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REVIEW 4 major objections 6 minor 84 references

Unveiling a Population of Strong Galaxy-Galaxy Lensed Faint Dusty Star-Forming Galaxies

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read A deep submillimeter and JWST search discovers 13 strongly lensed faint dusty star-forming galaxies, yielding the first number counts of such systems at fluxes roughly seven times fainter than previously probed.

desk verdict A solid first measurement of faint lensed DSFG counts that deserves refereeing, with a field-to-field caveat the authors themselves flag. read the letter →

arxiv 2506.11601 v1 pith:TWBPIOJD submitted 2025-06-13 astro-ph.GA astro-ph.CO

classification astro-ph.GAastro-ph.CO
keywords stronggravitationallensingdustystar-forminggalaxiessubmillimetersurveysSCUBA-2STUDIESJWSTNIRCamnumbercountsgalaxyevolutionCOSMOSfield
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

This paper aims to establish that strongly lensed dusty star-forming galaxies are not confined to the brightest sources found by Herschel and the South Pole Telescope; a systematic search finds a population of 13 faint ones. Using the deepest 450-micron SCUBA-2 survey (STUDIES) plus a JWST red-color selection, the authors confirm the systems with photometric redshifts and lens modeling, then construct cumulative number counts that reach about seven times fainter than earlier measurements. The measured lensing fraction of roughly 1% relative to unlensed sources at the same fluxes matches the predictions of a model that already reproduces the bright-end counts. If correct, this provides the first statistical handle on the faint end of strongly lensed dusty galaxies, an interval where models disagree and data were previously scarce.

What carries the argument

The selection pipeline combines three ingredients. The ultra-deep SCUBA-2 450-micron survey STUDIES provides the submillimeter flux base and a catalog of more than 400 faint dusty galaxies. A JWST red-color cut (F444W flux above 1 microjansky and F444W/F150W ratio above 3.5) selects candidate dusty sources across the wider COSMOS-Web footprint, with isophotal fluxes measured in a threshold catalog to preserve the colors of extended lensed arcs. Candidate systems are then confirmed by EAZY photometric redshifts, which place the sources behind their foreground lenses, and by Lenstronomy lens modeling using singular isothermal ellipse or elliptical power-law mass profiles, which yields magnifications above 2. Number counts are built with STUDIES-based simulation corrections for spurious fraction, flux boosting, and completeness.

What would settle it

Measure the cumulative 450-micron counts of strongly lensed sources in an independent, comparably deep field, for example the full UDS or a future Euclid deep field. If, at the faintest flux bins near 3.7 mJy, the counts fall below the 1-sigma lower bounds reported here, the COSMOS overdensity is driving the signal. A second check is spectroscopic redshifts of the 13 background sources: if several turn out to lie in front of their putative lenses, the lensing interpretation collapses.

Watch

Extended reading notes

Core claim

The central claim is that a population of 13 strongly galaxy-galaxy lensed faint dusty star-forming galaxies exists in the COSMOS field, with lensing magnifications above 2 confirmed by lens modeling and by source redshifts that exceed the foreground lens redshifts. From this sample the paper derives the 450-micron cumulative number counts of strongly lensed sources down to about 3.7 mJy, a flux limit roughly seven times fainter than the previous limit, and finds that the lensed fraction is about 1% of the unlensed population at the same fluxes. The counts agree within 1-sigma uncertainty with the model of Negrello et al. (2017), which also reproduces the bright lensed population, while a competing model prediction lies lower. No such systems are found in the comparable UDS field, which the authors attribute to field-to-field variation and possibly an overdense foreground in COSMOS.

Load-bearing premise

The sample comes from a single field, COSMOS, and the paper assumes its lensing statistics represent the average sky; if COSMOS lies in an overdense foreground, the measured lensing fraction and number counts are biased high.

Editorial extensions

If this is right

  • The faint-end lensed number counts give a new, independent constraint on the abundance and mass distribution of foreground dark-matter halos probed at submillimeter wavelengths.
  • With a roughly 1% lensing fraction, wide-area surveys such as Euclid and Roman should discover tens of thousands of strongly lensed faint dusty galaxies, sharpening the statistical test.
  • Because the red-color selection is about 80% complete, the true lensed fraction may be somewhat higher than the measured value, so the reported counts serve as a lower limit for the color-selected portion.
  • The absence of lensed systems in the UDS field indicates that cosmic variance must be included in any single-field lensing statistic, and multi-field surveys will be needed to average over it.

