Pith. sign in

REVIEW 3 major objections 5 minor 65 references

On The Very Bright Dropouts Selected Using the James Webb Space Telescope NIRCam Instrument

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Pith's one-line read JWST's brightest dropout galaxies are mostly low-z impostors, not early giants

desk verdict Careful and honest calibration of the bright-dropout problem; the MIRI subsample caveat is real but does not sink the paper. read the letter →

arxiv 2502.05751 v2 pith:PTRDJ2AU submitted 2025-02-09 astro-ph.GA

classification astro-ph.GA
keywords dropoutgalaxiesLymanbreakextremelyredobjectsJWSTNIRCamphotometricredshiftsSEDfittingMIRIhigh-redshift
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 asks a pointed question: when JWST's dropout technique turns up galaxies that are far too bright to be ordinary early-universe objects, what are they really? The authors systematically selected 300 very bright dropouts across 500 arcmin² of NIRCam imaging in four blank fields, then focused on 137 that also have MIRI mid-infrared coverage. Their SED analysis, using three independent fitting tools, indicates that this population is dominated by low-redshift galaxies at z≈1–4, with at least 67.9% of the sample being confident low-z interlopers, while at least 7% could still be genuine high-redshift galaxies. This matters because the high-redshift minority, if real, would be extraordinarily luminous objects that strain current galaxy formation models.

What carries the argument

The Lyman-break dropout selection: requiring no detection (S/N<2) in all bands bluer than the dropout band, a ≥0.8 mag break between adjacent bands, and a bright detection in F356W, which isolates objects whose ultraviolet continuum appears cut off. The 137-object main sample adds MIRI photometry at 5.6–25.5 µm to the NIRCam SEDs, and the paper classifies objects as 'High-z', 'Low-z', or 'Undecided' only when at least two of three independent SED codes (Le Phare with BC03 templates, EAZY with low-redshift galaxy templates, CIGALE with AGN and star-formation models) agree on the photometric redshift. This triple-coding, mid-IR-extended SED machinery is what lets the paper separate genuine high-z candidates from Balmer-break interlopers—though imperfectly, as the spectroscopic results show.

What would settle it

Spectroscopically observe the remaining ten 'High-z' candidates (especially the three Tier-1 objects) with NIRSpec: if the confirmed high-z fraction among them is below about 7% of the MIRI-overlap sample, the paper's claim that a non-negligible high-z fraction survives would be refuted.

Watch

Extended reading notes

Core claim

The central claim is that NIRCam-selected very bright dropouts are predominantly low-redshift galaxies, not the Lyman-break galaxies they were selected to find. Based on photometric-redshift agreement among three SED-fitting codes on 137 MIRI-covered objects, more than two-thirds (67.9%) are robustly classified as low-z (zphot<6, overwhelmingly z≈1–4), and only about 7% or more could be at high redshift. The paper's spectroscopic checks sharpen the claim: of seven secure NIRSpec redshifts, six are z≈3 galaxies, including two objects that had been placed in the 'High-z' category—showing that Balmer breaks can mimic Lyman breaks at this brightness even with mid-IR photometry. One Tier-2 'High-z' object is confirmed at z=8.679 with M_UV=-22.4 and stellar mass $10^{9}$.1 M_sun, a recovery of a known galaxy, demonstrating that true high-redshift objects do lurk in the sample.

Load-bearing premise

The 137 very bright dropouts that fall inside MIRI footprints are representative of all 300 very bright dropouts, even though the remaining 163 lack MIRI data and MIRI coverage depth varies strongly across the four fields.

