strategies. The circular step is confined to the simulator-based interpretation in Sec. 5.1. There, with SISSA parameters imported from Lapi et al. (2012) (an external, non-self citation), the total halo mass M_h is the sole free parameter, and Eq. A19 makes the Einstein-ring angle (and therefore the simulated gap position) a deterministic function of M_h. Choosing M_h ~ 5e14 M_sun so that the gap appears at the observed angular scale, and then describing this as an 'excellent agreement' with the 'predicted position', is a fitted-input-called-prediction: the central reproduction is by construction. However, the inference that strong lensing image displacement is the cause of the gap is not tautological, and the inferred M_h is compared with an independent SHMR relation (blue triangle in Fig. 7), which provides external support. No load-bearing self-citation chain is present: CRE24/CRE22 are used for context and prior motivation, and the deficit is re-established here. Overall, partial circularity in the central quantitative claim, score 6.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

The central inference rests on a small number of fitted and adopted parameters: the simulator halo mass is tuned to reproduce the gap, the satellite-profile normalization is adjusted to the lensing amplitude before NFW fitting, and the SISSA inner parameters and source-count inputs are borrowed from prior literature. No new physical entities are introduced; the Einstein Gap is an interpretation of an observed feature, not an independent object.

free parameters (3)
  • Simulator total halo mass M_h = ~5x10^14 solar masses
    Chosen so that the simulated Einstein Gap appears at the observed ~10 arcsec deficit; not independently constrained in this work.
  • Satellite-profile normalization factor = per mass bin, MCMC
    Rescales Sigma_sat to match the lensing CCF amplitude before NFW fitting; arbitrary normalization affects derived M_NFW and C_NFW.
  • NFW mass M_NFW and concentration C_NFW per BCG mass bin = Table 1 values, e.g., log M_NFW 13.13 to 13.96, C_NFW 0.11 to 1.12
    Free parameters of the NFW fits to the renormalized satellite profiles, used for SHMR and mass segregation conclusions.
axioms (5)
  • domain assumption The SISSA power-law approximation with log Sigma0=9.5, eta=0.8, n=4 and concentration ~5 describes the mass distribution of the stacked ZOU clusters in the radial range probed.
    Adopted from Lapi et al. (2012), Appendix A3; the paper states Sigma0 and eta depend weakly on mass distribution for fixed halo mass, but does not validate for the ZOU sample.
  • domain assumption The satellite galaxy distribution traces the underlying total mass distribution well enough that absence of a 10-arcsec dip in Sigma_sat rules out a genuine mass deficit.
    Section 4.1 uses the continuous Sigma_sat signal to reject the 'lack of mass' interpretation; this relies on literature tracer fidelity and is least secure in the BCG-dominated inner region.
  • domain assumption Background SMG positions from WISE have Gaussian errors with sigma=0.3 arcsec and the 2.4 arcsec convolution captures the effective positional uncertainty.
    Section 2.3; underpins the stacking resolution, but the exact effective positional error distribution is adopted from WISE/H-ATLAS cross-matching.
  • domain assumption Background source counts and angular power spectrum from Cai et al. (2013) and Lapi et al. (2011) accurately represent the SMG population in the simulated maps.
    Section 5.1; the simulator output depends on these external inputs.
  • standard math The magnification bias relation w = mu^(beta-1) - 1 holds in the strong lensing regime with a power-law source count slope beta.
    Appendix A derives this from standard lensing theory; it is a standard result under the stated assumptions.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Signal Drop in Magnification Profiles: Combining Lensing Simulations and Observations." pith.science (2026). https://pith.science/paper/KDVCLFWF

@misc{pith2026250902213,
  author       = {Pith},
  title        = {Pith review of: Signal Drop in Magnification Profiles: Combining Lensing Simulations and Observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KDVCLFWF}},
  note         = {Machine review of arXiv:2509.02213}
}
Share X Bluesky LinkedIn Reddit HN
abstract

