Pith. sign in

REVIEW 3 major objections 5 minor 5 cited by

At z=10, cosmic variance—not Poisson noise—dominates the uncertainty in galaxy number counts, reaching 100–240% per JWST field.

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-03 16:13 UTC pith:XKE7NCME

load-bearing objection First z~10 cosmic variance measurement from 34 sightlines is a genuinely useful new observable, but the model-ranking claim in the abstract rests on a tension statistic that ignores σ_CV. the 3 major comments →

arxiv 2512.14212 v2 pith:XKE7NCME submitted 2025-12-16 astro-ph.GA

Exploring Cosmic Dawn with PANORAMIC II: Cosmic Variance and Galaxy Clustering at zsim10

Andrea Weibel , Christian Kragh Jespersen , Pascal A. Oesch , Christina C. Williams , Rachel Bezanson , Gabriel Brammer , Aidan P. Cloonan , Pratika Dayal
show 5 more authors
Anne Hutter Zhiyuan Ji Michael V. Maseda Marko Shuntov Katherine E. Whitaker
This is my paper
classification astro-ph.GA
keywords cosmic variancegalaxy clusteringz~10 galaxiesLyman break galaxiesgalaxy biasUV luminosity functionpure parallel imagingcosmic dawn
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper measures the field-to-field scatter in the number of bright galaxies seen at z~10 by JWST. Using 34 widely separated sightlines, it finds that this cosmic variance is 0.96, 1.46, and 1.71 (fractional) for M_UV < -19.5, -20, and -20.5 respectively, implying a 100–240% uncertainty in counts from a single NIRCam pointing. Converting this to a galaxy bias gives b_g,CV ~30–50, meaning these galaxies must live in very massive, strongly clustered halos. Comparing the measured variance and abundance to simple models, the paper tentatively rules out models that raise star formation efficiency globally or boost scatter, and prefers models that decrease the mass-to-light ratio or scale star formation efficiency with halo mass.

Core claim

Using 34 independent sightlines, the paper finds z~10 Lyman-break galaxy counts vary with sigma_CV = 0.96, 1.46, and 1.71 per NIRCam pointing (M_UV<-19.5, -20, -20.5) — a 100–240% fractional scatter, implying b_g,CV~30–50. Combining this clustering signal with the UV luminosity function disfavors global star-formation-efficiency boosts and increased UV scatter models, favoring lower mass-to-light ratios or halo-mass-dependent SFE. This makes cosmic variance a new diagnostic that can break degeneracies among models for the overabundance of bright z~10 galaxies.

What carries the argument

Central is sigma_CV, the fractional field-to-field variance in completeness-corrected galaxy counts, sigma_CV^2 = (sigma^2_total - sigma^2_Poisson)/mu^2, where sigma^2_Poisson = <N>. The fiducial measurement fits a Gamma distribution to counts across 34 independent lines of sight via MCMC, which is robust to rare overdense outliers. sigma_CV is converted to a galaxy bias b_g,CV = sigma_CV/sigma_DM, with sigma_DM=0.031 computed for the NIRCam survey volume. Because small non-linear scales dominate such a small field, b_g,CV exceeds the linear bias by 3–5x; the UniverseMachine simulation is used to calibrate this non-linear enhancement and to translate the b_g,CV measurements into linear bias.

Load-bearing premise

The load-bearing premise is that the 34 lines of sight (28 pure-parallel pointings plus randomly placed mock pointings on legacy fields) are independent and statistically equivalent surveys, so that the observed spread in their counts is dominated by true cosmic variance rather than by field selection or residual large-scale correlation.

What would settle it

A revised measurement from a substantially larger set of independent pointings (e.g., ~100) that yields sigma_CV close to the Poisson expectation at M_UV<-20 — or a direct 2PCF measurement showing linear bias around 5 rather than the corrected ~10–14 — would falsify the claim that bright z~10 galaxies are this strongly clustered.

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

Share X Bluesky LinkedIn Reddit HN

If this is right

  • Cosmic variance, not Poisson noise, dominates the uncertainty in single- or few-field JWST counts of bright z~10 galaxies; UVLF estimates from small-area surveys can be off by factors of ~2–4.
  • The implied galaxy bias b_g,CV ~30–50 (linear bias ~10–14 after correction) means bright z~10 galaxies are hosted by very massive, strongly clustered halos, a datum any successful model of early galaxy formation must reproduce.
  • Models that raise global star-formation efficiency or increase the scatter in the UV–halo mass relation (e.g., bursty star formation) are disfavored relative to models that lower the mass-to-light ratio or make SFE grow with halo mass.
  • Adding ~50 independent NIRCam pure-parallel pointings would allow the method to distinguish between the remaining model families at >3 sigma significance.