Editorial extensions

If this is right

  • Brightness alone can pre-screen dropouts: using m356 ≳ 25–26 (rest-frame M_UV ≳ -22) would yield a much purer high-z sample for future JWST surveys.
  • The confirmed z=8.679 object, a starburst with SFR≈233 M_sun/yr at only 12.7 Myr old, shows that very luminous early galaxies can be explained by young bursts—no need to invoke impossibly massive galaxies.
  • The remaining very bright 'High-z' candidates, with M_UV ≤ -23 and stellar masses up to 10^11 M_sun, are exactly the regime where new galaxy formation models make testable predictions; their spectroscopic fate will decide whether any tension is real.
  • Any claim of 'impossibly bright' galaxies from NIRCam dropout samples must be checked against the Balmer-break degeneracy, since even mid-IR data do not break it when veto bands are shallow.

Reading between the lines

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

  • If the contamination rate is similar in the 163 MIRI-less objects, the true fraction of bright dropouts that are low-z could exceed 80%, strengthening the case for brightness-based pre-screening.
  • The Balmer/Lyman ambiguity may also affect other 'extremely red object' selections that lack deep blue veto bands; a systematic measure of veto-band depth against spectroscopic confirmation rate would quantify the effect.
  • One testable extension: the paper's sample could be re-analyzed with the redshift fixed at zspec for all ten NIRSpec objects to measure how often the high-z solution wins on χ², giving a quantitative prior for future SED classifiers.
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

3 major / 5 minor

Summary. This paper presents a systematic search for very bright dropout galaxies in JWST NIRCam data over four blank fields, identifies 300 objects, and focuses on the 137 with MIRI coverage. Using three independent SED-fitting codes, the authors categorize the MIRI-covered objects into High-z, Low-z, and Undecided, finding 93 Low-z, 10 High-z, and 34 Undecided objects. Ten objects have NIRSpec observations; seven secure redshifts confirm six low-redshift galaxies and one galaxy at z=8.679. The paper concludes that bright NIRCam dropouts are predominantly low-z EROs, that a small candidate high-z fraction remains, and that SED-based High-z selection is severely contaminated at the bright end.

Significance. If the result holds, this is a timely quantitative demonstration that the very brightest NIRCam dropouts are dominated by z~1-4 extremely red galaxies, and that apparent brightness can be used as a pre-screen for high-z candidates. The paper's strengths include the use of public JWST data, documented SExtractor photometry with explicit aperture choices, three independent SED-fitting codes with cross-tool consistency requirements, quoted uncertainties, and a small but genuine spectroscopic check. The authors also honestly report the severe contamination of their High-z category, which strengthens confidence in the low-z conclusion. The main weakness is the unsupported generalization from the MIRI-overlap subset to the full 300-object population.

major comments (3)
  1. [Section 4 and Section 8] The central quantitative claims—'>67.9% low-z' and '>7% high-z'—are computed for the 137-object MIRI-overlap main sample only, but the Summary presents them as conclusions about 'the NIRCam-selected very-bright dropouts' as a whole. Table 3 shows that MIRI overlap is strongly field- and passband-dependent (e.g., only 6 of 37 CEERS F115W dropouts enter the main sample, and CEERS contributes 11 of 52 dropouts overall), and Table A.1 provides only positions and m356 for the 163-object supplement, with no SED or spectroscopic check. Because the category mix varies across fields (CEERS main sample has 2/11 High-z, UDS 4/62), the unqualified population-level fractions are not established. Please either restrict all population statements to the MIRI-covered subset or add a representativeness test for the supplement.
  2. [Abstract and Section 4] The abstract's statement that 'the majority of them (>80%)' of the 300 objects have F115W-F356W>2.0 mag is not supported by the analysis in the text, where the 81% ERO fraction is derived for the 137 main-sample objects in Figure 2. If the full-sample color distribution was not computed, rephrase to attribute the 80% to the MIRI-covered subset, or provide the full-sample value.
  3. [Section 6, Section 7.1, Section 8] The conclusion that 'a non-negligible fraction (>7%) could still be at high-z' is based on the 10 SED-selected High-z candidates, but Section 6 reports that two of the three grade-I 'High-z' objects with spectroscopy are at z~3 and Section 7.1 calls the category 'severely contaminated.' The paper should explicitly distinguish the raw candidate fraction (10/137 = 7.3%) from an estimated true high-z fraction, and should quote the confirmed fraction (one of seven grade-I objects, or 1/137 of the main sample) so that the second conclusion is not overstated.
minor comments (5)
  1. [Section 2.1] The word 'overlaping' should be 'overlapping'.
  2. [Section 3.1] 'runSExtractor' should be 'run SExtractor'.
  3. [Table 1 and Table 3 captions] The captions begin with 'T able' and should read 'Table'.
  4. [Section 4 and Figure 2] Because many of the F115W colors are lower limits from 2-sigma non-detections, the 81% ERO fraction should be quoted as 'at least 81%'.
  5. [Section 7.1] The two previously refuted dropouts from the literature (f200dbrtceers264 and f150dbrtceers191) are in the supplement sample; this is worth highlighting as evidence that the supplement may also contain low-z interlopers, reinforcing the need for the representativeness test requested above.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the SED-based fractions are descriptive counts with an independent spectroscopic check; self-citations are methodological and non-load-bearing.