Gravitational lensing magnification bias is a valuable tool for studying mass density profiles, with submillimetre galaxies (SMGs) serving as ideal background sources. The satellite distribution in galaxy clusters also provides insights into their mass distribution.This study aims to investigate the signal drop in mass density profiles from magnification bias measurements, assessing the role of satellite galaxies through observational data and lensing simulations. Using a stacking technique, we analyze the radial distribution of satellites in clusters and measure the magnification bias on background SMGs via angular cross-correlations. A gravitational lensing simulator aids in interpreting the results. Our analysis confirms that satellite distributions align with a Navarro-Frenk-White profile on large scales but exceeding it in the inner part. However, the lack of a similar signal drop at $\sim$10 arcseconds as in the lensing measurements suggests a strong lensing effect from massive central galaxies. The study provides new insights into the mass density profiles derived from gravitational lensing and their relation to satellite distributions within galaxy clusters. The introduction of a gravitational lensing simulator helps explain the emergence of an ``Einstein Gap'' induced by strong lensing effects associated to a change in the apparent position of the sources that suppresses the expected signal. These findings provide a deeper understanding of how satellite galaxies influence gravitational lensing and offer a framework for improving mass density profile estimations in future studies.

Figures

Figures reproduced from arXiv: 2509.02213 by David Crespo, Hu Zou, Joaqu\'in Gonz\'alez-Nuevo, Jose M. Casas, Laura Bonavera, Marcos M. Cueli, Rebeca Fern\'andez-Fern\'andez.

Figure 1
Figure 1. Figure 1: Mass distribution of BCGs, divided into mass bins. The colour￾coded regions represent different stellar mass ranges, in log(𝑀𝐵𝐶𝐺/𝑀⊙), from less than 10.2 (pink) to greater than 11.7 (grey). distinction is particularly important and will be further addressed in later sections. According to this method, the typical search radius for identifying members and defining a cluster is 1 Mpc. The catalogue does not … view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between the lensing results (in magenta) of the clusters from the ZOU catalogue and the results of stacking the satellite positions. The members are randomly selected, using 4 subsamples of satellites with sizes of 102 (in red), 103 (in green), 104 (in orange), and the total set of satellites (in blue), approximately 1.2 · 107 (more information in the text). The difference between using the tota… view at source ↗
Figure 4
Figure 4. Figure 4: Satellite number density profile derived by stacking satellite posi￾tions around the BCG without any positional uncertainty (orange points) and assuming a 𝜎 = 2.4” one (blue points). The number of satellites used are indicated inside each panel. square map with a pixel size of 0.5 arcseconds and 800×800 pixels centred at the BCG position. By applying this method to each of the clusters, adding up all the m… view at source ↗
Figure 5
Figure 5. Figure 5: Mass density profile analysis for each mass bin. The panels are ordered by BCG stellar mass range from top to bottom and left to right, BIN1 being at the top left and BIN7 being the panel at the bottom. The red circles represent the results obtained using the lensing method, while the blue squares correspond to the renormalized Σ𝑠𝑎𝑡 points. The best NFW fit to the outer region of the satellite number densi… view at source ↗
Figure 7
Figure 7. Figure 7: Stellar mass of central galaxies as a function of halo mass, compar￾ing results from this work (blue stars for WL of the external region only and blue triangle for the result using the simulator) with several prior studies us￾ing different techniques: Tully-Fisher relation (Pizagno et al. (2005), TF, gray crosses), abundance matching (Moster et al. (2013), AM, green dashed line), weak lensing (Mandelbaum e… view at source ↗
Figure 9
Figure 9. Figure 9: Comparison between simulated mass density profiles and the mea￾sured one from ZOU clusters catalogue. The black crosses represent simula￾tion results using an average halo mass of 5 · 1014𝑀⊙, while violet circles correspond to observational data. the CORRSKY software (González-Nuevo et al. 2005), incorporating source number counts from Cai et al. (2013) and the angular power spectrum from Lapi et al. (2011… view at source ↗
Figure 8
Figure 8. Figure 8: Stacking maps visually showing the strong lensing effect produced by a point object with 𝑀ℎ = 7 · 1013𝑀⊙. The top panel shows the configura￾tion without the lens, while the bottom panel includes the lens, highlighting the resulting strong lensing effects and, in particular, the gap inside the Ein￾stein ring. its preliminary version, the simulator models foreground lenses using a NFW mass density profile (s… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

