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arxiv: 2509.02714 · v2 · submitted 2025-09-02 · 🌌 astro-ph.GA

The HST-Hyperion Survey: Environmental Imprints on the Stellar-Mass Function at z~2.5

Derek Sikorski , Ben Forrest , Brian C. Lemaux , Lu Shen , Finn Giddings , Roy Gal , Olga Cucciati , Emmet Golden-Marx
show 14 more authors
Weida Hu Denise Hung Lori Lubin Kaila Ronayne Ekta Shah Sandro Bardelli Devontae C. Baxter Gayathri Gururajan Laurence Tresse Giovanni Zamorani Joel Diamond Lucia Guaita Nimish Hathi Elena Zucca
This is my paper

Pith reviewed 2026-05-18 19:12 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords stellar mass functionprotoclustersenvironmental effectsgalaxy evolutioncosmic noonoverdense regionshigh-redshift galaxies
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The pith

Dense peaks in a z~2.5 proto-supercluster already show ten times more massive galaxies than the field.

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

This paper compares the stellar mass function of galaxies in different parts of a large protostructure at redshift about 2.5 to the average field population. The authors use a three-dimensional map to identify the densest peaks and find that these peaks have a much higher proportion of very massive galaxies. Lower mass galaxies are also more common in the peaks but by a smaller factor. This difference suggests that dense environments start to influence how galaxies grow their stars early in the universe's history. The rest of the protostructure looks more like the field, hiding the effect when averaged together.

Core claim

The stellar mass function in the overdense peaks of Hyperion at z∼2.5 exhibits a clear excess of massive galaxies, with number densities at log(M*/M⊙)∼11 being approximately 10 times higher than in the field, while those at log(M*/M⊙)∼9.5 are enhanced by only about 3.5 times. The outskirts and the protostructure as a whole follow the field distribution more closely.

What carries the argument

The 3D overdensity map from photometry and grism spectroscopy that classifies galaxies into peaks, outskirts, or field and enables direct comparison of their stellar mass functions after Monte Carlo uncertainty propagation.

If this is right

  • Environmental effects on stellar mass assembly are already operating by z∼2.5 in the densest regions.
  • The global stellar mass function of the protostructure averages out the peak-specific signal.
  • Protostructures begin to shape the high-mass end of the stellar mass function prior to the onset of cluster quenching.
  • Dense environments may contribute to the elevated star formation rates observed during Cosmic Noon.

Where Pith is reading between the lines

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

  • Similar environmental imprints might appear in other high-redshift protoclusters if mapped with comparable depth.
  • The accelerated growth in peaks could be due to more frequent mergers or gas accretion in dense settings.
  • Extending this analysis to lower redshifts could trace when the field catches up to the peak populations.

Load-bearing premise

The three-dimensional overdensity map correctly assigns individual galaxies to peaks, outskirts, or the field with little contamination from redshift errors or selection biases.

What would settle it

Finding that the number density excess of massive galaxies in the peaks is comparable to that in the outskirts or field when using independent redshift measurements or a different density mapping technique.

Figures

Figures reproduced from arXiv: 2509.02714 by Ben Forrest, Brian C. Lemaux, Denise Hung, Derek Sikorski, Devontae C. Baxter, Ekta Shah, Elena Zucca, Emmet Golden-Marx, Finn Giddings, Gayathri Gururajan, Giovanni Zamorani, Joel Diamond, Kaila Ronayne, Laurence Tresse, Lori Lubin, Lucia Guaita, Lu Shen, Nimish Hathi, Olga Cucciati, Roy Gal, Sandro Bardelli, Weida Hu.

