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REVIEW 3 major objections 4 minor 1 cited by

EIGER VII. The evolving relationship between galaxies and the intergalactic medium in the final stages of reionization

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read The correlation between galaxy density and intergalactic transparency reverses between z≈5.5 and z≈5.7, pointing to an inside-out finish of cosmic reionization.

desk verdict The sign reversal of the galaxy-IGM correlation is a genuinely new result, but the significance is weaker than the Spearman p-values suggest because of post-hoc binning and non-independent slices. read the letter →

arxiv 2506.03121 v1 pith:RD6XDDIO submitted 2025-06-03 astro-ph.GA astro-ph.CO

classification astro-ph.GAastro-ph.CO
keywords reionizationintergalacticmediumLyman-alphaforestgalaxy-environmentcorrelationsoxygenlineemittersJWSTslitlessspectroscopyquasarsightlinesinside-out
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

Using 948 [Oiii]-emitting galaxies along six quasar sightlines, this paper tries to establish that the relationship between galaxy density and the transparency of intergalactic hydrogen changes sign during the last stage of reionization. At $z<5.5$, overdense regions show lower Lyα transmission; at $z>5.7$, they show higher transmission, with a transitional behavior in between. The authors interpret the reversal as the handoff from local ionizing radiation produced by clustered star-forming galaxies to a nearly uniform background radiation field. If correct, this supports an inside-out reionization history in which ionized bubbles grow around galaxies and later merge into a homogeneous ionized IGM.

What carries the argument

The central object is the mean transmission curve $\langle T(r)\rangle$, the average Lyα transmission in radial comoving-distance bins around detected galaxies, together with the volume-centric correlation between galaxy number density and transmission measured in 5–20 cMpc slices. The paper judges the significance of features in these curves against a null hypothesis built from mock galaxy catalogs derived from light-cone simulations with star-formation-rate-to-[Oiii] abundance matching, which preserve realistic galaxy clustering while being uncorrelated with the transmission data. The field-to-field weighted normalization, which removes the already-known large-scale correlation before comparing shapes, is the step that lets the small-scale absorption and intermediate-scale excess be interpreted as genuine local signals.

What would settle it

Recompute the same cross-correlations with mock catalogs from an independent hydrodynamical simulation that matches the measured two-point clustering of the 948 [Oiii] emitters; if the key p-values (0.86%, 4.69%, 3.62%) rise above 5%, the claimed redshift reversal would not be supported. Alternatively, an independent set of more than a dozen new quasar sightlines at the same depth that fails to reproduce the positive correlation at $z>5.7$ would settle the question.

Watch

Extended reading notes

Core claim

The central discovery is the redshift reversal of the galaxy–transmission correlation. In volume-centric measurements, Lyα transmission anti-correlates with galaxy number density at $5.32<z<5.50$ (Spearman $\rho\approx -0.4$ to $-0.6$), is weak or non-monotonic at $5.50<z<5.70$, and becomes positive at $5.70<z<6.15$ ($\rho\approx0.2$–$0.4$). The galacto-centric transmission curves show excess absorption within about 8 cMpc of galaxies at low redshift and excess transmission at roughly 5–20 cMpc at high redshift. The authors argue that these signals reflect two competing effects: overdense regions absorb more Lyα light, while clustered star-forming galaxies produce local ionizing radiation that raises transmission; the local radiation wins at high redshift and is gradually overtaken by the rising uniform background at low redshift.

Load-bearing premise

The significance of the reversal depends on mock galaxy catalogs whose clustering only approximately matches the real [Oiii] emitters; if those mocks under-cluster, the null-hypothesis scatter is underestimated and the quoted p-values are too optimistic.

Editorial extensions

If this is right

  • At $z>5.7$, reionization proceeded inside-out: ionized regions formed around clustered star-forming galaxies and later expanded into lower-density voids.
  • The transition between $z\approx5.5$ and $5.7$ marks the end of the patchy phase, operating on a timescale of roughly 50–100 Myr.
  • Star-forming galaxies, not AGN, are the dominant ionizing sources during the final stages of reionization, since the positive correlation requires a source population that clusters like the [Oiii] emitters.
  • Galaxy–transmission curves can now serve as quantitative constraints on reionization simulations, linking galaxy properties to ionizing photon escape.

Reading between the lines

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

  • If the reversal is real, it should also be visible in other reionization observables, such as the 21-cm signal, where dense regions should be the first to become ionized.
  • The apparent cancellation of the positive signal at $r<5$ cMpc at $z>5.7$ could be used to constrain the local mean free path of ionizing photons and the escape fraction of galaxies.
  • A direct extension is to push the same analysis to $z>6.15$ with deeper spectroscopy; a strengthening positive correlation there would confirm the inside-out scenario earlier in reionization.
  • The Lyβ curve, though statistically weaker, could with more path length become a cleaner probe of local ionization because it is less saturated than Lyα.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper uses the complete EIGER dataset of 948 [OIII]-emitting galaxies around six quasar sightlines to measure the correlation between galaxy density and Ly-alpha/Ly-beta transmission during the tail end of reionization. The analysis is divided into three redshift bins (5.32<z<5.50, 5.50<z<5.70, 5.70<z<6.15). The authors report that galaxy density and Ly-alpha transmission are anti-correlated at z<5.50, uncorrelated in the intermediate bin, and positively correlated at 5.70<z<6.15. They also measure galacto-centric transmission curves and find excess absorption within ~8 cMpc at low redshift and excess transmission at ~6-20 cMpc at high redshift, with significance estimated from mock catalogs. The paper interprets these trends as evidence that local radiation from star-forming galaxies dominates at z>5.7, while a nearly uniform background dominates at z<5.5, supporting an inside-out reionization scenario.