full rationale

The paper is an observational census rather than a derivation chain, and its central claims do not reduce to their inputs by construction. The fractions in the Summary (>67.9% low-z, >7% high-z) are simple counts from the stated category definitions: 93/137 objects in the "Low-z" category and 10/137 in the "High-z" category, where the categories are defined a priori by requiring consistent zphot from at least two of three independent SED-fitting tools. No parameter is fitted to a subset and then renamed as a prediction; the category thresholds (zphot >= 6.0, chi^2 <= 100, tier assignments) are transparent and fixed before the counts are reported. The paper also provides an external check: seven objects with secure NIRSpec redshifts, two of which refute "High-z" assignments and one of which confirms a genuine z=8.679 galaxy. This spectroscopy is independent of the SED templates and therefore supports rather than completes a circle. The EAZY choice of GALSEDATLAS templates, which the authors state favors low-z fits, is a potential bias but not circularity: EAZY is only one of three tools, and an object is placed in the "Low-z" category only if at least two tools agree on zphot < 6.0, so the low-z template set alone cannot force the majority classification. The self-citations to H. Yan et al. (2023a,b) for the dropout criteria and C. Ling & H. Yan (2022) for PSF construction are methodological; the dropout criteria are restated explicitly in Section 4 and are standard Lyman-break selection conditions, so the argument does not rest on an unverified self-citation. The most notable weakness, that the 137-object MIRI overlap sample may not be representative of the full 300-object dropout population, is an extrapolation or selection-bias concern, not a circularity: the paper defines the main sample in Section 4 and computes the fractions only for that sample, and it does not claim to derive the fractions from the definition itself. Overall, the analysis is self-contained against external spectroscopic benchmarks, the load-bearing calculations are transparent counts, and the self-citations are not load-bearing.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the SED template assumptions and on four hand-chosen thresholds (brightness limits, dropout amplitude, zphot>6 cutoff, chi2<=100) that directly influence the reported category fractions. The representativeness of the MIRI-covered subsample is an additional untested premise. No new physical entities are introduced.