Where Pith is reading between the lines

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

  • Because the paper itself flags that its measurements may be biased high (a consequence of the skewed count distribution), the true sigma_CV could be lower; if future data confirm lower values, the tentative model ranking could shift back toward bursty/scatter models.
  • The same technique can be applied at z~13–17, where the UVLF is even more uncertain; measuring sigma_CV there would test whether the steep drop in galaxy number density from z~10 to z~17 is real or partly a cosmic-variance artifact.
  • The non-linear enhancement factor calibrated from the simulation is strong: b_g,CV / b_lin ~3–5 for NIRCam-sized fields. Direct 2PCF measurements in wide JWST fields could calibrate this factor empirically, removing the current reliance on the simulation's small-scale clustering.
  • If the high bias is real, targeted spectroscopy of the most overdense sightlines might reveal protocluster-scale overdensities at z~10, linking the variance measurement to large-scale structure formation.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper measures the cosmic variance (σ_CV) of z~10 Lyman-break galaxies using 34 independent NIRCam-pointing-sized fields, combining PANORAMIC pure parallels and legacy fields with mock pointings. Two estimators — bootstrap and MCMC Gamma-distribution fitting — give σ_CV ≈ 0.96–1.71 depending on M_UV limit, implying per-field cosmic-variance uncertainties of 100–240% and a large cosmic-variance-based galaxy bias. The authors compare with the fiducial UniverseMachine, which reproduces σ_CV but underproduces the abundance by a factor ≳5. They then implement simple UniverseMachine variants (global/halo-dependent M_UV boosts, constant/power-law SFE, global/halo-dependent scatter) and use the σ_CV–μ plane plus a 'combined model tension' statistic to argue that global SFE and enhanced-scatter models are disfavored relative to a power-law SFE model. Future prospects with additional parallel pointings are discussed.

Significance. If the measurement holds, this is one of the first direct cosmic-variance measurements at z~10, using a novel combination of pure-parallel and legacy sightlines. The consistency between two independent estimators and with UniverseMachine strengthens the empirical result. The model-comparison framework is a useful proof-of-concept for using σ_CV to break degeneracies among UV-bright-galaxy models. However, the quantitative model-ranking claim is weakened by the fact that the 'combined model tension' statistic is computed only from mean counts, not from σ_CV; the abstract's 'combined constraints on σ_CV and the UVLF' therefore overstates what the statistic demonstrates. With a corrected joint statistic or more carefully qualified claims, the paper would be a valuable contribution.

major comments (3)
  1. [§3.3, Figure 4] The 'combined model tension' m is defined as (μ_model − μ_data)(Σ_model + Σ_data)^−1(μ_model − μ_data)^T, where μ is the mean galaxy count per field in distinct M_UV bins. This statistic contains no term involving σ_CV. Section 5 confirms that the tension is computed 'across distinct M_UV bins (i.e. along the UVLF).' Consequently, the quantitative rankings in Figure 4 — B2 best, C1/C2/B1 disfavored at ≳2σ — are driven solely by abundance/UVLF constraints. The abstract's statement that 'combined constraints on σ_CV and the UVLF' disfavor these models is not supported by this statistic. Either build a joint statistic over (μ, σ_CV) using the full posteriors, or state explicitly that the quantitative ranking is UVLF-only and that σ_CV contributes only qualitative ~1σ support. As written, the headline model-discrimination claim is not demonstrated by the paper's own statistic.
  2. [§2.4, §3.1.2] The fiducial MCMC σ_CV values use a single random placement of one mock NIRCam pointing in each legacy field. The bootstrapping estimator (§2.4.1) re-samples new mock pointings, but the MCMC estimator (which provides the fiducial values) does not propagate the uncertainty arising from the choice of pointing position within the irregular legacy footprints. Because the legacy fields dominate the area and the mock pointings are treated as independent equal-area sightlines, a different realization of mock pointings could shift the inferred σ_CV. Please quantify this by re-running the MCMC fit over many mock-pointing realizations, and/or by reporting a PANORAMIC-only measurement. This is directly relevant to the claimed precision of the central σ_CV measurement.
  3. [§4.1–4.2] The conversion from the measured b_g,CV to a linear bias uses the ratio b_g,CV/b_g,lin measured from the fiducial UniverseMachine. The quoted conversion-factor uncertainties reflect only the scatter among UniverseMachine light cones; they do not include the systematic uncertainty in UniverseMachine's small-scale, non-linear clustering or in the assumed M_UV–M_halo relation. Since the conversion is then used to compare with literature linear-bias values, the comparison in §4.2 is model-dependent. I would ask the authors to state this limitation explicitly and, if possible, estimate the systematic error using an alternative clustering model or by varying the halo-occupation assumptions.
minor comments (5)
  1. [§5] The sentence 'A direct measurement of σ_UV at z∼10...' appears to be a typo; the paper measures σ_CV, not σ_UV.
  2. [§3.3 and §5] There is an inconsistency in identifying the best-fitting model: §3.3 and Figure 4 refer to the sharp DMSFE model with SFE∝M_halo^0.6 as best, while §5 states the best model has SFE∝M_halo^0.5. Please correct.
  3. [§3.3] Typo: 'difavored' should be 'disfavored'.
  4. [§2.4.2] The description of the Negative Binomial likelihood as an 'outlier-resistant Poisson-likelihood' is confusing. The Negative Binomial is the Gamma-Poisson mixture; the robustifying behavior should be explained more precisely.
  5. [§2.4.2] The exclusion of one of the five UniverseMachine light cones as 'unexplainable and significantly lower typical number densities' deserves more justification. Since the model comparison uses these light cones to estimate σ_CV and μ, a brief discussion of whether this light cone could instead be a rare fluctuation would be useful.