Significance. If the claimed redshift reversal is statistically robust, this would be one of the most direct observational constraints on the inside-out reionization scenario and on the role of galaxies as ionizing sources. The paper has notable strengths: it uses the complete six-field EIGER sample with a sixfold increase over earlier work; it builds per-field completeness cubes and uses them in mock construction; it uses clustering-based mocks rather than random catalogs to estimate null scatters; it compares with the THESAN simulation; and it is transparent about acknowledged limitations, including the oversimplified SFR-to-[OIII] conversion and the influence of large-scale correlations on normalization. The significance of the paper, however, rests on the statistical validity of the redshift-reversal claim, and that claim currently depends on several statistical choices that are not fully robust.

major comments (3)
  1. [Sections 3 and 4.2] The redshift bin boundaries z=5.5 and z=5.7 are chosen after inspecting the same dataset: Section 3 states that the trend in the mean transmission 'motivates us to conduct' the three-regime analysis, and Section 4.2 repeats that the division is motivated by the observed evolution of the average transmission. The Spearman p-values in Figure 16 are therefore conditional on these data-driven boundaries and do not account for the look-elsewhere effect of choosing boundaries that make the reversal most visible. The paper should present a formal interaction test between redshift and the density-transmission slope (e.g., a regression with continuous redshift as an interacting covariate) or show that the reversal survives a pre-specified bin choice. Without such a test, the claimed reversal is not formally established.
  2. [Section 4.2, Figure 16] The Spearman tests treat individual slices along each sightline as independent, but adjacent slices share the same transmission spectrum and a clustered galaxy field, so the effective number of independent samples is far smaller than the number of slices (roughly the number of sightlines per bin, i.e., 5-6). This explains why p-values such as 0.00005 and 0.0022 are likely overconfident. A valid significance estimate for the slice-based correlation should either use the mock framework of Section 4.3.2 applied to the same statistic, preserving line-of-sight correlations, or use a block bootstrap resampled by sightline. This issue is load-bearing because the reversal claim in the abstract and Section 5.1 relies primarily on these within-bin p-values.
  3. [Sections 4.3.2 and 4.3.3, Figure 20] The reported mock-based p-values (0.86%, 4.69%, and 3.62% for the key small-scale absorption and intermediate-scale transmission features) depend on the accuracy of the mock clustering. The paper itself describes the SFR-to-[OIII] abundance-matching conversion as 'certainly an oversimplification' and states that the resulting sample reproduces clustering only 'approximately.' If the UniverseMachine mocks under-cluster on the scales that dominate the null scatter, the 16-84 percentile bands in Figure 18 and the p-values in Figure 20 would be underestimated, which could erase already moderate significances. The authors should validate the mock clustering against the observed [OIII]-emitter clustering (e.g., by comparing the two-point correlation or slice-to-slice variance) or demonstrate that the conclusions are robust to plausible variations in the clustering model.
minor comments (4)
  1. [Section 4.3.2] There is a typo: 'which procides a reasonable estimate' should read 'which provides a reasonable estimate.'
  2. [Section 4.3.4] The paragraph describing the Ly-beta transmission curve is duplicated verbatim; one copy should be removed.
  3. [Figure 20] The p-values in Figure 20 are one-sided and are evaluated separately for several radial bins and redshift ranges; the text should state explicitly whether any multiple-comparison correction was considered, or explain why the uncorrected values are sufficient for the conclusions.
  4. [Section 4.2] The text says the intermediate bin shows 'a peak-like trend' and that the 20-cMpc binning yields a positive correlation (rho=0.251, p=0.227) that is not significant; the wording in Section 5.1 describing the intermediate bin as 'non-monotonic peak-like' should be reconciled with the fact that this feature is not statistically significant by the paper's own measures.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central galaxy-density/transmission correlation is measured from independent data sets and benchmarked against external mocks and simulations.