free parameters (4)
  • Brightness thresholds for 'very bright' definition = m356 <= 25.1 mag for F090W/F115W dropouts, <= 26.0 mag for F150W/F200W dropouts
    Hand-chosen in Section 4 to define the sample; changing them alters the sample and therefore the derived fractions.
  • Dropout amplitude threshold = 0.8 mag
    Adopted in Section 4 following the expected color of a Lyman break shifted halfway out of the dropout band; a different threshold would change the sample.
  • High-z photometric redshift cutoff = zphot >= 6.0
    Chosen in Section 5.2 to separate 'High-z' from 'Low-z' categories; this threshold directly sets the reported high-z fraction.
  • Good-fit chi2 threshold = chi2 <= 100
    Arbitrary threshold in Section 5.2 used to define Tier 1 versus Tier 2, affecting which objects are considered robustly classified.
assumptions (3)
  • domain assumption The adopted SED template libraries (BC03, Brown+14 GALSEDATLAS, CIGALE grids) and extinction laws adequately represent the true SEDs of both low-z EROs and high-z galaxies across 0.4-25 um.
    Invoked in Section 5.1; incorrect templates would bias the photometric redshifts and thus the category fractions.
  • domain assumption The Lyman-break dropout selection criteria (S/N>=5, dropout amplitude >=0.8 mag, S/N<2 in veto bands) select genuine high-z candidates subject only to Balmer-break contamination.
    This is the basis of the method described in Section 4; the paper itself shows that Balmer-break contaminants pass the selection when veto bands are shallow, so the assumption is only partially satisfied.
  • ad hoc to paper The 137-object MIRI-overlap sample is representative of the full 300-object very bright dropout sample.
    Assumed when the paper generalizes conclusions from the main sample to the bright dropout population (Section 4 and Summary); this representativeness is not tested.

how reviews work

0 comments
Cite this review

Pith. "Pith review of On The Very Bright Dropouts Selected Using the James Webb Space Telescope NIRCam Instrument." pith.science (2026). https://pith.science/paper/PTRDJ2AU

@misc{pith2026250205751,
  author       = {Pith},
  title        = {Pith review of: On The Very Bright Dropouts Selected Using the James Webb Space Telescope NIRCam Instrument},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PTRDJ2AU}},
  note         = {Machine review of arXiv:2502.05751}
}
abstract

The selection of candidate high-redshift galaxies using the dropout technique targeting the Lyman-break signature sometimes yields very bright objects that are too luminous to be easily explained if they are indeed at the expected redshifts. Here we present a systematic study of very bright dropouts selected through successive bands of the NIRCam instrument onboard the James Webb Space Telescope (JWST). Using the public NIRCam data in four blank fields over 500~arcmin$^2$, 300 such objects were found. They have F356W magnitudes $<25.1$~mag or $<26.0$~mag depending on the dropout passband, and the majority of them ($>80\%$) have very red F115W$-$F356W colors $> 2.0$~mag, qualifying them as ``extremely red objects'' (EROs). We focus on 137 objects that also have mid-IR observations from the JWST MIRI instrument. Their spectral energy distribution analysis shows that these objects are dominated by low-redshift ($z\sim1$--4) galaxies ($\gtrsim67\%$). However, a non-negligible fraction ($\gtrsim7\%$) could be at high redshifts. Seven of our objects have secure spectroscopic redshifts from JWST NIRSpec identifications, and the results confirm this picture: while six are low-redshift galaxies ($z\approx3$), one is a known galaxy at $z=8.679$ {(with $M_{\rm UV}=-22.4$~mag and stellar mass $M_*=10^{9.1}M_\odot$)} recovered in our sample. In light of recent theoretical models on early galaxy formation, this confirmed high-redshift galaxy does not pose a challenge. However, as our sample contains very luminous high-redshift candidates in the regime still underexplored ($M_{\rm UV}\leq -23$~mag and $M_*>10^{10.5}M_\odot$), spectroscopic identifications are necessary to ensure they do not create tension with these new models.

Figures

Figures reproduced from arXiv: 2502.05751 by the authors.