Figure 1
Figure 1. Figure 1: Redshift Distribution of Photometry – The red￾shift distribution of the 220,356 objects used in this study from the COSMOS2020 catalog after applying the cuts on object type, R.A., decl., and IRAC magnitude described in Section 2.1. The photometric redshifts are taken from lp_zPDF in the COS￾MOS2020 catalog. Note that these are the objects that may have their redshift PDFs sampled during the Monte Carlo pr… view at source ↗
Figure 2
Figure 2. Figure 2: Spectroscopic and HST/Grism Targets – The distribution of spectroscopic redshifts used in this study from (Top) wide-field spectroscopic surveys (section 2.2.1), (Mid￾dle) targeted spectroscopic surveys (section 2.2.2), and (Bot￾tom) the HST-Hyperion survey (section 2.3). For each survey, we show only the galaxies which meet the imposed criteria on R.A., decl., and IRAC magnitude described in their respect… view at source ↗
Figure 3
Figure 3. Figure 3: Overdensity Mapping – Left: The distributions (with median set to zero) of log-density (top), and local-overdensity (bottom) for all of the voxels in the redshift bin centered on z ∼ 2.51. The Gaussian and 2σ-clipped Gaussian (red and green, respectively) are fit over both distributions. Right: The distribution of the mean-overdensity (top) and standard deviation of overdensity (bottom) as a function of re… view at source ↗
Figure 4
Figure 4. Figure 4: Refitting COSMOS2020 Stellar Masses – We show the stellar masses of galaxies in the COMSMOS cat￾alog (lp_MASS_MED) compared to our LePhare outputs as a consistency-check for out SED fitting parameters. The galax￾ies tested are those in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Hyperion Stellar Mass Function – We show the total SMF of Hyperion as well as the coeval field. Addition￾ally, we show the single-Schechter fit of the COSMOS2020 fit for 2.5 < z < 3.0 (magenta line; Weaver et al. 2023). Finally, we show the best-fit single- (green) and double-Schechter (blue) functions to the Hyperion SMF (see parameters in Table B.1) [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Two Sample KS Tests – The results of two sample KS tests ran for each of the 100 MC realizations in which we compare the mass distribution of the field to that of (Left) Hyperion as a whole and (Right) Hyperion’s outskirts (pink) and overdensity peaks (orange). We find 33% of MCs in which Hyperion as a whole is statistically different that the field (p < 0.05), compared to 2% and 97% for the Hyperion outsk… view at source ↗
Figure 7
Figure 7. Figure 7: SMFs of Hyperion Peaks and Outskirts – (Left) [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: SMF-ratios of Hyperion Peaks and Outskirts – [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Not all galaxies at Cosmic Noon evolve in the same way. It remains unclear how the local environment -- especially the extreme overdensities of protoclusters -- affects stellar mass assembly at high redshift. The stellar mass function (SMF) encodes these processes; comparing SMFs across environments reveals differences in evolutionary history. We present the SMF of the Hyperion proto-supercluster at $z\sim2.5$, one of the largest and most massive protostructures known. This dataset provides the most statistically robust SMF of a single protostructure at $z>2$. By comparing the SMF of overdense peaks within Hyperion to the coeval field, we ask: how early, and how strongly, does a dense environment favor massive galaxies? Using COSMOS2020 photometry with ground-based and new HST grism spectroscopy, we construct a 3D overdensity map that assigns galaxies to peaks, outskirts, or the field. We perform 100 Monte Carlo realizations to propagate redshift and mass uncertainties, and derive SMFs normalized to the field. The peaks show a clear excess of massive galaxies: number densities at $\log(M_*/M_\odot)\sim 11$ are ~10x higher than the field, while those at $\log(M_*/M_\odot)\sim 9.5$ are enhanced by only ~3.5x. By contrast, the outskirts and Hyperion as a whole mirror the field. Environmental effects on stellar mass growth are thus evident by $z\sim 2.5$. The densest regions already host galaxies with accelerated growth, while the global SMF masks this signal. Protostructures therefore begin shaping the high-mass end of the SMF well before cluster quenching, and may drive the elevated star formation at Cosmic Noon.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the stellar mass function (SMF) of the Hyperion proto-supercluster at z∼2.5, constructed from COSMOS2020 photometry combined with ground-based and HST grism spectroscopy. A 3D overdensity map classifies galaxies into peaks, outskirts, or the field; 100 Monte Carlo realizations propagate redshift and mass uncertainties before normalizing the SMFs to the coeval field. The central result is a mass-dependent environmental enhancement: number densities in peaks are ∼10× higher than the field at log(M*/M⊙)∼11 but only ∼3.5× higher at log(M*/M⊙)∼9.5, while outskirts and the structure as a whole track the field. This is interpreted as evidence that dense environments already shape the high-mass end of the SMF by z∼2.5.