full rationale

The central signal is measured directly from two independent data sets: the EIGER [OIII]-emitter catalog (Section 2) and the Ly-alpha/Ly-beta transmission spectra (Section 3). The key volume-centric correlation (Figure 16, Section 4.2) uses only observed galaxy densities and observed transmissions in redshift slices; no parameter is fitted to the transmission data, and no model output is used to construct the correlation. The null-hypothesis scatter (Section 4.3.2) is generated from UniverseMachine lightcones with SFR-to-[OIII] abundance matching calibrated to the observed luminosity function; these mocks are by construction uncorrelated with the transmission field, so they provide an external benchmark for chance occurrence rather than an input to the measured signal. The field-to-field weighting in Equation 3 rescales the normalization of mock transmission curves using observed galaxy counts, but it does not impose the small-scale shape (excess absorption at r<8 cMpc or excess transmission at r~5-20 cMpc) that constitutes the central claim; the same weighting is applied to data and null, and the qualitative conclusions persist without it (Section 4.3.3). The THESAN comparison (Section 5.3) is an external simulation, and the qualitative inside-out interpretation is an inference, not a mathematical consequence of the definitions. The redshift bin boundaries (z=5.5, 5.7) are admittedly motivated by the observed mean transmission trend, which raises a multiple-testing caveat for the reported p-values; that is a statistical robustness concern, not a circularity, because the within-bin correlations are not definitionally determined by the choice of boundaries. No load-bearing argument reduces to a self-citation; Kashino et al. (2023) and Matthee et al. (2023) are cited for methodology and earlier single-sightline results, but the present six-sightline analysis is new and self-contained against external benchmarks.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central measurement rests on standard observational assumptions (continuum modeling, completeness, mock clustering) and one notable hand-chosen element: the redshift bin boundaries. No new physical entities or fitted constants are introduced; the only adopted prior values (e.g., tau_alpha/tau_beta=2.25) come from cited literature.

free parameters (1)
  • Redshift bin boundaries = z=5.50, 5.70
    The analysis splits the sample at z=5.50 and 5.70, explicitly motivated by the observed evolution of mean transmission in the same data (Sections 3 and 4.2), so the bin edges are chosen post-hoc rather than from external theory.
assumptions (3)
  • domain assumption The neural network and power-law extrapolation accurately predict the quasar intrinsic continuum blueward of Ly-alpha, so the transmission measurements are unbiased.
    Invoked in Section 3 to normalize quasar spectra; errors of 4-8% are neglected in the main correlation analysis.
  • ad hoc to paper UniverseMachine mocks with SFR-to-[OIII] abundance matching reproduce the true clustering of [OIII]-emitters well enough to estimate the null-hypothesis scatter.
    Section 4.3.2 uses these mocks to set the significance of the observed correlation features; the paper itself calls the conversion 'an oversimplification'.
  • domain assumption Foreground Ly-alpha absorption at z_fg~4.9 is corrected for using the cosmic mean transmission, ignoring spatial fluctuations.
    Section 3, lower panel; the paper notes fluctuations of factor ~1.4 can distort the Ly-beta measurements.

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Cite this review

Pith. "Pith review of EIGER VII. The evolving relationship between galaxies and the intergalactic medium in the final stages of reionization." pith.science (2026). https://pith.science/paper/RD6XDDIO

@misc{pith2026250603121,
  author       = {Pith},
  title        = {Pith review of: EIGER VII. The evolving relationship between galaxies and the intergalactic medium in the final stages of reionization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RD6XDDIO}},
  note         = {Machine review of arXiv:2506.03121}
}
abstract

We present a comprehensive analysis of the relationship between galaxies and the intergalactic medium (IGM) during the late stages of cosmic reionization, based on the complete JWST EIGER dataset. Using deep NIRCam $3.5\,\mathrm{\mu m}$ slitless spectroscopy, we construct a sample of 948 [\OIII]$\lambda5008$-emitting galaxies with $-21.4\lesssim M_\mathrm{UV}\lesssim -17.2$ spanning $5.33<z<6.97$ along six quasar sightlines. We correlate these galaxies with \Lya\ and \Lyb\ transmission measured from high-resolution quasar spectra across multiple redshift intervals. We find clear redshift evolution in the correlation between galaxy density and transmission: it is suppressed in overdense regions at $z<5.50$, while enhanced at $5.70<z<6.15$. The intermediate range exhibits a transitional behavior. Cross-correlation measurements further reveal excess absorption within $\sim 8$\,cMpc of galaxies at low redshifts, and enhanced transmission at intermediate scales ($\sim$5--20\,cMpc) at $z>5.70$. Statistical tests using mock catalogs with realistic galaxy clustering but no correlation with the transmission field confirm that the observed correlations are unlikely to arise by chance. The evolving signals can be explained by stronger absorption in overdense regions, combined with the competing influences of local radiation fields and the rising background radiation. While local radiation dominates ionization of the surrounding IGM at earlier times, the background becomes increasingly important, eventually surpassing the impact of nearby galaxies. These results support an inside-out progression of reionization, with ionized regions originating around clustered, star-forming galaxies and gradually extending into underdense regions.

Figures

Figures reproduced from arXiv: 2506.03121 by the authors.