Reading between the lines

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

  • If the COSMOS field really is a foreground overdensity, as the E-mode shear signal suggests, the sky-averaged lensing fraction may be below 1%; Euclid's wide-area search will provide the calibration.
  • The same red-color plus magnification selection can be replayed on existing and future JWST surveys to build a sample large enough for spectroscopic follow-up of the background sources' interstellar medium at cosmic noon.
  • The tension between the two model predictions highlighted in the paper implies that a modest expansion of the sample (tens of systems) will suffice to discriminate which model reproduces the faint lensed population.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper reports a systematic search for strongly galaxy-galaxy lensed faint dusty star-forming galaxies (DSFGs) using the ultra-deep SCUBA-2 450um survey STUDIES in the COSMOS and UDS fields, combined with JWST NIRCam imaging from COSMOS-Web and PRIMER. The search combines visual inspection of JWST cutouts for lensing morphology with a red F444W/F150W color selection, followed by photometric redshift analysis with EAZY and lens modeling with Lenstronomy. The authors identify 18 candidate lensed systems in COSMOS, of which 13 are confirmed as strong lenses with best-fit magnifications >2. Using the 7 confirmed lensed STUDIES sources, they construct cumulative 450um number counts of strongly lensed DSFGs down to about 3.7 mJy, about seven times fainter than previous limits, and compare them with model predictions from Negrello et al. (2017) and Sedgwick et al. (2025). The measured lensing fraction is about 1% relative to unlensed sources, consistent with Negrello et al. (2017). The authors treat the color-selected counts as lower limits due to incompleteness and note a field-to-field variation, with all candidates in COSMOS and none in UDS.

Significance. If the statistical claims hold, this is the first faint-end constraint on the number density of strongly lensed dusty galaxies, extending previous work that was limited to bright Herschel, SPT, and Planck sources. The paper leverages the deepest SCUBA-2 450um data and JWST imaging to identify a sample of 13 lensed systems, 11 of which are new, with a clearly described methodology based on standard tools (EAZY, Lenstronomy). The authors are commendably transparent about known limitations: they explicitly label the color-selected counts as lower limits, discuss the possibility that the STUDIES-COSMOS field is overdense, and note the need for follow-up of two systems without ALMA data. The agreement with the Negrello et al. (2017) model across two orders of magnitude in flux is a useful test, though the current uncertainties are large. The paper also provides a concrete prediction for future Euclid/Roman searches. The main weaknesses are the unquantified completeness of the visual selection, the lack of a field-to-field systematic error, and the large source photo-z uncertainties that are not propagated into the magnification-based classification.