Figure 1
Figure 1. Image stamps of example very bright dropouts in F090W, F115W, F150W, and F200W, arranged from top to bottom. Two example objects are shown for each group. The stamps are 2′′×2 ′′ in size and are oriented with north being up and east being left. The images are from the HST ACS, JWST NIRCam and JWST MIRI, with the passbands as noted. Most of the very bright dropouts are either disk-like (∼40%) or compact (∼45%) in mor… view at source ↗
Figure 2
Figure 2. Observed m115 − m356 color versus m356 of the very bright dropouts in the main sample. The F090W, F115W, F150W, and F200W dropouts are represented by circles, squares, triangles, and stars, respectively. The ones with upward arrows indicate the lower limits of the color if the objects have S/N < 2.0 in F115W, and the color lower limits are calculated using the 2 σ detection upper limits in this band. Adopting m115 −… view at source ↗
Figure 3
Figure 3. Examples of SED fitting results on one object in the “High-z” category (top panel) and one in the “Low-z” category (bottom panel). The blue symbols represent the observed values, and the curves are the spectra of the best-fit models. The red symbols are the synthesized magnitudes derived from the best-fit models. The insets show the probability distribution function of zphot. The quoted zphot value on top of each pa… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Left: F356W magnitude distributions for all objects, and objects in the “High-z”, “Low-z”, and “Undecided” categories, respectively. Right: Contour plots of Age vs. E(B-V) for the objects in the “High-z” (blue) and “Low-z” (red) categories. Both parameters are the 50th…
Figure 5
Figure 5. Figure 5: Comparison of SED fittings (using CIGALE for demonstration) for the two “High-z” candidates that were later confirmed as low redshift ones. In both cases, the prominent Balmer break at low-z mimics the high-z Lyman break signature very well. Nonetheless, the high-z sol…
Figure 6
Figure 6. Figure 6: Image stamps of f115d brt ceers 062 (top) and its SED fitting results from CIGALE (bottom). This is a known galaxy at zspec = 8.679 recovered in our selection. The image stamps are similar to those shown in [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 15 canonical work pages

  1. [1]

    J., Conselice, C

    Adams, N. J., Conselice, C. J., Austin, D., et al. 2024, ApJ, 965, 169, doi: 10.3847/1538-4357/ad2a7b Alcalde Pampliega, B., P´ erez-Gonz´ alez, P. G., Barro, G., et al. 2019, ApJ, 876, 135, doi: 10.3847/1538-4357/ab14f2

  2. [2]

    1999, MNRAS, 310, 540, doi: 10.1046/j.1365-8711.1999.02978.x Arrabal Haro, P., Dickinson, M., Finkelstein, S

    Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, MNRAS, 310, 540, doi: 10.1046/j.1365-8711.1999.02978.x Arrabal Haro, P., Dickinson, M., Finkelstein, S. L., et al. 2023a, Nature, 622, 707, doi: 10.1038/s41586-023-06521-7 Arrabal Haro, P., Dickinson, M., Finkelstein, S. L., et al. 2023b, ApJL, 951, L22, doi: 10.3847/2041-8213/acdd54

  3. [3]

    A., Weibel, A., et al

    Barrufet, L., Oesch, P. A., Weibel, A., et al. 2023, MNRAS, 522, 449, doi: 10.1093/mnras/stad947

  4. [4]

    1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

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

  5. [5]

    2019, A&A, 622, A103, doi: 10.1051/0004-6361/201834156

    Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103, doi: 10.1051/0004-6361/201834156

  6. [6]

    J., Stefanon, M., Brammer, G., et al

    Bouwens, R. J., Stefanon, M., Brammer, G., et al. 2023, MNRAS, 523, 1036, doi: 10.1093/mnras/stad1145

  7. [7]

    2023,, 0.6.17 Zenodo, doi: 10.5281/zenodo.7299500

    Brammer, G. 2023,, 0.6.17 Zenodo, doi: 10.5281/zenodo.7299500

  8. [8]

    B., van Dokkum, P

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

Show all 65 references
  1. [9]

    Brown, M. J. I., Moustakas, J., Smith, J. D. T., et al. 2014, ApJS, 212, 18, doi: 10.1088/0067-0049/212/2/18

  2. [10]

    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

  3. [11]

    2024,, 1.14.0 Zenodo, doi: 10.5281/zenodo.6984365

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2024,, 1.14.0 Zenodo, doi: 10.5281/zenodo.6984365

  4. [12]