Figure 1
Figure 1. Observations of three quasar fields (J0100, J1148 and J0148) out of the six targets. Left panels show the RGB images constructed from the NIRCam imaging data (F115W, F200W, and F356W). The axes indicate angular separation from the quasar position. Circles indicate the on-sky positions of the detected [O iii]-emitters, with color-coded redshift and size-coded relative [O iii] luminosity. Right panels show the transve… view at source ↗
Figure 2
Figure 2. Same as [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. shows the distribution of the ob￾served [O iii]λ5008 fluxes (F5008), which range from log(F5008/erg s−1 cm−2 ) ≈ −18.0 to −16.5 (2.5–97.5th percentiles), with a median of −17.42. There are no significant differences in the observed flux distribution between the survey fields: the median fluxes lie within log F5008(erg s−1 cm−2 ) = −17.57 to −17.34. The cor￾responding [O iii]λ5008 luminosities (L5008) range from log … view at source ↗
Figures from the paper (22 more)
Figure 5
Figure 5. Figure 5: Completeness over the entire survey volume (5.32 < z < 6.97) as a function of [O iii]λ5008 luminosity for each field. Solid lines indicate the average completeness functions. The blue and green shaded regions show the in￾terquartile range (IQR) of the spatial variation…
Figure 4
Figure 4. Figure 4: Completeness maps for the field of J1148+5251. The three panels show the completeness for [O iii]-emitters with L5008 = 1042 erg s−1 across the survey area at different redshifts (z = 5.5, 6.0, and 6.8, from top to bottom). Hori￾zontal trails of lower completeness are …
Figure 6
Figure 6. Figure 6: Quasar spectrum (black) and the intrinsic contin￾uum model (red) for each quasar. The extrapolated portions of the continua are shown in orange. Light and dark gray shaded regions represent the 1σ and 2σ confidence intervals of the model, respectively. I Telescope. The…
Figure 7
Figure 7. Figure 7: Upper panel: Lyα transmission measured in 20 cMpc bins along the sightlines toward our six tar￾get quasars. Different symbols represent different quasars. Down arrows indicate 2σ upper limits for non detections (< 1σ). The star symbols indicate the mean transmission in…
Figure 8
Figure 8. Figure 8: Redshift intervals of the Lyα (blue bars) and Lyβ (green bars) forests for the target quasars. Star symbols in￾dicate the redshifts of the quasars, while red bars correspond to their near-zones. The vertical dashed line marks the upper limit of the redshift range used …
Figure 9
Figure 9. Figure 9: The distribution of [O iii]-emitting galaxies, and the Lyα(blue) and Lyβ(green) transmission along the line of sight to QSO J0100+2802. The error spectrum, corresponding to Lyα transmission), is shown in brown. The red circles show the comoving transverse distances, r⊥…
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Same as [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: Mean transmission versus galaxy number den￾sity measured along individual sightlines within three red￾shift ranges: 5.32 < z < 5.50, 5.50 < z < 5.70, and 5.70 < z < 6.15, as labeled. Different symbols represent different sightlines, as indicated in the legend. Vertica…
Figure 16
Figure 16. Figure 16: Correlation between the number density of galaxies with [O iii]λ5008 luminosity L5008 ≥ 1042 erg s−1 and the Lyα transmission, for three redshift ranges. The three rows correspond to three different binning scales along the sightlines (5, 10, and 20 cMpc from top to b…
Figure 17
Figure 17. Figure 17: Correlation between galaxy number density and Lyβ transmission within 5.70 < z < 6.15, shown for different binning scales (5, 10, and 20 cMpc, from top to bottom). The symbols follow the same convention as in [PITH_FULL_IMAGE:figures/full_fig_p021_17.png]
Figure 18
Figure 18. Figure 18: Left panel: Lyα transmission curves measured in three redshift bins along the sightlines of our six quasars (symbols connected by solid lines). Horizontal error bars represent the grid size (2 cMpc) of the measurements. The horizontal dotted and dashed lines indicate …
Figure 19
Figure 19. Figure 19: Upper panel: Number of galaxies contributing to the transmission curve measurement within each radial bin. Horizontal lines indicate the total number of galaxies in each redshift bin (as shown in the legend). Lower panel: Cumulative total path length of the Lyα forest…
Figure 20
Figure 20. Figure 20: Distributions of mock transmission values measured using Equation 3, shown for different combined radial distance bins and redshift ranges. The transmission values are normalized by the field-to-field weighted mean, T¯wht. The vertical red line marks the observed valu…
Figure 21
Figure 21. Figure 21: Similar to [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: Schematic illustration of the evolution of the galaxy-transmission correlation signals. The upper left panel summarizes the three bottom panels in [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]
Figure 23
Figure 23. Figure 23: Locations of the detected [O iii]-emitters co￾incide with the strong Lyαand Lyβ transmission spikes at z ≈ 5.99 along the sightline toward QSO J0100 (gold star) within the survey field. The symbol sizes represent [O iii] luminosities, and colors indicate redshift. The…
Figure 24
Figure 24. Figure 24: presents the normalized Lyα transmission curves obtained from our observations (same as in the right panel of [PITH_FULL_IMAGE:figures/full_fig_p028_24.png]
Figure 25
Figure 25. Figure 25: We note that the methodology for deriving transmis￾sion curves differs between the two studies. In partic￾ular, the previous analysis applied Gaussian smoothing with σ = 2 cMpc to the transmission spectra and nor￾malized the transmission by the cosmic mean as a func￾t…