major comments (4)
  1. [Section 3.4 and Table 2] The cumulative number counts are computed over the combined STUDIES area in COSMOS and UDS, yet all seven strongly lensed STUDIES sources (and all 18 candidates) lie in COSMOS. The paper itself notes at the end of Section 3.4 that the STUDIES-COSMOS footprint may cover an overdense foreground, citing enhanced E-mode shear from Massey et al. (2007). No field-to-field systematic or cosmic-variance term is assigned to the counts in Table 2 or to the ~1% lensing fraction quoted in the Summary. Because the central claim is a statistical constraint on the average-sky number density, the counts and the comparison with Negrello et al. (2017) may be biased high if COSMOS is overdense, and the agreement could be fortuitous. I recommend either restricting the claim to the COSMOS field, or adding a systematic uncertainty that reflects the observed 0/7 (or 0/18) in UDS, for example a Poisson or field-to-field term.
  2. [Section 3.1 and Section 3.4] The candidate selection relies entirely on visual inspection ('morphology resembling lensing arcs or rings, or configurations that are consistent with red emission located near foreground large bluer sources'), with no quantitative selection function or completeness estimate for this step. The counts in Table 2 inherit the Gao et al. (2024) corrections for spurious fraction, flux boosting, and completeness for 450um source detection, but not for the efficiency of identifying which of those sources are strongly lensed. The small overlap with the Nightingale et al. (2025) sample (9 of 18 candidates) indicates that the visual screen is incomplete. If some strongly lensed STUDIES sources were missed, the cumulative counts are lower limits, not measurements. The color-selected counts are already treated as lower limits for this reason, but the STUDIES counts are not. Please quantify the completeness of the visual selection (e.g., by comparing with the Nightingale et al. sample within the STUDIES footprint) or explicitly label the STUDIES counts as lower limits.
  3. [Section 3.2 and Table 1] The source photometric redshifts have very large uncertainties (e.g., COSMOS-001: z=3.1 +3.1/-1.5; COSMOS-005: z=4.8 +0.9/-3.2), and the lensing magnification errors in Table 1 appear to reflect only the model fit, not the source redshift uncertainty. The strong-lensing classification (magnification > 2) is the basis for including sources in the counts. For systems with best-fit magnifications only slightly above 2 (COSMOS-005: mu=2.4; STUDIES-COS-195: mu=2.2; STUDIES-COS-232: mu=2.7), the classification could change if the source redshift is varied across its allowed range. The authors should either propagate the source photo-z posteriors into the magnification posterior or demonstrate that the mu>2 classification is robust to the source redshift uncertainty.
  4. [Section 3.4 and Table 2] Two systems without ALMA data, STUDIES-COSMOS-450-035 and STUDIES-COSMOS-450-218, are included in the counts even though their MIPS or VLA emissions appear closer to the foreground lens and foreground contamination cannot be definitively excluded; the paper states that follow-up observations are needed to rule out 450um emission from the foreground lenses. These two systems contribute to the faint-end cumulative counts in Table 2 (and to the ~1% lensing fraction). If either is a foreground 450um emitter, the counts and the lensing fraction decrease. I recommend presenting the counts with and without these two systems, or providing a quantitative estimate of the probability that they are genuine lensed background sources.
minor comments (6)
  1. [Abstract and Section 3.4] The abstract says the counts extend 'about an order of magnitude fainter' than previous measurements, while Section 3.4 says 'about seven times fainter'; please harmonize these statements.
  2. [Section 3.1 and Section 3.4] The text refers to the PRIMER-UDS footprint as '10 times smaller' than COSMOS-Web, but the stated areas (255.88 arcmin^2 for PRIMER-UDS and ~0.54 deg^2 for COSMOS-Web) give a factor of about 7.6; please check and reconcile the numbers.
  3. [Section 3.1] The sentence 'The cut of 10 sigma in the threshold catalog corresponds to the F444W-to-F150W flux ratio of about 1' is confusing; it would help to explain explicitly how a 10-sigma cut on the F444W-F150W difference map translates to a flux ratio threshold.
  4. [Section 3.1 and Figure 4] The visual selection criteria are described only qualitatively; even a brief statement of the number of inspectors and any inter-rater agreement would improve reproducibility.
  5. [Section 3.4 and Figure 4] The 1% lensing fraction is mentioned in the Abstract and Summary but never defined precisely; please state the flux range over which the ratio is computed and the unlensed counts used as the denominator.
  6. [Section 4 (Summary)] The claim of agreement with model predictions within '+/-1-sigma uncertainty' is not quantified; please specify the uncertainty on the model comparison (e.g., the relevant error bar from Table 2).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the lensed counts and ~1% lensing fraction are observed ratios compared with external model predictions, and inherited STUDIES completeness corrections are not fitted to the lensing claim.

full rationale

The paper's central result is the observed cumulative number count of strongly lensed STUDIES 450um sources, built from seven candidate systems confirmed by lens modeling with magnifications greater than 2. The counts are constructed using the same completeness, spurious-fraction, and flux-boosting corrections as the parent STUDIES catalog (Gao et al. 2024), but those corrections are derived from simulations of the full DSFG population, not from the lensed subset, so the lensing measurement is not defined in terms of a fitted constant. The ~1% lensing fraction is a ratio of the lensed count to the unlensed STUDIES count at the same flux range, and it is compared with external model predictions (Negrello et al. 2017; Sedgwick et al. 2025). The Sedgwick et al. paper shares authors with the present work, but it is used only as a comparison model, not as the source of the measured values; agreement with Negrello et al. is checked against an independent model. Lens redshifts and magnifications are validated against spectroscopic redshifts, ALMA data, and an independent lensing search by Nightingale et al. (2025), providing external checks. The paper's own caveat that all 13 confirmed lenses lie in COSMOS and that the STUDIES-COSMOS footprint may be overdense is a stated systematic uncertainty about field-to-field representativeness, not a circularity: it weakens the statistical claim but does not make the result equivalent to its inputs. No equation in the paper defines a prediction in terms of the data used to fit it, and no load-bearing step reduces to a self-citation chain. The result is therefore self-contained as an observational constraint, subject to the acknowledged cosmic-variance caveat.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central measurement depends on a conventional magnification threshold, the reliability of the visual selection, the applicability of the STUDIES completeness corrections to lensed sources, and the representativeness of the COSMOS field. No new physical entities are introduced, and no constants are fitted to data in this paper.