    2001, PASP, 113, 1449, doi: 10.1086/324269

    Calzetti, D. 2001, PASP, 113, 1449, doi: 10.1086/324269

  5. [13]

    L., & Storchi-Bergmann, T

    Calzetti, D., Kinney, A. L., & Storchi-Bergmann, T. 1994, ApJ, 429, 582, doi: 10.1086/174346

  6. [14]

    I., Micha lowski, M

    Caputi, K. I., Micha lowski, M. J., Krips, M., et al. 2014, ApJ, 788, 126, doi: 10.1088/0004-637X/788/2/126

  7. [15]

    2024, Nature, 633, 318, doi: 10.1038/s41586-024-07860-9

    Carniani, S., Hainline, K., D’Eugenio, F., et al. 2024, Nature, 633, 318, doi: 10.1038/s41586-024-07860-9

  8. [16]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

  9. [17]

    L., Barger, A

    Cowie, L. L., Barger, A. J., Wang, W. H., & Williams, J. P. 2009, ApJL, 697, L122, doi: 10.1088/0004-637X/697/2/L122

  10. [18]

    2023, MNRAS, 523, 3201, doi: 10.1093/mnras/stad1557

    Li, Z. 2023, MNRAS, 523, 3201, doi: 10.1093/mnras/stad1557

  11. [19]

    J., Willott, C., Alberts, S., et al

    Eisenstein, D. J., Willott, C., Alberts, S., et al. 2023, arXiv e-prints, arXiv:2306.02465, doi: 10.48550/arXiv.2306.02465

  12. [20]

    H., & Rieke, M

    Elston, R., Rieke, G. H., & Rieke, M. J. 1988, ApJL, 331, L77, doi: 10.1086/185239

  13. [21]

    2023, MNRAS, 522, 3986, doi: 10.1093/mnras/stad1095

    Ferrara, A., Pallottini, A., & Dayal, P. 2023, MNRAS, 522, 3986, doi: 10.1093/mnras/stad1095

  14. [22]

    J., et al

    Ferreira, L., Adams, N., Conselice, C. J., et al. 2022, ApJL, 938, L2, doi: 10.3847/2041-8213/ac947c

  15. [23]

    J., Sazonova, E., et al

    Ferreira, L., Conselice, C. J., Sazonova, E., et al. 2023, ApJ, 955, 94, doi: 10.3847/1538-4357/acec76

  16. [24]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Arrabal Haro, P., et al. 2025, arXiv e-prints, arXiv:2501.04085, doi: 10.48550/arXiv.2501.04085 15

  17. [25]

    K., & Sugahara, Y

    Fudamoto, Y., Inoue, A. K., & Sugahara, Y. 2022, ApJL, 938, L24, doi: 10.3847/2041- 8213/ac982b10.48550/arXiv.2208.00132

  18. [26]

    L., Nelson, E., Williams, C

    Gibson, J. L., Nelson, E., Williams, C. C., et al. 2024, ApJ, 974, 48, doi: 10.3847/1538-4357/ad64c2 G´ omez-Guijarro, C., Elbaz, D., Xiao, M., et al. 2022, A&A, 658, A43, doi: 10.1051/0004-6361/202141615 G´ omez-Guijarro, C., Magnelli, B., Elbaz, D., et al. 2023, A&A, 677, A3...

  19. [27]

    A., Kocevski, D

    Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35, doi: 10.1088/0067-0049/197/2/35

  20. [28]

    M., & Ridgway, S

    Hu, E. M., & Ridgway, S. E. 1994, AJ, 107, 1303, doi: 10.1086/116943

  21. [29]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841, doi: 10.1051/0004-6361:20065138

  22. [30]

    2023, ApJ, 959, 100, doi: 10.3847/1538-4357/ad09be

    Isobe, Y., Ouchi, M., Tominaga, N., et al. 2023, ApJ, 959, 100, doi: 10.3847/1538-4357/ad09be

  23. [31]