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Works this paper leans on

104 extracted references · 10 canonical work pages · cited by 1 Pith paper

  1. [1]

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

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

  2. [2]

    write newline

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

  3. [3]

    _O? ? 7'/d? S_htd@ӿǿ7 J , fLOl; _ ]Y*bƬ&sM: < 3Z \ iu6 &D7i:jM n[ [=1 Cm9bP ґC2VY ;V v, n _;H -7 M+ OOmˁUz?F Ĥ SD+O _<;& m:a' V,?ݾbtn6#e`GqRpۺT qțœMf)7 A]oXleײ

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

  4. [4]

    L., Shapley , A

    Adelberger , K. L., Shapley , A. E., Steidel , C. C., et al. 2005, , 629, 636, 10.1086/431753

  5. [5]

    L., Steidel , C

    Adelberger , K. L., Steidel , C. C., Shapley , A. E., & Pettini , M. 2003, , 584, 45, 10.1086/345660

  6. [6]

    G., Kulkarni , G., et al

    Asthana , S., Haehnelt , M. G., Kulkarni , G., et al. 2024, arXiv e-prints, arXiv:2409.15453, 10.48550/arXiv.2409.15453

  7. [7]

    P., Tollerud , E

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

  8. [8]

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

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

Show all 104 references
  1. [9]

    D., Bolton , J

    Becker , G. D., Bolton , J. S., & Lidz , A. 2015 a , , 32, e045, 10.1017/pasa.2015.45

  2. [10]

    D., Bolton , J

    Becker , G. D., Bolton , J. S., Madau , P., et al. 2015 b , , 447, 3402, 10.1093/mnras/stu2646

  3. [11]

    D., Davies , F

    Becker , G. D., Davies , F. B., Furlanetto , S. R., et al. 2018, , 863, 92, 10.3847/1538-4357/aacc73

  4. [12]

    H., Hearin , A

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

  5. [13]

    2024, , 531, 3406, 10.1093/mnras/stae1125

    Bhagwat , A., Costa , T., Ciardi , B., Pakmor , R., & Garaldi , E. 2024, , 531, 3406, 10.1093/mnras/stae1125

  6. [14]

    A., Matthee , J., et al

    Bordoloi , R., Simcoe , R. A., Matthee , J., et al. 2024, , 963, 28, 10.3847/1538-4357/ad1b63

  7. [15]

    2023, , 525, 5932, 10.1093/mnras/stad2523

    Borrow , J., Kannan , R., Garaldi , E., et al. 2023, , 525, 5932, 10.1093/mnras/stad2523

  8. [16]

    Bosman , S. E. I., Fan , X., Jiang , L., et al. 2018, , 479, 1055, 10.1093/mnras/sty1344

  9. [17]

    Bosman , S. E. I., Davies , F. B., Becker , G. D., et al. 2022, , 514, 55, 10.1093/mnras/stac104610.48550/arXiv.2108.03699

  10. [18]

    J., Illingworth , G

    Bouwens , R. J., Illingworth , G. D., Oesch , P. A., et al. 2015, , 811, 140, 10.1088/0004-637X/811/2/140

  11. [19]

    2018, , 479, 332, 10.1093/mnras/sty1490

    Boylan-Kolchin , M. 2018, , 479, 332, 10.1093/mnras/sty1490

  12. [20]

    G., Aubert , D., & Puchwein , E

    Chardin , J., Haehnelt , M. G., Aubert , D., & Puchwein , E. 2015, , 453, 2943, 10.1093/mnras/stv1786

  13. [21]

    Chardin , J., Puchwein , E., & Haehnelt , M. G. 2017, , 465, 3429, 10.1093/mnras/stw2943

  14. [22]

    R., Haehnelt , M

    Choudhury , T. R., Haehnelt , M. G., & Regan , J. 2009, , 394, 960, 10.1111/j.1365-2966.2008.14383.x

  15. [23]

    M., Becker , G

    Christenson , H. M., Becker , G. D., Furlanetto , S. R., et al. 2021, , 923, 87, 10.3847/1538-4357/ac2a34

  16. [24]

    M., Becker , G

    Christenson , H. M., Becker , G. D., D'Aloisio , A., et al. 2023, , 955, 138, 10.3847/1538-4357/acf450

  17. [25]

    S., Keating , L

    Conaboy , L., Bolton , J. S., Keating , L. C., et al. 2025, , 539, 2790, 10.1093/mnras/staf648

  18. [26]

    M., Fontanot , F., Vanzella , E., & Monaco , P

    Cristiani , S., Serrano , L. M., Fontanot , F., Vanzella , E., & Monaco , P. 2016, , 462, 2478, 10.1093/mnras/stw1810

  19. [27]

    R., McQuinn , M., Trac , H., & Shapiro , P

    D'Aloisio , A., Upton Sanderbeck , P. R., McQuinn , M., Trac , H., & Shapiro , P. R. 2017, , 468, 4691, 10.1093/mnras/stx711

  20. [28]

    2018, , 780, 1, 10.1016/j.physrep.2018.10.002

    Dayal , P., & Ferrara , A. 2018, , 780, 1, 10.1016/j.physrep.2018.10.002

  21. [29]