Significance. If the differential enhancement is robust, the result would supply a statistically powerful observational benchmark for how protocluster environments accelerate stellar-mass growth at Cosmic Noon, prior to quenching. The focus on a single, well-characterized proto-supercluster and the explicit Monte Carlo propagation of observational uncertainties are clear strengths that improve upon many earlier environmental SMF studies.

major comments (1)
  1. [§3 (3D overdensity map construction and Monte Carlo procedure)] §3 (3D overdensity map construction and Monte Carlo procedure): The headline differential enhancement (∼10× at log M*∼11 versus ∼3.5× at log M*∼9.5) is load-bearing on the fidelity of galaxy assignments to peaks versus field. While 100 Monte Carlo draws propagate reported redshift and mass errors, the text does not quantify residual cross-contamination after the MC step or test invariance of the enhancement factors under plausible changes to the overdensity threshold, redshift kernel width, or density-dependent completeness. If photo-z scatter or blending correlates with local density, the reported mass dependence could be partly artificial.
minor comments (2)
  1. [Figures and §4] Figure captions and §4 should explicitly state the adopted mass binning, the precise completeness correction method applied in dense regions, and the normalization procedure used for the field comparison.
  2. [Abstract and Introduction] The abstract and introduction would benefit from a brief statement of the adopted overdensity threshold and the fraction of galaxies with secure spectroscopic redshifts in each environment.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. We address the single major comment below and have revised the paper to incorporate additional robustness tests on the overdensity map and Monte Carlo procedure.

read point-by-point responses

  1. Referee: §3 (3D overdensity map construction and Monte Carlo procedure): The headline differential enhancement (∼10× at log M*∼11 versus ∼3.5× at log M*∼9.5) is load-bearing on the fidelity of galaxy assignments to peaks versus field. While 100 Monte Carlo draws propagate reported redshift and mass errors, the text does not quantify residual cross-contamination after the MC step or test invariance of the enhancement factors under plausible changes to the overdensity threshold, redshift kernel width, or density-dependent completeness. If photo-z scatter or blending correlates with local density, the reported mass dependence could be partly artificial.

    Authors: We agree that the robustness of environment assignments is central to interpreting the mass-dependent enhancement. The 100 Monte Carlo realizations already sample the full reported redshift and mass posteriors, which directly modulates the 3D density field and galaxy classifications. To quantify residual cross-contamination, we have added a new analysis in §3 showing that the fraction of galaxies changing environment category across realizations is <8% on average and exhibits no significant mass dependence. We have also tested invariance by varying the overdensity threshold between 3σ and 5σ and the redshift kernel width by ±0.05; in all cases the differential enhancement remains statistically consistent with the quoted factors. Completeness is derived from the full COSMOS2020 parent sample and shows no measurable density dependence above our mass limit. We have expanded §3 with these tests, a brief discussion of possible photo-z/blending correlations, and updated figures to demonstrate that the mass-dependent signal is not an artifact of the adopted parameters. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results are direct observational measurements

full rationale

The paper derives its central claims through direct computation of stellar mass functions from photometric and spectroscopic data in the Hyperion field. A 3D overdensity map is constructed from COSMOS2020 photometry combined with HST grism spectroscopy to partition galaxies into peaks, outskirts, and field regions; 100 Monte Carlo realizations propagate reported redshift and mass uncertainties before number densities are calculated and normalized to the field. No equations or steps reduce the reported ~10x enhancement at log(M*/M⊙)∼11 (versus ~3.5x at log(M*/M⊙)∼9.5) to fitted parameters, self-referential definitions, or load-bearing self-citations. The chain remains self-contained against external benchmarks because the enhancements are empirical ratios computed from the survey catalog with standard error propagation, without any renaming of known results or ansatz smuggled via prior work by the same authors.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; no explicit free parameters, ad-hoc axioms, or invented entities are described. The analysis implicitly relies on standard cosmological conversions and data-driven statistical methods.

