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REVIEW 3 major objections 6 minor 89 references

A Simulated Galaxy Laboratory: Exploring the Observational Effects on UV Spectral Absorption Line Measurements

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Mock observations of one simulated galaxy show that the partial covering model underestimates Si II column densities by 1.25 dex on average.

desk verdict The 1.25 dex PCM column-density bias is a solid, directly measured result, but the dust explanation is underdetermined until a dust-off radiative transfer run is done. read the letter →

arxiv 2412.02794 v1 pith:L52FOJ5U submitted 2024-12-03 astro-ph.GA astro-ph.IM

R. Michael Jennings , Alaina Henry , Valentin Mauerhofer , Timothy Heckman , Claudia Scarlata , Cody Carr , Xinfeng Xu , Mason Huberty
show 8 more authors
Simon Gazagnes Anne E. Jaskot Jeremy Blaizot Anne Verhamme Sophia R. Flury Alberto Saldana-Lopez Matthew J. Hayes Maxime Trebitsch
This is my paper · ORCID
classification astro-ph.GAastro-ph.IM
keywords ultravioletabsorptionlinesgalacticoutflowspartialcoveringmodelcolumndensitymockspectradustattenuationLymancontinuumescapespectralstacking
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 uses mock ultraviolet spectra of a simulated galaxy to test whether standard down-the-barrel absorption-line measurements recover the truth. It finds that residual flux, the depth of an absorption line, is systematically overestimated in low-resolution individual spectra and in stacked spectra. It also finds that the partial covering model, the standard method for turning Si II absorption into column densities, underestimates the true Si II column density by about 1.25 dex on average. The likely cause is dust: sightlines with the highest gas columns are also dust-obscured and therefore contribute little to the UV spectrum. If real galaxies behave like the simulation, outflow rates and Lyman-continuum escape fractions inferred from such spectra need corrections.

What carries the argument

The machinery is a set of mock observations built from one cosmological zoom-in galaxy with stellar mass near $10^9\,M_\odot$, evolved from $z=4.19$ to $z=3.0$, post-processed with Monte Carlo radiative transfer to produce Si II $\lambda\lambda1260,1526,1808$ spectra for 300 sightlines per snapshot, then convolved and noised to match the resolution and signal-to-noise of current UV surveys. The inference being tested is the partial covering model, which fits each velocity bin with $F_k(v)=1-C(v)+C(v)e^{-w_k\tau(v)}$ using three Si II transitions to solve for covering fraction $C(v)$ and optical depth $\tau(v)$, and converts the optical depth to column density via the apparent optical depth relation. Two additional objects carry the argument: a dust prescription with opacity proportional to hydrogen density and albedo 0.32, and Jensen's inequality applied to the spatially averaged transmission $\langle e^{-\tau}\rangle \ge e^{-\langle\tau\rangle}$, which guarantees that any spectrum averaging over sub-resolution structure will underestimate the mean optical depth.

What would settle it

Compare PCM-derived Si II column densities against an independent column-density tracer in real galaxies across a range of UV attenuation; if galaxies with high $A_{1520}$ still show agreement between PCM and independent estimates, the dust-hiding explanation fails. More directly, rerun the mock spectra with dust turned off: the paper predicts the median underestimate should drop by roughly 1 dex, leaving only the smaller optical-depth-averaging and saturation effects.

Watch

Extended reading notes

Core claim

The central claim is that a standard analysis of rest-frame UV Si II absorption, applied to the 22,500 mock sightlines produced from a radiation-hydrodynamic simulated galaxy, recovers the wrong column density. The apparent optical depth method with a partial covering model, using Si II $\lambda\lambda1260,1526,1808$ \AA, gives values that are on average 1.25 dex below the spatially resolved simulation column density, with most sightlines off by 1.0 to 1.5 dex. The paper argues the dominant cause is dust attenuation: because dust opacity is modeled as proportional to gas density, high-column-density gas is cospatial with high dust optical depth and is largely invisible in the UV, so the absorption spectrum is dominated by lower-column paths. It also shows that residual flux is overestimated when resolution is degraded, and that stacking spectra raises the measured residual flux above the average of the constituent spectra even when noise and resolution are perfect.

Load-bearing premise

The load-bearing premise is that one simulated galaxy, with its particular clumpy dust-gas geometry, is representative enough of real galaxies for the 1.25 dex bias to transfer; if real galaxies have a uniform dust screen or less cospatial dust, the underestimate would shrink.

Editorial extensions

If this is right

  • Residual flux measurements from low-resolution spectra should not be read as covering fractions without a correction for resolution and noise.
  • Stacked spectra reliably preserve average equivalent width when normalized, but not residual flux or PCM column density.
  • Mass outflow rates estimated from Si II column densities via the partial covering model are likely underestimates if dust is clumpy and cospatial with gas.
  • Lyman-continuum escape fractions predicted from low-resolution residual flux tend to be overestimated for galaxies with true escape fractions below roughly 1 percent.
  • Column densities from optically thin transitions via equivalent-width analysis are less biased than PCM fits on saturated lines.
  • If dust is cospatial with gas in real galaxies at similar levels, UV-selected samples will systematically miss the densest outflow gas, so mass, momentum, and metal loading inferred from down-the-barrel spectra would be biased low.
  • The 1.25 dex correction is a one-galaxy number; a grid of simulations spanning mass, metallicity, and dust-to-gas ratio could show whether the bias scales with UV attenuation or with the clumpiness of the ISM.
  • A straightforward test is to rerun the same sightlines with dust turned off; the paper indicates this is planned, and the prediction is that the median underestimate drops by about 1 dex.

