Pith. sign in

REVIEW 4 major objections 4 minor 57 references

Systematic Bias in Ionizing Radiation Escape Fraction Measurements from Foreground Large-Scale Structures

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read Foreground galaxy density modulates Lyman-alpha forest opacity, biasing escape-fraction measurements.

desk verdict Photometric measurement of the tau–delta relation is novel and plausible, but the SED extrapolation needs validation before the quantitative fesc bias claim is secure. read the letter →

arxiv 2501.19303 v2 pith:E5XQTYWO submitted 2025-01-31 astro-ph.GA

C. Scarlata , W. Hu , M. J. Hayes , S. Taamoli , A. A. Khostovan , C. M. Casey , A. L. Faisst , J. S. Kartaltepe
show 3 more authors
Y. Lin M. Salvato M. Rafelski
This is my paper · ORCID
classification astro-ph.GA
keywords Lyman-alphaforestescapefractionintergalacticmediumlarge-scalestructuregalaxyoverdensityphotometricmethodCOSMOSfieldreionization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper establishes that the Lyman-$\alpha$ forest\u2014the swarm of neutral-hydrogen absorption lines imprinted on light passing through the intergalactic medium\u2014is not the same along every sightline: it is thicker behind overdense regions of foreground galaxies. Using 268 spectroscopically confirmed galaxies at $2.65

What carries the argument

The central object is a photometric Lyman-$\alpha$ optical depth built from one medium-band filter: COSMOS IB427 (4170\u20134370 \AA) isolates absorption by gas at $2.4<z<2.6$ along the sightline to a background galaxy at $2.65<z<3.0$. The intrinsic flux in that band, $f_{\rm model}$, is predicted by fitting stellar population synthesis models to rest-frame 1216\u20132000 \AA photometry with the redshift fixed at the spectroscopic value, and the measurement is $\tau_{\mathrm{Ly}\alpha}=-\ln(f_{\rm observed}/f_{\rm model})$. The density axis comes from a weighted kernel-density map of $1+\delta$ at $2.4<z<2.6$ constructed from COSMOS2020 photometric-redshift probability distributions. The reality check is a 1,000-realization shuffle of galaxy coordinates, which generates the null distribution for the Spearman correlation.

What would settle it

Take the same 268 sightlines and obtain medium-resolution ultraviolet spectra that cover the IB427 bandpass, then compare the spectroscopic Lyman-$\alpha$ optical depth with the photometric one; if the two diverge increasingly with $1+\delta$, the claimed correlation is produced by the SED extrapolation rather than the IGM. Alternatively, stack background quasar spectra at the same sky positions to measure the optical depth directly in the same density field.

Watch

Extended reading notes

Core claim

The paper's central claim is that the effective Lyman-$\alpha$ optical depth at $z\approx2.5$ correlates with foreground galaxy overdensity, so the intervening large-scale structure imprints a systematic pattern on any measurement of ionizing escape fraction. Overdense sightlines are on average less transparent; the best-fit relation is $\tau_{\mathrm{Ly}\alpha}=(0.12\pm0.04)(1+\delta)+0.22\pm0.05$ (Eq. 2), and a 1,000-realization shuffled-coordinate null test makes chance unlikely. The authors note that their slope agrees within errors with earlier quasar-based studies, while their normalization is somewhat lower and their mean $\tau_{\mathrm{Ly}\alpha}=0.33$ exceeds the quasar cosmic average $\sim0.2$, a shift they do not fully explain. Their IGM simulation indicates that the Lyman-$\alpha$ forest contributes 30\u201390% of the total absorption at 840\u2013910 \AA, so the environment dependence maps directly onto escape-fraction corrections. In practical terms, the correction for a galaxy behind an overdense region is larger, and behind an underdense region smaller, than the standard average correction.

Load-bearing premise

The load-bearing premise is that the unabsorbed IB427 flux can be predicted from stellar-population fits to longer wavelengths; if that prediction drifts with galaxy environment, the optical depth\u2013density correlation could arise without any real change in intergalactic neutral gas.