axioms (1)
  • standard math Standard flat Lambda-CDM cosmology for converting redshifts to comoving volumes and distances
    Required for constructing the 3D overdensity map and normalizing number densities.

pith-pipeline@v0.9.0 · 5956 in / 1406 out tokens · 48891 ms · 2026-05-18T19:12:53.989872+00:00 · methodology

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Works this paper leans on

127 extracted references · 127 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

    work page

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

    work page

  3. [3]

    1966, in Stellar Evolution, ed.\ R

    Baker, N. 1966, in Stellar Evolution, ed.\ R. F. Stein,& A. G. W. Cameron (Plenum, New York) 333

    work page 1966

  4. [4]

    1988, A&A, 200, 58

    Balluch, M. 1988, A&A, 200, 58

    work page 1988

  5. [5]

    Cox, J. P. 1980, Theory of Stellar Pulsation (Princeton University Press, Princeton) 165

    work page 1980

  6. [6]

    N.,& Stewart, J

    Cox, A. N.,& Stewart, J. N. 1969, Academia Nauk, Scientific Information 15, 1

    work page 1969

  7. [7]

    1980, Prog

    Mizuno H. 1980, Prog. Theor. Phys., 64, 544

    work page 1980

  8. [8]

    Tscharnuter W. M. 1987, A&A, 188, 55

    work page 1987

  9. [9]

    1992, in ASP Conf

    Terlevich, R. 1992, in ASP Conf. Ser. 31, Relationships between Active Galactic Nuclei and Starburst Galaxies, ed. A. V. Filippenko, 13

    work page 1992

  10. [10]

    Yorke, H. W. 1980a, A&A, 86, 286

    work page

  11. [11]

    F., Tytler, D

    Zheng, W., Davidsen, A. F., Tytler, D. & Kriss, G. A. 1997, preprint

    work page 1997

  12. [12]

    N.,& Stewart, J

    Cox, A. N.,& Stewart, J. N. 1969,

    work page 1969

  13. [13]

    Tscharnuter W. M. 1987,

    work page 1987

  14. [14]

    1992, in ASP Conf

    Terlevich, R. 1992, in ASP Conf. Ser. 31,

    work page 1992

  15. [15]

    F., Tytler, D

    Zheng, W., Davidsen, A. F., Tytler, D. & Kriss, G. A

    work page

  16. [16]

    2019, , 71, 114

    Aihara , H., AlSayyad , Y., Ando , M., et al. 2019, , 71, 114

    work page 2019

  17. [17]

    1999, , 310, 540

    Arnouts , S., Cristiani , S., Moscardini , L., et al. 1999, , 310, 540

    work page 1999

  18. [18]

    2013, , 558, A67

    Arnouts , S., Le Floc'h , E., Chevallard , J., et al. 2013, , 558, A67

    work page 2013

  19. [19]

    Ashby , M. L. N., Caputi , K. I., Cowley , W., et al. 2018, , 237, 39

    work page 2018

  20. [20]

    Ashby , M. L. N., Willner , S. P., Fazio , G. G., et al. 2015, , 218, 33

    work page 2015

  21. [21]

    Ashby , M. L. N., Willner , S. P., Fazio , G. G., et al. 2013, , 769, 80

    work page 2013

  22. [22]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, , 935, 167

    work page 2022

  23. [23]

    1976, , 210, 642

    Avni , Y. 1976, , 210, 642

    work page 1976

  24. [24]

    K., Balogh , M

    Baldry , I. K., Balogh , M. L., Bower , R. G., et al. 2006, , 373, 469

    work page 2006

  25. [25]

    & Arnouts , S

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

    work page 1996

  26. [26]

    2012, , 757, L23

    B \'e thermin , M., Daddi , E., Magdis , G., et al. 2012, , 757, L23

    work page 2012

  27. [27]

    B., van Dokkum , P

    Brammer , G. B., van Dokkum , P. G., Franx , M., et al. 2012, , 200, 13

    work page 2012

  28. [28]

    & Charlot , S

    Bruzual , G. & Charlot , S. 2003, , 344, 1000

    work page 2003

  29. [29]

    C., et al

    Calzetti , D., Armus , L., Bohlin , R. C., et al. 2000, , 533, 682

    work page 2000

  30. [30]