102 extracted references · 34 canonical work pages · 1 internal anchor

  1. [1]

    A., 1964, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables

    Abramowitz M., Stegun I. A., 1964, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. National Bureau of Standards Applied Mathematics Series Vol. 55, Dover Publications, New York

  2. [2]

    W., Evrard A

    Allen S. W., Evrard A. E., Mantz A. B., 2011, @doi [ ] 10.1146/annurev-astro-081710-102514 , https://ui.adsabs.harvard.edu/abs/2011ARA&A..49..409A 49, 409

  3. [4]

    K., et al., 2014, @doi [ ] 10.1093/mnras/stu727 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.2440B 441, 2440

    Baldry I. K., et al., 2014, @doi [ ] 10.1093/mnras/stu727 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.2440B 441, 2440

  4. [5]

    Rep.] 10.1016/S0370-1573(00)00082-X , http://adsabs.harvard.edu/abs/2001PhR...340..291B 340, 291

    Bartelmann M., Schneider P., 2001, @doi [Phys. Rep.] 10.1016/S0370-1573(00)00082-X , http://adsabs.harvard.edu/abs/2001PhR...340..291B 340, 291

  5. [6]

    H., Gazta \ n aga E., Mart \' P., Miquel R., 2014, @doi [ ] 10.1093/mnras/stu530 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440.3701B 440, 3701

    Bauer A. H., Gazta \ n aga E., Mart \' P., Miquel R., 2014, @doi [ ] 10.1093/mnras/stu530 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440.3701B 440, 3701

  6. [7]

    Bellstedt S., et al., 2016, Monthly Notices of the Royal Astronomical Society, 460, 2862

  7. [8]

    L., 2018, @doi [ ] 10.3847/1538-4357/aacafd , http://adsabs.harvard.edu/abs/2018ApJ...862...81B 862, 81

    Bianchini F., Reichardt C. L., 2018, @doi [ ] 10.3847/1538-4357/aacafd , http://adsabs.harvard.edu/abs/2018ApJ...862...81B 862, 81

  8. [9]

    Bianchini F., Fabbian G., Lapi A., Gonzalez-Nuevo J., Gilli R., Baccigalupi C., 2019, @doi [ ] 10.3847/1538-4357/aaf86b , http://adsabs.harvard.edu/abs/2019ApJ...871..136B 871, 136

  9. [10]

    W., Smail I., Ivison R

    Blain A. W., Smail I., Ivison R. J., Kneib J. P., 1999, @doi [ ] 10.1046/j.1365-8711.1999.02178.x , https://ui.adsabs.harvard.edu/abs/1999MNRAS.302..632B 302, 632

  10. [11]

    Bonavera L., Gonz \'a lez-Nuevo J., Arg \"u eso F., Toffolatti L., 2017a, @doi [ ] 10.1093/mnras/stx1020 , http://adsabs.harvard.edu/abs/2017MNRAS.469.2401B 469, 2401

  11. [12]

    Bonavera L., Gonz \'a lez-Nuevo J., De Marco B., Arg \"u eso F., Toffolatti L., 2017b, @doi [ ] 10.1093/mnras/stx2102 , http://adsabs.harvard.edu/abs/2017MNRAS.472..628B 472, 628

  12. [13]