Editorial extensions

If this is right

  • Small-field escape-fraction surveys are not immune: overdense and underdense structures span several arcminutes, roughly the size of a WFC3 field, so a survey field can sit inside a single structure.
  • The quantitative effect is large enough to matter: across a 200 \AA window, Lyman-alpha transmission rises from 58% at $\delta=1.5$ to 74% at $\delta=-0.5$.
  • Escape-fraction measurements from narrow-band spectroscopy will be more affected than broad-band imaging, because the Lyman-alpha forest contribution is stronger over shorter wavelength intervals.
  • Choosing survey fields separated by many degrees on the sky decorrelates sightlines and reduces the bias; wide-area photometric surveys with photometric redshifts, such as Euclid, supply the density maps needed to apply environment-dependent corrections.

Reading between the lines

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

  • Editorial: If the relation holds, the field-to-field scatter in published escape fractions is not purely statistical, and comparing measurements from two fields requires knowing the overdensity of the foreground slice in front of each field.
  • Editorial: A spectroscopic check of the $f_{\rm model}$ extrapolation on a subset of these galaxies would separate a real IGM signal from an SED-modeling artifact, since the current spectra cannot measure the forest directly.
  • Editorial: Applied to all-sky surveys, the same photometric technique could produce a tomographic map of neutral-gas absorption at $z\sim2.5$, turning foreground large-scale structure from a nuisance into a measured field for per-sightline corrections.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper uses 268 spectroscopically confirmed galaxies at 2.7<z<3.0 in COSMOS to measure the Lyman-alpha forest optical depth photometrically. For each galaxy, the unabsorbed flux in the medium-band filter IB427 (rest-frame ~1040-1200 A) is predicted from BAGPIPES stellar population fits to photometry at rest wavelengths 1216-2000 A, and tau_Lyalpha is computed as -ln(f_obs/f_model). These values are then compared with a foreground galaxy density map at z~2.5 from Taamoli et al. (2024). The authors report a weak positive correlation between tau_Lyalpha and overdensity, quantified by Spearman rho=0.16 (p=0.007) and a linear fit tau_Lyalpha = (0.12 +/- 0.04)(1+delta) + 0.22 +/- 0.05 (Eq. 2). They argue that this environment-dependent IGM transmission introduces a systematic bias into escape fraction measurements and propose a photometric method, extendable to Euclid-era surveys, to map and correct for this effect.

Significance. If the measured tau-delta relation is robust, the paper makes a useful contribution: it increases the number of IGM sight lines by orders of magnitude relative to QSO studies, it provides a quantitative relation that can be used to correct escape fraction measurements, and it highlights an often-neglected systematic in JWST-era reionization analyses. The empirical nature of the correlation and the consistency with earlier independent QSO-based studies (Mukae et al. 2017; Liang et al. 2021) are strengths, as is the explicit discussion of how the effect depends on wavelength interval and field size. However, the central measurement rests on an unvalidated SED extrapolation into the rest-frame Lyman-alpha forest region, and the statistical analysis does not propagate individual measurement uncertainties. These issues do not invalidate the qualitative conclusion, but they must be addressed before Eq. 2 can be used as a quantitative environmental correction.