    E., et al

    Dayal , P., Volonteri , M., Greene , J. E., et al. 2024, arXiv e-prints, arXiv:2401.11242, 10.48550/arXiv.2401.11242

  22. [30]

    Duncan , K., & Conselice , C. J. 2015, , 451, 2030, 10.1093/mnras/stv1049

  23. [31]

    B., & Hennawi , J

    Eilers , A.-C., Davies , F. B., & Hennawi , J. F. 2018, , 864, 53, 10.3847/1538-4357/aad4fd

  24. [32]

    A., Yue , M., et al

    Eilers , A.-C., Simcoe , R. A., Yue , M., et al. 2023, , 950, 68, 10.3847/1538-4357/acd776

  25. [33]

    2024, , 974, 275, 10.3847/1538-4357/ad778b

    Eilers , A.-C., Mackenzie , R., Pizzati , E., et al. 2024, , 974, 275, 10.3847/1538-4357/ad778b

  26. [34]

    L., & Keating , B

    Fan , X., Carilli , C. L., & Keating , B. 2006 a , , 44, 415, 10.1146/annurev.astro.44.051905.092514

  27. [35]

    A., Becker , R

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

  28. [36]

    2009, , 703, 1416, 10.1088/0004-637X/703/2/1416

    Faucher-Gigu \`e re , C.-A., Lidz , A., Zaldarriaga , M., & Hernquist , L. 2009, , 703, 1416, 10.1088/0004-637X/703/2/1416

  29. [37]

    L., D'Aloisio , A., Paardekooper , J.-P., et al

    Finkelstein , S. L., D'Aloisio , A., Paardekooper , J.-P., et al. 2019, , 879, 36, 10.3847/1538-4357/ab1ea8

  30. [38]

    R., Zaldarriaga , M., & Hernquist , L

    Furlanetto , S. R., Zaldarriaga , M., & Hernquist , L. 2004, , 613, 1, 10.1086/423025

  31. [39]

    G., Davies , F

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

  32. [40]

    D., & Christenson , H

    Gangolli , N., D'Aloisio , A., Cain , C., Becker , G. D., & Christenson , H. 2025, , 2025, 069, 10.1088/1475-7516/2025/03/069

  33. [41]

    2024 a , arXiv e-prints, arXiv:2410.02850, 10.48550/arXiv.2410.02850

    Garaldi , E., & Bellscheidt , V. 2024 a , arXiv e-prints, arXiv:2410.02850, 10.48550/arXiv.2410.02850

  34. [42]

    2024 b , arXiv e-prints, arXiv:2410.02853, 10.48550/arXiv.2410.02853

    ---. 2024 b , arXiv e-prints, arXiv:2410.02853, 10.48550/arXiv.2410.02853

  35. [43]

    2022, , 512, 4909, 10.1093/mnras/stac257

    Garaldi , E., Kannan , R., Smith , A., et al. 2022, , 512, 4909, 10.1093/mnras/stac257

  36. [44]

    Gnedin , N. Y. 2000, , 535, 530, 10.1086/308876

  37. [45]

    Y., & Madau , P

    Gnedin , N. Y., & Madau , P. 2022, Living Reviews in Computational Astrophysics, 8, 3, 10.1007/s41115-022-00015-5

  38. [46]

    2018, , 613, A44, 10.1051/0004-6361/201732385

    Grazian , A., Giallongo , E., Boutsia , K., et al. 2018, , 613, A44, 10.1051/0004-6361/201732385

  39. [47]

    2017, , 465, 4838, 10.1093/mnras/stw3026

    Greig , B., & Mesinger , A. 2017, , 465, 4838, 10.1093/mnras/stw3026

  40. [48]

    2012, , 746, 125, 10.1088/0004-637X/746/2/125

    Haardt , F., & Madau , P. 2012, , 746, 125, 10.1088/0004-637X/746/2/125

  41. [49]

    R., Millman, K

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

  42. [50]

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

  43. [51]

    2021, , 503, 3698, 10.1093/mnras/stab602

    Hutter , A., Dayal , P., Yepes , G., et al. 2021, , 503, 3698, 10.1093/mnras/stab602

  44. [52]

    A., Seiler , J., et al

    Hutter , A., Watkinson , C. A., Seiler , J., et al. 2020, , 492, 653, 10.1093/mnras/stz3139

  45. [54]

    2022, , 515, 5914, 10.1093/mnras/stac1972

    Ishimoto , R., Kashikawa , N., Kashino , D., et al. 2022, , 515, 5914, 10.1093/mnras/stac1972

  46. [55]

    2025, arXiv e-prints, arXiv:2502.03683, 10.48550/arXiv.2502.03683

    Jiang , D., Jiang , L., Sun , S., Liu , W., & Fu , S. 2025, arXiv e-prints, arXiv:2502.03683, 10.48550/arXiv.2502.03683

  47. [56]

    2024, , 976, 93, 10.3847/1538-4357/ad82de

    Jin , X., Yang , J., Fan , X., et al. 2024, , 976, 93, 10.3847/1538-4357/ad82de

  48. [57]

    F., Ono , Y., et al

    Kakiichi , K., Hennawi , J. F., Ono , Y., et al. 2023, , 523, 1772, 10.1093/mnras/stad1376

  49. [58]

    S., Laporte , N., et al

    Kakiichi , K., Ellis , R. S., Laporte , N., et al. 2018, , 479, 43, 10.1093/mnras/sty1318

  50. [59]

    2025, arXiv e-prints, arXiv:2503.07074, 10.48550/arXiv.2503.07074

    Kakiichi , K., Jin , X., Wang , F., et al. 2025, arXiv e-prints, arXiv:2503.07074, 10.48550/arXiv.2503.07074

  51. [60]

    2022, , 511, 4005, 10.1093/mnras/stab3710

    Kannan , R., Garaldi , E., Smith , A., et al. 2022, , 511, 4005, 10.1093/mnras/stab3710

  52. [61]

    J., Matthee , J., et al

    Kashino , D., Lilly , S. J., Matthee , J., et al. 2023, , 950, 66, 10.3847/1538-4357/acc588

  53. [62]

    J., Shibuya , T., Ouchi , M., & Kashikawa , N

    Kashino , D., Lilly , S. J., Shibuya , T., Ouchi , M., & Kashikawa , N. 2020, , 888, 6, 10.3847/1538-4357/ab5a7d

  54. [63]

    2020, , 494, 2200, 10.1093/mnras/staa639

    Katz , H., Ramsoy , M., Rosdahl , J., et al. 2020, , 494, 2200, 10.1093/mnras/staa639

  55. [64]

    C., Weinberger , L

    Keating , L. C., Weinberger , L. H., Kulkarni , G., et al. 2020, , 491, 1736, 10.1093/mnras/stz3083

  56. [65]

    2021, , 502, 3510, 10.1093/mnras/stab177

    Liu , B., & Bordoloi , R. 2021, , 502, 3510, 10.1093/mnras/stab177

  57. [66]

    2021, , 504, 4062, 10.1093/mnras/stab1132

    Ma , X., Quataert , E., Wetzel , A., Faucher-Gigu \`e re , C.-A., & Boylan-Kolchin , M. 2021, , 504, 4062, 10.1093/mnras/stab1132

  58. [67]

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

  59. [68]

    J., Volonteri , M., Haardt , F., & Oh , S

    Madau , P., Rees , M. J., Volonteri , M., Haardt , F., & Oh , S. P. 2004, , 604, 484, 10.1086/381935

  60. [69]

    2006, , 369, 1719, 10.1111/j.1365-2966.2006.10408.x

    Mapelli , M., Ferrara , A., & Pierpaoli , E. 2006, , 369, 1719, 10.1111/j.1365-2966.2006.10408.x

  61. [70]

    A., et al

    Matthee , J., Mackenzie , R., Simcoe , R. A., et al. 2023, , 950, 67, 10.3847/1538-4357/acc846

  62. [71]

    2024 a , , 529, 2794, 10.1093/mnras/stae673

    Matthee , J., Golling , C., Mackenzie , R., et al. 2024 a , , 529, 2794, 10.1093/mnras/stae673

  63. [72]

    P., Brammer , G., et al

    Matthee , J., Naidu , R. P., Brammer , G., et al. 2024 b , , 963, 129, 10.3847/1538-4357/ad2345

  64. [73]

    S., Steidel , C

    McLean , I. S., Steidel , C. C., Epps , H. W., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV, ed. I. S. McLean , S. K. Ramsay , & H. Takami , 84460J, 10.1117/12.924794

  65. [74]

    2007, , 377, 1043, 10.1111/j.1365-2966.2007.11489.x

    McQuinn , M., Lidz , A., Zahn , O., et al. 2007, , 377, 1043, 10.1111/j.1365-2966.2007.11489.x

  66. [75]

    A., Kakiichi , K., Bosman , S

    Meyer , R. A., Kakiichi , K., Bosman , S. E. I., et al. 2020, , 494, 1560, 10.1093/mnras/staa746

  67. [76]

    F., Dijkstra , M., Laurent , P., Loeb , A., & Pritchard , J

    Mirabel , I. F., Dijkstra , M., Laurent , P., Loeb , A., & Pritchard , J. R. 2011, , 528, A149, 10.1051/0004-6361/201016357

  68. [77]

    2021, , 909, 117, 10.3847/1538-4357/abd2af

    Momose , R., Shimasaku , K., Kashikawa , N., et al. 2021, , 909, 117, 10.3847/1538-4357/abd2af

  69. [78]

    2017, , 835, 281, 10.3847/1538-4357/835/2/281

    Mukae , S., Ouchi , M., Kakiichi , K., et al. 2017, , 835, 281, 10.3847/1538-4357/835/2/281

  70. [79]

    P., Tacchella , S., Mason , C

    Naidu , R. P., Tacchella , S., Mason , C. A., et al. 2020, , 892, 109, 10.3847/1538-4357/ab7cc9

  71. [80]

    G., et al

    Ocvirk , P., Aubert , D., Sorce , J. G., et al. 2020, , 496, 4087, 10.1093/mnras/staa1266

  72. [81]

    P., & Furlanetto , S

    Oh , S. P., & Furlanetto , S. R. 2005, , 620, L9, 10.1086/428610

  73. [82]

    2015, , 451, 2544, 10.1093/mnras/stv1114

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

  74. [83]

    S., & McLure , R

    Parsa , S., Dunlop , J. S., & McLure , R. J. 2018, , 474, 2904, 10.1093/mnras/stx2887

  75. [84]

    2018, , 473, 4077, 10.1093/mnras/stx2656

    Pillepich , A., Springel , V., Nelson , D., et al. 2018, , 473, 4077, 10.1093/mnras/stx2656

  76. [85]

    2020, , 641, A6, 10.1051/0004-6361/201833910

    Planck Collaboration , Aghanim , N., Akrami , Y., et al. 2020, , 641, A6, 10.1051/0004-6361/201833910

  77. [86]

    A., Combet , C., & Wilkinson , M

    Power , C., Wynn , G. A., Combet , C., & Wilkinson , M. I. 2009, , 395, 1146, 10.1111/j.1365-2966.2009.14628.x

  78. [87]

    X., Hennawi , J., Cooke , R., et al

    Prochaska , J. X., Hennawi , J., Cooke , R., et al. 2019, PypeIt: Python spectroscopic data reduction pipeline , Astrophysics Source Code Library, record ascl:1911.004. 1911.004

  79. [88]

    2025, , 42, e049, 10.1017/pasa.2025.35

    Qin , Y., Mesinger , A., Prelogovi \'c , D., et al. 2025, , 42, e049, 10.1017/pasa.2025.35

  80. [89]

    2002, , 336, L33, 10.1046/j.1365-8711.2002.05990.x

    Ricotti , M. 2002, , 336, L33, 10.1046/j.1365-8711.2002.05990.x

  81. [90]

    E., Ellis , R

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

  82. [91]

    2018, , 479, 994, 10.1093/mnras/sty1655

    Rosdahl , J., Katz , H., Blaizot , J., et al. 2018, , 479, 994, 10.1093/mnras/sty1655

  83. [92]

    R., & Giroux , M

    Shapiro , P. R., & Giroux , M. L. 1987, , 321, L107, 10.1086/185015

  84. [93]

    I., Bolte , M., Epps , H

    Sheinis , A. I., Bolte , M., Epps , H. W., et al. 2002, , 114, 851, 10.1086/341706

  85. [94]

    A., Burgasser , A

    Simcoe , R. A., Burgasser , A. J., Schechter , P. L., et al. 2013, , 125, 270, 10.1086/670241

  86. [95]

    2024, , 527, 6139, 10.1093/mnras/stad3605

    Simmonds , C., Tacchella , S., Hainline , K., et al. 2024, , 527, 6139, 10.1093/mnras/stad3605

  87. [96]

    2022, , 512, 3243, 10.1093/mnras/stac713

    Smith , A., Kannan , R., Garaldi , E., et al. 2022, , 512, 3243, 10.1093/mnras/stac713

  88. [97]

    2025, arXiv e-prints, arXiv:2503.15587, 10.48550/arXiv.2503.15587

    Sun , F., Fudamoto , Y., Lin , X., et al. 2025, arXiv e-prints, arXiv:2503.15587, 10.48550/arXiv.2503.15587

  89. [98]

    2024, , 969, 162, 10.3847/1538-4357/ad4888

    D urov c \' kov \'a , D., Eilers , A.-C., Chen , H., et al. 2024, , 969, 162, 10.3847/1538-4357/ad4888

  90. [99]

    2011, , 536, A105, 10.1051/0004-6361/201117752

    Vernet , J., Dekker , H., D'Odorico , S., et al. 2011, , 536, A105, 10.1051/0004-6361/201117752

  91. [100]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, 10.1038/s41592-019-0686-2

  92. [101]

    F., et al

    Wang , F., Yang , J., Hennawi , J. F., et al. 2023, , 951, L4, 10.3847/2041-8213/accd6f

  93. [102]

    H., Abel , T., Turk , M

    Wise , J. H., Abel , T., Turk , M. J., Norman , M. L., & Smith , B. D. 2012, , 427, 311, 10.1111/j.1365-2966.2012.21809.x

  94. [103]

    2014, , 440, 776, 10.1093/mnras/stu299

    Yajima , H., Li , Y., Zhu , Q., et al. 2014, , 440, 776, 10.1093/mnras/stu299

  95. [104]

    2022, in American Astronomical Society Meeting Abstracts, Vol

    Yeh , Y.-C., Smith , A., Garaldi , E., et al. 2022, in American Astronomical Society Meeting Abstracts, Vol. 240, American Astronomical Society Meeting \#240, 126.04

  96. [105]

    A., et al

    Yue , M., Eilers , A.-C., Simcoe , R. A., et al. 2024, , 966, 176, 10.3847/1538-4357/ad3914

Pith tools

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