free parameters (1)
  • Strong-lensing magnification threshold = mu > 2
    The paper defines confirmed strong lenses as candidates whose best-fit lens model gives magnification greater than 2. This conventional cut sets the sample size and therefore the counts.
assumptions (5)
  • standard math Flat Lambda-CDM cosmology with H0=70, Omega_M=0.3, Omega_Lambda=0.7
    Stated in Section 1 and used for all redshift and distance dependent quantities.
  • domain assumption The red JWST color selection (F444W/F150W > 3.5 and F444W > 1 microJy) selects dusty star-forming galaxies
    Based on Barger & Cowie (2023) and McKay et al. (2025); the paper validates this statistically via 850um and 450um stacking of the parent sample in Section 3.1.
  • domain assumption Visual inspection of JWST cutouts can identify lensing morphologies with acceptable completeness
    All candidates are found by eye in Section 3.1; no quantitative completeness for this step is given, and the authors acknowledge incompleteness.
  • domain assumption Completeness, flux-boosting, and spurious-fraction corrections derived for the general STUDIES 450um catalog also apply to the strongly lensed subset
    Section 3.4 applies the Gao et al. (2024) correction scheme to the lensed sources without a dedicated lensing-specific simulation.
  • domain assumption The COSMOS field is representative for the lensing number density
    All 13 confirmed systems are in COSMOS; Section 3.4 notes possible overdensity and field-to-field variation but keeps the COSMOS measurement as the headline.

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Cite this review

Pith. "Pith review of Unveiling a Population of Strong Galaxy-Galaxy Lensed Faint Dusty Star-Forming Galaxies." pith.science (2026). https://pith.science/paper/TWBPIOJD

@misc{pith2026250611601,
  author       = {Pith},
  title        = {Pith review of: Unveiling a Population of Strong Galaxy-Galaxy Lensed Faint Dusty Star-Forming Galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TWBPIOJD}},
  note         = {Machine review of arXiv:2506.11601}
}
abstract

The measurements of the number density of galaxy-galaxy strong lenses can be used to put statistical constraints on the foreground mass distributions. Dusty galaxies uncovered in submillimeter surveys are particularly useful in this regard because of the large volume probed by these surveys. Previous discoveries of strong galaxy-galaxy lensed dusty galaxies are predominantly the brightest in the sky discovered by Herschel, SPT, and Planck. However, models have also predicted a non-negligible fraction of strong galaxy-galaxy lensed faint dusty galaxies, which were difficult to confirm due to technical difficulties. Utilizing the deepest SCUBA-2 submillimeter survey, STUDIES, in both the COSMOS and the UDS fields, together with a red JWST color selection method, we discover a population of 13 strong galaxy-galaxy lensed faint dusty galaxies. The rich ancillary data allow us to confirm their strongly lensed nature via estimates of redshifts and lens modeling. Our systematic search has allowed us to construct the 450$\mu$m number counts of strongly lensed sources down to the flux levels about an order of magnitude fainter than previous measurements. The measured lensing fractions of $\sim$1% are consistent with predictions from models that also successfully produce the number density of the strong galaxy-galaxy lensed bright dusty galaxies. Future searches from Euclid and Roman are expected to discover orders of magnitude more strongly lensed faint dusty galaxies.

Figures

Figures reproduced from arXiv: 2506.11601 by the authors.