Circularity Check

0 steps flagged

No significant circularity: the σ_CV measurement is empirical and the model σ_CV tracks are genuine predictions; the §3.3 tension statistic is UVLF-only, a correctness gap rather than a circular reduction.

full rationale

The central σ_CV measurement is not circular: Eq. 4 and the MCMC Gamma-distribution fit operate on observed completeness-corrected counts per field, with no σ_CV input fed into the estimator that is then reported as the measurement. The bootstrap and MCMC agree within uncertainties, and the MCMC prior/likelihood choices are stated and motivated by Jespersen et al. (2025c), a published, reproducible method paper; the paper explicitly notes alternative distributions (negative binomial) are equally adequate, so no ansatz is smuggled by citation. The sample from W25 is a standard self-use of a companion catalog, not a definitional input: σ_CV is not defined by W25's UVLF values. The model comparison is also not circular in its σ_CV content: the UniverseMachine-based models are tuned to the UVLF (μ) and then their σ_CV is computed and compared to the data, so σ_CV is an independent prediction; the baseline UniverseMachine is a calibrated external simulation. The one real flaw is in §3.3: the 'combined model tension' m is explicitly a function of μ_model and μ_data only, with no σ_CV term, and §5 confirms the ranking is 'along the UVLF.' Thus Figure 4 and the relative model significances do not actually include σ_CV, contradicting the abstract's 'combined constraints on σ_CV and the UVLF' wording. This is a missing/incomplete statistic and a correctness risk, not a circular reduction: the empirical σ_CV measurement and the qualitative joint contours (Figures 2/3) remain independent. Appendix B's caveat that quoted uncertainties ignore model misspecification further weakens the quantitative rankings, again a statistical limitation rather than circularity. Overall: no load-bearing step reduces a prediction to its fitted input; minor self-citations (W25, Jespersen et al. 2025c) are not load-bearing. Score 2.

Axiom & Free-Parameter Ledger

8 free parameters · 6 axioms · 0 invented entities

The paper introduces no new physical entities. The central measurement is empirical; the main extra assumptions are the statistical model for number counts, UniverseMachine as a clustering/non-linear benchmark, and the simplified model implementations. The 8 fitted model parameters in Table 1 are the only numbers tuned to the data.

free parameters (8)
  • ΔM_UV (global boost, model A1) = 1.02^{+0.08}_{-0.07} mag
    Fitted to match observed z~10 number counts and σ_CV constraints; Table 1.
  • ΔM_UV (halo mass > 10^10 M_sun, model A2) = 1.01^{+0.08}_{-0.08} mag
    Fitted to match observed number counts; Table 1.
  • ΔM_UV (halo mass > 10^10.5 M_sun, model A2) = 0.93^{+0.13}_{-0.19} mag
    Fitted to match observed number counts; Table 1.
  • SFE (global constant, model B1) = 0.098^{+0.004}_{-0.006}
    Fitted to match observed number counts and σ_CV; Table 1.
  • SFE_peak (DMSFE slope 0.5, model B2) = 0.63^{+0.03}_{-0.02}
    Fitted to match observed number counts and σ_CV; Table 1.
  • SFE_peak (DMSFE slope 0.6, model B2) = 0.94^{+0.06}_{-0.06}
    Fitted to match observed number counts and σ_CV; Table 1.
  • σ_UV (global scatter, model C1) = 1.21^{+0.04}_{-0.04} mag
    Fitted to match observed number counts and σ_CV; Table 1.
  • σ_UV,norm (halo-mass dependent scatter, model C2) = 1.11^{+0.06}_{-0.05} mag
    Fitted to match observed number counts and σ_CV; Table 1.
axioms (6)
  • domain assumption ΛCDM cosmology with WMAP9 parameters (h=0.6932, Ω_m,0=0.2865) is assumed throughout.
    Used for all physical scales, σ_DM, and UniverseMachine comparisons (§1).
  • domain assumption The F115W-dropout color selection isolates a pure z~10 LBG sample; the 18/18 spectroscopic confirmation rate is taken as evidence of high purity.
    Underlies all counts; contamination would dilute clustering (§2.2, §4.5).
  • domain assumption Field-to-field galaxy number counts follow a Gamma distribution with variance = mean + μ²σ_CV², and the Negative-Binomial likelihood down-weights outliers.
    Core statistical model for MCMC σ_CV inference (§2.4.2), justified by Jespersen et al. 2025c.
  • domain assumption UniverseMachine light cones reproduce small-scale/non-linear clustering at z~10 well enough to convert b_g,CV to linear bias and to serve as the fiducial comparison.
    Used throughout §3.2–§4.2; if UniverseMachine clustering is wrong, the non-linear correction factors (2.8–5.4) are wrong.
  • ad hoc to paper The simple UniverseMachine modifications (uniform ΔM_UV, constant SFE, power-law SFE, global/halo-dependent σ_UV) faithfully represent the proposed physical model classes.
    New simplified models in §3.2 are proxies for top-heavy IMF/dust/AGN, DMSFE, and burstiness; they are not full physical implementations.
  • ad hoc to paper Dropping one UniverseMachine CANDELS light cone with 'unexplainable and significantly lower typical number densities' is valid; the remaining 32 lines of sight are representative.
    Data-selection choice in §2.4.2; dropping an unfavorable realization can bias the model comparison.

pith-pipeline@v1.3.0-alltime-deepseek · 26461 in / 17515 out tokens · 137169 ms · 2026-08-03T16:13:32.169535+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Exploring Cosmic Dawn with PANORAMIC II: Cosmic Variance and Galaxy Clustering at $z\sim10$." pith.science (2026). https://pith.science/paper/XKE7NCME