major comments (4)
  1. [Section 2.4, Eq. (1)] The inferred tau_Lyalpha is entirely determined by f_model, the unabsorbed IB427 flux predicted from BAGPIPES fits to photometry at rest wavelengths 1216-2000 A, extrapolated to rest ~1040-1180 A. If intrinsic SED properties such as stellar age, metallicity, dust attenuation, or nebular continuum correlate with environment, then f_model errors will be spatially correlated with the density map even for a perfectly uniform IGM, producing a spurious tau-delta correlation. The single alternate dust-law test (Reddy et al. 2016) does not span the full model degeneracy. The paper should validate the SED extrapolation against direct spectroscopic measurements of the same galaxies or against QSO transmission along co-spatial sight lines; without this, Eq. (2) cannot be treated as a quantitative environmental correction.
  2. [Section 3, average tau_Lyalpha] The reported average tau_Lyalpha = 0.33+0.18/-0.21 is offset from the QSO-based cosmic mean of ~0.2 at similar redshift, and the authors note that this offset is difficult to reconcile given that the Mukae et al. (2017) measurement was made in the same field and redshift range. This unresolved zero-point offset indicates a systematic in f_model that could also shear the slope of the tau-delta relation. The paper should investigate and quantify the source of this offset (e.g., photometric zero-points, SED template choice, or selection of LBG sight lines) rather than leaving it as an unexplained possibility.
  3. [Section 3.1, Spearman analysis] The Spearman rho = 0.16 (p = 0.007) and the linear fit in Eq. (2) are computed without propagating the individual uncertainties on tau_Lyalpha (which range from S/N > 3 in IB427 to much higher) or the errors on the extracted overdensity values. Heteroscedastic measurement errors could affect the rank correlation and the fitted slope. A bootstrap or errors-in-variables treatment, or at minimum a demonstration that the correlation is not driven by the lowest-S/N points, is needed to support the quoted significance.
  4. [Section 3.2, use of Eq. (2) for fesc bias] The quantitative bias estimate (transmission changing from 58% to 74% between delta = 1.5 and delta = -0.5) uses Eq. (2) with its current slope and intercept uncertainties. Propagating the errors on both parameters yields a wide range of predicted transmission differences, and the extrapolation to delta = 1.5 may be outside the range well sampled by the 268 galaxies. The qualitative conclusion that overdense regions produce larger IGM corrections is plausible, but the numerical values quoted in the abstract and conclusions should be presented with the associated uncertainty.
minor comments (4)
  1. [Section 2.2] The parent spectroscopic catalog contains ~10^5 sources, but the final sample is only 268 galaxies after the redshift, quality, and IB427 S/N cuts; the paper should describe the selection function more explicitly, since the environment dependence of spectroscopic follow-up could affect the density sampling.
  2. [Figure 4] The conversion of the Mukae et al. (2017) and Liang et al. (2021) results into tau using T_cosmic = 0.78 should be explained in the text or figure caption, including the uncertainty on this conversion, so the comparison of slopes is transparent.
  3. [Abstract and Section 3.2] The statement that Euclid photometric redshifts will make it possible to account for this bias assumes that photometric redshifts at z~2.5 are sufficiently accurate to construct the density maps; a brief discussion of the required accuracy would strengthen the claim.
  4. [Title] The title contains an apparent typo: 'F raction' should be 'Fraction'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the tau-delta correlation is an empirical measurement benchmarked against external QSO-based results, and the density map is an independent data product.

full rationale

The paper's central claim is an empirical correlation between a photometrically estimated Lyman-alpha forest optical depth (tau_Lyalpha) and a foreground galaxy overdensity map (1+delta). tau_Lyalpha is defined as -ln(f_observed/f_model), where f_observed is the IB427 flux and f_model is an SED-model flux fitted to rest-frame 1216-2000 Angstrom photometry; the overdensity map comes from Taamoli et al. (2024) and is constructed from photometric redshifts in COSMOS2020. Neither quantity is defined in terms of the other: no parameter entering the tau measurement is fitted to the density map, and the density map was not constructed to reproduce IB427 transmission or the final tau-delta relation. The paper also benchmarks its result against external QSO-based measurements (Mukae et al. 2017; Liang et al. 2021) and against the cosmic-mean QSO optical depth, so the central trend has independent anchors. The main caveat, that f_model is extrapolated from rest-frame >1216 Angstrom photometry into the IB427 band and could carry environment-dependent SED errors, is a systematic-error risk rather than a circular reduction: it does not make the tau-delta correlation equal to its inputs by construction. Self-citations such as Taamoli et al. (2024) and Khostovan et al. (2025) supply data products and spectral catalogs, not the load-bearing correlation itself, and no fitted parameter is relabeled as a prediction. The paper is therefore self-contained with respect to its central empirical claim, and no circular step is exhibited.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper is an observational correlation study. It does not introduce new physical entities. Its results rest on two fitted coefficients and on assumptions that the SED model accurately predicts the unabsorbed IB427 flux and that the galaxy density map traces the Ly-alpha absorber distribution. The dependences on model-dependent photometric corrections are the main unverified inputs.