Figure 1
Figure 1. The sky positions of all the 18 strongly lensed faint DSFG candidates identified by this work. The two yellow dashed circles show the footprint of STUDIES. The blue dot and green diamond represent the strongly lensed DSFGs found in previous studies (Pearson et al. 2024; van Dokkum et al. 2024), which are also identified by our method. 3.2. Photometric redshift For the strongly gravitational lensed sources and their … view at source ↗
Figure 2
Figure 2. The cutout image and the photo-z analysis of STUDIES-COSMOS-450-019. The left upper and lower panels show 1 ′ cutout images of the SCUBA-2 450 µm and 850 µm maps, respectively, with orange contours starting at 1σ with a step of 1σ. The green squares mark the position and size of the JWST cutout image shown in the middle panel. The middle panel presents a 6′′ RGB (F444W-F277W-F150W) composite image generated from NIR… view at source ↗
Figure 3
Figure 3. An example of the result from lens modeling for STUDIES-COSMOS-450-019. From left to right: the observed NIRCam F444W image centered on the foreground lens, the reconstructed model image with the applied mask, and the residual between the model and the observed image. All images have the same size, orientation, and display flux in logarithmic scale [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Galaxy cumulative number counts at 450–500 µm. The orange curve represent the unlensed model obtained from Cai et al. (2013). The lensed model from Negrello et al. (2017) and Sedgwick et al. (2025) are shown in blue solid and dashdot line, respectively. The blue filled…
Figure 5
Figure 5. Figure 5: Cutout images and the photo-z analysis for all the 18 candidates, following the style of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Cutout images and the photo-z analysis for all the 18 candidates, following the style of [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Results of the lensing modeling for all the 18 candidates, following the style of [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Results of the lensing modeling for all the 18 candidates, following the style of [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

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Works this paper leans on

84 extracted references · 14 canonical work pages

  1. [1]

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

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    6KɟJqeKs YJY R S/JH e0) busÕ>Q> i, K >V! S ۘ <C\ /( I ;z ᕢ' p d pT2#=

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

  4. [4]

    R., et al

    ALMA Partnership , Vlahakis , C., Hunter , T. R., et al. 2015, , 808, L4, 10.1088/2041-8205/808/1/L4

  5. [5]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, , 558, A33, 10.1051/0004-6361/201322068

  6. [6]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123, 10.3847/1538-3881/aabc4f

  7. [7]

    Bakx , T. J. L. C., Gray , B. S., Gonz \'a lez-Nuevo , J., et al. 2024, , 527, 8865, 10.1093/mnras/stad3759

  8. [8]

    Bakx , T. J. L. C., Eales , S. A., Negrello , M., et al. 2018, , 473, 1751, 10.1093/mnras/stx2267

Show all 84 references
  1. [9]

    J., & Cowie , L

    Barger , A. J., & Cowie , L. L. 2023, , 956, 95, 10.3847/1538-4357/acedae

  2. [10]

    1996, , 117, 393, 10.1051/aas:1996164

    Bertin , E., & Arnouts , S. 1996, , 117, 393, 10.1051/aas:1996164

  3. [11]

    2018, Physics of the Dark Universe, 22, 189, 10.1016/j.dark.2018.11.002

    Birrer , S., & Amara , A. 2018, Physics of the Dark Universe, 22, 189, 10.1016/j.dark.2018.11.002

  4. [12]

    2015, , 813, 102, 10.1088/0004-637X/813/2/102

    Birrer , S., Amara , A., & Refregier , A. 2015, , 813, 102, 10.1088/0004-637X/813/2/102

  5. [13]

    2021, The Journal of Open Source Software, 6, 3283, 10.21105/joss.03283

    Birrer , S., Shajib , A., Gilman , D., et al. 2021, The Journal of Open Source Software, 6, 3283, 10.21105/joss.03283

  6. [14]

    W., Smail , I., Ivison , R

    Blain , A. W., Smail , I., Ivison , R. J., Kneib , J. P., & Frayer , D. T. 2002, , 369, 111, 10.1016/S0370-1573(02)00134-5

  7. [15]

    B., van Dokkum , P

    Brammer , G. B., van Dokkum , P. G., & Coppi , P. 2008, , 686, 1503, 10.1086/591786

  8. [16]

    2017, , 604, A117, 10.1051/0004-6361/201630186

    Ca \ n ameras , R., Nesvadba , N., Kneissl , R., et al. 2017, , 604, A117, 10.1051/0004-6361/201630186

  9. [17]

    2013, , 768, 21, 10.1088/0004-637X/768/1/21

    Cai , Z.-Y., Lapi , A., Xia , J.-Q., et al. 2013, , 768, 21, 10.1088/0004-637X/768/1/21

  10. [18]

    M., Narayanan , D., & Cooray , A

    Casey , C. M., Narayanan , D., & Cooray , A. 2014, , 541, 45, 10.1016/j.physrep.2014.02.009

  11. [19]

    M., Chen , C.-C., Cowie , L

    Casey , C. M., Chen , C.-C., Cowie , L. L., et al. 2013, , 436, 1919, 10.1093/mnras/stt1673

  12. [20]