@misc{pith2026251214212,
  author       = {Pith},
  title        = {Pith review of: Exploring Cosmic Dawn with PANORAMIC II: Cosmic Variance and Galaxy Clustering at $z\sim10$},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XKE7NCME}},
  note         = {Machine review of arXiv:2512.14212}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Observational campaigns with JWST have revealed a higher-than-expected abundance of UV-bright galaxies at $z\gtrsim10$, with various proposed theoretical explanations. A powerful complementary constraint to break degeneracies between different models is galaxy clustering. In this paper, we combine PANORAMIC pure parallel and legacy imaging along 34 independent sightlines to measure the cosmic variance ($\sigma_{\rm CV}$) in the number counts of Lyman break galaxies at $z\sim10$ which is directly related to their clustering strength. We find $\sigma_{\rm CV}=0.96^{+0.20}_{-0.18}$, $1.46^{+0.54}_{-0.44}$, and $1.71^{+0.72}_{-0.59}$ per NIRCam pointing ($\sim9.7\,{\rm arcmin}^2$, $\lesssim1.5\,{\rm pMpc}$ at $z\sim10$) for galaxies with M$_{\rm UV}<-19.5$, $-20$, and $-20.5$. Comparing to galaxies in the fiducial UniverseMachine, we find that $\sigma_{\rm CV}$ is consistent with our measurements, but that the number densities are a factor $\gtrsim5$ lower. We implement simple models in the UniverseMachine that represent different physical mechanisms to enhance the number density of UV-bright galaxies. All models decrease $\sigma_{\rm CV}$ by placing galaxies at fixed M$_{\rm UV}$ in lower mass halos, but to varying degrees. Combined constraints on $\sigma_{\rm CV}$ and the UVLF thus tentatively disfavor models that globally increase the star formation efficiency (SFE) or the scatter in the M$_{\rm UV}$-$M_{\rm halo}$ relation, while models that decrease the mass-to-light ratio, or assume a power-law scaling of the SFE with $M_{\rm halo}$ agree better with the data. We show that with sufficient additional independent sightlines, robust discrimination between models is possible, paving the way for powerful constraints on the physics of early galaxy evolution through NIRCam pure parallel imaging.

Figures

Figures reproduced from arXiv: 2512.14212 by Aidan P. Cloonan, Andrea Weibel, Anne Hutter, Christian Kragh Jespersen, Christina C. Williams, Gabriel Brammer, Katherine E. Whitaker, Marko Shuntov, Michael V. Maseda, Pascal A. Oesch, Pratika Dayal, Rachel Bezanson, Zhiyuan Ji.

Figure 1
Figure 1. Figure 1: Cosmic variance σCV in the galaxy number count at z ∼ 10 for a NIRCam pointing sized survey (9.7 arcmin2 ). We plot values inferred through two different methods: boot￾strapping Equation 4 over 34 independent fields (crosses), and MCMC-fitting to the distribution of number counts per field (stars). The secondary y-axis shows the galaxy bias in￾ferred by cosmic variance, as defined in Equation 2. Measure￾me… view at source ↗
Figure 2
Figure 2. Figure 2: Combined constraints on the abundance of galaxies at z ∼ 10, quantified as the mean number of galaxies per field (i.e. per NIRCam pointing), µ, and their clustering, quantified by the cosmic variance σCV. Each row of panels corresponds to a simple model implemented in the UniverseMachine to represent a class of models invoked to explain the abundance of UV-bright galaxies at z ∼ 10. From left to right, pan… view at source ↗
Figure 3
Figure 3. Figure 3: Same as [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Combined model tension, calculated as the equivalent posterior distance in Gaussian units, across three distinct MUV bins (−20 < MUV < −19.5, −20.5 < MUV < −20, and MUV < −20.5), for the different models presented in Section 3.2. The secondary y-axis on the right shows the combined model tension relative to the best-fitting model (sharp DMSFE), indicated as the horizontal dashed line. This shows that model… view at source ↗
Figure 5
Figure 5. Figure 5: Galaxy bias bg, CV, as defined in Equation 2, as a function of the field side length in arcmin for MUV < −19.5, -20, and -20.5, measured from the UniverseMachine at z ∼ 10. The secondary x-axis corresponds to the phys￾ical scale for the respective field side length in co-moving Mpc at z = 10. Linear bias values are shown as horizontal dashed lines, and the shaded regions are 1σ uncertainties re￾spectively.… view at source ↗
Figure 6
Figure 6. Figure 6: Combined constraints µ and σCV in analogy to Figures 2 and 3. The gray contours and red star represent our measurement in the MUV < −20 bin. The blue contours are derived assuming that our measurements represent the ground truth and simulating the constraining power of 50 additional lines of sight, as could be easily obtained in a dedicated pure parallel NIRCam imaging program. The colored markers represen… view at source ↗
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Posterior distributions of the MCMC-fitting described in Section 2.4.2. The fitted quantities are the mean and variance. Contours correspond to 0.5, 1 , 1.5 and 2σ confidence regions [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗

discussion (0)

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

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The Impact of Cosmic Variance and Satellites on JWST Clustering Measurements at Redshift around 6

    astro-ph.CO 2026-05 unverdicted novelty 7.0

    Using 1000 mock realizations matched to the ASPIRE survey, the authors find cosmic variance increases clustering errors by ~3x over Poisson estimates and widens minimum halo mass uncertainties by 1.5-3x for z~6 quasar...

  2. DeepDive: Simultaneous Formation of Massive Quiescent Galaxies in High-Redshift Galaxy Proto-clusters

    astro-ph.GA 2026-04 unverdicted novelty 6.0

    JWST data show massive quiescent galaxies in high-redshift proto-clusters formed and quenched simultaneously, with AGN signatures, indicating environmental triggering of quenching.

  3. Winding Back the Clock: Recent Star Formation Histories of Massive Quiescent Galaxies Are Consistent With Their Rapid Number Density Evolution Since $\mathbf{z\sim7}$

    astro-ph.GA 2026-04 conditional novelty 6.0

    Star formation histories inferred for z=2-5 massive quiescent galaxies imply past number densities that align with observed rapid evolution since z~7.