free parameters (2)
  • Slope of the tau_Lyalpha - (1+delta) relation = 0.12 +/- 0.04
    Linear fit to the 268 sightlines in Eq. (2); used for the fesc bias estimates in Section 3.2, so the impact calculation inherits the fit.
  • Intercept of the tau_Lyalpha - (1+delta) relation = 0.22 +/- 0.05
    Linear fit to the same data in Eq. (2); sets the zero-point of the environment-dependent transmission correction.
assumptions (3)
  • domain assumption The SED model fit to rest-frame 1216-2000 Angstrom photometry accurately predicts the intrinsic flux in the IB427 band at rest ~1090-1200 Angstrom.
    Section 2.4 defines tau_Lyalpha = -ln(f_observed/f_model), where f_model is extrapolated from longer-wavelength photometry. Environmental dependence in template mismatch or nebular continuum could mimic the reported correlation.
  • domain assumption The galaxy density map at z~2.5 from Taamoli et al. (2024) traces the neutral gas distribution relevant to Ly-alpha forest absorption.
    Section 2.3 uses the photometric-redshift galaxy density map as the environmental tracer; the physical interpretation assumes galaxies trace the absorbing gas on the relevant scales.
  • domain assumption The Inoue et al. (2014) parameterization of IGM absorber distributions, derived from QSO sightlines, is a valid baseline for simulating the relative Ly-alpha forest contribution to total IGM absorption.
    Section 3.2 uses this model for Figure 5 and the fesc bias estimate; the paper notes the baseline assumes a spatially uncorrelated absorber distribution.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Systematic Bias in Ionizing Radiation Escape Fraction Measurements from Foreground Large-Scale Structures." pith.science (2026). https://pith.science/paper/E5XQTYWO

@misc{pith2026250119303,
  author       = {Pith},
  title        = {Pith review of: Systematic Bias in Ionizing Radiation Escape Fraction Measurements from Foreground Large-Scale Structures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E5XQTYWO}},
  note         = {Machine review of arXiv:2501.19303}
}
read the original abstract

We investigate the relationship between the Lyman-alpha (Lya) forest transmission in the intergalactic medium (IGM) and the environmental density of galaxies, focusing on its implications for the measurement of ionizing radiation escape fractions. Using a sample of 268 spectroscopically confirmed background galaxies at 2.7<z<3.0 and a galaxy density map at z~2.5 within the COSMOS field, we measure the Lya transmission photometrically, leveraging the multiwavelength data available from the COSMOS2020 catalog. Our results reveal a weak but statistically significant positive correlation between Lya optical depth and galaxy density contrast, suggesting that overdense regions are enriched in neutral gas, which could bias escape fraction measurements. This emphasizes the need to account for the large-scale structure of the IGM in analyses of ionizing radiation escape fractions, and highlights the advantages of a photometric approach for increasing the number of sampled lines of sight across large fields. The photometric redshifts provided by upcoming all-sky surveys, such as Euclid, will make it possible to account for this effect across widely separated fields.

Figures

Figures reproduced from arXiv: 2501.19303 by the authors.