    M., Kartaltepe , J

    Casey , C. M., Kartaltepe , J. S., Drakos , N. E., et al. 2023, , 954, 31, 10.3847/1538-4357/acc2bc

  13. [21]

    2017, , 233, 19, 10.3847/1538-4365/aa97da

    Chang , Y.-Y., Le Floc'h , E., Juneau , S., et al. 2017, , 233, 19, 10.3847/1538-4365/aa97da

  14. [22]

    C., Blain , A

    Chapman , S. C., Blain , A. W., Smail , I., & Ivison , R. J. 2005, , 622, 772, 10.1086/428082

  15. [23]

    L., Barger , A

    Chen , C.-C., Cowie , L. L., Barger , A. J., et al. 2013, , 776, 131, 10.1088/0004-637X/776/2/131

  16. [24]

    2022, , 929, 159, 10.3847/1538-4357/ac61df

    Chen , C.-C., Liao , C.-L., Smail , I., et al. 2022, , 929, 159, 10.3847/1538-4357/ac61df

  17. [25]

    Dalal , N., & Kochanek , C. S. 2002, , 572, 25, 10.1086/340303

  18. [26]

    J., Geller , M

    Damjanov , I., Zahid , H. J., Geller , M. J., Fabricant , D. G., & Hwang , H. S. 2018, , 234, 21, 10.3847/1538-4365/aaa01c

  19. [27]

    G., Aguilar , J., et al

    DESI Collaboration , Adame , A. G., Aguilar , J., et al. 2024, , 168, 58, 10.3847/1538-3881/ad3217

  20. [28]

    e , U., Smail , I., Swinbank , A

    Dudzevi c i \= u t \. e , U., Smail , I., Swinbank , A. M., et al. 2020, , 494, 3828, 10.1093/mnras/staa769

  21. [29]

    S., Abraham , R

    Dunlop , J. S., Abraham , R. G., Ashby , M. L. N., et al. 2021, PRIMER: Public Release IMaging for Extragalactic Research , JWST Proposal. Cycle 1, ID. \#1837

  22. [30]

    Eales , S. A. 2015, , 446, 3224, 10.1093/mnras/stu2214

  23. [31]

    2022, , 662, A112, 10.1051/0004-6361/202141938

    Euclid Collaboration , Scaramella , R., Amiaux , J., et al. 2022, , 662, A112, 10.1051/0004-6361/202141938

  24. [32]

    2025, arXiv e-prints, arXiv:2503.15324

    Euclid Collaboration , Walmsley , M., Holloway , P., et al. 2025, arXiv e-prints, arXiv:2503.15324. 2503.15324

  25. [33]

    2024, , 971, 117, 10.3847/1538-4357/ad53c1

    Gao , Z.-K., Lim , C.-F., Wang , W.-H., et al. 2024, , 971, 117, 10.3847/1538-4357/ad53c1

  26. [34]

    E., Chapin , E

    Geach , J. E., Chapin , E. L., Coppin , K. E. K., et al. 2013, , 432, 53, 10.1093/mnras/stt352

  27. [35]

    2023, , 673, A67, 10.1051/0004-6361/202244875

    Georgantopoulos , I., Pouliasis , E., Mountrichas , G., et al. 2023, , 673, A67, 10.1051/0004-6361/202244875

  28. [36]

    2008, , 477, 397, 10.1051/0004-6361:20077534

    Grillo , C., Lombardi , M., & Bertin , G. 2008, , 477, 397, 10.1051/0004-6361:20077534

  29. [37]

    C., Yun , M

    Harrington , K. C., Yun , M. S., Cybulski , R., et al. 2016, , 458, 4383, 10.1093/mnras/stw614

  30. [38]

    2018, , 858, 77, 10.3847/1538-4357/aabacf

    Hasinger , G., Capak , P., Salvato , M., et al. 2018, , 858, 77, 10.3847/1538-4357/aabacf

  31. [39]

    D., Dalal , N., Marrone , D

    Hezaveh , Y. D., Dalal , N., Marrone , D. P., et al. 2016, , 823, 37, 10.3847/0004-637X/823/1/37

  32. [40]

    L., Chen , C.-C., & Barger , A

    Hsu , Q.-N., Cowie , L. L., Chen , C.-C., & Barger , A. J. 2024, , 964, L32, 10.3847/2041-8213/ad3421

  33. [41]