    Bonavera L., et al., 2019, @doi [ ] 10.1088/1475-7516/2019/09/021 , https://ui.adsabs.harvard.edu/abs/2019JCAP...09..021B 2019, 021

  13. [14]

    Bonavera L., et al., 2020, , 639, A128

  14. [15]

    M., Gonzalez-Nuevo J., 2022, Proceedings of the MG16 Meeting on General Relativity, R

    Bonavera L., Cueli M. M., Gonzalez-Nuevo J., 2022, Proceedings of the MG16 Meeting on General Relativity, R. Ruffini & G. Vereshchagin eds., World Scientific., https://ui.adsabs.harvard.edu/abs/2021arXiv211202959B p. arXiv:2112.02959

  15. [16]

    Bourne N., et al., 2016, @doi [ ] 10.1093/mnras/stw1654 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.1714B 462, 1714

  16. [17]

    Budzynski J., Koposov S., McCarthy I., McGee S., Belokurov V., 2012, Monthly Notices of the Royal Astronomical Society, 423, 104

  17. [18]

    J., 1978, @doi [ ] 10.1086/156640 , https://ui.adsabs.harvard.edu/abs/1978ApJ...226..559B 226, 559

    Butcher H., Oemler A. J., 1978, @doi [ ] 10.1086/156640 , https://ui.adsabs.harvard.edu/abs/1978ApJ...226..559B 226, 559

  18. [19]

    J., 1984, @doi [ ] 10.1086/162519 , https://ui.adsabs.harvard.edu/abs/1984ApJ...285..426B 285, 426

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

  19. [20]

    Cai Z.-Y., et al., 2013, @doi [ ] 10.1088/0004-637X/768/1/21 , https://ui.adsabs.harvard.edu/abs/2013ApJ...768...21C 768, 21

  20. [21]

    F., 2004, @doi [ ] 10.1051/0004-6361:20031696 , https://ui.adsabs.harvard.edu/abs/2004A&A...415..839C 415, 839

    Cardone V. F., 2004, @doi [ ] 10.1051/0004-6361:20031696 , https://ui.adsabs.harvard.edu/abs/2004A&A...415..839C 415, 839

  21. [22]

    Ferguson H.C

    Carollo C.M. Ferguson H.C. W. R. e., 1999, The formation of galactic bulges. CUP, http://gen.lib.rus.ec/book/index.php?md5=05496f340a9c4ec93b80590a3addb4aa

  22. [23]

    Ciotti L., 1991, , https://ui.adsabs.harvard.edu/abs/1991A&A...249...99C 249, 99

  23. [24]

    A., 2010, @doi [ ] 10.1088/0004-637X/723/2/1678 , https://ui.adsabs.harvard.edu/abs/2010ApJ...723.1678C 723, 1678

    Coe D., Ben \' tez N., Broadhurst T., Moustakas L. A., 2010, @doi [ ] 10.1088/0004-637X/723/2/1678 , https://ui.adsabs.harvard.edu/abs/2010ApJ...723.1678C 723, 1678

  24. [25]

    Conroy C., et al., 2007, @doi [ ] 10.1086/509632 , https://ui.adsabs.harvard.edu/abs/2007ApJ...654..153C 654, 153

  25. [26]

    M., Casas J

    Crespo D., Gonz \'a lez-Nuevo J., Bonavera L., Cueli M. M., Casas J. M., Goitia E., 2022, @doi [ ] 10.1051/0004-6361/202244016 , https://ui.adsabs.harvard.edu/abs/2022A&A...667A.146C 667, A146

  26. [27]

    M., Casas J

    Crespo D., Gonz \'a lez-Nuevo J., Bonavera L., Cueli M. M., Casas J. M., 2024, @doi [ ] 10.1051/0004-6361/202347426 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.109C 684, A109

  27. [28]

    arXiv:1611.00036

    DESI Collaboration et al., 2016, @doi [arXiv e-prints] 10.48550/arXiv.1611.00036 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100036D p. arXiv:1611.00036

  28. [29]

    Davis M., Peebles P., 1983, The Astrophysical Journal, 267, 465

  29. [30]

    Dressler A., 1980, @doi [ ] 10.1086/157753 , https://ui.adsabs.harvard.edu/abs/1980ApJ...236..351D 236, 351

  30. [32]

    Eales S., et al., 2010, @doi [ ] 10.1086/653086 , https://ui.adsabs.harvard.edu/abs/2010PASP..122..499E 122, 499

  31. [33]

    El \' asd \'o ttir \'A ., M \"o ller O., 2007, @doi [ ] 10.1088/1475-7516/2007/07/006 , https://ui.adsabs.harvard.edu/abs/2007JCAP...07..006E 2007, 006

  32. [34]

    M., Casas J

    Fern \'a ndez-Fern \'a ndez R., Bonavera L., Crespo D., Gonz \'a lez-Nuevo J., Cueli M. M., Casas J. M., Cabo S. R., 2024, @doi [ ] 10.1051/0004-6361/202348806 , https://ui.adsabs.harvard.edu/abs/2024A&A...685A.155F 685, A155

  33. [35]

    M., Gonz \'a lez-Nuevo J., Bonavera L., Crespo D., Casas J

    Fern \'a ndez L., Cueli M. M., Gonz \'a lez-Nuevo J., Bonavera L., Crespo D., Casas J. M., Lapi A., 2022, @doi [ ] 10.1051/0004-6361/202141905 , https://ui.adsabs.harvard.edu/abs/2022A&A...658A..19F 658, A19

  34. [36]

    Fox C., Mahler G., Sharon K., Gonz \'a lez J. D. R., 2022, The Astrophysical Journal, 928, 87

  35. [37]

    D., Koopmans L

    Gavazzi R., Treu T., Rhodes J. D., Koopmans L. V., Bolton A. S., Burles S., Massey R. J., Moustakas L. A., 2007, The Astrophysical Journal, 667, 176

  36. [38]

    Gonz \'a lez-Nuevo J., Toffolatti L., Arg \"u eso F., 2005, @doi [ ] 10.1086/427425 , https://ui.adsabs.harvard.edu/abs/2005ApJ...621....1G 621, 1

  37. [39]

    Gonz \'a lez-Nuevo J., et al., 2014, @doi [ ] 10.1093/mnras/stu1041 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.442.2680G 442, 2680

  38. [40]

    Gonz \'a lez-Nuevo J., et al., 2017, @doi [ ] 10.1088/1475-7516/2017/10/024 , https://ui.adsabs.harvard.edu/abs/2017JCAP...10..024G 2017, 024

  39. [41]

    M., Bonavera L., Lapi A., Migliaccio M., Arg \"u eso F., Toffolatti L., 2021, @doi [ ] 10.1051/0004-6361/202039043 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.152G 646, A152

    Gonz \'a lez-Nuevo J., Cueli M. M., Bonavera L., Lapi A., Migliaccio M., Arg \"u eso F., Toffolatti L., 2021, @doi [ ] 10.1051/0004-6361/202039043 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.152G 646, A152

  40. [42]

    L., 2003, @doi [ ] 10.1046/j.1365-2966.2003.07114.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.346..601G 346, 601

    Goto T., Yamauchi C., Fujita Y., Okamura S., Sekiguchi M., Smail I., Bernardi M., Gomez P. L., 2003, @doi [ ] 10.1046/j.1365-2966.2003.07114.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.346..601G 346, 601

  41. [43]

    W., Driver S

    Graham A. W., Driver S. P., 2005, @doi [ ] 10.1071/AS05001 , https://ui.adsabs.harvard.edu/abs/2005PASA...22..118G 22, 118

  42. [44]

