Pith. sign in

REVIEW 3 major objections 6 minor 4 cited by

In simulated galaxies at z≈6–9, dust attenuation is not uniform: star-forming clumps attenuate light with grayer curves, harbor roughly ten times the dust column density of the galaxy as a whole, and show co-spatial dust-star geometry, whil

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

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

T0 review · deepseek-v4-flash

2026-08-03 03:37 UTC pith:GP7C3MXK

load-bearing objection This paper delivers the first clear quantitative clump-vs-diffuse vs integrated dust attenuation picture for z=6–9 galaxies and a usable IRX–Δβ diagnostic; the main claims are credible, with the fixed DTM assumption being the largest physical caveat. the 3 major comments →

arxiv 2602.07347 v3 pith:GP7C3MXK submitted 2026-02-07 astro-ph.GA

Clump-Scale Dust Attenuation in Epoch of Reionization Galaxies: Spatially Resolved Properties from FirstLight Simulations

Yurina Nakazato , Kosei Matsumoto , Akio K. Inoue , Daniel Ceverino , Takashi Hosokawa , Daisuke Toyouchi This is my paper
classification astro-ph.GA
keywords dust attenuationstar-forming clumpsepoch of reionizationradiative transferIRX–β relationcosmological zoom-in simulationsattenuation curveshigh-redshift galaxies
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper asks whether dust attenuation inside high-redshift galaxies is the same everywhere or varies from one star-forming clump to the surrounding diffuse gas. Using cosmological zoom-in simulations with post-processing dust radiative transfer, it finds that the answer is no: within z≈6–9 clumpy galaxies, individual clumps attenuate light with much grayer attenuation curves and about ten times higher dust column densities than the galaxy-integrated average, while diffuse regions show steeper curves. To separate the effects of dust amount from dust–star geometry, the authors build a two-parameter toy model on the IRX–Δβ plane and show that clumps are co-spatial or dust-extended while the system-integrated signal looks star-extended. This matters because spatially resolved JWST/ALMA observations are now mapping attenuation pixel-by-pixel; if attenuation laws vary within galaxies, assuming a single fixed law biases inferred stellar masses, ages, and star formation rates.

Core claim

On the paper's own terms, the central discovery is a component-wise decomposition of dust attenuation in reionization-era galaxies: star-forming clumps (identified by SFR surface density) have median attenuation-curve slope S ≡ A_UV/A_V ≈ 1.60, grayer than the system-integrated value of 1.84 and much grayer than diffuse regions at 2.90. In the IRX–Δβ diagnostic, clumps sit at fiducial UV optical depths τ_fid ~ 10^(1.6) with dust-to-star scale-height ratio R ≈ 1.0 (co-spatial) extending to R > 1 (dust-extended), while system-integrated values have R < 1 (star-extended) with τ_fid ~ 10^(0.85). The paper interprets this as clumps having roughly ten times higher dust column densities than the sy

What carries the argument

The load-bearing tool is a toy radiative-transfer model on the IRX–Δβ plane. IRX is the infrared-to-UV luminosity ratio, and Δβ is the difference between attenuated and intrinsic UV spectral slope. The model describes a galaxy as uniform stellar and dust layers with scale heights H_* and H_d, parameterized by the scale-height ratio R = H_d/H_* and a fiducial optical depth τ_fid. Four analytic escape-probability formulas—no dust, sandwich (R<1), well-mixed (R=1), and mixed-plus-screen (R>1)—map every (τ_fid, R) pair to a point on the IRX–Δβ plane. This lets the authors read off dust column density and geometry from simulated or observed positions, breaking the degeneracy that makes attenuatio

Load-bearing premise

The quantitative results—especially the factor-of-ten clump-to-system column-density contrast and the dust-extended geometry—rest on assuming that dust everywhere contains 40% of the metal mass, with no spatial variation in the dust-to-metal ratio.

What would settle it

Measure resolved dust continuum and UV emission for a gravitationally lensed z≈7 galaxy with clumps at ~100 pc scale, and place individual clumps on the IRX–Δβ plane. If clumps scatter along the foreground-screen grid lines (R ≫ 1) rather than clustering at the well-mixed R ≈ 1 curves, or if their directly measured dust column densities are within a factor of three of the system-integrated value, the paper's central claim would be contradicted.