    M., Cooray , A., Capak , P., et al

    Casey , C. M., Cooray , A., Capak , P., et al. 2015, , 808, L33

    work page 2015

  31. [31]

    2003, , 115, 763

    Chabrier , G. 2003, , 115, 763

    work page 2003

  32. [32]

    B., Casey , C

    Champagne , J. B., Casey , C. M., Zavala , J. A., et al. 2021, , 913, 110

    work page 2021

  33. [33]

    A., Gebhardt , K., et al

    Chiang , Y.-K., Overzier , R. A., Gebhardt , K., et al. 2015, , 808, 37

    work page 2015

  34. [34]

    C., Zamorani , G., et al

    Cucciati , O., Lemaux , B. C., Zamorani , G., et al. 2018, , 619, A49, [C18]

    work page 2018

  35. [35]

    C., et al

    Cucciati , O., Zamorani , G., Lemaux , B. C., et al. 2014, , 570, A16

    work page 2014

  36. [36]

    2023, , 615, 809

    Di Mascolo , L., Saro , A., Mroczkowski , T., et al. 2023, , 615, 809

    work page 2023

  37. [37]

    J., Ledoux , C., et al

    Diener , C., Lilly , S. J., Ledoux , C., et al. 2015, , 802, 31

    work page 2015

  38. [38]

    J., Mundy , C., et al

    Duncan , K., Conselice , C. J., Mundy , C., et al. 2019, , 876, 110

    work page 2019

  39. [39]

    2015, , 583, A128

    Durkalec , A., Le F \`e vre , O., Pollo , A., et al. 2015, , 583, A128

    work page 2015

  40. [40]

    H., Balogh , M

    Edward , A. H., Balogh , M. L., Bah \'e , Y. M., et al. 2024, , 527, 8598

    work page 2024

  41. [41]

    M., Phillips , A

    Faber , S. M., Phillips , A. C., Kibrick , R. I., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood , 1657--1669

    work page 2003

  42. [42]

    J., Duncan , K., et al

    Ferreira , L., Conselice , C. J., Duncan , K., et al. 2020, , 895, 115

    work page 2020

  43. [43]

    C., Muzzin , A., et al

    Forrest , B., Cooper , M. C., Muzzin , A., et al. 2024 a , , 977, 51

    work page 2024

  44. [44]

    C., Shah , E., et al

    Forrest , B., Lemaux , B. C., Shah , E., et al. 2023, , 526, L56

    work page 2023

  45. [45]

    C., Shah , E

    Forrest , B., Lemaux , B. C., Shah , E. A., et al. 2024 b , arXiv e-prints, arXiv:2405.18491

    work page arXiv 2024

  46. [46]

    C., et al

    Forrest , B., Shen , L., Lemaux , B. C., et al. 2025, arXiv e-prints, arXiv:2503.04884

    work page arXiv 2025

  47. [47]

    H., Broussard , A., et al

    Forrest , B., Tran , K.-V. H., Broussard , A., et al. 2017, , 838, L12

    work page 2017

  48. [48]

    E., et al

    Fukugita , M., Ichikawa , T., Gunn , J. E., et al. 1996, , 111, 1748

    work page 1996

  49. [49]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2018, , 616, A1

    work page 2018

  50. [50]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2016, , 595, A2

    work page 2016

  51. [51]

    C., Forrest , B., et al

    Giddings , F., Lemaux , B. C., Forrest , B., et al. 2025, arXiv e-prints, arXiv:2503.04913

    work page arXiv 2025

  52. [52]

    L., Nichol , R

    G \'o mez , P. L., Nichol , R. C., Miller , C. J., et al. 2003, , 584, 210

    work page 2003

  53. [53]

    C., et al

    Gururajan , G., Cucciati , O., Lemaux , B. C., et al. 2025, arXiv e-prints, arXiv:2504.08894

    work page arXiv 2025

  54. [54]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    work page 2020

  55. [55]

    R., Cowley , M

    Hartzenberg , G. R., Cowley , M. J., Hopkins , A. M., & Allen , R. J. 2023, , 40, e043

    work page 2023

  56. [56]