Figure 1
Figure 1. COSMOS filter transmission curves used in this work, overlaid to the spectral template of a star-forming galaxy at z = 2.67 see text for details. The thick and thin black lines show the spectrum before and after absorption by the Lyα forest. The IB427 filter samples the portion of the absorption due to material in the 2.4 ≲ z ≲ 2.6 redshift range. nation of photometric bands available in COSMOS and shown in the Figu… view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Example SED modeling on a z = 2.78 galaxy. The orange dots represent the measured fluxes in the COS￾MOS2020 catalog. The black dots are computed using the best-fit spectrum (shown in black). are typically identified on the basis of a strong Lyman break feature. 3.1. The Lyα optical depth and foreground galaxy density The main result of our analysis is presented in Fig￾ure 4 where we show the ⟨z⟩ = 2.5 effective Lyα … view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: (Left) Lyα optical depth as a function of galaxy overdensity for the spectroscopically confirmed galaxies in COSMOS (gray points) and best linear fit with corresponding uncertainty (black line and gray areas). The correlation is weak but significant and agrees with pre…
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 41 canonical work pages

  1. [1]

    B., et al

    Adamo, A., Atek, H., Bagley, M. B., et al. 2024, arXiv e-prints, arXiv:2405.21054 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

    arXiv 2024

  2. [2]

    D., Bolton, J

    Becker, G. D., Bolton, J. S., Madau, P., et al. 2015, Monthly Notices of the Royal Astronomical Society, 447, 3402

    work page 2015

  3. [3]

    D., D’Aloisio, A., Christenson, H

    Becker, G. D., D’Aloisio, A., Christenson, H. M., et al. 2021, MNRAS, 508, 1853

    2021

  4. [4]

    D., Davies, F

    Becker, G. D., Davies, F. B., Furlanetto, S. R., et al. 2018, ApJ, 863, 92

    work page 2018

  5. [5]

    2025, arXiv e-prints, arXiv:2503.20878

    Beckett, A., Rafelski, M., Scarlata, C., et al. 2025, arXiv e-prints, arXiv:2503.20878

    work page arXiv 2025

  6. [6]

    R., Teplitz, H

    Bridge, C. R., Teplitz, H. I., Siana, B., et al. 2010, ApJ, 720, 465

    work page 2010

  7. [7]

    2003, Monthly Notices of the Royal Astronomical Society, 344, 1000

    Bruzual, G., & Charlot, S. 2003, Monthly Notices of the Royal Astronomical Society, 344, 1000

    work page 2003

  8. [8]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, The Astrophysical Journal, 533, 682, publisher: IOP Publishing

    work page 2000

Show all 57 references
  1. [9]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Dav´ e, R. 2018, MNRAS, 480, 4379

    2018

  2. [10]

    A., Sales de Andrade, E., Lee, A., et al

    Caswell, T. A., Sales de Andrade, E., Lee, A., et al. 2023, Matplotlib/Matplotlib: REL: V3.7.4, ,

    2023

  3. [11]

    M., Mantha, K

    Citro, A., Scarlata, C. M., Mantha, K. B., et al. 2024, arXiv e-prints, arXiv:2406.07618 Euclid Collaboration: Mellier, Y., Abdurro’uf, Acevedo Barroso, J., et al. 2024, A&A, accepted, arXiv:2405.13491

    2024 arXiv

  4. [12]

    A., Becker, R

    Fan, X., Strauss, M. A., Becker, R. H., et al. 2006, The Astronomical Journal, 132, 117, publisher: IOP Publishing Faucher-Gigu` ere, C.-A., Prochaska, J. X., Lidz, A.,

    2006

  5. [13]

    2008, The Astrophysical Journal, 681, 831, publisher: IOP Publishing

    Hernquist, L., & Zaldarriaga, M. 2008, The Astrophysical Journal, 681, 831, publisher: IOP Publishing

    2008

  6. [14]

    L., Leung, G

    Finkelstein, S. L., Leung, G. C. K., Bagley, M. B., et al. 2024, ApJL, 969, L2

    2024

  7. [15]

    J., Tang, M., Robertson, B

    Fletcher, T. J., Tang, M., Robertson, B. E., et al. 2019, The Astrophysical Journal, 878, 87, publisher: The American Astronomical Society

    2019

  8. [16]

    R., Jaskot, A

    Flury, S. R., Jaskot, A. E., Ferguson, H. C., et al. 2022, The Astrophysical Journal, 930, 126, publisher: The American Astronomical Society

    2022

  9. [17]