    2023, ApJL, 948, L13, doi: 10.3847/2041-8213/accd6d

    Jacobs, C., Glazebrook, K., Calabr` o, A., et al. 2023, ApJL, 948, L13, doi: 10.3847/2041-8213/accd6d

  24. [32]

    M., Faber, S

    Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36, doi: 10.1088/0067-0049/197/2/36

  25. [33]

    2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb Labb´ e, I., van Dokkum, P., Nelson, E., et al

    Kuhn, V., Guo, Y., Martin, A., et al. 2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb Labb´ e, I., van Dokkum, P., Nelson, E., et al. 2023, Nature, 616, 266, doi: 10.1038/s41586-023-05786-2

  26. [34]

    L., Hutchison, T

    Larson, R. L., Hutchison, T. A., Bagley, M., et al. 2023a, ApJ, 958, 141, doi: 10.3847/1538-4357/acfed4

  27. [35]

    L., Finkelstein, S

    Larson, R. L., Finkelstein, S. L., Kocevski, D. D., et al. 2023b, ApJL, 953, L29, doi: 10.3847/2041-8213/ace619

  28. [36]

    2022, ApJ, 929, 40, doi: 10.3847/1538-4357/ac57c1

    Ling, C., & Yan, H. 2022, ApJ, 929, 40, doi: 10.3847/1538-4357/ac57c1

  29. [37]

    A., Trenti, M., & Treu, T

    Mason, C. A., Trenti, M., & Treu, T. 2023, MNRAS, 521, 497, doi: 10.1093/mnras/stad035

  30. [38]

    J., Suess, K

    Nelson, E. J., Suess, K. A., Bezanson, R., et al. 2023, ApJL, 948, L18, doi: 10.3847/2041-8213/acc1e1

  31. [39]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

  32. [40]

    E., Tacchella, S., Johnson, B

    Robertson, B. E., Tacchella, S., Johnson, B. D., et al. 2023, ApJL, 942, L42, doi: 10.3847/2041- 8213/aca08610.48550/arXiv.2208.11456

  33. [41]

    2023, MNRAS, 518, L19, doi: 10.1093/mnrasl/slac115

    Rodighiero, G., Bisigello, L., Iani, E., et al. 2023, MNRAS, 518, L19, doi: 10.1093/mnrasl/slac115

  34. [42]

    Scodeggio, M., & Silva, D. R. 2000, A&A, 359, 953, doi: 10.48550/arXiv.astro-ph/0004228

  35. [43]

    2023, MNRAS, 525, 3254, doi: 10.1093/mnras/stad2508

    Shen, X., Vogelsberger, M., Boylan-Kolchin, M., Tacchella, S., & Kannan, R. 2023, MNRAS, 525, 3254, doi: 10.1093/mnras/stad2508

  36. [44]

    Stalevski, M., Fritz, J., Baes, M., Nakos, T., & Popovi´ c, L. ˇC. 2012, MNRAS, 420, 2756, doi: 10.1111/j.1365-2966.2011.19775.x

  37. [45]

    2016, MNRAS, 458, 2288, doi: 10.1093/mnras/stw444

    Stalevski, M., Ricci, C., Ueda, Y., et al. 2016, MNRAS, 458, 2288, doi: 10.1093/mnras/stw444

  38. [46]

    C., & Hamilton, D

    Steidel, C. C., & Hamilton, D. 1992, AJ, 104, 941, doi: 10.1086/116287

  39. [47]

    C., & Hamilton, D

    Steidel, C. C., & Hamilton, D. 1993, AJ, 105, 2017, doi: 10.1086/116579

  40. [48]

    C., Pettini, M., & Hamilton, D

    Steidel, C. C., Pettini, M., & Hamilton, D. 1995, AJ, 110, 2519, doi: 10.1086/117709

  41. [49]

    P., Chen, Z., et al

    Tang, M., Stark, D. P., Chen, Z., et al. 2023, MNRAS, 526, 1657, doi: 10.1093/mnras/stad2763