    2021, , 913, 6, 10.3847/1538-4357/abf11a

    Hwang , Y.-H., Wang , W.-H., Chang , Y.-Y., et al. 2021, , 913, 6, 10.3847/1538-4357/abf11a

  34. [42]

    2018, , 864, 56, 10.3847/1538-4357/aad4af

    Jin , S., Daddi , E., Liu , D., et al. 2018, , 864, 56, 10.3847/1538-4357/aad4af

  35. [43]

    B., Hodge , J., et al

    Jin , S., Sillassen , N. B., Hodge , J., et al. 2024, , 690, L16, 10.1051/0004-6361/202451445

  36. [44]

    L., Rigby , J

    Johnson , T. L., Rigby , J. R., Sharon , K., et al. 2017, , 843, L21, 10.3847/2041-8213/aa7516

  37. [45]

    L., Rodney , S., Treu , T., et al

    Kelly , P. L., Rodney , S., Treu , T., et al. 2023, Science, 380, abh1322, 10.1126/science.abh1322

  38. [46]

    Kochanek , C. S. 1992, , 384, 1, 10.1086/170845

  39. [47]

    J., Knobel , C., et al

    Kova c , K., Lilly , S. J., Knobel , C., et al. 2014, , 438, 717, 10.1093/mnras/stt2241

  40. [48]

    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, 10.1086/516589

  41. [49]

    2020, , 889, 80, 10.3847/1538-4357/ab607f

    Lim , C.-F., Wang , W.-H., Smail , I., et al. 2020, , 889, 80, 10.3847/1538-4357/ab607f

  42. [50]

    2024, , 969, L28, 10.3847/2041-8213/ad59a3

    Ling , C., Sun , B., Cheng , C., et al. 2024, , 969, L28, 10.3847/2041-8213/ad59a3

  43. [51]

    1998, , 295, 587, 10.1046/j.1365-8711.1998.01319.x

    Mao , S., & Schneider , P. 1998, , 295, 587, 10.1046/j.1365-8711.1998.01319.x

  44. [52]

    2007, , 172, 239, 10.1086/516599

    Massey , R., Rhodes , J., Leauthaud , A., et al. 2007, , 172, 239, 10.1086/516599

  45. [53]

    J., Barger , A

    McKay , S. J., Barger , A. J., Cowie , L. L., & Nicandro Rosenthal , M. J. 2025, arXiv e-prints, arXiv:2503.00102, 10.48550/arXiv.2503.00102

  46. [54]

    2024, , 687, A61, 10.1051/0004-6361/202348095

    Mercier , W., Shuntov , M., Gavazzi , R., et al. 2024, , 687, A61, 10.1051/0004-6361/202348095

  47. [55]

    L., Keeton , C

    Mitchell , J. L., Keeton , C. R., Frieman , J. A., & Sheth , R. K. 2005, , 622, 81, 10.1086/427910

  48. [56]

    2012, , 761, 142, 10.1088/0004-637X/761/2/142

    Muzzin , A., Labb \'e , I., Franx , M., et al. 2012, , 761, 142, 10.1088/0004-637X/761/2/142

  49. [57]

    C., Acevedo Barroso , J

    Nagam , B. C., Acevedo Barroso , J. A., Wilde , J., et al. 2025, arXiv e-prints, arXiv:2502.09802, 10.48550/arXiv.2502.09802

  50. [58]

    2016, , 823, 17, 10.3847/0004-637X/823/1/17

    Nayyeri , H., Keele , M., Cooray , A., et al. 2016, , 823, 17, 10.3847/0004-637X/823/1/17

  51. [59]

    2010, Science, 330, 800, 10.1126/science.1193420

    Negrello , M., Hopwood , R., De Zotti , G., et al. 2010, Science, 330, 800, 10.1126/science.1193420

  52. [60]

    2017, , 465, 3558, 10.1093/mnras/stw2911

    Negrello , M., Amber , S., Amvrosiadis , A., et al. 2017, , 465, 3558, 10.1093/mnras/stw2911

  53. [61]

    2025, arXiv e-prints, arXiv:2503.08777, 10.48550/arXiv.2503.08777

    Nightingale , J., Mahler , G., McCleary , J., et al. 2025, arXiv e-prints, arXiv:2503.08777, 10.48550/arXiv.2503.08777

  54. [62]

    2024, , 527, 12044, 10.1093/mnras/stad3916

    Pearson , J., Serjeant , S., Wang , W.-H., et al. 2024, , 527, 12044, 10.1093/mnras/stad3916