Reading between the lines

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

  • If the dust-hiding mechanism operates in real galaxies, UV-selected samples will systematically miss the densest outflow gas, so mass, momentum, and metal loading inferred from down-the-barrel spectra would be biased low.
  • The 1.25 dex correction is a one-galaxy number; a grid of simulations spanning mass, metallicity, and dust-to-gas ratio could show whether the bias scales with UV attenuation or with the clumpiness of the ISM.
  • A straightforward test is to rerun the same sightlines with dust turned off; the paper indicates this is planned, and the prediction is that the median underestimate drops by about 1 dex.
  • Spatially resolved UV spectroscopy, if it becomes available, could identify which regions dominate the residual flux and directly measure the column-density distribution the PCM tries to recover.
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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 / 6 minor

Summary. This paper uses a radiation-hydrodynamic zoom-in simulation of a z ~ 3-4, M* ~ 10^9 Msun galaxy post-processed with the RASCAS Monte Carlo radiative transfer code to generate 22,500 mock Si II and Ly-beta spectra. The authors downgrade the spectral resolution, binning, and noise to match the LzLCS/G140L, CLASSY, and VANDELS surveys, then measure equivalent width, residual flux, and v50 on individual and stacked spectra. They apply the apparent optical depth method with a partial covering model (PCM) using Si II 1260, 1526, and 1808 and compare the inferred column densities to simulation-derived 'true' columns, finding a median underestimate of log(N_PCM/N_sim) = -1.25. They attribute much of this bias to dust attenuation hiding high-column-density sightlines, with spatial optical-depth averaging as a secondary effect, and show that stacking further biases residual flux and PCM column-density estimates.

Significance. If the measured 1.25 dex PCM bias holds, it has direct implications for mass-outflow rates and Lyman-continuum escape predictions inferred from UV absorption lines. The paper's forward-model design is a clear strength: the 'true' column density is defined independently of the spectral fitting, and the main result is robust to uniform versus star-weighted column sampling. The residual-flux and stacking results are demonstrated with large statistical samples (11.25 million noise realizations) and are internally consistent with the known convolution and averaging effects. However, the causal attribution of the bias to dust is not yet supported at the same standard as the measurement itself; the paper presents a simplified analytic screen model rather than a dust-off radiative transfer run, and it explicitly defers that run to future work. The current version is best viewed as establishing a large, systematic PCM bias and identifying two plausible mechanisms, one of which (optical-depth averaging) is guaranteed by Jensen's inequality but is not quantitatively separated from dust.

major comments (3)
  1. [Section 6.4.1, Eq. (9), Figure 13] The central claim that dust causes roughly 1 dex of the PCM underestimate rests on N_dust_sim = N_star_sim * exp(-tau_dust), an analytic foreground-screen attenuation with a fixed sigma_1260. This is not the dust treatment used in the RASCAS post-processing, where dust is cospatial with the gas, has albedo 0.32, and participates in scattering. A foreground screen can overstate dust's ability to hide high-column gas, so the quantitative 'at least ~1 dex' attribution is not established. The manuscript itself states that a dust-off radiative transfer run is needed (Section 7 and Section 6.4.1), so the abstract's 'likely caused by high-column-density sight-lines that are optically-thick to dust' goes beyond the presented evidence. Please either run the dust-off comparison or revise the abstract and summary to present dust attenuation as one candidate mechanism rather than the established cause.
  2. [Section 6.4.2, Eq. (10)] Optical-depth averaging is a competing, guaranteed bias: by Jensen's inequality, <e^{-tau}> >= e^{-<tau>}, so the apparent optical depth tau_a = ln(1/<e^{-tau}>) is always an underestimate whenever tau varies across the aperture. The correlations shown in Figure 15, where the PCM error tracks the skew and variance of the column-density distribution, are exactly what this mechanism predicts. The paper does not quantify the fraction of the -1.25 dex that arises from this effect versus dust, so the statement in Section 7 that 'dust attenuation has the highest potential' and the assignment of ~1 dex to dust are underdetermined. A dust-off run would separate the two; without it, please present the two mechanisms as degenerate in this data and avoid claiming that dust is the dominant cause.
  3. [Section 6.4.1, Figure 14 and Section 2.1] The transferability of the bias to real galaxies rests on a single simulated galaxy (M* ~ 10^9 Msun, Z ~ 0.4 Zsun, z ~ 3-4) and on the overlap in A1520 values with LzLCS and CLASSY. The overlap in integrated attenuation does not establish that the clumpy dust-gas geometry of this simulation is typical of those survey galaxies; a uniform dust screen or less cospatial dust would reduce the dust-related bias. The paper explicitly disclaims that the simulation is a ground truth for real galaxies, but the abstract and Section 7 nevertheless generalize the dust conclusion. Please either temper the generalizing statements or add a test with a different dust geometry or galaxy mass, or state this representativeness limitation explicitly in the abstract.
minor comments (6)
  1. [Abstract and Section 6.3] The terms 'under-predictions' and 'underestimates' are used interchangeably; please choose one for consistency.
  2. [Section 4.2, Figure 6] There is a typo in 'overstimate' in the paragraph explaining why stacked spectra overestimate <R>; please correct it.
  3. [Section 6.2, Eq. (7)] Please state explicitly that N_PCM(v) has units of cm^-2 per km/s, so that the integration in Eq. (8) yields the total column density in cm^-2.
  4. [Section 6.4.2, footnote 4] The statement that 'Cf(v) is unity' in the continuous-spaxel representation may confuse readers, since Eq. (6) fits C(v) as a free parameter; please clarify that the covering fraction is absorbed into the spatial average of e^{-tau} in this representation.
  5. [Table 1] The VANDELS row leaves the metallicity column blank; please use a '—' or explicit 'no reported value' for clarity, as done for other missing entries.
  6. [Section 5, Eq. (3)] The symbol f_pre_esc is used before its meaning is defined; please define 'pre' as the dust-corrected, predicted escape fraction at the first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the PCM column-density underestimate is an independent forward-model comparison; the dust attribution is underdetermined but is explicitly flagged by the authors as requiring a dust-off radiative-transfer run.

full rationale

The paper's central result, log(NPCM/Nuniform_sim) = -1.25, is obtained by comparing PCM fits to the three Si II transitions (Eqs. 6-8) against column densities integrated directly from the simulation grid (Eq. 5). These are independent definitions: the simulation truth does not use the spectrum, and no parameter is fitted to force the comparison to -1.25. The stacked-spectrum residual-flux result is likewise a forward-modeled consequence of averaging line shapes (Figure 6) with a Jensen-inequality explanation (Eq. 10), not a redefinition. Self-citations to Mauerhofer et al. 2021 and Gazagnes et al. 2023 provide the simulation and its validation against CLASSY; these are code-based, externally comparable inputs rather than an unverified uniqueness theorem, so they do not make the argument circular. The one caveat is the abstract-level claim that the underestimate is 'likely caused by dust': Section 6.4.1 supports it with an analytic foreground-screen attenuation (Eq. 9), which the authors themselves call 'an exaggeration of the effects of dust attenuation treating all dust to lie in a single plane in front of the Si II gas,' and they state that disentangling dust from optical-depth averaging requires a dedicated dust-off run ('In a future paper we will generate spectra with dust turned off to better quantify its sole effect'). That is an underdetermination or correctness risk, not circularity, because Equation 9 is not fitted to NPCM and the claim is presented as probable rather than derived from the target. The paper is self-contained against external benchmarks (e.g., the LzLCS/CLASSY overlap in Figure 14), and no load-bearing step reduces by construction to its own input.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. The central bias measurements are directly computed from the simulation, but their interpretation depends on several modeling choices and on the realism of the dust implementation.

free parameters (2)
  • True column integration radius = 1 kpc (diameter 2 kpc)
    The true Si II column density is the mean over a 135x135 uniform grid in a 1 kpc radius column. The claimed 1.25 dex underestimate is relative to this definition; using a larger radius lowers Nsim and reduces the bias, as shown in Appendix A and Figure 18.
  • PCM velocity integration threshold = 0.80 of continuum flux of Si II 1260
    Hand-chosen threshold to restrict the NPCM(v) integral; changing the threshold would change the inferred column and the reported underestimate.
assumptions (5)
  • domain assumption The zoom-in RAMSES-RT simulation produces a representative star-forming galaxy with a realistic ISM and CGM.
    All true quantities come from this single simulation (Mauerhofer et al. 2021); results are assumed to be instructive for real galaxies (Section 2.1).
  • domain assumption Dust opacity is proportional to hydrogen density with albedo 0.32 and the adopted pseudo-density model.
    Used to produce the spectra and to interpret the PCM discrepancy (Section 2.2, 6.4.1); from Mauerhofer et al. 2021 and Li & Draine 2001.
  • domain assumption Solar abundance ratios and the KROME/PyNeb ionization modeling give correct Si II ionic fractions.
    Column densities and spectra depend on the modeled ionization balance (Section 2.2).
  • domain assumption RASCAS Monte Carlo radiative transfer correctly captures resonant scattering, fluorescent emission, and dust absorption.
    Mock spectra are generated with RASCAS; correctness of the underlying RT is assumed (Section 2.2).
  • standard math The partial covering model equations (Savage & Sembach 1991; Arav et al. 2005) are the standard interpretation framework.
    Used to fit C(v) and tau(v) with three Si II transitions (Section 6.2).

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Cite this review

Pith. "Pith review of A Simulated Galaxy Laboratory: Exploring the Observational Effects on UV Spectral Absorption Line Measurements." pith.science (2026). https://pith.science/paper/L52FOJ5U

@misc{pith2026241202794,
  author       = {Pith},
  title        = {Pith review of: A Simulated Galaxy Laboratory: Exploring the Observational Effects on UV Spectral Absorption Line Measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L52FOJ5U}},
  note         = {Machine review of arXiv:2412.02794}
}
read the original abstract

Ultraviolet absorption line spectroscopy is a sensitive diagnostic for the properties of interstellar and circumgalactic gas. Down-the-barrel observations, where the absorption is measured against the galaxy itself, are commonly used to study feedback from galactic outflows and to make predictions about the leakage of HI ionizing photons into the intergalactic medium. Nonetheless, the interpretation of these observations is challenging and observational compromises are often made in terms of signal-to-noise, spectral resolution, or the use of stacking analyses. In this paper, we present a novel quantitative assessment of UV absorption line measurement techniques by using mock observations of a hydrodynamical simulation. We use a simulated galaxy to create 22,500 spectra in the commonly used SiII lines while also modeling the signal-to-noise and spectral resolution of recent rest-frame UV galaxy surveys at both high and low redshifts. We show that the residual flux of absorption features is easily overestimated for single line measurements and for stacked spectra. Additionally, we explore the robustness of the partial covering model for estimating column densities from spectra and find under-predictions on average of 1.25 dex. We show that the under-prediction is likely caused by high-column-density sight-lines that are optically-thick to dust making them invisible in UV spectra.

Figures

Figures reproduced from arXiv: 2412.02794 by the authors.

Figure 1
Figure 1. Example spectral observations of the simulated galaxy for SiII λ1260˚A. Each row shows a different observed sightline of the galaxy with decreasing line strength from top to bottom with Wsim = 2.4, 0.6, 0.3˚A; Rsim = 0.04, 0.44, 0.7; v 50 sim = −2.47, −62.26, 24.14. Each column shows the survey data the mock spectra represent. The true simulation spectra are shown in red with the mock observation spectra plotted on … view at source ↗
Figure 2
Figure 2. SiII λ1260 differences of equivalent width, residual flux, and 50% velocity for the three mock surveys. Each difference is plotted as a function of “true” equivalent width. The solid lines show the median, and the shaded regions show the 1σ and 3σ range respectively. The size of the markers is proportional to the count of sightlines within each Wsim bin. The vertical dotted, dashed, and solid lines for each survey c… view at source ↗
Figure 3
Figure 3. Same as the middle row of [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Example stacks of normalized SiII λ1260 spectra for each survey. Each stack consists of the same 30 sightlines. lines. For saturated lines it is often interpreted as a gas covering fraction of the source (Vasei et al. 2016; Reddy et al. 2016; Gazagnes et al. 2018; Stei…
Figure 5
Figure 5. Figure 5: Measurement residuals on stacked spectra for each survey. Our three line measurements are shown in each column starting with equivalent width on the left, residual flux, then 50% velocity on the right. Two methods of stacking are shown with normalized flux average on t…
Figure 6
Figure 6. Figure 6: A stacked spectrum of two sightlines, j and j ′ , illustrating the effects on measuring residual flux. Due to the troughs of sightline j and j ′ not existing at the same wavelength bin, i ̸= i ′ , the residual flux of the stacked spec￾trum does not equal the average of…
Figure 7
Figure 7. Figure 7: Correlation between true LyC escape fraction and predicted escape fraction from dust corrected SiII 1260 residual flux using Equation 3. Left: The 22,500 true base sightlines are plotted for the simulation panel with a linear fit to the data shown by the dotted red lin…
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: SiII column density diagnostics for a single representative sightline where the partial covering model produces an underestimation by ∼ 1 order of magnitude. Left: Column density map, 2kpc wide, with histogram of all pixel samples. The dashed and dotted vertical lines…
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: A comparison of “true” and inferred SiII column densities for all 22,500 galaxy sightlines. The “a”, “b” and “c” markers indicate the values corresponding to the sightlines shown in Figures 10, 11 and 13 respectively. Left: The simulation column densities are compared…
Figure 13
Figure 13. Figure 13: SiII column density diagnostics for a single example galaxy sightline using different sampling methods. From left to right, the three sampling methods are a uniform spatial sample, column densities in front of all stars, and column densities in front of all stars atte…
Figure 14
Figure 14. Figure 14: Top: True SiII column density as a func￾tion of integrated sightline UV attenuation at 1520˚A for all 22,500 galaxy sightlines. Middle: SiII column density un￾derestimates as a function integrated sightline UV attenua￾tion. Bottom: UV attenuations for the simulation s…
Figure 15
Figure 15. Figure 15: Relationships between column density underestimates and spatial column density distribution statistics. The “a”, “b” and “c” markers indicate the values corresponding to the sightlines shown in Figures 10, 11 and 13 respectively. Left: The ratio of the mean to median …
Figure 16
Figure 16. Figure 16: A comparison of “true” and inferred SiII column densities for the different spectral resolutions and binnings of each mock survey. Sightline data is binned by log Nsim and the median value per bin is shown. Marker sizes are proportional to the sightline counts per bin…
Figure 17
Figure 17. Figure 17: Outcome of 1000 measurements comparing PCM column densities from stacked spectra Nstack to the average PCM column densities of the individual sightlines ⟨N⟩. The vertical dashed line shows the median outcome where Nstack underestimates ⟨N⟩ by 20%. mock survey spectra,…
Figure 18
Figure 18. Figure 18: Left: Galaxy diagram showing a central dense ISM surrounded by a more diffuse extended wind. The region between the stellar continuum in the ISM and the observer is labelled as the absorption region. The absorption feature in the spectrum used for the PCM originates f…

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Reference graph

Works this paper leans on

89 extracted references · 2 canonical work pages

  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]

    u;V4 lewĪ gj

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -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 on reference command E...

    2021

  4. [4]

    M., Heckman, T

    Alexandroff, R. M., Heckman, T. M., Borthakur, S., Overzier, R., & Leitherer, C. 2015, The Astrophysical Journal, 810, 104, 10.1088/0004-637X/810/2/104

    doi:10.1088/0004-637x/810/2/104 2015

  5. [5]

    A., et al

    Arav, N., Kaastra, J., Kriss, G. A., et al. 2005, The Astrophysical Journal, 620, 665, 10.1086/425560

    doi:10.1086/425560 2005

  6. [6]

    Barrow, K. S. S., Robertson, B. E., Ellis, R. S., et al. 2020, The Astrophysical Journal Letters, 902, L39, 10.3847/2041-8213/abbd8e

    doi:10.3847/2041-8213/abbd8e 2020

  7. [7]

    A., James, B

    Berg, D. A., James, B. L., King, T., et al. 2022, The Astrophysical Journal Supplement Series, 261, 31, 10.3847/1538-4365/ac6c03

    doi:10.3847/1538-4365/ac6c03 2022

  8. [8]

    2023, Monthly Notices of the Royal Astronomical Society, 523, 3749, 10.1093/mnras/stad1523

    Blaizot, J., Garel, T., Verhamme, A., et al. 2023, Monthly Notices of the Royal Astronomical Society, 523, 3749, 10.1093/mnras/stad1523

    doi:10.1093/mnras/stad1523 2023

Show all 89 references
  1. [9]

    M., Leitherer , C., & Overzier , R

    Borthakur , S., Heckman , T. M., Leitherer , C., & Overzier , R. A. 2014, Science, 346, 216, 10.1126/science.1254214

    2014 doi

  2. [10]

    2023, The Astrophysical Journal, 952, 88, 10.3847/1538-4357/acd331

    Carr, C., Michel-Dansac, L., Blaizot, J., et al. 2023, The Astrophysical Journal, 952, 88, 10.3847/1538-4357/acd331

    2023 doi

  3. [11]

    2021, The Astrophysical Journal, 906, 104, 10.3847/1538-4357/abc7c3

    Carr, C., Scarlata, C., Henry, A., & Panagia, N. 2021, The Astrophysical Journal, 906, 104, 10.3847/1538-4357/abc7c3

    2021 doi

  4. [12]

    2018, The Astrophysical Journal, 860, 143, 10.3847/1538-4357/aac48e

    Carr, C., Scarlata, C., Panagia, N., & Henry, A. 2018, The Astrophysical Journal, 860, 143, 10.3847/1538-4357/aac48e

    2018 doi

  5. [13]

    2017, Astronomy and Astrophysics, 605, 1, 10.1051/0004-6361/201730610

    Chisholm, J., Orlitová, I., Schaerer, D., et al. 2017, Astronomy and Astrophysics, 605, 1, 10.1051/0004-6361/201730610

    2017 doi

  6. [14]

    R., Bayliss, M., et al

    Chisholm, J., Rigby, J. R., Bayliss, M., et al. 2019, The Astrophysical Journal, 882, 182, 10.3847/1538-4357/ab3104

    2019 doi

  7. [15]

    A., Leitherer , C., & Chen , Y

    Chisholm , J., Tremonti , C. A., Leitherer , C., & Chen , Y. 2017, , 469, 4831, 10.1093/mnras/stx1164

    2017 doi

  8. [16]

    A., Leitherer , C., Chen , Y., & Wofford , A

    Chisholm , J., Tremonti , C. A., Leitherer , C., Chen , Y., & Wofford , A. 2016 a , , 457, 3133, 10.1093/mnras/stw178

    2016 doi

  9. [17]

    2016 b , , 463, 541, 10.1093/mnras/stw1951

    Chisholm , J., Tremonti Christy , A., Leitherer , C., & Chen , Y. 2016 b , , 463, 541, 10.1093/mnras/stw1951

    2016 doi

  10. [18]

    2018, Astronomy & Astrophysics, 616, A30, 10.1051/0004-6361/201832758

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

    2018 doi

  11. [19]

    R., Kereš, D., Hopkins, P

    Choban, C. R., Kereš, D., Hopkins, P. F., et al. 2022, Monthly Notices of the Royal Astronomical Society, 514, 4506, 10.1093/mnras/stac1542

    2022 doi

  12. [20]

    M., Schneider, E

    de la Cruz, L. M., Schneider, E. E., & Ostriker, E. C. 2021, The Astrophysical Journal, 919, 112, 10.3847/1538-4357/ac04ac

    2021 doi

  13. [21]

    E., Reddy, N

    Du, X., Shapley, A. E., Reddy, N. A., et al. 2018, The Astrophysical Journal, 860, 75, 10.3847/1538-4357/aabfcf

    2018 doi

  14. [22]

    2011, The Astrophysical Journal, 739, 7, 10.1088/0004-637X/739/1/7

    Edmonds, D., Borguet, B., Arav, N., et al. 2011, The Astrophysical Journal, 739, 7, 10.1088/0004-637X/739/1/7

    2011 doi

  15. [23]

    K., Quider, A

    Erb, D. K., Quider, A. M., Henry, A. L., & Martin, C. L. 2012, The Astrophysical Journal, 759, 26, 10.1088/0004-637X/759/1/26

    2012 doi

  16. [24]

    R., Jaskot, A

    Flury, S. R., Jaskot, A. E., Ferguson, H. C., et al. 2022 a . http://arxiv.org/abs/2201.11716

    2022 arXiv

  17. [25]

    ---. 2022 b . http://arxiv.org/abs/2203.15649

    2022 arXiv

  18. [26]

    2021, Astronomy & Astrophysics, 647, A150, 10.1051/0004-6361/202040059

    Garilli, B., McLure, R., Pentericci, L., et al. 2021, Astronomy & Astrophysics, 647, A150, 10.1051/0004-6361/202040059

    2021 doi

  19. [27]

    2020, Astronomy & Astrophysics, 639, A85, 10.1051/0004-6361/202038096

    Gazagnes, S., Chisholm, J., Schaerer, D., Verhamme, A., & Izotov, Y. 2020, Astronomy & Astrophysics, 639, A85, 10.1051/0004-6361/202038096

    2020 doi

  20. [28]

    2018, Astronomy & Astrophysics, 616, A29, 10.1051/0004-6361/201832759

    Gazagnes, S., Chisholm, J., Schaerer, D., et al. 2018, Astronomy & Astrophysics, 616, A29, 10.1051/0004-6361/201832759

    2018 doi

  21. [29]

    A., et al

    Gazagnes, S., Mauerhofer, V., Berg, D. A., et al. 2023, The Astrophysical Journal, 952, 164, 10.3847/1538-4357/acda2c

    2023 doi

  22. [30]

    T., & Sahnow , D

    Ghavamian , P., Froning , C., Osterman , S., Keyes , C. T., & Sahnow , D. 2010, in Hubble after SM4. Preparing JWST, 46

    2010

  23. [31]

    Grassi, T., Bovino, S., Schleicher, D. R. G., et al. 2014, Monthly Notices of the Royal Astronomical Society, 439, 2386, 10.1093/mnras/stu114

    2014 doi

  24. [32]

    J., & Scott, P

    Grevesse, N., Asplund, M., Sauval, A. J., & Scott, P. 2010, Astrophysics and Space Science, 328, 179, 10.1007/s10509-010-0288-z

    2010 doi

  25. [34]

    2011, Monthly Notices of the Royal Astronomical Society, 415, 2101, 10.1111/j.1365-2966.2011.18820.x

    Hahn, O., & Abel, T. 2011, Monthly Notices of the Royal Astronomical Society, 415, 2101, 10.1111/j.1365-2966.2011.18820.x

    2011

  26. [35]

    M., Alexandroff, R

    Heckman, T. M., Alexandroff, R. M., Borthakur, S., Overzier, R., & Leitherer, C. 2015, Astrophysical Journal, 809, 147, 10.1088/0004-637X/809/2/147

    2015 doi

  27. [36]

    M., Borthakur, S., Overzier, R., et al

    Heckman, T. M., Borthakur, S., Overzier, R., et al. 2011, Astrophysical Journal, 730, 10.1088/0004-637X/730/1/5

    2011 doi

  28. [37]

    L., & Erb, D

    Henry, A., Scarlata, C., Martin, C. L., & Erb, D. 2015, Astrophysical Journal, 809, 19, 10.1088/0004-637X/809/1/19

    2015 doi

  29. [38]

    Huberty, M., Carr, C., Scarlata, C., et al. 2024. http://arxiv.org/abs/2406.03646

    2024 arXiv

  30. [39]

    I., Worseck, G., Schaerer, D., et al

    Izotov, Y. I., Worseck, G., Schaerer, D., et al. 2021, Monthly Notices of the Royal Astronomical Society, 503, 1734, 10.1093/mnras/stab612

    2021 doi

  31. [40]

    L., Auger , M., Pettini , M., et al

    James , B. L., Auger , M., Pettini , M., et al. 2018, , 476, 1726, 10.1093/mnras/sty315

    2018 doi

  32. [41]

    L., Berg , D

    James , B. L., Berg , D. A., King , T., et al. 2022, , 262, 37, 10.3847/1538-4365/ac8008

    2022 doi

  33. [42]

    E., Silveyra, A

    Jaskot, A. E., Silveyra, A. C., Plantinga, A., et al. 2024 a . http://arxiv.org/abs/2406.10171

    2024 arXiv

  34. [43]

    ---. 2024 b . http://arxiv.org/abs/2406.10179

    2024 arXiv

  35. [44]

    Jenkins, E. B. 2009, The Astrophysical Journal, 700, 1299, 10.1088/0004-637X/700/2/1299

    2009 doi

  36. [45]

    P., & Ellis, R

    Jones, T., Stark, D. P., & Ellis, R. S. 2012, Astrophysical Journal, 751, 10.1088/0004-637X/751/1/51

    2012 doi

  37. [46]

    A., Ellis , R

    Jones , T. A., Ellis , R. S., Schenker , M. A., & Stark , D. P. 2013, , 779, 52, 10.1088/0004-637X/779/1/52

    2013 doi

  38. [47]

    A., Danforth, C

    Keeney, B. A., Danforth, C. W., Stocke, J. T., France, K., & Green, J. C. 2012, Publications of the Astronomical Society of the Pacific, 124, 830, 10.1086/667392

    2012 doi

  39. [48]

    C., Jones , T., Sanders , R

    Keerthi Vasan , G. C., Jones , T., Sanders , R. L., et al. 2023, , 959, 124, 10.3847/1538-4357/acf462

    2023 doi

  40. [49]

    2017, Monthly Notices of the Royal Astronomical Society, stx052, 10.1093/mnras/stx052

    Kimm, T., Katz, H., Haehnelt, M., et al. 2017, Monthly Notices of the Royal Astronomical Society, stx052, 10.1093/mnras/stx052

    2017 doi

  41. [50]

    A., Shapley, A

    Kornei, K. A., Shapley, A. E., Martin, C. L., et al. 2012, The Astrophysical Journal, 758, 135, 10.1088/0004-637X/758/2/135

    2012 doi

  42. [51]

    Li, A., & Draine, B. T. 2001, The Astrophysical Journal, 554, 778, 10.1086/323147

    2001 doi

  43. [52]

    J., Carollo, C

    Lilly, S. J., Carollo, C. M., Pipino, A., Renzini, A., & Peng, Y. 2013, The Astrophysical Journal, 772, 119, 10.1088/0004-637X/772/2/119

    2013 doi

  44. [53]

    L., Shapley, A

    Martin, C. L., Shapley, A. E., Coil, A. L., et al. 2012, Astrophysical Journal, 760, 10.1088/0004-637X/760/2/127

    2012 doi

  45. [54]

    2021, Astronomy & Astrophysics, 646, A80, 10.1051/0004-6361/202039449

    Mauerhofer, V., Verhamme, A., Blaizot, J., et al. 2021, Astronomy & Astrophysics, 646, A80, 10.1051/0004-6361/202039449

    2021 doi

  46. [55]

    2016, Monthly Notices of the Royal Astronomical Society, 459, 2342, 10.1093/mnras/stw831

    Mesinger, A., Greig, B., & Sobacchi, E. 2016, Monthly Notices of the Royal Astronomical Society, 459, 2342, 10.1093/mnras/stw831

    2016 doi

  47. [56]

    2020, Astronomy & Astrophysics, 635, A154, 10.1051/0004-6361/201834961

    Michel-Dansac, L., Blaizot, J., Garel, T., et al. 2020, Astronomy & Astrophysics, 635, A154, 10.1051/0004-6361/201834961

    2020 doi

  48. [57]

    2019, Computational Astrophysics and Cosmology, 6, 2, 10.1186/s40668-019-0028-x

    Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computational Astrophysics and Cosmology, 6, 2, 10.1186/s40668-019-0028-x

    2019 doi

  49. [58]

    J., Shapley, A., Faisst, A

    Pahl, A. J., Shapley, A., Faisst, A. L., et al. 2020, Monthly Notices of the Royal Astronomical Society, 493, 3194, 10.1093/mnras/staa355

    2020 doi

  50. [59]

    S., Sofia, U

    Parvathi, V. S., Sofia, U. J., Murthy, J., & Babu, B. R. S. 2012, The Astrophysical Journal, 760, 36, 10.1088/0004-637X/760/1/36

    2012 doi

  51. [60]

    X., Kasen, D., & Rubin, K

    Prochaska, J. X., Kasen, D., & Rubin, K. 2011, Astrophysical Journal, 734, 10.1088/0004-637X/734/1/24

    2011 doi

  52. [62]

    M., Shapley, A

    Quider, A. M., Shapley, A. E., Pettini, M., Steidel, C. C., & Stark, D. P. 2010, Monthly Notices of the Royal Astronomical Society, 402, 1467, 10.1111/j.1365-2966.2009.16005.x

    2010

  53. [63]

    A., Steidel, C

    Reddy, N. A., Steidel, C. C., Pettini, M., & Bogosavljević, M. 2016, The Astrophysical Journal, 828, 107, 10.3847/0004-637X/828/2/107

    2016 doi

  54. [64]

    R., Bayliss, M

    Rigby, J. R., Bayliss, M. B., Sharon, K., et al. 2018, The Astronomical Journal, 155, 104, 10.3847/1538-3881/aaa2ff

    2018 doi

  55. [65]

    E., Hayes, M., Östlin, G., et al

    Rivera-Thorsen, T. E., Hayes, M., Östlin, G., et al. 2015, The Astrophysical Journal, 805, 14, 10.1088/0004-637X/805/1/14

    2015 doi

  56. [66]

    2013, Monthly Notices of the Royal Astronomical Society, 436, 2188, 10.1093/mnras/stt1722

    Rosdahl, J., Blaizot, J., Aubert, D., Stranex, T., & Teyssier, R. 2013, Monthly Notices of the Royal Astronomical Society, 436, 2188, 10.1093/mnras/stt1722

    2013 doi

  57. [67]

    2015, Monthly Notices of the Royal Astronomical Society, 449, 4380, 10.1093/mnras/stv567

    Rosdahl, J., & Teyssier, R. 2015, Monthly Notices of the Royal Astronomical Society, 449, 4380, 10.1093/mnras/stv567

    2015 doi

  58. [68]

    S., Veilleux, S., & Sanders, D

    Rupke, D. S., Veilleux, S., & Sanders, D. B. 2005, The Astrophysical Journal Supplement Series, 160, 87, 10.1086/432886

    2005 doi

  59. [69]

    2021, a&a, in press

    Saldana-Lopez, A., Schaerer, D., Chisholm, J., et al. 2021, a&a, in press

    2021

  60. [70]

    2022, Astronomy & Astrophysics, 663, A59, 10.1051/0004-6361/202141864

    ---. 2022, Astronomy & Astrophysics, 663, A59, 10.1051/0004-6361/202141864

    2022 doi

  61. [71]

    2023, Monthly Notices of the Royal Astronomical Society, 522, 6295, 10.1093/mnras/stad1283

    ---. 2023, Monthly Notices of the Royal Astronomical Society, 522, 6295, 10.1093/mnras/stad1283

    2023 doi

  62. [72]

    D., & Sembach, K

    Savage, B. D., & Sembach, K. R. 1991, The Astrophysical Journal, 379, 245, 10.1086/170498

    1991 doi

  63. [73]

    2015, Astrophysical Journal, 801, 43, 10.1088/0004-637X/801/1/43

    Scarlata, C., & Panagia, N. 2015, Astrophysical Journal, 801, 43, 10.1088/0004-637X/801/1/43

    2015 doi

  64. [74]

    I., et al

    Scarlata, C., Colbert, J., Teplitz, H. I., et al. 2009, Astrophysical Journal, 704, 10.1088/0004-637X/704/2/L98

    2009 doi

  65. [75]

    E., Steidel, C

    Shapley, A. E., Steidel, C. C., Pettini, M., & Adelberger, K. L. 2003, The Astrophysical Journal, 588, 65, 10.1086/373922

    2003 doi

  66. [76]

    Silk, J., & Mamon, G. A. 2012, Research in Astronomy and Astrophysics, 12, 917, 10.1088/1674-4527/12/8/004

    2012 doi

  67. [77]

    2015, Monthly Notices of the Royal Astronomical Society, 449, 4336, 10.1093/mnras/stv565

    Smith, A., Safranek-Shrader, C., Bromm, V., & Milosavljević, M. 2015, Monthly Notices of the Royal Astronomical Society, 449, 4336, 10.1093/mnras/stv565

    2015 doi

  68. [78]

    S., & Dav \'e , R

    Somerville , R. S., & Dav \'e , R. 2015, , 53, 51, 10.1146/annurev-astro-082812-140951

    2015 doi

  69. [79]

    C., Bogosavljević, M., Shapley, A

    Steidel, C. C., Bogosavljević, M., Shapley, A. E., et al. 2018, The Astrophysical Journal, 869, 123, 10.3847/1538-4357/aaed28

    2018 doi

  70. [80]

    C., Erb, D

    Steidel, C. C., Erb, D. K., Shapley, A. E., et al. 2010, The Astrophysical Journal, 717, 289, 10.1088/0004-637X/717/1/289

    2010 doi

  71. [81]

    2002, Astronomy & Astrophysics, 385, 337, 10.1051/0004-6361:20011817

    Teyssier, R. 2002, Astronomy & Astrophysics, 385, 337, 10.1051/0004-6361:20011817

    2002 doi

  72. [82]

    F., Steidel, C

    Trainor, R. F., Steidel, C. C., Strom, A. L., & Rudie, G. C. 2015, The Astrophysical Journal, 809, 89, 10.1088/0004-637X/809/1/89

    2015 doi

  73. [83]

    2017, Monthly Notices of the Royal Astronomical Society, 470, 224, 10.1093/mnras/stx1060

    Trebitsch, M., Blaizot, J., Rosdahl, J., Devriendt, J., & Slyz, A. 2017, Monthly Notices of the Royal Astronomical Society, 470, 224, 10.1093/mnras/stx1060

    2017 doi

  74. [84]

    A., Heckman, T

    Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, The Astrophysical Journal, 613, 898, 10.1086/423264

    2004 doi

  75. [85]

    E., et al

    Vasei, K., Siana, B., Shapley, A. E., et al. 2016, The Astrophysical Journal, 831, 38, 10.3847/0004-637X/831/1/38

    2016 doi

  76. [86]

    2013, Monthly Notices of the Royal Astronomical Society, 436, 3031, 10.1093/mnras/stt1789

    Vogelsberger, M., Genel, S., Sijacki, D., et al. 2013, Monthly Notices of the Royal Astronomical Society, 436, 3031, 10.1093/mnras/stt1789

    2013 doi

  77. [87]

    M., et al

    Wang, W., Kassin, S., Faber, S. M., et al. 2021. http://arxiv.org/abs/2109.12133

    2021 arXiv

  78. [88]

    J., Coil, A

    Weiner, B. J., Coil, A. L., Prochaska, J. X., et al. 2009, The Astrophysical Journal, 692, 187, 10.1088/0004-637X/692/1/187

    2009 doi

  79. [89]

    2018, The Astrophysical Journal, 858, 39, 10.3847/1538-4357/aab7ea

    Xu, X., Arav, N., Miller, T., & Benn, C. 2018, The Astrophysical Journal, 858, 39, 10.3847/1538-4357/aab7ea

    2018 doi

  80. [90]

    2022, The Astrophysical Journal, 933, 222, 10.3847/1538-4357/ac6d56

    Xu, X., Heckman, T., Henry, A., et al. 2022, The Astrophysical Journal, 933, 222, 10.3847/1538-4357/ac6d56

    2022 doi

  81. [91]

    2023, The Astrophysical Journal, 948, 28, 10.3847/1538-4357/acbf46

    ---. 2023, The Astrophysical Journal, 948, 28, 10.3847/1538-4357/acbf46

    2023 doi

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