    D., Clayton, G

    Gordon, K. D., Clayton, G. C., Decleir, M., et al. 2023, ApJ, 950, 86

    2023

  10. [18]

    2024, ApJ, 960, 56

    Harikane, Y., Nakajima, K., Ouchi, M., et al. 2024, ApJ, 960, 56

    2024

  11. [19]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    2020

  12. [20]

    J., Runnholm, A., Gronke, M., & Scarlata, C

    Hayes, M. J., Runnholm, A., Gronke, M., & Scarlata, C. 2021, The Astrophysical Journal, 908, 36, publisher: The American Astronomical Society

    2021

  13. [21]

    J., Saldana-Lopez, A., Citro, A., et al

    Hayes, M. J., Saldana-Lopez, A., Citro, A., et al. 2024, arXiv e-prints, arXiv:2411.09262

    2024 arXiv

  14. [22]

    K., Shimizu, I., Iwata, I., & Tanaka, M

    Inoue, A. K., Shimizu, I., Iwata, I., & Tanaka, M. 2014, MNRAS, 442, 1805

    2014

  15. [23]

    K., Shimizu, I., Iwata, I., & Tanaka, M

    Inoue, A. K., Shimizu, I., Iwata, I., & Tanaka, M. 2014, Monthly Notices of the Royal Astronomical Society, 442, 1805 9

    2014

  16. [24]

    A., Kartaltepe, J

    Khostovan, A. A., Kartaltepe, J. S., Salvato, M., et al. 2025, COSMOS Spectroscopic Redshift Compilation (First Data Release): 165k Redshifts Encompassing Two Decades of Spectroscopy, , , arXiv:2503.00120

    2025

  17. [25]

    2005, Monthly Notices of the Royal Astronomical Society, 360, 1373

    Kirkman, D., Tytler, D., Suzuki, N., et al. 2005, Monthly Notices of the Royal Astronomical Society, 360, 1373

    2005

  18. [26]

    2016, Jupyter Notebooks—a Publishing Format for Reproducible Computational Workflows, 87–90

    Kluyver, T., Ragan-Kelley, B., P´ erez, F., et al. 2016, Jupyter Notebooks—a Publishing Format for Reproducible Computational Workflows, 87–90

    2016

  19. [27]

    2001, Monthly Notices of the Royal Astronomical Society, 322, 231

    Kroupa, P. 2001, Monthly Notices of the Royal Astronomical Society, 322, 231

    2001

  20. [28]

    2023, MNRAS, 518, 2546

    Laloux, B., Georgakakis, A., Andonie, C., et al. 2023, MNRAS, 518, 2546

    2023

  21. [29]

    C., Cucciati, O., F` evre, O

    Lemaux, B. C., Cucciati, O., F` evre, O. L., et al. 2022, Astronomy & Astrophysics, 662, A33, publisher: EDP Sciences

    2022

  22. [30]

    2021, ApJ, 907, 3

    Liang, Y., Kashikawa, N., Cai, Z., et al. 2021, ApJ, 907, 3

    2021

  23. [31]

    2024, Monthly Notices of the Royal Astronomical Society, 527, 4173

    Lin, Y.-H., Scarlata, C., Williams, H., et al. 2024, Monthly Notices of the Royal Astronomical Society, 527, 4173

    2024

  24. [32]

    K., Williger, G

    Liske, J., Webb, J. K., Williger, G. M., Fern´ andez-Soto, A., & Carswell, R. F. 2000, MNRAS, 311, 657

    2000

  25. [33]

    2024, ApJ, 971, 75

    Madau, P., Giallongo, E., Grazian, A., & Haardt, F. 2024, ApJ, 971, 75

    2024

  26. [34]

    K., Yamada, T., et al

    Mawatari, K., Inoue, A. K., Yamada, T., et al. 2023, AJ, 165, 208

    2023

  27. [35]

    S., Prochaska, J

    Monzon, J. S., Prochaska, J. X., Lee, K.-G., & Chisholm, J. 2020, The Astronomical Journal, 160, 37