  4. Tilting at the Turnover: Modeling the Faint-End of the UV Luminosity Function Behind Abell s1063 with JWST

    astro-ph.GA 2026-07 unverdicted novelty 5.0

    Using lensed JWST data behind Abell S1063, the UVLF at z~6-11 is fit with gradual quadratic turnover, yielding no evidence for turnover to M_UV=-13.5 at z=6, exclusion limits on turnover models, and lower bounds showi...

  5. JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: Photometrically Selected Galaxy Candidates at z > 8

    astro-ph.GA 2026-01 unverdicted novelty 5.0

    JADES DR5 delivers 2081 z_phot > 8 galaxy candidates with UV slope trends, morphological evidence of clumpy growth, and improved photo-z methods tested on a spectroscopic subsample.

Reference graph

Works this paper leans on

116 extracted references · 9 canonical work pages · cited by 5 Pith papers · 1 internal anchor

  1. [1]

    J., Conselice, C

    Adams, N. J., Conselice, C. J., Ferreira, L., et al. 2023, MNRAS, 518, 4755, doi: 10.1093/mnras/stac3347

    doi:10.1093/mnras/stac3347 2023

  2. [2]

    J., Conselice, C

    Adams, N. J., Conselice, C. J., Austin, D., et al. 2024, ApJ, 965, 169, doi: 10.3847/1538-4357/ad2a7b Arrabal Haro, P., Dickinson, M., Finkelstein, S. L., et al. 2023, Nature, 622, 707, doi: 10.1038/s41586-023-06521-7

    doi:10.3847/1538-4357/ad2a7b 2024

  3. [3]

    2025, arXiv e-prints, arXiv:2507.03124

    Asada, Y., Willott, C., Muzzin, A., et al. 2025, arXiv e-prints, arXiv:2507.03124. https://arxiv.org/abs/2507.03124

    arXiv 2025

  4. [4]

    2023, MNRAS, 524, 5486, doi: 10.1093/mnras/stad1998

    Atek, H., Chemerynska, I., Wang, B., et al. 2023, MNRAS, 524, 5486, doi: 10.1093/mnras/stad1998

    doi:10.1093/mnras/stad1998 2023

  5. [5]

    L., Trenti, M., Wyithe, J

    Barone-Nugent, R. L., Trenti, M., Wyithe, J. S. B., et al. 2014, ApJ, 793, 17, doi: 10.1088/0004-637X/793/1/17

    doi:10.1088/0004-637x/793/1/17 2014

  6. [6]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182

    doi:10.1093/mnras/stz1182 2019

  7. [7]

    A., & Weinberg, D

    Berlind, A. A., & Weinberg, D. H. 2002, ApJ, 575, 587, doi: 10.1086/341469

    doi:10.1086/341469 2002

  8. [8]

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

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

    doi:10.1051/aas:1996164 1996

  9. [9]

    A., Lira, P., Barrientos, L

    Blanc, G. A., Lira, P., Barrientos, L. F., et al. 2008, ApJ, 681, 1099, doi: 10.1086/588018

    work page doi:10.1086/588018 2008

  10. [10]

    J., Oesch, P

    Bouwens, R. J., Oesch, P. A., Labb´ e, I., et al. 2016, ApJ, 830, 67, doi: 10.3847/0004-637X/830/2/67

    doi:10.3847/0004-637x/830/2/67 2016

  11. [11]

    Accelerated by Dark Matter: a High-redshift Pathway to Efficient Galaxy-scale Star Formation

    Boylan-Kolchin, M. 2024, arXiv e-prints, arXiv:2407.10900, doi: 10.48550/arXiv.2407.10900

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2407.10900 2024

  12. [12]

    2023, grizli, 1.9.11, Zenodo, Zenodo, doi: 10.5281/zenodo.8370018

    Brammer, G. 2023, grizli, 1.9.11, Zenodo, Zenodo, doi: 10.5281/zenodo.8370018

    doi:10.5281/zenodo.8370018 2023

  13. [13]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, The Astrophysical Journal, 686, 1503 Budav´ ari, T., Connolly, A. J., Szalay, A. S., et al. 2003, ApJ, 595, 59, doi: 10.1086/377168

    doi:10.1086/377168 2008

  14. [14]

    2019, MNRAS, 483, 1922, doi: 10.1093/mnras/sty3069

    Velden, C. 2019, MNRAS, 483, 1922, doi: 10.1093/mnras/sty3069

    work page doi:10.1093/mnras/sty3069 2019

  15. [15]

    C., McLure, R

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

    doi:10.1093/mnras/sty2169 2018

  16. [16]

    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

    doi:10.1038/s41586-024-07860-9 2024

  17. [17]

    Carrasco, D., Trenti, M., Mutch, S., & Oesch, P. A. 2018, PASA, 35, e022, doi: 10.1017/pasa.2018.17

    doi:10.1017/pasa.2018.17 2018

  18. [18]

    2025, arXiv e-prints, arXiv:2507.13160, doi: 10.48550/arXiv.2507.13160

    Carvajal-Bohorquez, C., Ciesla, L., Laporte, N., et al. 2025, arXiv e-prints, arXiv:2507.13160, doi: 10.48550/arXiv.2507.13160

    work page doi:10.48550/arxiv.2507.13160 2025

  19. [19]

    M., Kartaltepe, J

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

    doi:10.3847/1538-4357/acc2bc 2023

  20. [20]