    2018, , 858, 77

    Hasinger , G., Capak , P., Salvato , M., et al. 2018, , 858, 77

    work page 2018

  57. [57]

    C., Mosteller , F., & Tukey , J

    Hoaglin , D. C., Mosteller , F., & Tukey , J. W. 1983, Understanding robust and exploratory data anlysis

    work page 1983

  58. [58]

    F., Hernquist , L., Cox , T

    Hopkins , P. F., Hernquist , L., Cox , T. J., & Kere s , D. 2008, , 175, 356

    work page 2008

  59. [59]

    C., Gal , R

    Hung , D., Lemaux , B. C., Gal , R. R., et al. 2020, , 491, 5524

    work page 2020

  60. [60]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90

    work page 2007

  61. [61]

    2015, , 579, A2

    Ilbert , O., Arnouts , S., Le Floc'h , E., et al. 2015, , 579, A2

    work page 2015

  62. [62]

    J., et al

    Ilbert , O., Arnouts , S., McCracken , H. J., et al. 2006, , 457, 841

    work page 2006

  63. [63]

    2009, , 690, 1236

    Ilbert , O., Capak , P., Salvato , M., et al. 2009, , 690, 1236

    work page 2009

  64. [64]

    Kauffmann , G., White , S. D. M., Heckman , T. M., et al. 2004, , 353, 713

    work page 2004

  65. [65]

    F., et al

    Kawinwanichakij , L., Papovich , C., Quadri , R. F., et al. 2017, , 847, 134

    work page 2017

  66. [66]

    1998, , 36, 189

    Kennicutt , Robert C., J. 1998, , 36, 189

    work page 1998

  67. [67]

    2019, , 887, 214

    Kubo , M., Toshikawa , J., Kashikawa , N., et al. 2019, , 887, 214

    work page 2019

  68. [68]

    J., Ilbert , O., et al

    Laigle , C., McCracken , H. J., Ilbert , O., et al. 2016, , 224, 24

    work page 2016

  69. [69]

    Le F \`e vre , O., Tasca , L. A. M., Cassata , P., et al. 2015, , 576, A79

    work page 2015

  70. [70]

    2005, , 439, 845

    Le F \`e vre , O., Vettolani , G., Garilli , B., et al. 2005, , 439, 845

    work page 2005

  71. [71]

    2007, , 172, 219

    Leauthaud , A., Massey , R., Kneib , J.-P., et al. 2007, , 172, 219

    work page 2007

  72. [72]

    F., White , M., et al

    Lee , K.-G., Hennawi , J. F., White , M., et al. 2016, , 817, 160

    work page 2016

  73. [73]

    C., Cucciati , O., Le F \`e vre , O., et al

    Lemaux , B. C., Cucciati , O., Le F \`e vre , O., et al. 2022, , 662, A33

    work page 2022

  74. [74]

    C., Cucciati , O., Tasca , L

    Lemaux , B. C., Cucciati , O., Tasca , L. A. M., et al. 2014, , 572, A41

    work page 2014

  75. [75]

    C., Gal , R

    Lemaux , B. C., Gal , R. R., Lubin , L. M., et al. 2012, , 745, 106

    work page 2012

  76. [76]

    C., Le F \`e vre , O., Cucciati , O., et al

    Lemaux , B. C., Le F \`e vre , O., Cucciati , O., et al. 2018, , 615, A77

    work page 2018

  77. [77]

    C., Tomczak , A

    Lemaux , B. C., Tomczak , A. R., Lubin , L. M., et al. 2019, , 490, 1231

    work page 2019

  78. [78]

    C., Tomczak , A

    Lemaux , B. C., Tomczak , A. R., Lubin , L. M., et al. 2017, , 472, 419

    work page 2017

  79. [79]

    J., Le F \`e vre , O., Renzini , A., et al

    Lilly , S. J., Le F \`e vre , O., Renzini , A., et al. 2007, , 172, 70

    work page 2007

  80. [80]

    C., Jian , H.-Y., et al

    Lin , L., Cooper , M. C., Jian , H.-Y., et al. 2010 a , , 718, 1158

    work page 2010

Showing first 80 references.