Pith. sign in

REVIEW 2 major objections 4 minor 75 references

SLICE: SPT-CL J0546-5345 -- A prominent strong-lensing cluster at $z=1.07$

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read A galaxy cluster at redshift 1.07 is one of the strongest known gravitational lenses.

desk verdict First strong-lensing model of SPT-CL J0546-5345 shows a genuinely prominent lens at z=1.07, with the main caveat being photo-z-dependent mass scale. read the letter →

arxiv 2507.08949 v2 pith:X5QMJWET submitted 2025-07-11 astro-ph.CO astro-ph.GA

classification astro-ph.COastro-ph.GA
keywords gravitationallensing:stronggalaxyclustersSPT-CLJ0546-5345EinsteinradiusJWST/NIRCamphotometricredshiftslight-traces-massmodelingmultiplyimagedAGN
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents the first strong-lensing analysis of the massive galaxy cluster SPT-CL J0546-5345 at $z_l=1.07$, using new JWST/NIRCam and archival HST imaging. The authors identify at least 10 secure and 6 candidate multiply imaged background galaxies and build a Light-Traces-Mass lens model from them. They derive effective Einstein radii $\theta_{\rm E}=18.1\pm1.8''$ for a source at $z_s=3$ and $\theta_{\rm E}=27.9\pm2.8''$ for $z_s=9$, with a projected mass $M(<200\,\mathrm{kpc})=(1.9\pm0.3)\times10^{14}\,M_\odot$ inside the strong-lensing region. These values resemble those of the best-studied lower-redshift lensing clusters, even though this cluster is seen when the universe was about 3--4 Gyr younger, making it a rare high-redshift probe of cluster formation.

What carries the argument

The central object is the Light-Traces-Mass (LTM) lens model: it assigns power-law mass profiles to cluster galaxies in proportion to their luminosity, smooths the galaxy map into a dark matter component, adds external shear, and leaves the weights of bright galaxies free; parameters are optimized with Markov-chain Monte Carlo to minimize the scatter between predicted and observed multiple-image positions. The model is anchored to System 3, whose photometric redshift is set to $z_s=3.5$, and the fit is repeated at $z_s=3.25$ and $3.75$ so the quoted uncertainties include the anchor's photometric-redshift uncertainty. Because strong-lensing masses scale with the angular-diameter distance ratio $D_{LS}/D_S$, this anchor is the lever arm that sets the absolute mass and Einstein-radius scales.

What would settle it

Measure a spectrum of System 3, the anchor of the whole model. If its spectroscopic redshift lies outside $z=3.5\pm0.25$, the model must be re-anchored and the quoted Einstein radii and masses would shift with the distance ratio $D_{LS}/D_S$; likewise, if the point-like images of System 5 do not share a common redshift, the AGN identification and its time-delay predictions would collapse.

Watch

Extended reading notes

Core claim

SPT-CL J0546-5345, a cluster first detected through the Sunyaev-Zel'dovich effect and spectroscopically confirmed at $z=1.07$, is a prominent gravitational lens. The paper's mass model, constrained by 30 multiple-image positions from at least 10 secure systems, yields an effective Einstein radius $\theta_{\rm E}=18.1\pm1.8''$ for a source at $z_s=3$ and $27.9\pm2.8''$ for a source at $z_s=9$, with a projected mass $M(<200\,\mathrm{kpc})=(1.9\pm0.3)\times10^{14}\,M_\odot$. These lensing properties are comparable to those of the well-studied low-redshift lensing clusters, a similarity the authors emphasize is surprising because the cluster is seen when the universe was roughly 3--4 Gyr younger and such prominent lenses are expected to be rare. The same analysis identifies a candidate sextuply lensed point-like source that may be an AGN, along with a hyperbolic-umbilic-like image configuration.

Load-bearing premise

The photometric redshifts of the lensed background galaxies, especially the anchor System 3 at $z=3.5$, are accurate enough that rescaling by the source-lens distance ratio does not push the derived Einstein radii and masses outside the quoted uncertainties.

Editorial extensions

If this is right

  • The cluster joins a small set of well-modeled strong lenses at $z_l>1$, showing that JWST depth and wavelength coverage can reveal prominent lensing features around high-redshift clusters.
  • If the point-like System 5 is confirmed spectroscopically as a multiply imaged AGN, it would be one of only a handful of cluster-lensed AGN, with model-predicted time delays of roughly 40--50 years between image groups, making it a target for time-delay cosmology and black-hole reverberation mapping.
  • The model's mass within 500 kpc, extrapolated to $M_{500,\rm c}=(7.2\pm0.5)\times10^{14}\,M_\odot$, provides a lensing-based comparison for X-ray, SZE, and weak-lensing mass estimates of this cluster, and the implied hydrostatic-to-lensing mass ratio of about 0.74 is consistent with other clusters.
  • The paper's semi-analytic estimate says lenses as strong as this at $z\simeq1.07$ should be rare, so each additional $z>1$ cluster analyzed with JWST tests whether current halo mass functions and concentration relations under-predict strong lensing at high redshift.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A systematic error in the photometric redshift of the anchor System 3 would rescale every quoted mass and Einstein radius through the distance ratio $D_{LS}/D_S$, and the three anchor runs only span the photometric 1-sigma range; spectroscopic redshifts of the multiply imaged systems are therefore the decisive check.
  • The rarity calculation relies on a simulation-based mass function that the authors note likely under-counts the most massive halos, so a larger JWST sample of $z>1$ clusters or a larger cosmological volume would test whether such large Einstein radii are really as exceptional as the estimate suggests.
  • If Systems 4 and 8 turn out to be parts of the same background galaxy, the hyperbolic-umbilic-like configuration would offer a rare caustic-geometry measurement of the cluster mass distribution that is independent of the usual image-count constraints.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. The paper presents the first strong-lensing analysis of the SZ-selected cluster SPT-CL J0546-5345 at z_l=1.067, using new JWST/NIRCam and archival HST imaging. The authors identify 10 secure and 6 candidate multiply imaged systems, build an LTM mass model, and report effective Einstein radii of 18.1±1.8 arcsec for z_s=3 and 27.9±2.8 arcsec for z_s=9, together with a projected mass M(<200 kpc)=(1.9±0.3)×10^14 M_sun. They compare these values to Hubble Frontier Fields clusters, estimate the rarity of such a lens at z>1, and highlight a hyperbolic-umbilic-like configuration and a candidate multiply imaged AGN.

Significance. If the quantitative values hold, this is one of the few z>1 clusters with prominent strong-lensing features, demonstrating the power of JWST to reveal such systems and providing an important target for spectroscopic follow-up. The multiple-image identifications are supported by imaging morphology and colors, and the paper is transparent about the lack of spectroscopic redshifts for the lensed sources. The lens model is publicly released, which is a useful resource. The principal caveat is that the mass scale and Einstein radii rest on a single photometric-redshift anchor for System 3, with only a 1-sigma range explored.

major comments (2)
  1. [§3 and Table 1] The global normalization of the model, and therefore all quoted Einstein radii and enclosed masses, is set by the assumed redshift of System 3, z_3=3.5. The three anchor runs at z_3=3.25, 3.5, and 3.75 cover only the Bagpipes 16–84% percentiles from five broad bands. For z_l=1.07, the lensing efficiency factor D_LS/D_S decreases steeply at low source redshift: it is roughly 0.49 at z_s=3.5 and 0.21 at z_s=1.5. Thus a systematic photo-z error of Δz≈−2 would change the inferred mass normalization by a factor of about 2 and shift the reported Einstein radii and masses far outside the quoted 1σ uncertainties. Since no multiply imaged source has a spectroscopic redshift, the quantitative comparisons to HFF clusters and the rarity statement in §4.3 are not robust against this systematic uncertainty. I recommend presenting the scaling of θ_E and M with z_3 over a wider range (e.g., z_3=2–5), or, if that is not feasible, tempering the quantitative comparisons and making the conditional nature of the numbers explicit in the abstract.
  2. [§4.1] The model has 28 free parameters and 39 constraints with reduced χ²≈68/11 and an r.m.s. of about 1.2 arcsec. The quoted uncertainties appear to propagate the MCMC scatter and the three anchor runs, but they do not account for systematic uncertainty in the LTM parameterization itself (power-law slope, smoothing scale, galaxy weights, external shear, and the light-to-mass mapping). Because the headline Einstein radii and masses are outputs of this single model, I would like to see either an independent check with a different modeling technique or an explicit discussion of how variations of the LTM hyper-parameters change θ_E and M(<200 kpc). Without this, the quantitative claims should be regarded as model-dependent estimates rather than robust measurements.
minor comments (4)
  1. [§2.3] The photo-z analysis adopts a lower limit of z>1.2; because the five-band photometry is sparse, this prior can substantially affect the quoted intervals. Please state explicitly how the prior shapes the reported 16–84% ranges and whether any systems, such as System 4 with its wide asymmetric interval, are sensitive to this choice.
  2. [§4.3] The rarity calculation concludes that a cluster with this Einstein radius should be rare across the sky, but the following paragraph lists several recently analyzed z~1 clusters with similar or stronger lensing properties, which appears contradictory. Please clarify whether the calculation is intended as a lower limit and how the recent discoveries affect the expected abundance.
  3. [Figure 3] The model reproduction panels are visually compelling, but the figure does not show the positional residuals for each system. Adding a residual vector plot or a table of observed versus predicted image positions would help quantify the reported 1.2 arcsec r.m.s. and make the fit quality easier to assess.
  4. [§4.2] The time-delay predictions for the AGN candidate, including the 40–50 year gap between image groups, are stated rather precisely. Given the model's r.m.s. and the absence of a confirmed source redshift, these should be labeled as order-of-magnitude, model-dependent estimates.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the strong-lensing identification is anchored by direct multiple-image observations, and the mass model is an independently established technique whose key outputs are externally benchmarked.

full rationale

The paper's central claims—the identification of at least 10 secure and 6 candidate multiply imaged systems, the derived Einstein radii, and the enclosed projected mass—are anchored in direct JWST/HST imaging of multiple images, not in a self-referential definition. The LTM mass-modeling method is self-cited to Zitrin et al., but it is an established, independently used technique with a documented predictive step: an initial model built only from cluster-member photometry is used to delens and relens arclets and predict counterimages that are then searched for in the data (Section 3), which is a genuine prediction rather than a fit result relabeled. The final model is constrained by 30 multiple-image positions with a reported r.m.s. of ~1.2 arcseconds, and the results are compared against external benchmarks including X-ray, SZE, and weak-lensing mass estimates and an independent expected Einstein-radius distribution. The acknowledged dependence on the photometric redshift of System 3 (z3 = 3.5) is a real limitation—a systematic photo-z error would rescale the inferred masses and radii—but the paper explicitly discloses this and brackets it with three anchor runs at z3 = 3.25, 3.5, and 3.75. That is a sensitivity analysis attached to an input assumption, not a circular reduction of an output to an input. No step in the derivation chain is equivalent to its inputs by construction, and no load-bearing argument reduces to a self-citation chain.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The central mass and Einstein radius claims rest on the LTM model with 28 free parameters and on photometric redshifts derived from only five wide bands, plus the light-traces-mass assumption. These are standard tools in cluster lensing, but each introduces systematic uncertainty that is not fully propagated into the quoted errors. The authors do propagate the anchor redshift uncertainty by running three models, but not the full photo-z systematics.

free parameters (8)
  • Galaxy mass profile power-law exponent
    Exponent of the power-law density profile assigned to each cluster galaxy; a free parameter of the LTM model (Section 3).
  • Dark matter smoothing kernel width
    Gaussian smoothing scale applied to the galaxy light map to construct the dark matter distribution; free parameter.
  • Galaxy-to-dark-matter weight ratio
    Relative normalization between the galaxy and dark matter components; free parameter.
  • Overall mass normalization
    Global scaling of the total mass; free parameter.
  • External shear
    Additional shear term adding effective ellipticity to the mass model; free parameter.
  • Weights of key bright galaxies
    Mass normalizations of a few bright galaxies left free to optimize, marked in Figure 1.
  • Source redshifts of lensed systems = Optimized by model; see Table 1, z_model column
    Redshifts of multiple image systems without spectroscopic measurements were varied over wide uniform priors and optimized by MCMC.
  • Anchor redshift z3 (System 3) = 3.5, with variants at 3.25 and 3.75
    LTM model scaled to a reference source redshift z3 taken from the photometric redshift estimate of System 3.
assumptions (6)
  • domain assumption Light-traces-mass (LTM) assumption
    The total mass distribution is assumed to follow the distribution of cluster galaxy light, smoothed and weighted (Section 3). If dark matter is not aligned with the light, the mass profile and Einstein radii would be biased.
  • domain assumption Red-sequence cluster member selection identifies true members
    Cluster members are selected by color-magnitude relation plus by-eye inspection (Section 2.2). Spectroscopic validation covers part of the sample but not all 160 members.
  • domain assumption Photometric redshifts from Bagpipes are reliable
    SED fitting with five wide bands and a lower redshift limit of 1.2 (Section 2.3). Photometric redshift errors propagate into the mass model and Einstein radii.
  • domain assumption Flat Lambda CDM cosmology with H0=70, Omega_m=0.3, Omega_L=0.7
    Adopted cosmology for distance and kpc conversions (Section 1). Parameters are not fitted to the data.
  • domain assumption NFW profile for extrapolation to R500
    Used to compare strong-lensing mass to X-ray/SZ/weak-lensing masses at R500 (Section 4.1). Real profiles may deviate from NFW.
  • domain assumption Tinker mass function and Meneghetti c-M relation for rarity estimate
    Semi-analytic rarity estimate in Section 4.3 relies on these simulation-based inputs, which may underpredict massive halos.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SLICE: SPT-CL J0546-5345 -- A prominent strong-lensing cluster at $z=1.07$." pith.science (2026). https://pith.science/paper/X5QMJWET

@misc{pith2026250708949,
  author       = {Pith},
  title        = {Pith review of: SLICE: SPT-CL J0546-5345 -- A prominent strong-lensing cluster at $z=1.07$},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X5QMJWET}},
  note         = {Machine review of arXiv:2507.08949}
}
abstract

Massive galaxy clusters act as prominent strong-lenses. Due to a combination of observational biases, cluster evolution and lensing efficiency, most of the known cluster lenses lie typically at $z_{l}\sim0.2-0.7$, with only a few prominent examples at higher redshifts. Here we report a first strong-lensing analysis of the massive galaxy cluster SPT-CL J0546-5345 at a redshift $z_l=1.07$. This cluster was first detected through the Sunyaev-Zel'dovich effect, with a high estimated mass for its redshift of $M_{200,c} = (7.95 \pm 0.92) \times 10^{14}\,M_{\odot}$. Using recent JWST/NIRCam and archival HST imaging, we identify at least 10 secure and 6 candidate sets of multiply imaged background galaxies, which we use to constrain the mass distribution in the cluster. We derive effective Einstein radii of $\theta_{E}= 18.1 \pm 1.8 ''$ for a source at $z_{s}=3$, and $\theta_{E}= 27.9 \pm 2.8 ''$ for a source at $z_{s}=9$. The total projected mass within a $200$ kpc radius around the strong-lensing region is $M(<200\,\mathrm{kpc}) = (1.9 \pm 0.3) \times 10^{14}\,M_{\odot}$. While our results rely on photometric redshifts warranting spectroscopic follow-up, this central mass resembles that of the Hubble Frontier Fields clusters - although SPT-CL J0546-5345 is observed when the Universe was $\sim 3-4$ Gyr younger. Amongst the multiply-imaged sources, we identify a hyperbolic-umbilic-like configuration, and, thanks to its point-like morphology, a possible Active Galactic Nucleus (AGN). If confirmed spectroscopically, it will add to just a handful of other quasars and AGN known to be multiply lensed by galaxy clusters.

Figures

Figures reproduced from arXiv: 2507.08949 by the authors.

Figure 1
Figure 1. Color-composite image of SPT-CL J0546-5345 constructed from SLICE JWST and archival HST imaging of the cluster (Red: F322W2; Green: F150W2; Blue: F814W). Strong-lensing multiple images are numbered and labeled in green. The yellow, blue and red lines represent, respectively, the critical curves for source redshifts zs = 3.5 (System 3), zs = 4.5 and zs = 6, as computed from our strong lensing model of the cluster. Ga… view at source ↗
Figure 2
Figure 2. Surface mass density map (left), in units of the critical density for lensing, and magnification map (right), both for an assumed source redshift of zs = 3.5, from the best-fit LTM model. dict the observed appearance of counter images, which are then searched for in the observations. The method is described in greater detail in A. Zitrin et al. (2015) and we briefly summarize it here. The model is composed of three … view at source ↗
Figure 3
Figure 3. Reproduction of several systems by our model. For each system we delens one image – typically the first one – to the source plane and back to the image plane, displaying the reproduction of the other images in that system. The upper row, for each system, shows the images as they appear in the RGB color image of the cluster ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The expected distribution of Einstein radii around zl ∼ 1.07 for a source at zs = 3, across the sky (shaded red), and for the SPT survey region (2,500 deg2 ; shaded green), versus the Einstein radius of SP￾T-CL J0546-5345 θE = 18.1 ± 1.8 ′′ (vertical blue shaded region…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

75 extracted references · 18 canonical work pages

  1. [1]

    Allingham et al

    10J. Allingham et al. T able 1.Multiple Images and Candidates Arc ID R.A. Dec.z phot 50% [16%–84%]z model [16%–84%] Comments 1.1 05:46:37.1684 -53:45:29.876 — 2.76 [2.58–2.79] Relensed images predicted in the west, 1.2 05:46:36.9976 -53:45:30.179 — ” maybe corresponding to System 11 2.1 05:46:36.9549 -53:45:27.906 1.58 [1.33–1.95] 1.73 [1.72–1.94] 2.2 05:...

  2. [2]

    H., Wilson, G., Balogh, M

    Abdulshafy, S., Abdullah, M. H., Wilson, G., Balogh, M. L., & Mabrouk, R. H. 2025, arXiv e-prints, arXiv:2505.12110, doi: 10.48550/arXiv.2505.12110

  3. [3]

    2020, ApJ, 898, 6, doi: 10.3847/1538-4357/ab929d

    Acebron, A., Zitrin, A., Coe, D., et al. 2020, ApJ, 898, 6, doi: 10.3847/1538-4357/ab929d

  4. [4]

    R., Baker, A

    Aguirre, P., Lindner, R. R., Baker, A. J., et al. 2018, ApJ, 855, 26, doi: 10.3847/1538-4357/aaaab0

  5. [5]

    A., Ade, P

    Andersson, K., Benson, B. A., Ade, P. A. R., et al. 2011, ApJ, 738, 48, doi: 10.1088/0004-637X/738/1/48

  6. [6]

    L., van der Burg, R

    Balogh, M. L., van der Burg, R. F. J., Muzzin, A., et al. 2021, MNRAS, 500, 358, doi: 10.1093/mnras/staa3008

  7. [7]

    1996, A&AS, 117, 393

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393

  8. [8]

    2002, Astronomical Society of the Pacific Conference Series, Vol

    Bertin, E., Mellier, Y., Radovich, M., et al. 2002, Astronomical Society of the Pacific Conference Series, Vol. 281, The TERAPIX Pipeline (Bohlender, David A. and Durand, Daniel and Handley, Thomas H.), 228

Show all 75 references
  1. [9]

    E., Stalder, B., de Haan, T., et al

    Bleem, L. E., Stalder, B., de Haan, T., et al. 2015, ApJS, 216, 27, doi: 10.1088/0067-0049/216/2/27

  2. [10]

    2022, grizli, 1.5.0 Zenodo, doi: 10.5281/zenodo.6672538

    Brammer, G., Strait, V., Matharu, J., & Momcheva, I. 2022, grizli, 1.5.0 Zenodo, doi: 10.5281/zenodo.6672538

  3. [11]

    2005, ApJ, 621, 53, doi: 10.1086/426494

    Broadhurst, T., Ben ´ ıtez, N., Coe, D., et al. 2005, ApJ, 621, 53, doi: 10.1086/426494

  4. [12]

    Brodwin, M., Ruel, J., Ade, P. A. R., et al. 2010, ApJ, 721, 90, doi: 10.1088/0004-637X/721/1/90

  5. [13]

    2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

  6. [14]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Dav´ e, R. 2018, MNRAS, 480, 4379, doi: 10.1093/mnras/sty2169

  7. [15]

    2020, MNRAS, 491, 3778, doi: 10.1093/mnras/stz3040

    Carrasco, M., Zitrin, A., & Seidel, G. 2020, MNRAS, 491, 3778, doi: 10.1093/mnras/stz3040

  8. [16]

    2025, arXiv e-prints, arXiv:2503.17498, doi: 10.48550/arXiv.2503.17498

    Cerny, C., Mahler, G., Sharon, K., et al. 2025, arXiv e-prints, arXiv:2503.17498, doi: 10.48550/arXiv.2503.17498

  9. [17]

    P., Khullar, G., Napier, K

    Cloonan, A. P., Khullar, G., Napier, K. A., et al. 2025, ApJ, 987, 194, doi: 10.3847/1538-4357/addabf

  10. [18]

    2019, ApJ, 884, 85, doi: 10.3847/1538-4357/ab412b

    Coe, D., Salmon, B., Bradaˇ c, M., et al. 2019, ApJ, 884, 85, doi: 10.3847/1538-4357/ab412b

  11. [19]

    M., Meena, A

    Diego, J. M., Meena, A. K., Adams, N. J., et al. 2023, A&A, 672, A3, doi: 10.1051/0004-6361/202245238

  12. [20]

    2011, PASP, 123, 288, doi: 10.1086/658908

    Dressler, A., Bigelow, B., Hare, T., et al. 2011, PASP, 123, 288, doi: 10.1086/658908

  13. [21]

    C., Mantz, A., et al

    Ebeling, H., Edge, A. C., Mantz, A., et al. 2010, MNRAS, 407, 83, doi: 10.1111/j.1365-2966.2010.16920.x

  14. [22]

    Fox, C., Mahler, G., Sharon, K., & Remolina Gonz´ alez, J. D. 2022, ApJ, 928, 87, doi: 10.3847/1538-4357/ac5024

  15. [23]

    J., Zitrin, A., Weaver, J

    Furtak, L. J., Zitrin, A., Weaver, J. R., et al. 2023a, MNRAS, 523, 4568, doi: 10.1093/mnras/stad1627

  16. [24]

    J., Mainali, R., Zitrin, A., et al

    Furtak, L. J., Mainali, R., Zitrin, A., et al. 2023b, MNRAS, 522, 5142, doi: 10.1093/mnras/stad1321

  17. [25]

    J., Zitrin, A., Richard, J., et al

    Furtak, L. J., Zitrin, A., Richard, J., et al. 2024a, MNRAS, 533, 2242, doi: 10.1093/mnras/stae1943

  18. [26]

    J., Labb´ e, I., Zitrin, A., et al

    Furtak, L. J., Labb´ e, I., Zitrin, A., et al. 2024b, Nature, 628, 57, doi: 10.1038/s41586-024-07184-8

  19. [27]

    D., & Yee, H

    Gladders, M. D., & Yee, H. K. C. 2000, AJ, 120, 2148, doi: 10.1086/301557

  20. [28]

    D., & Yee, H

    Gladders, M. D., & Yee, H. K. C. 2005, ApJS, 157, 1, doi: 10.1086/427327

  21. [29]

    L., Zitrin, A., et al

    Golubchik, M., Steinhardt, C. L., Zitrin, A., et al. 2024, ApJ, 976, 108, doi: 10.3847/1538-4357/ad8441

  22. [30]

    2021, ApJS, 253, 3, doi: 10.3847/1538-4365/abd023

    Hilton, M., Sif´ on, C., Naess, S., et al. 2021, ApJS, 253, 3, doi: 10.3847/1538-4365/abd023

  23. [31]

    C., et al

    Jauzac, M., Mahler, G., Edge, A. C., et al. 2019, MNRAS, 483, 3082, doi: 10.1093/mnras/sty3312

  24. [32]

    2025, arXiv e-prints, arXiv:2501.13082, doi: 10.48550/arXiv.2501.13082

    Ji, X., Maiolino, R., ¨Ubler, H., et al. 2025, arXiv e-prints, arXiv:2501.13082, doi: 10.48550/arXiv.2501.13082

  25. [33]

    2016, ApJ, 819, 114, doi: 10.3847/0004-637X/819/2/114

    Ouchi, M. 2016, ApJ, 819, 114, doi: 10.3847/0004-637X/819/2/114

  26. [34]

    V., & Borgani, S

    Kravtsov, A. V., & Borgani, S. 2012, ARA&A, 50, 353, doi: 10.1146/annurev-astro-081811-125502

  27. [35]

    J., Richard, J., Ebeling, H., et al

    Lagattuta, D. J., Richard, J., Ebeling, H., et al. 2023, MNRAS, 522, 1091, doi: 10.1093/mnras/stad803

  28. [36]

    2025, A&A, 693, A33, doi: 10.1051/0004-6361/202451969

    Limousin, M., Beauchesne, B., Niemiec, A., et al. 2025, A&A, 693, A33, doi: 10.1051/0004-6361/202451969

  29. [37]

    M., Koekemoer, A., Coe, D., et al

    Lotz, J. M., Koekemoer, A., Coe, D., et al. 2017, ApJ, 837, 97, doi: 10.3847/1538-4357/837/1/97

  30. [38]

    2019, ApJ, 873, 96, doi: 10.3847/1538-4357/ab042b

    Mahler, G., Sharon, K., Fox, C., et al. 2019, ApJ, 873, 96, doi: 10.3847/1538-4357/ab042b

  31. [39]

    D., et al

    Mahler, G., Sharon, K., Gladders, M. D., et al. 2020, ApJ, 894, 150, doi: 10.3847/1538-4357/ab886b

  32. [40]

    2016, arXiv e-prints, arXiv:1608.04388, doi: 10.48550/arXiv.1608.04388

    Martizzi, D., & Agrusa, H. 2016, arXiv e-prints, arXiv:1608.04388, doi: 10.48550/arXiv.1608.04388

  33. [41]

    K., & Bagla, J

    Meena, A. K., & Bagla, J. S. 2024, The Open Journal of Astrophysics, 7, 91, doi: 10.33232/001c.124634

  34. [42]

    2003, MNRAS, 346, 67, doi: 10.1046/j.1365-2966.2003.07068.x

    Meneghetti, M., Bartelmann, M., & Moscardini, L. 2003, MNRAS, 346, 67, doi: 10.1046/j.1365-2966.2003.07068.x

  35. [43]

    2010, A&A, 514, A93+, doi: 10.1051/0004-6361/200913222

    Meneghetti, M., Rasia, E., Merten, J., et al. 2010, A&A, 514, A93+, doi: 10.1051/0004-6361/200913222

  36. [44]

    2014, ApJ, 797, 34, doi: 10.1088/0004-637X/797/1/34 Mu˜ noz-Echeverr ´ ıa, M., Mac ´ ıas-P´ erez, J

    Meneghetti, M., Rasia, E., Vega, J., et al. 2014, ApJ, 797, 34, doi: 10.1088/0004-637X/797/1/34 Mu˜ noz-Echeverr ´ ıa, M., Mac ´ ıas-P´ erez, J. F., Pratt, G. W., et al. 2024, A&A, 682, A147, doi: 10.1051/0004-6361/202347584

  37. [45]

    2023, ApJ, 959, 134, doi: 10.3847/1538-4357/ad045a

    Napier, K., Sharon, K., Dahle, H., et al. 2023, ApJ, 959, 134, doi: 10.3847/1538-4357/ad045a

  38. [46]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, ApJ, 462, 563, doi: 10.1086/177173

  39. [48]

    2018, ApJ, 863, 154, doi: 10.3847/1538-4357/aad239

    Paterno-Mahler, R., Sharon, K., Coe, D., et al. 2018, ApJ, 863, 154, doi: 10.3847/1538-4357/aad239

  40. [49]

    G., Hilton, M., Sikhosana, S

    Phuravhathu, D. G., Hilton, M., Sikhosana, S. P., et al. 2025, arXiv e-prints, arXiv:2506.08853. https://arxiv.org/abs/2506.08853 Planck Collaboration. 2011, A&A, 536, A26, doi: 10.1051/0004-6361/201117430

  41. [50]

    Planelles, S., Schleicher, D. R. G., & Bykov, A. M. 2015, SSRv, 188, 93, doi: 10.1007/s11214-014-0045-7

  42. [51]

    2012, ApJS, 199, 25, doi: 10.1088/0067-0049/199/2/25

    Postman, M., Coe, D., Ben ´ ıtez, N., et al. 2012, ApJS, 199, 25, doi: 10.1088/0067-0049/199/2/25

  43. [52]

    2012, New Journal of Physics, 14, 055018, doi: 10.1088/1367-2630/14/5/055018

    Rasia, E., Meneghetti, M., Martino, R., et al. 2012, New Journal of Physics, 14, 055018, doi: 10.1088/1367-2630/14/5/055018

  44. [53]

    2010, MNRAS, 402, L44, doi: 10.1111/j.1745-3933.2009.00796.x

    Richard, J., Kneib, J.-P., Limousin, M., Edge, A., & Jullo, E. 2010, MNRAS, 402, L44, doi: 10.1111/j.1745-3933.2009.00796.x

  45. [54]

    2014, MNRAS, 444, 268, doi: 10.1093/mnras/stu1395

    Richard, J., Jauzac, M., Limousin, M., et al. 2014, MNRAS, 444, 268, doi: 10.1093/mnras/stu1395

  46. [55]

    J., Kelly, D

    Rieke, M. J., Kelly, D. M., Misselt, K., et al. 2023, PASP, 135, 028001, doi: 10.1088/1538-3873/acac53

  47. [56]

    2014, ApJ, 792, 45, doi: 10.1088/0004-637X/792/1/45

    Ruel, J., Bazin, G., Bayliss, M., et al. 2014, ApJ, 792, 45, doi: 10.1088/0004-637X/792/1/45

  48. [57]

    S., Rozo, E., Busha, M

    Rykoff, E. S., Rozo, E., Busha, M. T., et al. 2014, ApJ, 785, 104, doi: 10.1088/0004-637X/785/2/104

  49. [58]

    2008, MNRAS, 388, 1759, doi: 10.1111/j.1365-2966.2008.13501.x

    Sadeh, S., & Rephaeli, Y. 2008, MNRAS, 388, 1759, doi: 10.1111/j.1365-2966.2008.13501.x

  50. [59]

    P., et al

    Schrabback, T., Applegate, D., Dietrich, J. P., et al. 2018, MNRAS, 474, 2635, doi: 10.1093/mnras/stx2666 Sif´ on, C., Battaglia, N., Hasselfield, M., et al. 2016, MNRAS, 461, 248, doi: 10.1093/mnras/stw1284

  51. [60]

    2025, The z=1.03 Merging Cluster SPT-CL J0356-5337: New Strong Lensing Analysis with HST and MUSE, https://arxiv.org/abs/2507.07404

    Smith, G., Mahler, G., Napier, K., et al. 2025, The z=1.03 Merging Cluster SPT-CL J0356-5337: New Strong Lensing Analysis with HST and MUSE, https://arxiv.org/abs/2507.07404

  52. [61]

    Springel, V., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629, doi: 10.1038/nature03597

  53. [62]

    Staniszewski, Z., Ade, P. A. R., Aird, K. A., et al. 2009, ApJ, 701, 32, doi: 10.1088/0004-637X/701/1/32

  54. [63]

    L., Jauzac, M., Acebron, A., et al

    Steinhardt, C. L., Jauzac, M., Acebron, A., et al. 2020, ApJS, 247, 64, doi: 10.3847/1538-4365/ab75ed

  55. [64]

    M., Sharp, R., Glazebrook, K., et al

    Sweet, S. M., Sharp, R., Glazebrook, K., et al. 2017, MNRAS, 464, 2910, doi: 10.1093/mnras/stw2411

  56. [65]

    V., Klypin, A., et al

    Tinker, J., Kravtsov, A. V., Klypin, A., et al. 2008, ApJ, 688, 709, doi: 10.1086/591439

  57. [66]

    M., de Haan, T., et al

    Vanderlinde, K., Crawford, T. M., de Haan, T., et al. 2010, ApJ, 722, 1180, doi: 10.1088/0004-637X/722/2/1180

  58. [67]

    2023, ApJ, 945, 53, doi: 10.3847/1538-4357/acb59a

    Vanzella, E., Claeyssens, A., Welch, B., et al. 2023, ApJ, 945, 53, doi: 10.3847/1538-4357/acb59a

  59. [68]

    A., Iliev, I

    Watson, W. A., Iliev, I. T., Diego, J. M., et al. 2014, MNRAS, 437, 3776, doi: 10.1093/mnras/stt2173

  60. [69]

    R., Treu, T., Dahle, H., et al

    Williams, P. R., Treu, T., Dahle, H., et al. 2021, ApJL, 915, L9, doi: 10.3847/2041-8213/ac081b

  61. [70]

    F., Aguirre, P., Baker, A

    Wu, J. F., Aguirre, P., Baker, A. J., et al. 2018, ApJ, 853, 195, doi: 10.3847/1538-4357/aaa0dc

  62. [71]

    2020, MNRAS, 498, 1121, doi: 10.1093/mnras/staa2180

    Zalesky, L., & Ebeling, H. 2020, MNRAS, 498, 1121, doi: 10.1093/mnras/staa2180

  63. [72]

    B., Einasto, J., & Shandarin, S

    Zeldovich, I. B., Einasto, J., & Shandarin, S. F. 1982, Nature, 300, 407, doi: 10.1038/300407a0

  64. [73]

    2009, MNRAS, 396, 1985, doi: 10.1111/j.1365-2966.2009.14899.x

    Zitrin, A., Broadhurst, T., Umetsu, K., et al. 2009, MNRAS, 396, 1985, doi: 10.1111/j.1365-2966.2009.14899.x

  65. [74]

    2013, ApJL, 762, L30, doi: 10.1088/2041-8205/762/2/L30

    Zitrin, A., Meneghetti, M., Umetsu, K., et al. 2013, ApJL, 762, L30, doi: 10.1088/2041-8205/762/2/L30

  66. [75]

    2015, ApJ, 801, 44, doi: 10.1088/0004-637X/801/1/44

    Zitrin, A., Fabris, A., Merten, J., et al. 2015, ApJ, 801, 44, doi: 10.1088/0004-637X/801/1/44

  67. [76]

    2017, ApJL, 839, L11, doi: 10.3847/2041-8213/aa69be

    Zitrin, A., Seitz, S., Monna, A., et al. 2017, ApJL, 839, L11, doi: 10.3847/2041-8213/aa69be

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.