    M., Akins, H

    Casey, C. M., Akins, H. B., Shuntov, M., et al. 2024, ApJ, 965, 98, doi: 10.3847/1538-4357/ad2075

    doi:10.3847/1538-4357/ad2075 2024

  21. [21]

    2022, ApJL, 938, L15, doi: 10.3847/2041-8213/ac94d0

    Castellano, M., Fontana, A., Treu, T., et al. 2022, ApJL, 938, L15, doi: 10.3847/2041-8213/ac94d0

    doi:10.3847/2041-8213/ac94d0 2022

  22. [22]

    2024, ApJ, 972, 143, doi: 10.3847/1538-4357/ad5f88

    Castellano, M., Napolitano, L., Fontana, A., et al. 2024, ApJ, 972, 143, doi: 10.3847/1538-4357/ad5f88

    doi:10.3847/1538-4357/ad5f88 2024

  23. [23]

    2024, ApJ, 965, 101, doi: 10.3847/1538-4357/ad2b6c

    Genel, S. 2024, ApJ, 965, 101, doi: 10.3847/1538-4357/ad2b6c

    doi:10.3847/1538-4357/ad2b6c 2024

  24. [24]

    2002, PhR, 372, 1, doi: 10.1016/S0370-1573(02)00276-4

    Cooray, A., & Sheth, R. 2002, PhR, 372, 1, doi: 10.1016/S0370-1573(02)00276-4

    doi:10.1016/s0370-1573(02)00276-4 2002

  25. [25]

    R., Hutter, A., Dayal, P., et al

    Cueto, E. R., Hutter, A., Dayal, P., et al. 2024, A&A, 686, A138, doi: 10.1051/0004-6361/202349017

    doi:10.1051/0004-6361/202349017 2024

  26. [26]

    2023, Nature Astronomy, 7, 622, doi: 10.1038/s41550-023-01918-w

    Curtis-Lake, E., Carniani, S., Cameron, A., et al. 2023, Nature Astronomy, 7, 622, doi: 10.1038/s41550-023-01918-w

    doi:10.1038/s41550-023-01918-w 2023

  27. [27]

    2024, MNRAS, 533, 2391, doi: 10.1093/mnras/stae2006

    Dalmasso, N., Leethochawalit, N., Trenti, M., & Boyett, K. 2024, MNRAS, 533, 2391, doi: 10.1093/mnras/stae2006

    doi:10.1093/mnras/stae2006 2024

  28. [28]

    S., & Pacucci, F

    Dayal, P., Ferrara, A., Dunlop, J. S., & Pacucci, F. 2014, MNRAS, 445, 2545, doi: 10.1093/mnras/stu1848

    doi:10.1093/mnras/stu1848 2014

  29. [29]

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

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

    doi:10.1093/mnras/stad1557 2023

  30. [30]

    T., Dunlop, J

    Donnan, C. T., Dunlop, J. S., McLure, R. J., McLeod, D. J., & Cullen, F. 2025, arXiv e-prints, arXiv:2501.03217, doi: 10.48550/arXiv.2501.03217

    Pith/arXiv arXiv doi:10.48550/arxiv.2501.03217 2025

  31. [31]

    T., McLeod, D

    Donnan, C. T., McLeod, D. J., Dunlop, J. S., et al. 2023, MNRAS, 518, 6011, doi: 10.1093/mnras/stac3472

    doi:10.1093/mnras/stac3472 2023

  32. [32]

    T., McLure, R

    Donnan, C. T., McLure, R. J., Dunlop, J. S., et al. 2024, MNRAS, 533, 3222, doi: 10.1093/mnras/stae2037

    doi:10.1093/mnras/stae2037 2024

  33. [33]

    S., et al

    Feldmann, R., Boylan-Kolchin, M., Bullock, J. S., et al. 2025, MNRAS, 536, 988, doi: 10.1093/mnras/stae2633

    doi:10.1093/mnras/stae2633 2025

  34. [34]

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

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

    doi:10.1093/mnras/stad1095 2023

  35. [35]

    L., Bagley, M

    Finkelstein, S. L., Bagley, M. B., Ferguson, H. C., et al. 2023, ApJL, 946, L13, doi: 10.3847/2041-8213/acade4

    doi:10.3847/2041-8213/acade4 2023

  36. [36]

    L., Leung, G

    Finkelstein, S. L., Leung, G. C. K., Bagley, M. B., et al. 2024, ApJL, 969, L2, doi: 10.3847/2041-8213/ad4495

    doi:10.3847/2041-8213/ad4495 2024

  37. [37]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    doi:10.1086/670067 2013

  38. [38]

    R., et al

    Fujimoto, S., Wang, B., Weaver, J. R., et al. 2024, ApJ, 977, 250, doi: 10.3847/1538-4357/ad9027

    doi:10.3847/1538-4357/ad9027 2024

  39. [39]

    Gelli, V., Mason, C., & Hayward, C. C. 2024, ApJ, 975, 192, doi: 10.3847/1538-4357/ad7b36

    doi:10.3847/1538-4357/ad7b36 2024

  40. [40]

    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

    doi:10.1088/0067-0049/197/2/35 2011

  41. [41]

    N., Johnson, B

    Hainline, K. N., Johnson, B. D., Robertson, B., et al. 2024, ApJ, 964, 71, doi: 10.3847/1538-4357/ad1ee4

    doi:10.3847/1538-4357/ad1ee4 2024

  42. [42]

    2024, ApJ, 960, 56, doi: 10.3847/1538-4357/ad0b7e

    Harikane, Y., Nakajima, K., Ouchi, M., et al. 2024, ApJ, 960, 56, doi: 10.3847/1538-4357/ad0b7e

    doi:10.3847/1538-4357/ad0b7e 2024

  43. [43]