    S., Shao S., 2022, Monthly Notices of the Royal Astronomical Society, 514, 390

    Gu Q., Guo Q., Zhang T., Cautun M., Lacey C., Frenk C. S., Shao S., 2022, Monthly Notices of the Royal Astronomical Society, 514, 390

  43. [45]

    Guo Q., Cole S., Eke V., Frenk C., Helly J., 2013, Monthly Notices of the Royal Astronomical Society, 434, 1838

  44. [46]

    Hennig C., et al., 2017, Monthly Notices of the Royal Astronomical Society, 467, 4015

  45. [47]

    Hildebrandt H., et al., 2013, @doi [ ] 10.1093/mnras/sts585 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.429.3230H 429, 3230

  46. [48]

    D., 2007, @doi [Computing In Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing In Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

  47. [49]

    E., et al., 2007, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2007arXiv0709.1159J p

    Johnston D. E., et al., 2007, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2007arXiv0709.1159J p. arXiv:0709.1159

  48. [50]

    Jones E., Oliphant T., Peterson P., et al., 2001, SciPy : Open source scientific tools for Python , http://www.scipy.org/

  49. [51]

    Lapi A., et al., 2011, @doi [ ] 10.1088/0004-637X/742/1/24 , https://ui.adsabs.harvard.edu/abs/2011ApJ...742...24L 742, 24

  50. [52]

    Y., De Zotti G., Danese L., 2012, @doi [ ] 10.1088/0004-637X/755/1/46 , https://ui.adsabs.harvard.edu/abs/2012ApJ...755...46L 755, 46

    Lapi A., Negrello M., Gonz \'a lez-Nuevo J., Cai Z. Y., De Zotti G., Danese L., 2012, @doi [ ] 10.1088/0004-637X/755/1/46 , https://ui.adsabs.harvard.edu/abs/2012ApJ...755...46L 755, 46

  51. [53]

    Leauthaud A., et al., 2012, @doi [ ] 10.1088/0004-637X/744/2/159 , https://ui.adsabs.harvard.edu/abs/2012ApJ...744..159L 744, 159

  52. [54]

    Luo W., et al., 2024, @doi [ ] 10.3847/1538-4357/ad86b5 , https://ui.adsabs.harvard.edu/abs/2024ApJ...977...59L 977, 59

  53. [55]

    J., et al., 2018, @doi [ ] 10.3847/1538-4365/aab8fc , https://ui.adsabs.harvard.edu/abs/2018ApJS..236...30M 236, 30

    Maddox S. J., et al., 2018, @doi [ ] 10.3847/1538-4365/aab8fc , https://ui.adsabs.harvard.edu/abs/2018ApJS..236...30M 236, 30

  54. [56]

    M., Brinkmann J., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10156.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.368..715M 368, 715

    Mandelbaum R., Seljak U., Kauffmann G., Hirata C. M., Brinkmann J., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10156.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.368..715M 368, 715

  55. [57]

    E., Angulo R

    Marian L., Smith R. E., Angulo R. E., 2015, Monthly Notices of the Royal Astronomical Society, 451, 1418

  56. [58]

    M \'e nard B., Scranton R., Fukugita M., Richards G., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16486.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.1025M 405, 1025

  57. [59]

    2021, @doi [Science] 10.1126/science.aax5164 , 369, 1347

    Meneghetti M., et al. 2021, @doi [Science] 10.1126/science.aax5164 , 369, 1347

  58. [60]

    2024, @doi [Astronomy & Astrophysics] 10.1051/0004-6361/202346058 , 670, A142

    Meneghetti M., et al. 2024, @doi [Astronomy & Astrophysics] 10.1051/0004-6361/202346058 , 670, A142

  59. [61]

    D., 1998, Monthly Notices of the Royal Astronomical Society, 295, 319

    Mo H., Mao S., White S. D., 1998, Monthly Notices of the Royal Astronomical Society, 295, 319

  60. [62]