  42. [50]

    Thompson, D., Beckwith, S. V. W., Fockenbrock, R., et al. 1999, ApJ, 523, 100, doi: 10.1086/307708

  43. [51]

    2012, Nature, 486, 233, doi: 10.1038/nature11073

    Walter, F., Decarli, R., Carilli, C., et al. 2012, Nature, 486, 233, doi: 10.1038/nature11073

  44. [52]

    2016, ApJ, 816, 84, doi: 10.3847/0004-637X/816/2/84

    Wang, T., Elbaz, D., Schreiber, C., et al. 2016, ApJ, 816, 84, doi: 10.3847/0004-637X/816/2/84

  45. [53]

    2019, Nature, 572, 211, doi: 10.1038/s41586-019-1452-4

    Wang, T., Schreiber, C., Elbaz, D., et al. 2019, Nature, 572, 211, doi: 10.1038/s41586-019-1452-4

  46. [54]

    J., & Cowie, L

    Wang, W.-H., Barger, A. J., & Cowie, L. L. 2012, ApJ, 744, 155, doi: 10.1088/0004-637X/744/2/155

  47. [55]

    C., Oesch, P

    Williams, C. C., Oesch, P. A., Weibel, A., et al. 2024, arXiv e-prints, arXiv:2410.01875, doi: 10.48550/arXiv.2410.01875

  48. [56]

    Y., Elbaz, D., G´ omez-Guijarro, C., et al

    Xiao, M. Y., Elbaz, D., G´ omez-Guijarro, C., et al. 2023, A&A, 672, A18, doi: 10.1051/0004-6361/202245100

  49. [57]

    2023a, ApJL, 942, L9, doi: 10.3847/2041-8213/aca80c

    Yan, H., Ma, Z., Ling, C., Cheng, C., & Huang, J.-S. 2023a, ApJL, 942, L9, doi: 10.3847/2041-8213/aca80c

  50. [58]

    2024, ApJ, 975, 44, doi: 10.3847/1538-4357/ad7de9

    Yan, H., Sun, B., & Ling, C. 2024, ApJ, 975, 44, doi: 10.3847/1538-4357/ad7de9

  51. [59]

    2023b, arXiv e-prints, arXiv:2311.15121, doi: 10.48550/arXiv.2311.15121

    Yan, H., Sun, B., Ma, Z., & Ling, C. 2023b, arXiv e-prints, arXiv:2311.15121, doi: 10.48550/arXiv.2311.15121

  52. [60]

    Yan, H., Dickinson, M., Eisenhardt, P. R. M., et al. 2004, ApJ, 616, 63, doi: 10.1086/424898

  53. [61]

    J., Weymann, R

    Yan, L., McCarthy, P. J., Weymann, R. J., et al. 2000, AJ, 120, 575, doi: 10.1086/301454

  54. [62]

    B., et al

    Yang, G., Papovich, C., Bagley, M. B., et al. 2023, ApJL, 956, L12, doi: 10.3847/2041-8213/acfaa0

  55. [63]

    A., Buat, V., Casey, C

    Zavala, J. A., Buat, V., Casey, C. M., et al. 2023, ApJL, 943, L9, doi: 10.3847/2041-8213/acacfe

  56. [64]

    2020, A&A, 642, A155, doi: 10.1051/0004-6361/202038059

    Zhou, L., Elbaz, D., Franco, M., et al. 2020, A&A, 642, A155, doi: 10.1051/0004-6361/202038059

  57. [65]

    UNDECIDED

    Zitrin, A., Labb´ e, I., Belli, S., et al. 2015, ApJL, 810, L12, doi: 10.1088/2041-8205/810/1/L12 16 APPENDIX A. THE SUPPLEMENT SAMPLE OF VERY BRIGHT DROPOUTS Among the 300 very bright dropouts, 163 of them are outside of the MIRI coverage, which we did not perform SED analysi...

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.