    2020

  28. [36]

    2017, The Astrophysical Journal, 835, 281, publisher: The American Astronomical Society

    Mukae, S., Ouchi, M., Kakiichi, K., et al. 2017, The Astrophysical Journal, 835, 281, publisher: The American Astronomical Society

    2017

  29. [37]

    P., Oesch, P

    Naidu, R. P., Oesch, P. A., Reddy, N., et al. 2017, ApJ, 847, 12

    2017

  30. [38]

    Perez, F., & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21

    2007

  31. [39]

    Prochaska, J. X. 2019, Saas-Fee Advanced Course, 46, 111

    2019

  32. [40]

    A., Steidel, C

    Reddy, N. A., Steidel, C. C., Pettini, M., Bogosavljevi´ c, M., & Shapley, A. E. 2016, The Astrophysical Journal, 828, 108, publisher: The American Astronomical Society

    2016

  33. [41]

    C., Steidel, C

    Rudie, G. C., Steidel, C. C., Shapley, A. E., & Pettini, M. 2013, The Astrophysical Journal, 769, 146, publisher: The American Astronomical Society

    2013

  34. [42]

    S., et al

    Saxena, A., Pentericci, L., Ellis, R. S., et al. 2022, MNRAS, 511, 120

    2022

  35. [43]

    T., et al

    Scarlata, C., Alavi, A., Bruton, S. T., et al. 2022, The Parallel Ionizing Emissivity Survey, HST Proposal. Cycle 30, ID. #17147, ,

    2022

  36. [44]

    2007, ApJS, 172, 1

    Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1

    2007

  37. [45]

    E., Steidel, C

    Shapley, A. E., Steidel, C. C., Pettini, M., & Adelberger, K. L. 2003, The Astrophysical Journal, 588, 65, publisher: IOP Publishing

    2003

  38. [46]

    2019, ApJ, 879, 9

    Shi, K., Huang, Y., Lee, K.-S., et al. 2019, ApJ, 879, 9

    2019

  39. [47]

    I., Colbert, J., et al

    Siana, B., Teplitz, H. I., Colbert, J., et al. 2007, ApJ, 668, 62

    2007

  40. [48]

    E., Kulas, K

    Siana, B., Shapley, A. E., Kulas, K. R., et al. 2015, ApJ, 804, 17

    2015

  41. [49]

    M., et al

    Slosar, A., Font-Ribera, A., Pieri, M. M., et al. 2011, JCAP, 2011, 001

    2011

  42. [50]

    C., Bogosavljevi´ c, M., Shapley, A

    Steidel, C. C., Bogosavljevi´ c, M., Shapley, A. E., et al. 2018, ApJ, 869, 123

    2018

  43. [51]

    C., Bogosavljevi´ c, M., Shapley, A

    Steidel, C. C., Bogosavljevi´ c, M., Shapley, A. E., et al. 2018, The Astrophysical Journal, 869, 123, publisher: The American Astronomical Society

    2018

  44. [52]

    2024, The Astrophysical Journal, 966, 18, publisher: The American Astronomical Society

    Taamoli, S., Mobasher, B., Chartab, N., et al. 2024, The Astrophysical Journal, 966, 18, publisher: The American Astronomical Society

    2024

  45. [53]

    2016, ApJ, 826, 114

    Toshikawa, J., Kashikawa, N., Overzier, R., et al. 2016, ApJ, 826, 114

    2016

  46. [54]

    2018, MNRAS, 476, L15

    Vanzella, E., Nonino, M., Cupani, G., et al. 2018, MNRAS, 476, L15

    2018

  47. [55]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  48. [56]

    R., Kauffmann, O

    Weaver, J. R., Kauffmann, O. B., Ilbert, O., et al. 2022, The Astrophysical Journal Supplement Series, 258, 11, publisher: The American Astronomical Society

    2022

  49. [57]

    L., Chen, W., et al

    Williams, H., Kelly, P. L., Chen, W., et al. 2023, Science, 380, 416

    2023

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.