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

If this is right

  • Spatially resolved SED fitting that assumes one attenuation law for all pixels will systematically bias stellar masses: clump masses would be underestimated and diffuse-region masses overestimated.
  • Attenuation curves at z≈6–9 can be grayer than the local starburst law even when the dust grains are Milky-Way-like; the grayness is driven by geometry and optical depth rather than exotic dust compositions.
  • Clump-scale IRX–Δβ measurements, now becoming possible with JWST and ALMA, can directly constrain dust column densities and dust–star geometry in individual star-forming regions.
  • Because diffuse regions dominate the system-integrated light in these galaxies, galaxy-integrated attenuation appears star-extended even though its most actively star-forming regions are dust-extended.
  • The two-parameter decomposition is not limited to the epoch of reionization and can be applied to spatially resolved observations at lower redshifts.

Where Pith is reading between the lines

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

  • A higher dust-to-metal ratio in dense clumps (from grain growth) would steepen the inferred clump-to-diffuse column-density contrast, possibly making the factor of ten even larger; conversely, efficient dust destruction in clumps would shrink it, so the factor-of-ten is a direct consequence of the fixed dust-to-metal ratio.
  • The dust-extended geometry inferred for clumps suggests that clumps are surrounded by dusty envelopes; one testable consequence is that dust-attenuated near-IR images of clumps should appear smaller than their intrinsic UV images, as the paper's example galaxy shows.
  • The agreement with observed z≈7 galaxies is currently limited to system-integrated values; clump-level observations, once available, would provide a sharper test of the geometry claims.
  • If clumps were identified by stellar mass rather than SFR density, the inferred geometry might shift, since older clumps could have less co-spatial dust and therefore fall closer to the screen-model region of the IRX–Δβ plane.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper uses the FirstLight cosmological zoom-in simulations at z=6–9, post-processed with the SKIRT Monte Carlo dust radiative transfer code, to study dust attenuation and re-emission at sub-galactic scales. Star-forming clumps are identified in 376 clumpy systems (1059 clumps) by SFR surface density, and the authors compare attenuation properties of clumps, diffuse regions, and whole systems. The main claims are that system-integrated attenuation curves are grayer than Calzetti, that clumps are even grayer (median S ≡ A_UV/A_V ≈ 1.60) with dust column densities about an order of magnitude higher than system-integrated values, that clumps have well-mixed or dust-extended dust–star geometry (R ≳ 1), and that system-integrated light is star-extended (R < 1). These trends are interpreted with a two-parameter IRX–Δβ toy model, and the system-integrated predictions are compared with REBELS-IFU galaxies at z ~ 7, finding broad consistency.

Significance. If the results hold, this is the first spatially resolved theoretical characterization of clump-scale dust attenuation in the epoch of reionization, with concrete predictions for JWST and ALMA observations. The paper is careful in several respects: SKIRT convergence and stochastic-heating/self-absorption effects are tested in Appendix A, the SMC-dust case is explored in Appendix C, and the toy-model-inferred clump parameters are checked against direct measurements of r_d/r_* and optical depth in Section 4.2. The IRX–Δβ toy model is a simple and potentially reusable diagnostic for separating optical depth from geometry. These strengths make the paper a valuable contribution, but the central quantitative claims rest on a fixed dust-to-metal ratio and on a statistical treatment of 376 snapshots that may not be independent.

major comments (3)
  1. [§2.2.2 and §4.4] The entire dust distribution is constructed by scaling the gas-phase metal distribution with a fixed dust-to-metal ratio DTM=0.4. The central quantitative conclusions—clumps having ~10× higher dust column densities than the system and clump geometry R≥1 versus system R<1—are direct outputs of these dust maps. Because grain growth and SN destruction are density- and metallicity-dependent, a spatially varying DTM could change the clump/diffuse contrast and the inferred geometry. The caveat is acknowledged in §4.4, but the abstract and summary state the ~10× and R≥1 results without conditioning on this assumption. Moreover, the §4.2 validation uses the same DTM=0.4 maps, so it cannot independently test the assumption. I request a sensitivity test (e.g., recomputing the RT with a clump-enhanced or clump-depleted DTM distribution, or adopting a time-dependent DTM model from the cited literatu
  2. [§4.2 and Figure 8] The 'order of magnitude' column-density contrast is not fully supported by the toy-model medians in Figure 8. The clump median is log10 τ_fid ~ 1.6 and the system-integrated median is ~0.85, i.e., a factor of ~6, not 10. The text also reports direct clump optical depths with median ~1.9, but no corresponding direct system-integrated measurement is given. In addition, the toy-model validation for R is performed only for compact clumps via r_d/r_*; the system-integrated R<1 and the statement that the star-extended geometry is 'driven by diffuse components' are not validated against any direct 3D measure, and the text acknowledges that scale heights are difficult to define for extended components. Please report direct system (and diffuse) optical-depth and geometry measurements, quote the actual median contrast, and clarify whether the factor 10 refers to extremes rather than the median.
  3. [§2.3 and Figures 4–8] The statistical sample is described as 376 clumpy systems, but these are drawn from 62 distinct halos with snapshots spaced only 7–10 Myr. Since clumpy phases can persist over multiple snapshots, the same halo may contribute several of the 376 systems, and the contours/medians in Figures 4–8 treat all of these as independent. This likely overstates the statistical weight of the sample and can bias effective scatter and significance. I ask for a per-halo bootstrap or a reduced sample with one snapshot per clumpy epoch (or snapshots separated by more than a dynamical time) to demonstrate that the medians, scatters, and REBELS-IFU comparison are robust to this pseudo-replication.
minor comments (6)
  1. [Appendix A] The sentence 'By default, we use (n_p, n_λ) = (10^7, 150)' appears twice in the same paragraph.
  2. [Title page] The first author's name appears as 'YURINANAKAZATO' without a space; should be 'Yurina Nakazato'.
  3. [Table 1 caption] The phrase 'with a40 cMpc/hbox' should read 'with a 40 cMpc/h box'.
  4. [Eq. (7)] The text states that R=0 corresponds to 'no dust layer' and P_esc=1, but plugging R=0 into Eq. (7) gives P_esc=(1+e^{-τ_fid})/2 unless τ_fid=0. Consider clarifying that R=0 is realized only in the τ_fid=0 limit, while finite τ_fid with R→0 represents a thin central sheet.
  5. [Figure 8] The left panel does not clearly distinguish the simulated system-integrated contours from the REBELS-IFU gray circles in the figure itself; a legend entry or a more explicit caption would help.
  6. [Tables 1 and C.1] Median values are quoted without uncertainties or interquartile ranges; since scatter is a central part of the paper, reporting quartiles would be informative.

Circularity Check

0 steps flagged

No significant circularity: results come from radiative transfer; the toy model is a validated diagnostic, and the fixed DTM is an explicitly stated assumption, not a fitted prediction.

full rationale

The paper's central results are produced by post-processing dust radiative transfer (SKIRT) on FirstLight zoom-in simulations, not by fitting a model to the target observables. The dust distribution is set by assuming a fixed dust-to-metal ratio DTM=0.4 (Section 2.2.2), which is an input assumption explicitly caveated in Section 4.4; it is not a parameter tuned to reproduce the paper's attenuation-curve slopes, IRX-delta-beta positions, or clump/system column-density ratios, so the results do not reduce to it by construction. The IRX-delta-beta toy model (Section 4.2) is an analytic slab model following Popping et al. (2017) and Lin et al. (2021), with R and tau_fid defined from physical dust column and geometry (Eq. 5); using it to interpret simulated IRX-delta-beta positions is an inversion/diagnostic step, and the paper validates the inferred values against direct measurements of r_d/r_* and dust column-density maps. That validation is a consistency check between an approximate analytic model and the full RT calculation, not circularity. Self-citations to FirstLight (Ceverino et al. 2017) and Nakazato et al. (2024) describe the simulation suite and clump-identification method; they are not used as a uniqueness argument, nor do they forbid alternative interpretations. The REBELS-IFU comparison uses external observational data and is not forced by any fitted parameter. Overall, the derivation chain is self-contained: assumptions are explicit, and the central quantitative claims follow from the simulations and are independently checked rather than being equivalent to the input by definition.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The central claims rest on a small set of model inputs: fixed DTM, fixed dust grain properties, clump definition thresholds, and the analytic toy-model geometry. No new physical entities are introduced. The paper explicitly lists the main caveats, which correspond to these assumptions.

free parameters (4)
  • Dust-to-metal ratio (DTM) = 0.4
    Fixed in Section 2.2.2; sets the absolute dust column densities and optical depths. Chosen from literature, not derived from the simulations. Directly affects the inferred τ_fid and the ~10× clump-to-system column ratio.
  • Clump identification thresholds = Σ_SFR > 10^1.5 M_sun/yr/kpc^2; N_grid ≥ 16
    Defines what counts as a clump (Section 2.3). Varying these thresholds changes the clump/diffuse decomposition and hence the reported attenuation-curve differences.
  • Star formation density/temperature thresholds = ρ_th = 0.035 M_sun/pc^3, T_th = 10^4 K
    Subgrid star formation parameters from the FirstLight suite (Ceverino et al. 2017). They affect where clumps form and the dust-star geometry, but are not fitted in this paper.
  • Dust grain model choice = MW (default) or SMC (Weingartner & Draine 2001)
    The extinction/attenuation curve shape is an input. The paper checks both MW and SMC but does not explore other compositions (e.g., larger grains or varying PAH fractions), which could shift the absolute attenuation curves.
axioms (4)
  • domain assumption Energy balance: absorbed UV luminosity is re-emitted as IR, giving IRX = (1 - P_esc)/P_esc.
    Equation 12 in Section 4.2. Assumes all absorbed UV is re-emitted in the IR and that no other energy losses or sources exist. The paper notes component-wise energy balance is approximate for diffuse regions (footnote 3).
  • standard math Toy-model escape probability formulas for sandwich, well-mixed, and screen geometries.
    Equations 7-11, following Xu & Buat 1995, Popping et al. 2017, and Lin et al. 2021. These are analytic approximations for idealized 1D symmetric geometries and are assumed to represent the effective dust-star geometry of clumps and systems.
  • domain assumption The dust extinction curve is fixed by the adopted dust model and is the same for all components.
    Used for τ_fid_λ1 = τ_fid_UV × (A_λ1/A_UV) (Eq. 14) and for the toy-model grids. Real EoR dust may differ, as the paper acknowledges in caveats.
  • domain assumption FirstLight galaxies are representative of z=6–9 EoR galaxies.
    The sample is 62 halos selected by V_circ > 178 km/s at z=5 (Section 2.1). This biases toward massive galaxies and may not capture lower-mass or different morphological populations.

pith-pipeline@v1.3.0-alltime-deepseek · 26752 in / 14104 out tokens · 131036 ms · 2026-08-03T03:37:13.732084+00:00 · methodology

0 comments
read the original abstract

Understanding dust attenuation in galaxies at both integrated and spatially resolved scales is fundamental for accurately determining the physical properties of galaxies. Recent high-spatial-resolution observations with ALMA and JWST enable investigations of spatially resolved properties in high-redshift galaxies ($z \gtrsim 6$), but spatial variations in dust observables remain poorly constrained. We use cosmological zoom-in simulations combined with post-processing dust radiative transfer calculations for 376 clumpy galaxies at $z=6$-$9$ with stellar masses of $M_* \gtrsim 10^9 \, M_\odot$. For each system, we investigate dust attenuation and re-emission properties for three components: system-integrated, individual clumps, and diffuse regions. We find that system-integrated attenuation curves are grayer than the Calzetti curve, even when assuming MW- or SMC-type dust. Attenuation curves of individual clumps are even grayer, while diffuse regions exhibit steeper curves owing to enhanced scattering in optically thin environments. Since the effects of optical depth and dust-star geometry are intrinsically degenerate in attenuation curves, we introduce a toy model based on the IRX-$\Delta\beta$ plane, where $\Delta\beta$ denotes the difference between attenuated and intrinsic UV slopes. Applying this framework, we find that clumps have dust column densities approximately an order of magnitude higher than system-integrated values and exhibit co-spatial or dust-extended geometries. In contrast, system-integrated attenuation reflects star-extended geometries driven by contributions from optically thin diffuse regions. We apply this framework to REBELS-IFU galaxies at $z \sim 7$ and find good agreement with our simulation predictions.

Figures

Figures reproduced from arXiv: 2602.07347 by Akio K. Inoue, Daisuke Toyouchi, Daniel Ceverino, Kosei Matsumoto, Takashi Hosokawa, Yurina Nakazato.

Figure 1
Figure 1. Figure 1: Histogram of clump properties. The top panel shows the mass-weighted stellar age distributions for each component: clumps (cyan), diffuse (orange), and system-integrated (black). The bottom panel shows the surface density distributions of SFR (cyan) and stel￾lar mass (blue) for clump components. Note that these surface den￾sities are only well-defined for clumps due to their compact nature. Clumps are iden… view at source ↗
Figure 2
Figure 2. Figure 2: Projected distribution of simulated galaxy FL957 at z = 7.7, one of the identified clumpy galaxies. Upper panels: (a) projected gas number density, (b) surface SFR density with yellow lines identifying star-forming clumps (see Section 2.3), (c) dust column density, and (d) mass-weighted dust temperature. Lower panels: (e) rest-frame UV surface brightness (νLν at 1600 A), (f) total IR emission integrated ov… view at source ↗
Figure 3
Figure 3. Figure 3: Left: SEDs for the four clumps. The dashed and solid lines indicate the intrinsic stellar continuum and the attenuated (with re￾emission) SEDs, respectively. Right: SEDs for the system-integrated (black) and diffuse (orange) components. The system-integrated SED is calculated over the entire 10 kpc ×10 kpc region, while the diffuse component SED is obtained by subtracting the clump emission from the system… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of dust properties between system-integrated values and individual components for all identified clumpy galaxies at z = 6 − 9. The x-axis of each panel shows the system-integrated value calculated over 10 kpc × 10 kpc regions, while the y-axis shows values for individual clumps (cyan) and diffuse components (orange). Contours enclose 20%, 50%, 80%, and 95% of the data. Top left: peak dust temper… view at source ↗
Figure 5
Figure 5. Figure 5: Top panel: Dust attenuation curves for the clumpy galax￾ies we identified in our simulations. The black solid line shows the median system-integrated attenuation curve, colored dashed lines show the median attenuation curves of individual clumps, and the orange dotted line shows the median of the diffuse component. Each shaded region shows the 5-95% range. For comparison, we plot the Calzetti attenuation c… view at source ↗
Figure 6
Figure 6. Figure 6: Illustration of the four simplified dust-star geometry models characterized by different dust-to-star scale height ratios (R ≡ Hd/H∗). Stars represent the stellar layer with scale height H∗, and gray shaded areas indicate the uniform dust layer with scale height Hd. Orange arrows schematically represent escaping UV ra￾diation. The escape probability for each geometry type is derived explicitly in the main … view at source ↗
Figure 7
Figure 7. Figure 7: IRX–∆β relation with toy model predictions, where ∆β ≡ βUV − βUV,0 represents the change in UV slope due to dust attenuation. We show theoretical curves for different combinations of the dust-to-star scale-height ratio (R) and fiducial UV optical depth (τ fid UV), which parametrize the dust-star geometry and dust column density, respectively. The solid black lines represent the limiting case of a foregroun… view at source ↗
Figure 8
Figure 8. Figure 8: Left: Distributions of the dust-to-star scale-height ratio (R) and fiducial UV optical depth (τ fid UV) for clump components (cyan) and system-integrated values (black). These contours and the gray circles show values inferred from the toy-model grid matching in [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

discussion (0)

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

Forward citations

Cited by 4 Pith papers

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

  1. Resolving dust and Ly{\alpha} emission in a lensed galaxy at the epoch of reionization with JWST/CANUCS

    astro-ph.GA 2025-12 conditional novelty 7.0

    Spatially resolved JWST observations of lensed LAE HCM 6A at z=6.57 reveal Lyα escaping from a dust-cleared central clump in the youngest region, with age-dependent Calzetti-like attenuation curves.

  2. PhotoIFU: NIRCam as a Photometric Integral Field Unit for Mapping Feedback in Galaxies

    astro-ph.GA 2026-07 conditional novelty 6.0

    PhotoIFU maps stellar mass, star-formation rate, dust, and metallicity pixel-by-pixel from JWST imaging, and finds gas-outflow regions are distinct from host galaxies in three z≈1.3–3.7 systems.

  3. SDSS+JWST Census of Stellar and Nebular Dust Attenuation at $z \sim 0$-7: Mass Dependence and Redshift Evolution

    astro-ph.GA 2026-05 unverdicted novelty 5.0

    Dust attenuation follows a universal mass-dependent relation from z=0 to 7 with a transition at 10^9 solar masses where nebular attenuation steepens relative to stellar.

  4. DREAMS. JWST Spectroscopy of a $z=8.3$ Galaxy with an ALMA Dust Continuum Detection: Early Dust, Very High $T_{\rm dust}$, and a Multi-wavelength [OIII] Ratio Discrepancy

    astro-ph.GA 2026-05 unverdicted novelty 5.0

    New JWST spectrum of a z=8.3 galaxy with prior ALMA dust detection shows AGN signatures, 0.15 Zsun metallicity, Mdust ~10^6 Msun, Tdust~91 K, and an [OIII] line ratio higher than nebular models allow.

Reference graph

Works this paper leans on

132 extracted references · 17 canonical work pages · cited by 4 Pith papers · 4 internal anchors

  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]

    @F& @ 2 @0 @ # L A d C& dd2! c 22 ￷,lA d C& 3&<<\*> c 22  W^y[n W'es

    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]

    2023, , 945, 117, 10.3847/1538-4357/acba06

    Abdurro'uf , Coe , D., Jung , I., et al. 2023, , 945, 117, 10.3847/1538-4357/acba06

    doi:10.3847/1538-4357/acba06 2023

  5. [5]

    Algera , H. S. B., Inami , H., Sommovigo , L., et al. 2024, , 527, 6867, 10.1093/mnras/stad3111

    doi:10.1093/mnras/stad3111 2024

  6. [6]

    2017, , 466, 105, 10.1093/mnras/stw3061

    Aoyama , S., Hou , K.-C., Shimizu , I., et al. 2017, , 466, 105, 10.1093/mnras/stw3061

    doi:10.1093/mnras/stw3061 2017

  7. [7]

    2011, , 196, 22, 10.1088/0067-0049/196/2/22

    Baes , M., Verstappen , J., De Looze , I., et al. 2011, , 196, 22, 10.1088/0067-0049/196/2/22

    doi:10.1088/0067-0049/196/2/22 2011

  8. [8]

    Bakx , T. J. L. C., Sommovigo , L., Tamura , Y., et al. 2025, arXiv e-prints, arXiv:2511.08327, 10.48550/arXiv.2511.08327

    work page doi:10.48550/arxiv.2511.08327 2025

  9. [9]

    2018, , 477, 552, 10.1093/mnras/sty552

    Behrens , C., Pallottini , A., Ferrara , A., Gallerani , S., & Vallini , L. 2018, , 477, 552, 10.1093/mnras/sty552

    doi:10.1093/mnras/sty552 2018

  10. [10]

    Bowler , R. A. A., Cullen , F., McLure , R. J., Dunlop , J. S., & Avison , A. 2022, , 510, 5088, 10.1093/mnras/stab3744

    doi:10.1093/mnras/stab3744 2022

  11. [11]

    Bowler , R. A. A., Inami , H., Sommovigo , L., et al. 2024, , 527, 5808, 10.1093/mnras/stad3578

    doi:10.1093/mnras/stad3578 2024

  12. [12]

    D., Casey , C

    Burnham , A. D., Casey , C. M., Zavala , J. A., et al. 2021, , 910, 89, 10.3847/1538-4357/abe401

    doi:10.3847/1538-4357/abe401 2021

  13. [13]

    M., Stanway , E

    Byrne , C. M., Stanway , E. R., Eldridge , J. J., McSwiney , L., & Townsend , O. T. 2022, , 512, 5329, 10.1093/mnras/stac807

    doi:10.1093/mnras/stac807 2022

  14. [14]

    C., et al

    Calzetti , D., Armus , L., Bohlin , R. C., et al. 2000, , 533, 682, 10.1086/308692

    doi:10.1086/308692 2000

  15. [15]

    L., & Storchi-Bergmann , T

    Calzetti , D., Kinney , A. L., & Storchi-Bergmann , T. 1994, , 429, 582, 10.1086/174346

    doi:10.1086/174346 1994

  16. [16]

    J., Katz , H., Witten , C., et al

    Cameron , A. J., Katz , H., Witten , C., et al. 2024, , 534, 523, 10.1093/mnras/stae1547

    doi:10.1093/mnras/stae1547 2024

  17. [17]

    2015, Astronomy and Computing, 9, 20, 10.1016/j.ascom.2014.10.004

    Camps , P., & Baes , M. 2015, Astronomy and Computing, 9, 20, 10.1016/j.ascom.2014.10.004

    doi:10.1016/j.ascom.2014.10.004 2015

  18. [18]

    2020, Astronomy and Computing, 31, 100381, 10.1016/j.ascom.2020.100381

    ---. 2020, Astronomy and Computing, 31, 100381, 10.1016/j.ascom.2020.100381

    arXiv 2020

  19. [19]

    A., Clayton , G

    Cardelli , J. A., Clayton , G. C., & Mathis , J. S. 1989, , 345, 245, 10.1086/167900

    doi:10.1086/167900 1989

  20. [20]

    M., Scoville , N

    Casey , C. M., Scoville , N. Z., Sanders , D. B., et al. 2014, , 796, 95, 10.1088/0004-637X/796/2/95

    doi:10.1088/0004-637x/796/2/95 2014

  21. [21]

    M., Zavala , J

    Casey , C. M., Zavala , J. A., Spilker , J., et al. 2018, , 862, 77, 10.3847/1538-4357/aac82d

    doi:10.3847/1538-4357/aac82d 2018

  22. [22]

    J., Ceverino , D., & Bignone , L

    Cataldi , P., Pedrosa , S., Pellizza , L. J., Ceverino , D., & Bignone , L. A. 2025, arXiv e-prints, arXiv:2510.05299, 10.48550/arXiv.2510.05299

    work page doi:10.48550/arxiv.2510.05299 2025

  23. [23]

    2010, , 404, 2151, 10.1111/j.1365-2966.2010.16433.x

    Ceverino , D., Dekel , A., & Bournaud , F. 2010, , 404, 2151, 10.1111/j.1365-2966.2010.16433.x

    arXiv 2010

  24. [24]

    Ceverino , D., Glover , S. C. O., & Klessen , R. S. 2017, , 470, 2791, 10.1093/mnras/stx1386

    doi:10.1093/mnras/stx1386 2017

  25. [25]

    S., et al

    Ceverino , D., Hirschmann , M., Klessen , R. S., et al. 2021, , 504, 4472, 10.1093/mnras/stab1206

    doi:10.1093/mnras/stab1206 2021

  26. [26]

    S., & Glover , S

    Ceverino , D., Klessen , R. S., & Glover , S. C. O. 2018, , 480, 4842, 10.1093/mnras/sty2124

    doi:10.1093/mnras/sty2124 2018

  27. [27]

    2019, , 484, 1366, 10.1093/mnras/stz079

    ---. 2019, , 484, 1366, 10.1093/mnras/stz079

    doi:10.1093/mnras/stz079 2019

  28. [29]

    2009 b , , 695, 292, 10.1088/0004-637X/695/1/292

    ---. 2009 b , , 695, 292, 10.1088/0004-637X/695/1/292

    doi:10.1088/0004-637x/695/1/292 2009

  29. [30]

    S., et al

    Ceverino , D., Klypin , A., Klimek , E. S., et al. 2014, , 442, 1545, 10.1093/mnras/stu956

    doi:10.1093/mnras/stu956 2014

  30. [31]

    S., & Glover , S

    Ceverino , D., Nakazato , Y., Yoshida , N., Klessen , R. S., & Glover , S. C. O. 2024, , 689, A244, 10.1051/0004-6361/202450224

    doi:10.1051/0004-6361/202450224 2024

  31. [32]

    P., Endsley , R., et al

    Chen , Z., Stark , D. P., Endsley , R., et al. 2023, , 518, 5607, 10.1093/mnras/stac3476

    doi:10.1093/mnras/stac3476 2023

  32. [33]

    2013, , 432, 2061, 10.1093/mnras/stt523

    Chevallard , J., Charlot , S., Wandelt , B., & Wild , V. 2013, , 432, 2061, 10.1093/mnras/stt523

    doi:10.1093/mnras/stt523 2013

  33. [34]

    2024, , 691, A325, 10.1051/0004-6361/202449750

    Crespo G \'o mez , A., Colina , L., \'A lvarez-M \'a rquez , J., et al. 2024, , 691, A325, 10.1051/0004-6361/202449750

    work page doi:10.1051/0004-6361/202449750 2024

  34. [35]

    J., McLeod , D

    Cullen , F., McLure , R. J., McLeod , D. J., et al. 2023, , 520, 14, 10.1093/mnras/stad073

    doi:10.1093/mnras/stad073 2023

  35. [36]

    J., McLure , R

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

    doi:10.1093/mnras/stae1211 2024

  36. [37]

    R., et al

    da Cunha , E., Walter , F., Smail , I. R., et al. 2015, , 806, 110, 10.1088/0004-637X/806/1/110

    doi:10.1088/0004-637x/806/1/110 2015

  37. [38]

    2022, , 512, 989, 10.1093/mnras/stac537

    Dayal , P., Ferrara , A., Sommovigo , L., et al. 2022, , 512, 989, 10.1093/mnras/stac537

    doi:10.1093/mnras/stac537 2022

  38. [39]

    2019, , 486, 743, 10.1093/mnras/stz805

    Decleir , M., De Looze , I., Boquien , M., et al. 2019, , 486, 743, 10.1093/mnras/stz805

    doi:10.1093/mnras/stz805 2019

  39. [40]

    2024, , 527, 2139, 10.1093/mnras/stad3239

    Dome , T., Tacchella , S., Fialkov , A., et al. 2024, , 527, 2139, 10.1093/mnras/stad3239

    doi:10.1093/mnras/stad3239 2024

  40. [41]

    2024, arXiv e-prints, arXiv:2412.01623, 10.48550/arXiv.2412.01623

    Dottorini , D., Calabr \`o , A., Pentericci , L., et al. 2024, arXiv e-prints, arXiv:2412.01623, 10.48550/arXiv.2412.01623

    Pith/arXiv arXiv doi:10.48550/arxiv.2412.01623 2024

  41. [42]

    T., Aniano , G., Krause , O., et al

    Draine , B. T., Aniano , G., Krause , O., et al. 2014, , 780, 172, 10.1088/0004-637X/780/2/172

    doi:10.1088/0004-637x/780/2/172 2014

  42. [43]

    1998, , 501, 643, 10.1086/305829

    Dwek , E. 1998, , 501, 643, 10.1086/305829

    doi:10.1086/305829 1998

  43. [44]

    J., Stanway , E

    Eldridge , J. J., Stanway , E. R., Xiao , L., et al. 2017, , 34, e058, 10.1017/pasa.2017.51

    doi:10.1017/pasa.2017.51 2017

  44. [45]

    J., Korista , K

    Ferland , G. J., Korista , K. T., Verner , D. A., et al. 1998, , 110, 761, 10.1086/316190

    doi:10.1086/316190 1998

  45. [46]

    2025, , 695, L15, 10.1051/0004-6361/202453214

    Fern \'a ndez Aranda , R., D \' az Santos , T., Hatziminaoglou , E., et al. 2025, , 695, L15, 10.1051/0004-6361/202453214

    doi:10.1051/0004-6361/202453214 2025

  46. [47]

    Fisher , R., Bowler , R. A. A., Stefanon , M., et al. 2025, , 539, 109, 10.1093/mnras/staf485

    doi:10.1093/mnras/staf485 2025

  47. [48]

    K., & Sugahara , Y

    Fudamoto , Y., Inoue , A. K., & