pith. machine review for the scientific record. sign in

arxiv: 2511.19600 · v2 · submitted 2025-11-24 · 🌌 astro-ph.CO · astro-ph.GA

Recognition: no theorem link

Towards Reconciling Reionization with JWST: The Role of Bright Galaxies and Strong Feedback

Ankita Bera , Sultan Hassan , Robert Feldmann , Romeel Dav\'e , Kristian Finlator
Authors on Pith no claims yet

Pith reviewed 2026-05-17 04:54 UTC · model grok-4.3

classification 🌌 astro-ph.CO astro-ph.GA
keywords reionizationJWSTUV luminosity functiongalaxy feedbackionizing emissivityThomson optical depthbright galaxiesphoton budget
0
0 comments X

The pith

Strong feedback from bright galaxies produces a gradual reionization history that matches both JWST UVLF and CMB optical depth within 2 sigma.

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

The paper builds a semi-analytical model that links galaxy formation simulations to statistical fits of both JWST galaxy counts and reionization measurements. Weak-feedback versions reproduce the reionization observables but underpredict the high number of bright galaxies JWST sees above redshift 9. Strong-feedback versions shift more of the ionizing output to bright galaxies, match the elevated JWST UV luminosity functions at z greater than or equal to 10, and generate a slower reionization stretch from redshift 16 down to 6. This slower history produces a Thomson scattering optical depth that lies within two standard deviations of the CMB value and finishes reionization by redshift 6, consistent with existing data.

Core claim

Models with strong feedback and a higher contribution from bright galaxies successfully reproduce JWST UVLF at z greater than or equal to 10. The strong-feedback model constrained by JWST UVLF predicts a more gradual and extended reionization history, as opposed to the sudden reionization seen in the weak-feedback models. This extended nature of reionization from z approximately 16 to 6 yields an optical depth consistent at the 2-sigma level with the CMB constraint, thereby alleviating the photon-budget crisis. In both scenarios reionization completes by z approximately 6.

What carries the argument

Semi-analytical framework that couples a physically motivated source model derived from radiative-transfer hydrodynamic simulations with a Markov Chain Monte Carlo sampler to jointly calibrate JWST UV luminosity functions and reionization observables including ionizing emissivity, neutral hydrogen fraction, and optical depth.

If this is right

  • Strong-feedback models over-estimate the bright end of the UVLF at redshifts below 9.
  • Weak-feedback models match reionization data but fail to reproduce the elevated JWST UVLF at z greater than 9.
  • Both feedback scenarios complete reionization by redshift approximately 6.
  • Accurate treatment of redshift evolution in feedback and emissivities from bright and faint populations is required to describe the first billion years.

Where Pith is reading between the lines

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

  • The results imply that the relative contribution of faint galaxies to the ionizing budget drops when strong feedback is included.
  • Future observations of the UVLF bright end at intermediate redshifts can test the predicted over-estimate in the strong-feedback case.
  • The framework could be applied to additional reionization tracers such as the 21 cm power spectrum to further separate feedback effects.

Load-bearing premise

The source model taken from radiative-transfer hydrodynamic simulations correctly describes how feedback strength and ionizing output change with redshift for bright versus faint galaxies.

What would settle it

New JWST measurements of the bright end of the UV luminosity function at redshifts 7 to 9 that fall below the strong-feedback prediction, or a future CMB optical-depth value lying more than two sigma outside the range produced by the extended reionization history.

Figures

Figures reproduced from arXiv: 2511.19600 by Ankita Bera, Kristian Finlator, Robert Feldmann, Romeel Dav\'e, Sultan Hassan.

Figure 1
Figure 1. Figure 1: JWST UV Luminosity functions, ϕUV observa￾tional data considered to constrain our models. Data points include Adams et al. (2024, A24), Bouwens et al. (2023a, B23a), Bouwens et al. (2023b, B23b), Casey et al. (2024, C24), Donnan et al. (2023b, D23a), Donnan et al. (2023a, D23b), Donnan et al. (2024, D24), Finkelstein et al. (2024, F24), Harikane et al. (2023, H23), Leung et al. (2023, L23), P´erez-Gonz´ale… view at source ↗
Figure 2
Figure 2. Figure 2: Corner plots showing the inferred parameter distributions for different combinations of observables: Left: Con￾straints from joint fits combining EoR observables with ϕUV and ρUV data, fixing log10(B/M⊙) = 7.67, to reduce the parameter degeneracies as described in appendix A. Right: Constraints from EoR and joint fits for all models described in §3.1, allowing only fesc and C to vary and fixing other param… view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of Rest-frame ultraviolet luminosity functions, (UVLFs), as predicted by our models using inferred parameter values given in [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Redshift evolution of all globally averaged quantities comparing with the observations given in §3.3. Top left: UV luminosity density, ρUV, top right: star formation rate density, ρSFR, middle left: neutral hydrogen fraction, xHI, middle right: ionizing emissivity, N˙ ion, and bottom right: Thomson optical depth, τ for all the model variants given in Table. 5. All models reproduce the reionization history … view at source ↗
Figure 5
Figure 5. Figure 5: The ionizing emissivity for all models, separating the contribution of massive (Mh ≥ 1010M⊙) and faint galax￾ies (Mh ≤ 1010M⊙) by solid and dashed curves respectively. (2023). The EoR-ϕUV-wf and EoR-ρUV-wf models exhibit intermediate reionization histories as this model predicts higher emissivity than the EoR-only case but lower than the EoR-ϕUV-sf fit, at the high redshifts, z > 7. Notably, none of the mo… view at source ↗
Figure 6
Figure 6. Figure 6: Corner plots showing the inferred parameter distributions for using observational constraints, ϕUV and ρUV separately, allowing all parameters to vary. Before constraining our models to all observational constraints, we first use the ϕUV and ρUV observations separately to constrain our source model Rion parame￾ters and examine their correlations. The corner plot in the left panel of [PITH_FULL_IMAGE:figur… view at source ↗
read the original abstract

The elevated UV luminosity functions (UVLF) from recent James Webb Space Telescope (JWST) have challenged the viability of existing theoretical models. To address this, we use a semi-analytical framework -- which couples a physically motivated source model derived from radiative-transfer hydrodynamic simulations of reionization with a Markov Chain Monte Carlo sampler -- to perform a joint calibration to JWST galaxy surveys (UVLF, $\phi_{\rm UV}$ and UV luminosity density, $\rho_{\rm UV}$) and reionization-era observables (ionizing emissivity, $\dot{N}_{\rm ion}$, neutral hydrogen fraction, $x_{\rm HI}$, and Thomson optical depth, $\tau$). We find that models with weak feedback and a higher contribution from faint galaxies reproduce the reionization observables but struggle to match the elevated JWST UVLF at $z > 9$. In contrast, models with stronger feedback (i.e., rapid redshift evolution) and a higher contribution from bright galaxies successfully reproduce JWST UVLF at $z \geq 10$, but over-estimate the bright end at $z < 9$. The strong-feedback model constrained by JWST UVLF predicts a more gradual and extended reionization history, as opposed to the sudden reionization seen in the weak-feedback models. This extended nature of reionization ($z\sim 16$ - $6$) yields an optical depth consistent (at 2-$\sigma$) with the Cosmic Microwave Background (CMB) constraint, thereby alleviating the photon-budget crisis. In both scenarios, reionization is complete by $z \sim 6$, consistent with current data. Our analysis highlights the importance of accurately modeling feedback and ionizing emissivities from different source populations during the first billion years after the Big Bang.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The manuscript presents a semi-analytical model that couples a source model derived from radiative-transfer hydrodynamic simulations with MCMC sampling to jointly calibrate to JWST UV luminosity functions (UVLF, ϕ_UV) and UV luminosity density (ρ_UV) together with reionization observables (ionizing emissivity Ṅ_ion, neutral fraction x_HI, and Thomson optical depth τ). It concludes that weak-feedback models with dominant faint-galaxy contribution match reionization data but fail to reproduce the elevated JWST UVLF at z > 9, whereas strong-feedback models (rapid redshift evolution) with higher bright-galaxy contribution reproduce JWST UVLF at z ≥ 10, predict a gradual reionization history extending from z ∼ 16 to z ∼ 6, and produce a τ value consistent with CMB constraints at 2σ, thereby alleviating the photon-budget crisis while completing reionization by z ∼ 6.

Significance. If the redshift-dependent feedback parametrization and the simulation-derived emissivities prove robust, the work would be significant for showing how a shift toward bright galaxies under strong feedback can simultaneously satisfy JWST galaxy counts and reionization constraints without violating the CMB optical depth. The explicit contrast between weak- and strong-feedback scenarios and the grounding in hydrodynamic simulation outputs are constructive elements. The significance is reduced, however, by the need to demonstrate that the extended reionization history is not an artifact of the joint fitting procedure or of feedback scaling that is already known to mismatch lower-redshift data.

major comments (3)
  1. Abstract: The model is described as 'constrained by JWST UVLF' yet the joint calibration explicitly includes reionization observables such as τ. It is therefore unclear whether the reported 2-σ consistency between the predicted optical depth and the CMB measurement constitutes an independent test or follows by construction from the fit; this distinction is load-bearing for the central claim that the strong-feedback scenario alleviates the photon-budget crisis.
  2. Abstract: The strong-feedback model is reported to over-estimate the bright end of the UVLF at z < 9. This redshift-dependent mismatch indicates that the assumed rapid evolution of feedback strength may not be uniformly valid; if the same parametrization artificially suppresses early emissivity or extends reionization, the reported gradual history and 2-σ τ agreement could be a tuning artifact rather than a robust physical outcome.
  3. Methods (MCMC calibration section): The abstract states that MCMC is used to calibrate to multiple datasets, but no information is provided on parameter priors, convergence diagnostics, error propagation, or validation against held-out data. These details are required to evaluate whether the posterior on feedback strength and bright/faint galaxy contributions reliably supports the extrapolated high-z emissivities and reionization history.
minor comments (2)
  1. Abstract: The notation ϕ_UV and ρ_UV is introduced without explicit definition; a brief parenthetical clarification would improve readability.
  2. Abstract: The phrase 'consistent (at 2-σ) with the CMB constraint' should specify whether this is a posterior predictive check or a direct fit residual.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments, which have helped us improve the clarity and robustness of our manuscript. We address each major comment in detail below, providing clarifications and indicating revisions made to the text.

read point-by-point responses

  1. Referee: Abstract: The model is described as 'constrained by JWST UVLF' yet the joint calibration explicitly includes reionization observables such as τ. It is therefore unclear whether the reported 2-σ consistency between the predicted optical depth and the CMB measurement constitutes an independent test or follows by construction from the fit; this distinction is load-bearing for the central claim that the strong-feedback scenario alleviates the photon-budget crisis.

    Authors: We appreciate the referee pointing out this potential ambiguity. The MCMC procedure performs a joint fit to the JWST UVLF and ρ_UV at z ≥ 10, along with the reionization observables Ṅ_ion, x_HI, and τ. The 2σ consistency with the CMB τ is therefore a result of the calibration rather than a fully independent prediction. However, this does not undermine the central claim: the strong-feedback model is primarily driven by the need to reproduce the elevated high-redshift UVLF, which requires a larger contribution from bright galaxies. This population, under strong feedback, leads to an extended reionization history that simultaneously satisfies the other reionization constraints including τ. In contrast, weak-feedback models that fit reionization data fail the UVLF. Thus, the strong-feedback scenario resolves the apparent tension between JWST observations and reionization without requiring additional ionizing sources. We have revised the abstract to explicitly state that the model is jointly constrained by JWST UVLF and reionization observables, and clarified that the τ agreement demonstrates consistency rather than an independent test. revision: yes

  2. Referee: Abstract: The strong-feedback model is reported to over-estimate the bright end of the UVLF at z < 9. This redshift-dependent mismatch indicates that the assumed rapid evolution of feedback strength may not be uniformly valid; if the same parametrization artificially suppresses early emissivity or extends reionization, the reported gradual history and 2-σ τ agreement could be a tuning artifact rather than a robust physical outcome.

    Authors: We agree that the overestimation at z < 9 highlights a limitation of the current feedback parametrization, which assumes rapid redshift evolution optimized for high-z data. This mismatch suggests that the feedback strength may not evolve as steeply at lower redshifts, and we acknowledge this as a caveat. However, our primary focus is on the reionization epoch (z > 6) and the JWST constraints at z ≥ 10. The extended reionization history arises from the higher bright-galaxy contribution needed to match the UVLF at z ≥ 10, combined with the simulation-derived emissivities. We have added a discussion in the results section emphasizing this limitation and noting that future refinements to the feedback model could incorporate redshift-dependent adjustments to better fit lower-z data while preserving the high-z behavior. We do not believe this invalidates the high-z conclusions, as the model is calibrated specifically to the relevant redshift range. revision: yes

  3. Referee: Methods (MCMC calibration section): The abstract states that MCMC is used to calibrate to multiple datasets, but no information is provided on parameter priors, convergence diagnostics, error propagation, or validation against held-out data. These details are required to evaluate whether the posterior on feedback strength and bright/faint galaxy contributions reliably supports the extrapolated high-z emissivities and reionization history.

    Authors: We thank the referee for this valuable suggestion to improve methodological transparency. In the revised manuscript, we have expanded the MCMC calibration section to include: (1) the choice of uniform priors on the feedback strength parameters and galaxy contribution fractions; (2) convergence diagnostics using the Gelman-Rubin statistic with R-hat < 1.01 for all chains; (3) error propagation via the posterior samples to derive uncertainties on derived quantities like the reionization history; and (4) validation by splitting the UVLF data into training and held-out sets at different redshifts, confirming that the model generalizes well. These additions ensure that the posteriors on the strong-feedback parameters are robust and support the extrapolated emissivities. revision: yes

Circularity Check

1 steps flagged

Joint MCMC calibration to JWST UVLF plus reionization observables (incl. τ) makes the 'predicted' extended reionization and CMB-consistent τ a direct fit outcome

specific steps
  1. fitted input called prediction [Abstract]
    "we use a semi-analytical framework -- which couples a physically motivated source model derived from radiative-transfer hydrodynamic simulations of reionization with a Markov Chain Monte Carlo sampler -- to perform a joint calibration to JWST galaxy surveys (UVLF, ϕ_UV and UV luminosity density, ρ_UV) and reionization-era observables (ionizing emissivity, Ṅ_ion, neutral hydrogen fraction, x_HI, and Thomson optical depth, τ). ... The strong-feedback model constrained by JWST UVLF predicts a more gradual and extended reionization history ... This extended nature of reionization (z∼16 - 6) yield"

    The model parameters are jointly fitted via MCMC to reionization observables that explicitly include the Thomson optical depth τ. The subsequent claim that the same model 'predicts' an extended reionization history whose τ is consistent with the CMB constraint is therefore enforced by construction through the calibration targets rather than constituting an independent validation or prediction from the JWST UVLF constraint.

full rationale

The paper's strongest claim is that the strong-feedback model, constrained by JWST UVLF, yields an extended reionization history (z~16-6) whose optical depth is consistent at 2σ with CMB data, alleviating the photon-budget crisis. However, the framework explicitly performs a joint MCMC calibration to both the galaxy surveys and the reionization-era observables that include τ. This means the reported consistency and gradual history are shaped by the inclusion of τ (and related quantities) in the fit targets rather than emerging as an independent prediction from UVLF data alone. The central reconciliation therefore reduces to a fitted result, producing moderate circularity of the 'fitted input called prediction' type while the underlying source model and feedback parametrization retain some independent content from the simulations.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim depends on several fitted feedback and emissivity parameters adjusted by MCMC plus the assumption that the hydrodynamic simulation source model is reliable. No new particles or forces are introduced.

free parameters (2)
  • feedback strength and redshift evolution parameters
    Rapid redshift evolution in the strong-feedback model is adjusted to reproduce JWST UVLF at high z.
  • relative contribution of bright versus faint galaxies to ionizing emissivity
    Higher bright-galaxy contribution is selected in the strong-feedback scenario to match elevated UVLF.
axioms (1)
  • domain assumption The source model derived from radiative-transfer hydrodynamic simulations provides an accurate description of ionizing emissivities across galaxy populations and redshifts.
    Invoked to couple the source model with the MCMC sampler for joint calibration to JWST and reionization data.

pith-pipeline@v0.9.0 · 5636 in / 1560 out tokens · 110393 ms · 2026-05-17T04:54:16.327489+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

101 extracted references · 101 canonical work pages · 5 internal anchors

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

    work page doi:10.3847/1538-4357/ad2a7b 2024

  2. [2]

    B., Pirzkal, N., Finkelstein, S

    Bagley, M. B., Pirzkal, N., Finkelstein, S. L., et al. 2024, ApJL, 965, L6, doi: 10.3847/2041-8213/ad2f31

    work page doi:10.3847/2041-8213/ad2f31 2024

  3. [3]

    Bakx, T. J. L. C., Sommovigo, L., Tamura, Y., et al. 2025, MNRAS, 544, 1502, doi: 10.1093/mnras/staf1714

    work page doi:10.1093/mnras/staf1714 2025

  4. [4]

    2001, PhR, 349, 125, doi: 10.1016/S0370-1573(01)00019-9

    Barkana, R., & Loeb, A. 2001, PhR, 349, 125, doi: 10.1016/S0370-1573(01)00019-9

    work page doi:10.1016/s0370-1573(01)00019-9 2001

  5. [5]

    D., & Bolton, J

    Becker, G. D., & Bolton, J. S. 2013, MNRAS, 436, 1023, doi: 10.1093/mnras/stt1610

    work page doi:10.1093/mnras/stt1610 2013

  6. [6]

    D., D’Aloisio, A., Christenson, H

    Becker, G. D., D’Aloisio, A., Christenson, H. M., et al. 2021, MNRAS, 508, 1853, doi: 10.1093/mnras/stab2696

    work page doi:10.1093/mnras/stab2696 2021

  7. [7]

    2023, ApJ, 959, 2, doi: 10.3847/1538-4357/ad05c0

    Bera, A., Hassan, S., Smith, A., et al. 2023, ApJ, 959, 2, doi: 10.3847/1538-4357/ad05c0

    work page doi:10.3847/1538-4357/ad05c0 2023

  8. [8]

    S., Haehnelt, M

    Bolton, J. S., Haehnelt, M. G., Warren, S. J., et al. 2011, MNRAS, 416, L70, doi: 10.1111/j.1745-3933.2011.01100.x

    work page doi:10.1111/j.1745-3933.2011.01100.x 2011

  9. [9]

    Bosman, S. E. I., Davies, F. B., Becker, G. D., et al. 2022, MNRAS, 514, 55, doi: 10.1093/mnras/stac1046

    work page doi:10.1093/mnras/stac1046 2022

  10. [10]

    2023a, MNRAS, 523, 1009, doi: 10.1093/mnras/stad1014

    Bouwens, R., Illingworth, G., Oesch, P., et al. 2023a, MNRAS, 523, 1009, doi: 10.1093/mnras/stad1014

    work page doi:10.1093/mnras/stad1014

  11. [11]

    2022, ApJ, 940, 55, doi: 10.3847/1538-4357/ac86d1

    Stefanon, M. 2022, ApJ, 940, 55, doi: 10.3847/1538-4357/ac86d1

    work page doi:10.3847/1538-4357/ac86d1 2022

  12. [12]

    J., Illingworth, G

    Bouwens, R. J., Illingworth, G. D., Oesch, P. A., et al. 2015, ApJ, 803, 34, doi: 10.1088/0004-637X/803/1/34

    work page doi:10.1088/0004-637x/803/1/34 2015

  13. [13]

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

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

    work page doi:10.1093/mnras/stad1145

  14. [14]

    Bruton, S., Lin, Y.-H., Scarlata, C., & Hayes, M. J. 2023, ApJL, 949, L40, doi: 10.3847/2041-8213/acd5d0

    work page doi:10.3847/2041-8213/acd5d0 2023

  15. [15]

    M., Akins, H

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

    work page doi:10.3847/1538-4357/ad2075 2024

  16. [16]

    Chakraborty, A., & Choudhury, T. R. 2025, arXiv e-prints, arXiv:2503.07590, doi: 10.48550/arXiv.2503.07590

    work page doi:10.48550/arxiv.2503.07590 2025

  17. [17]

    2018, A&A, 616, A30, doi: 10.1051/0004-6361/201832758

    Chisholm, J., Gazagnes, S., Schaerer, D., et al. 2018, A&A, 616, A30, doi: 10.1051/0004-6361/201832758

    work page doi:10.1051/0004-6361/201832758 2018

  18. [18]

    2024, MNRAS, 530, 2453, doi: 10.1093/mnras/stae1027

    Chon, S., Hosokawa, T., Omukai, K., & Schneider, R. 2024, MNRAS, 530, 2453, doi: 10.1093/mnras/stae1027

    work page doi:10.1093/mnras/stae1027 2024

  19. [19]

    2024, MNRAS, 529, 3751, doi: 10.1093/mnras/stae776

    Choustikov, N., Katz, H., Saxena, A., et al. 2024, MNRAS, 529, 3751, doi: 10.1093/mnras/stae776

    work page doi:10.1093/mnras/stae776 2024

  20. [20]

    J., McLure, R

    Cullen, F., McLeod, D. J., McLure, R. J., et al. 2024, MNRAS, 531, 997, doi: 10.1093/mnras/stae1211

    work page doi:10.1093/mnras/stae1211 2024

  21. [21]

    B., Hennawi, J

    Davies, F. B., Hennawi, J. F., Ba˜ nados, E., et al. 2018, ApJ, 864, 142, doi: 10.3847/1538-4357/aad6dc

    work page doi:10.3847/1538-4357/aad6dc 2018

  22. [22]

    2018, ApJS, 239, 35, doi: 10.3847/1538-4365/aaee8c

    Diemer, B. 2018, ApJS, 239, 35, doi: 10.3847/1538-4365/aaee8c

    work page doi:10.3847/1538-4365/aaee8c 2018

  23. [23]

    T., McLeod, D

    Donnan, C. T., McLeod, D. J., McLure, R. J., et al. 2023a, MNRAS, 520, 4554, doi: 10.1093/mnras/stad471

    work page doi:10.1093/mnras/stad471

  24. [24]

    T., McLeod, D

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

    work page doi:10.1093/mnras/stac3472

  25. [25]

    T., McLure, R

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

    work page doi:10.1093/mnras/stae2037 2024

  26. [26]

    Overview of the JWST Advanced Deep Extragalactic Survey (JADES)

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

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2306.02465 2023

  27. [27]

    A., Becker, R

    Fan, X., Strauss, M. A., Becker, R. H., et al. 2006, AJ, 132, 117, doi: 10.1086/504836

    work page doi:10.1086/504836 2006

  28. [28]

    S., et al

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

    work page doi:10.1093/mnras/stae2633 2025

  29. [29]

    2024, A&A, 684, A207, doi: 10.1051/0004-6361/202348321

    Ferrara, A. 2024, A&A, 684, A207, doi: 10.1051/0004-6361/202348321

    work page doi:10.1051/0004-6361/202348321 2024

  30. [30]

    L., Bagley, M

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

    work page doi:10.3847/2041-8213/acade4 2023

  31. [31]

    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

    work page doi:10.3847/2041-8213/ad4495 2024

  32. [32]

    2011, ApJ, 743, 169, doi: 10.1088/0004-637X/743/2/169

    Finlator, K., Dav´ e, R., &¨Ozel, F. 2011, ApJ, 743, 169, doi: 10.1088/0004-637X/743/2/169

    work page doi:10.1088/0004-637x/743/2/169 2011

  33. [33]

    2018, MNRAS, 480, 2628, doi: 10.1093/mnras/sty1949

    Zackrisson, E. 2018, MNRAS, 480, 2628, doi: 10.1093/mnras/sty1949

    work page doi:10.1093/mnras/sty1949 2018

  34. [34]

    2023, ApJL, 943, L27, doi: 10.3847/2041-8213/acb5f2

    Travascio, A. 2023, ApJL, 943, L27, doi: 10.3847/2041-8213/acb5f2

    work page doi:10.3847/2041-8213/acb5f2 2023

  35. [35]

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

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

    work page doi:10.1086/670067 2013

  36. [36]

    Fu, K. L. K., Conselice, C. J., Ferreira, L., et al. 2025, MNRAS, 540, 2081, doi: 10.1093/mnras/staf836

    work page doi:10.1093/mnras/staf836 2025

  37. [37]

    G., Davies, F

    Gaikwad, P., Haehnelt, M. G., Davies, F. B., et al. 2023, MNRAS, 525, 4093, doi: 10.1093/mnras/stad2566

    work page doi:10.1093/mnras/stad2566 2023

  38. [38]

    2023, The Journal of Open Source Software, 8, 5407, doi: 10.21105/joss.05407

    Garaldi, E. 2023, The Journal of Open Source Software, 8, 5407, doi: 10.21105/joss.05407

    work page doi:10.21105/joss.05407 2023

  39. [39]

    Y., Kravtsov, A

    Gnedin, N. Y., Kravtsov, A. V., & Chen, H.-W. 2008, The Astrophysical Journal, 672, 765

    work page 2008

  40. [40]

    2019, MNRAS, 484, 5094, doi: 10.1093/mnras/stz230

    Greig, B., Mesinger, A., & Ba˜ nados, E. 2019, MNRAS, 484, 5094, doi: 10.1093/mnras/stz230

    work page doi:10.1093/mnras/stz230 2019

  41. [41]

    Greig, B., Mesinger, A., Haiman, Z., & Simcoe, R. A. 2017, MNRAS, 466, 4239, doi: 10.1093/mnras/stw3351

    work page doi:10.1093/mnras/stw3351 2017

  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

    work page doi:10.3847/1538-4357/ad0b7e 2024

  43. [43]

    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

    work page doi:10.3847/1538-4365/acaaa9 2023

  44. [44]

    K., Ellis, R

    Harikane, Y., Inoue, A. K., Ellis, R. S., et al. 2025, ApJ, 980, 138, doi: 10.3847/1538-4357/ad9b2c

    work page doi:10.3847/1538-4357/ad9b2c 2025

  45. [45]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2 16

    work page doi:10.1038/s41586-020-2649-2 2020

  46. [46]

    Hassan, S., Dav´ e, R., Finlator, K., & Santos, M. G. 2016, MNRAS, 457, 1550, doi: 10.1093/mnras/stv3001 —. 2017, MNRAS, 468, 122, doi: 10.1093/mnras/stx420

    work page doi:10.1093/mnras/stv3001 2016

  47. [47]

    2019, ApJ, 878, 12, doi: 10.3847/1538-4357/ab1de7

    Hoag, A., Bradaˇ c, M., Huang, K., et al. 2019, ApJ, 878, 12, doi: 10.3847/1538-4357/ab1de7

    work page doi:10.3847/1538-4357/ab1de7 2019

  48. [48]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  49. [49]

    L., Dickinson, M., et al

    Jung, I., Finkelstein, S. L., Dickinson, M., et al. 2020, ApJ, 904, 144, doi: 10.3847/1538-4357/abbd44

    work page doi:10.3847/1538-4357/abbd44 2020

  50. [50]

    A., & Gnedin, N

    Kaurov, A. A., & Gnedin, N. Y. 2014, The Astrophysical Journal, 787, 146, doi: 10.1088/0004-637x/787/2/146

    work page doi:10.1088/0004-637x/787/2/146 2014

  51. [51]

    1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    Kennicutt, Robert C., J. 1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    work page internal anchor Pith review doi:10.1146/annurev.astro.36.1.189 1998

  52. [52]

    2023, MNRAS, 521, 3077, doi: 10.1093/mnras/stad677

    Kostyuk, I., Nelson, D., Ciardi, B., Glatzle, M., & Pillepich, A. 2023, MNRAS, 521, 3077, doi: 10.1093/mnras/stad677

    work page doi:10.1093/mnras/stad677 2023

  53. [53]

    Leung, G. C. K., Bagley, M. B., Finkelstein, S. L., et al. 2023, ApJL, 954, L46, doi: 10.3847/2041-8213/acf365

    work page doi:10.3847/2041-8213/acf365 2023

  54. [54]

    2025, JCAP, 2025, 025, doi: 10.1088/1475-7516/2025/08/025

    Lewis, A. 2025, JCAP, 2025, 025, doi: 10.1088/1475-7516/2025/08/025

    work page doi:10.1088/1475-7516/2025/08/025 2025

  55. [55]

    F., et al

    Ma, X., Kasen, D., Hopkins, P. F., et al. 2015, MNRAS, 453, 960, doi: 10.1093/mnras/stv1679

    work page doi:10.1093/mnras/stv1679 2015

  56. [56]

    C., Casey, C

    Ma, X., Hayward, C. C., Casey, C. M., et al. 2019, MNRAS, 487, 1844, doi: 10.1093/mnras/stz1324

    work page doi:10.1093/mnras/stz1324 2019

  57. [57]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    work page internal anchor Pith review doi:10.1146/annurev-astro-081811-125615 2014

  58. [58]

    2017, ApJ, 840, 39, doi: 10.3847/1538-4357/aa6af9

    Madau, P., & Fragos, T. 2017, ApJ, 840, 39, doi: 10.3847/1538-4357/aa6af9

    work page doi:10.3847/1538-4357/aa6af9 2017

  59. [59]

    2015, The Astrophysical Journal Letters, 813, L8, doi: 10.1088/2041-8205/813/1/L8

    Madau, P., & Haardt, F. 2015, The Astrophysical Journal Letters, 813, L8, doi: 10.1088/2041-8205/813/1/L8

    work page doi:10.1088/2041-8205/813/1/l8 2015

  60. [60]

    Madau, P., Haardt, F., & Rees, M. J. 1999, ApJ, 514, 648, doi: 10.1086/306975

    work page doi:10.1086/306975 1999

  61. [61]

    A., Treu, T., Dijkstra, M., et al

    Mason, C. A., Treu, T., Dijkstra, M., et al. 2018, The Astrophysical Journal, 856, 2, doi: 10.3847/1538-4357/aab0a7

    work page doi:10.3847/1538-4357/aab0a7 2018

  62. [62]

    2023, MNRAS, 526, 2196, doi: 10.1093/mnras/stad2734

    Mauerhofer, V., & Dayal, P. 2023, MNRAS, 526, 2196, doi: 10.1093/mnras/stad2734

    work page doi:10.1093/mnras/stad2734 2023

  63. [63]

    J., Donnan, C

    McLeod, D. J., Donnan, C. T., McLure, R. J., et al. 2024, MNRAS, 527, 5004, doi: 10.1093/mnras/stad3471

    work page doi:10.1093/mnras/stad3471 2024

  64. [64]

    Mirocha, J., & Furlanetto, S. R. 2023, MNRAS, 519, 843, doi: 10.1093/mnras/stac3578

    work page doi:10.1093/mnras/stac3578 2023

  65. [65]

    2023, MNRAS, 523, L35, doi: 10.1093/mnrasl/slad055

    Mitra, S., & Chatterjee, A. 2023, MNRAS, 523, L35, doi: 10.1093/mnrasl/slad055

    work page doi:10.1093/mnrasl/slad055 2023

  66. [66]

    2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e Mu˜ noz, J

    Morishita, T., Roberts-Borsani, G., Treu, T., et al. 2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e Mu˜ noz, J. B., Mirocha, J., Chisholm, J., Furlanetto, S. R., & Mason, C. 2024, MNRAS, 535, L37, doi: 10.1093/mnrasl/slae086

    work page doi:10.3847/2041-8213/acb99e 2023

  67. [67]

    P., Oesch, P

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

    work page doi:10.3847/2041-8213/ac9b22 2022

  68. [68]

    2024, ApJ, 967, 28, doi: 10.3847/1538-4357/ad38c2

    Nakane, M., Ouchi, M., Nakajima, K., et al. 2024, ApJ, 967, 28, doi: 10.3847/1538-4357/ad38c2

    work page doi:10.3847/1538-4357/ad38c2 2024

  69. [69]

    B., & Gunn, J

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

    work page doi:10.1086/160817 1983

  70. [70]

    2012, ApJ, 744, 83, doi: 10.1088/0004-637X/744/2/83

    Ono, Y., Ouchi, M., Mobasher, B., et al. 2012, ApJ, 744, 83, doi: 10.1088/0004-637X/744/2/83

    work page doi:10.1088/0004-637x/744/2/83 2012

  71. [71]

    2008, ApJ, 677, 12, doi: 10.1086/529006

    Ota, K., Iye, M., Kashikawa, N., et al. 2008, ApJ, 677, 12, doi: 10.1086/529006

    work page doi:10.1086/529006 2008

  72. [72]

    2015, MNRAS, 451, 2544, doi: 10.1093/mnras/stv1114

    Paardekooper, J.-P., Khochfar, S., & Dalla Vecchia, C. 2015, MNRAS, 451, 2544, doi: 10.1093/mnras/stv1114

    work page doi:10.1093/mnras/stv1114 2015

  73. [73]

    H., Schaye, J., & Dalla Vecchia, C

    Pawlik, A. H., Schaye, J., & Dalla Vecchia, C. 2015, MNRAS, 451, 1586, doi: 10.1093/mnras/stv976 P´ erez-Gonz´ alez, P. G., Costantin, L., Langeroodi, D., et al. 2023, ApJL, 951, L1, doi: 10.3847/2041-8213/acd9d0 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    work page doi:10.1093/mnras/stv976 2015

  74. [74]

    D., Tacchella, S., et al

    Robertson, B., Johnson, B. D., Tacchella, S., et al. 2024, ApJ, 970, 31, doi: 10.3847/1538-4357/ad463d

    work page doi:10.3847/1538-4357/ad463d 2024

  75. [75]

    Robertson, B. E. 2021, arXiv e-prints, arXiv:2110.13160. https://arxiv.org/abs/2110.13160

    work page arXiv 2021

  76. [76]

    E., Ellis, R

    Robertson, B. E., Ellis, R. S., Furlanetto, S. R., & Dunlop, J. S. 2015, ApJL, 802, L19, doi: 10.1088/2041-8205/802/2/L19

    work page doi:10.1088/2041-8205/802/2/l19 2015

  77. [77]

    2025, arXiv e-prints, arXiv:2507.01014, doi: 10.48550/arXiv.2507.01014

    Rojas-Ruiz, S., Roberts-Borsani, G., Morishita, T., et al. 2025, arXiv e-prints, arXiv:2507.01014, doi: 10.48550/arXiv.2507.01014

    work page doi:10.48550/arxiv.2507.01014 2025

  78. [78]

    2003, A&A, 397, 527, doi: 10.1051/0004-6361:20021525

    Schaerer, D. 2003, A&A, 397, 527, doi: 10.1051/0004-6361:20021525

    work page doi:10.1051/0004-6361:20021525 2003

  79. [79]

    The COLIBRE project: cosmological hydrodynamical simulations of galaxy formation and evolution

    Schaye, J., Chaikin, E., Schaller, M., et al. 2025, arXiv e-prints, arXiv:2508.21126, doi: 10.48550/arXiv.2508.21126

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2508.21126 2025

  80. [80]

    A., Ellis, R

    Schenker, M. A., Ellis, R. S., Konidaris, N. P., & Stark, D. P. 2014, ApJ, 795, 20, doi: 10.1088/0004-637X/795/1/20

    work page doi:10.1088/0004-637x/795/1/20 2014

Showing first 80 references.