    2016, ApJ, 821, 123, doi: 10.3847/0004-637X/821/2/123 —

    Harikane, Y., Ouchi, M., Ono, Y., et al. 2016, ApJ, 821, 123, doi: 10.3847/0004-637X/821/2/123 —. 2018, PASJ, 70, S11, doi: 10.1093/pasj/psx097 19

    doi:10.3847/0004-637x/821/2/123 2016

  44. [44]

    2023, ApJS, 265, 5, doi: 10.3847/1538-4365/acaaa9

    Harikane, Y., Ouchi, M., Oguri, M., et al. 2023, ApJS, 265, 5, doi: 10.3847/1538-4365/acaaa9

    doi:10.3847/1538-4365/acaaa9 2023

  45. [45]

    2016, MNRAS, 460, 2552, doi: 10.1093/mnras/stw840

    Campbell, D., & Tollerud, E. 2016, MNRAS, 460, 2552, doi: 10.1093/mnras/stw840

    doi:10.1093/mnras/stw840 2016

  46. [46]

    M., & Furlanetto, S

    Hegde, S., Wyatt, M. M., & Furlanetto, S. R. 2024, JCAP, 2024, 025, doi: 10.1088/1475-7516/2024/08/025

    doi:10.1088/1475-7516/2024/08/025 2024

  47. [47]

    2009, A&A, 498, 725, doi: 10.1051/0004-6361/200811042

    Hildebrandt, H., Pielorz, J., Erben, T., et al. 2009, A&A, 498, 725, doi: 10.1051/0004-6361/200811042

    doi:10.1051/0004-6361/200811042 2009

  48. [48]

    2013, ApJS, 208, 19, doi: 10.1088/0067-0049/208/2/19

    Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19, doi: 10.1088/0067-0049/208/2/19

    doi:10.1088/0067-0049/208/2/19 2013

  49. [49]

    W., Bovy, J., & Lang, D

    Hogg, D. W., Bovy, J., & Lang, D. 2010, arXiv e-prints, arXiv:1008.4686, doi: 10.48550/arXiv.1008.4686

    Pith/arXiv arXiv doi:10.48550/arxiv.1008.4686 2010

  50. [50]

    R., Dayal, P., et al

    Hutter, A., Cueto, E. R., Dayal, P., et al. 2025, A&A, 694, A254, doi: 10.1051/0004-6361/202452460

    doi:10.1051/0004-6361/202452460 2025

  51. [51]

    2018, ApJ, 854, 73, doi: 10.3847/1538-4357/aaa544

    Ishigaki, M., Kawamata, R., Ouchi, M., et al. 2018, ApJ, 854, 73, doi: 10.3847/1538-4357/aaa544

    doi:10.3847/1538-4357/aaa544 2018

  52. [52]

    2017, ApJ, 841, 8, doi: 10.3847/1538-4357/aa6d64

    Ishikawa, S., Kashikawa, N., Toshikawa, J., et al. 2017, ApJ, 841, 8, doi: 10.3847/1538-4357/aa6d64

    doi:10.3847/1538-4357/aa6d64 2017

  53. [53]

    1946, Proceedings of the Royal Society of London

    Jeffreys, H. 1946, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 186, 453

    1946

  54. [54]

    K., Carnall, A

    Jespersen, C. K., Carnall, A. C., & Lovell, C. C. 2025a, ApJL, 988, L19, doi: 10.3847/2041-8213/adeb7c

    doi:10.3847/2041-8213/adeb7c 2041

  55. [55]

    K., Cranmer, M., Melchior, P., et al

    Jespersen, C. K., Cranmer, M., Melchior, P., et al. 2022, ApJ, 941, 7, doi: 10.3847/1538-4357/ac9b18

    doi:10.3847/1538-4357/ac9b18 2022

  56. [56]

    K., Melchior, P., Spergel, D

    Jespersen, C. K., Melchior, P., Spergel, D. N., et al. 2025b, arXiv e-prints, arXiv:2503.03816, doi: 10.48550/arXiv.2503.03816

    Pith/arXiv arXiv doi:10.48550/arxiv.2503.03816

  57. [57]

    Lovell, C. C. 2025c, ApJ, 982, 23, doi: 10.3847/1538-4357/adb422

    doi:10.3847/1538-4357/adb422

  58. [58]

    M., Lacey, C

    Jose, C., Baugh, C. M., Lacey, C. G., & Subramanian, K. 2017, MNRAS, 469, 4428, doi: 10.1093/mnras/stx1014

    doi:10.1093/mnras/stx1014 2017

  59. [59]

    G., & Baugh, C

    Jose, C., Lacey, C. G., & Baugh, C. M. 2016, MNRAS, 463, 270, doi: 10.1093/mnras/stw1702

    doi:10.1093/mnras/stw1702 2016

  60. [60]

    2016, MNRAS, 457, 4340, doi: 10.1093/mnras/stw248

    Klypin, A., Yepes, G., Gottl¨ ober, S., Prada, F., & Heß, S. 2016, MNRAS, 457, 4340, doi: 10.1093/mnras/stw248

    doi:10.1093/mnras/stw248 2016

  61. [61]

    2024, arXiv e-prints, arXiv:2405.04578, doi: 10.48550/arXiv.2405.04578

    Kravtsov, A., & Belokurov, V. 2024, arXiv e-prints, arXiv:2405.04578, doi: 10.48550/arXiv.2405.04578

    Pith/arXiv arXiv doi:10.48550/arxiv.2405.04578 2024

  62. [62]

    2022, MNRAS, 509, 5836, doi: 10.1093/mnras/stab3265

    Roberts-Borsani, G., & Treu, T. 2022, MNRAS, 509, 5836, doi: 10.1093/mnras/stab3265

    doi:10.1093/mnras/stab3265 2022

  63. [63]

    P., et al

    Li, C., Kauffmann, G., Jing, Y. P., et al. 2006, MNRAS, 368, 21, doi: 10.1111/j.1365-2966.2006.10066.x

    arXiv 2006

  64. [64]

    C., et al

    Li, Z., Dekel, A., Sarkar, K. C., et al. 2024, A&A, 690, A108, doi: 10.1051/0004-6361/202348727

    doi:10.1051/0004-6361/202348727 2024

  65. [65]

    2025, arXiv e-prints, arXiv:2511.09618, doi: 10.48550/arXiv.2511.09618

    Schaye, J. 2025, arXiv e-prints, arXiv:2511.09618, doi: 10.48550/arXiv.2511.09618

    doi:10.48550/arxiv.2511.09618 2025

  66. [66]

    2012, ApJ, 756, 71, doi: 10.1088/0004-637X/756/1/71 L´ opez-Sanjuan, C., Cenarro, A

    Lin, L., Dickinson, M., Jian, H.-Y., et al. 2012, ApJ, 756, 71, doi: 10.1088/0004-637X/756/1/71 L´ opez-Sanjuan, C., Cenarro, A. J., Hern´ andez-Monteagudo, C., et al. 2015, A&A, 582, A16, doi: 10.1051/0004-6361/201526731

    work page doi:10.1088/0004-637x/756/1/71 2012

  67. [67]

    S., Bose, S., et al

    Lu, S., Frenk, C. S., Bose, S., et al. 2025, MNRAS, 536, 1018, doi: 10.1093/mnras/stae2646

    doi:10.1093/mnras/stae2646 2025

  68. [68]

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

    Mason, C. A., Trenti, M., & Treu, T. 2015, ApJ, 813, 21, doi: 10.1088/0004-637X/813/1/21 —. 2023, MNRAS, 521, 497, doi: 10.1093/mnras/stad035

    doi:10.1088/0004-637x/813/1/21 2015

  69. [69]

    G., et al

    Mauerhofer, V., Dayal, P., Haehnelt, M. G., et al. 2025, A&A, 696, A157, doi: 10.1051/0004-6361/202554042

    doi:10.1051/0004-6361/202554042 2025

  70. [70]

    J., Wolk, M., Colombi, S., et al

    McCracken, H. J., Wolk, M., Colombi, S., et al. 2015, MNRAS, 449, 901, doi: 10.1093/mnras/stv305

    doi:10.1093/mnras/stv305 2015

  71. [71]

    2009, A&A, 505, 463, doi: 10.1051/0004-6361/200912314

    Meneux, B., Guzzo, L., de la Torre, S., et al. 2009, A&A, 505, 463, doi: 10.1051/0004-6361/200912314

    work page doi:10.1051/0004-6361/200912314 2009

  72. [72]

    2020, MNRAS, 499, 4534, doi: 10.1093/mnras/staa3150

    Mirocha, J. 2020, MNRAS, 499, 4534, doi: 10.1093/mnras/staa3150

    doi:10.1093/mnras/staa3150 2020

  73. [73]

    J., & White, S

    Mo, H. J., & White, S. D. M. 1996, MNRAS, 282, 347, doi: 10.1093/mnras/282.2.347

    doi:10.1093/mnras/282.2.347 1996

  74. [74]

    A., Kreilgaard, K

    Morishita, T., Mason, C. A., Kreilgaard, K. C., et al. 2025, ApJ, 983, 152, doi: 10.3847/1538-4357/adbbdc

    doi:10.3847/1538-4357/adbbdc 2025

  75. [75]

    P., Somerville, R

    Moster, B. P., Somerville, R. S., Newman, J. A., & Rix, H.-W. 2011, ApJ, 731, 113, doi: 10.1088/0004-637X/731/2/113 Mu˜ noz, J. B., Mirocha, J., Furlanetto, S., & Sabti, N. 2023, MNRAS, 526, L47, doi: 10.1093/mnrasl/slad115

    doi:10.1088/0004-637x/731/2/113 2011

  76. [76]

    P., Oesch, P

    Naidu, R. P., Oesch, P. A., van Dokkum, P., et al. 2022, ApJL, 940, L14, doi: 10.3847/2041-8213/ac9b22

    doi:10.3847/2041-8213/ac9b22 2022

  77. [77]

    P., Oesch, P

    Naidu, R. P., Oesch, P. A., Brammer, G., et al. 2025, arXiv e-prints, arXiv:2505.11263. https://arxiv.org/abs/2505.11263

    arXiv 2025

  78. [78]

    A., & Davis, M

    Newman, J. A., & Davis, M. 2002, ApJ, 564, 567, doi: 10.1086/324148

    doi:10.1086/324148 2002

  79. [79]

    A., & Moster, B

    Newman, J. A., & Moster, B. P. 2014, QUICKCV: Cosmic variance calculator, Astrophysics Source Code Library, record ascl:1402.012

    2014

  80. [80]

    M., Hawkins, E., et al

    Norberg, P., Baugh, C. M., Hawkins, E., et al. 2002, MNRAS, 332, 827, doi: 10.1046/j.1365-8711.2002.05348.x

    arXiv 2002

Showing first 80 references.