  55. [63]

    M., Vegetti , S., McKean , J

    Powell , D. M., Vegetti , S., McKean , J. P., et al. 2023, , 524, L84, 10.1093/mnrasl/slad074

  56. [64]

    1964, , 128, 307, 10.1093/mnras/128.4.307

    Refsdal , S. 1964, , 128, 307, 10.1093/mnras/128.4.307

  57. [65]

    D., Spilker , J

    Reuter , C., Vieira , J. D., Spilker , J. S., et al. 2020, , 902, 78, 10.3847/1538-4357/abb599

  58. [66]

    2025, , 10.1093/mnras/staf414

    Sedgwick , C., Serjeant , S., & Weiner , C. 2025, , 10.1093/mnras/staf414

  59. [67]

    J., Vernardos , G., Collett , T

    Shajib , A. J., Vernardos , G., Collett , T. E., et al. 2024, , 220, 87, 10.1007/s11214-024-01105-x

  60. [68]

    M., Smail , I., Swinbank , A

    Simpson , J. M., Smail , I., Swinbank , A. M., et al. 2019, , 880, 43, 10.3847/1538-4357/ab23ff

  61. [69]

    S., Hayward , C

    Spilker , J. S., Hayward , C. C., Marrone , D. P., et al. 2022, , 929, L3, 10.3847/2041-8213/ac61e6

  62. [70]

    H., Marshall , P

    Suyu , S. H., Marshall , P. J., Auger , M. W., et al. 2010, , 711, 201, 10.1088/0004-637X/711/1/201

  63. [71]

    H., & Marshall , P

    Treu , T., Suyu , S. H., & Marshall , P. J. 2022, , 30, 8, 10.1007/s00159-022-00145-y

  64. [72]

    2021, , 653, A151, 10.1051/0004-6361/202140830

    Trombetti , T., Burigana , C., Bonato , M., et al. 2021, , 653, A151, 10.1051/0004-6361/202140830

  65. [73]

    A., Bendo , G

    Urquhart , S. A., Bendo , G. J., Serjeant , S., et al. 2022, , 511, 3017, 10.1093/mnras/stac150

  66. [74]

    van der Vlugt , D., Algera , H. S. B., Hodge , J. A., et al. 2021, , 907, 5, 10.3847/1538-4357/abcaa3

  67. [75]

    2024, Nature Astronomy, 8, 119, 10.1038/s41550-023-02103-9

    van Dokkum , P., Brammer , G., Wang , B., Leja , J., & Conroy , C. 2024, Nature Astronomy, 8, 119, 10.1038/s41550-023-02103-9

  68. [76]

    Vegetti , S., Koopmans , L. V. E., Bolton , A., Treu , T., & Gavazzi , R. 2010, , 408, 1969, 10.1111/j.1365-2966.2010.16865.x

  69. [77]

    D., Marrone , D

    Vieira , J. D., Marrone , D. P., Chapman , S. C., et al. 2013, , 495, 344, 10.1038/nature12001

  70. [78]

    2017, , 850, 37, 10.3847/1538-4357/aa911b

    Wang , W.-H., Lin , W.-C., Lim , C.-F., et al. 2017, , 850, 37, 10.3847/1538-4357/aa911b

  71. [79]

    A., Eales , S

    Ward , B. A., Eales , S. A., Pons , E., et al. 2022, , 510, 2261, 10.1093/mnras/stab3300

  72. [81]

    2013 b , , 762, 59, 10.1088/0004-637X/762/1/59

    ---. 2013 b , , 762, 59, 10.1088/0004-637X/762/1/59

  73. [82]

    R., Kauffmann , O

    Weaver , J. R., Kauffmann , O. B., Ilbert , O., et al. 2022, , 258, 11, 10.3847/1538-4365/ac3078

  74. [83]

    J., et al

    Wilson , S., Hilton , M., Rooney , P. J., et al. 2016, , 463, 413, 10.1093/mnras/stw1947

  75. [84]

    C., Suyu , S

    Wong , K. C., Suyu , S. H., Chen , G. C. F., et al. 2020, , 498, 1420, 10.1093/mnras/stz3094

  76. [85]

    A., Aretxaga , I., Geach , J

    Zavala , J. A., Aretxaga , I., Geach , J. E., et al. 2017, , 464, 3369, 10.1093/mnras/stw2630

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.