    P., Naab T., White S

    Moster B. P., Naab T., White S. D. M., 2013, @doi [ ] 10.1093/mnras/sts261 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.3121M 428, 3121

  61. [63]

    I., Hatch N

    Muldrew S. I., Hatch N. A., Cooke E. A., 2018, Monthly Notices of the Royal Astronomical Society, 473, 2335

  62. [64]

    D., Outram P

    Myers A. D., Outram P. J., Shanks T., Boyle B. J., Croom S. M., Loaring N. S., Miller L., Smith R. J., 2003, Monthly Notices of the Royal Astronomical Society, 342, 467

  63. [65]

    V., 2005, The Astrophysical Journal, 618, 557

    Nagai D., Kravtsov A. V., 2005, The Astrophysical Journal, 618, 557

  64. [66]

    F., Frenk C

    Navarro J. F., Frenk C. S., White S. D. M., 1996, @doi [ ] 10.1086/177173 , https://ui.adsabs.harvard.edu/abs/1996ApJ...462..563N 462, 563

  65. [67]

    P \^a ris I., et al., 2017, Astronomy & Astrophysics, 597, A79

  66. [68]

    E., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.53 , 9, 21

    P\'erez F., Granger B. E., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.53 , 9, 21

  67. [69]

    L., et al., 2010, @doi [ ] 10.1051/0004-6361/201014759 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...1P 518, L1

    Pilbratt G. L., et al., 2010, @doi [ ] 10.1051/0004-6361/201014759 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...1P 518, L1

  68. [70]

    Pizagno J., et al., 2005, @doi [ ] 10.1086/491614 , https://ui.adsabs.harvard.edu/abs/2005ApJ...633..844P 633, 844

  69. [71]

    Planck Collaboration et al., 2014, @doi [ ] 10.1051/0004-6361/201321526 , http://adsabs.harvard.edu/abs/2014A\

  70. [72]

    Planck Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201525831 , 594, A21

  71. [73]

    Planck Collaboration et al., 2021, @doi [ ] 10.1051/0004-6361/201833910e , https://ui.adsabs.harvard.edu/abs/2021A&A...652C...4P 652, C4

  72. [74]

    E., 1992, Gravitational Lenses

    Schneider P., Ehlers J., Falco E. E., 1992, Gravitational Lenses. Springer-Verlag, @doi 10.1007/978-3-662-03758-4

  73. [75]

    Saas-Fee Advanced Course Vol

    Schneider P., Kochanek C., Wambsganss J., 2006, Gravitational Lensing: Strong, Weak and Micro. Saas-Fee Advanced Course Vol. 33, Springer Science & Business Media

  74. [76]

    P., et al., 2010, The Astronomical Journal, 139, 2360

    Schneider D. P., et al., 2010, The Astronomical Journal, 139, 2360

  75. [77]

    Schrabback T., et al., 2018, Monthly Notices of the Royal Astronomical Society, 474, 2635

  76. [78]

    Scranton R., et al., 2005, @doi [ ] 10.1086/431358 , https://ui.adsabs.harvard.edu/abs/2005ApJ...633..589S 633, 589

  77. [79]

    L., 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, https://ui.adsabs.harvard.edu/abs/1963BAAA....6...41S 6, 41

    S \'e rsic J. L., 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, https://ui.adsabs.harvard.edu/abs/1963BAAA....6...41S 6, 41

  78. [80]

    L., 1968, Cordoba

    Sersic J. L., 1968, Cordoba

  79. [81]

    M., Keller B

    Stil J. M., Keller B. W., George S. J., Taylor A. R., 2014, @doi [ ] 10.1088/0004-637X/787/2/99 , http://adsabs.harvard.edu/abs/2014ApJ...787...99S 787, 99

  80. [82]

    A., Van Dokkum P

    Tal T., Wake D. A., Van Dokkum P. G., 2012, The Astrophysical Journal Letters, 751, L5

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.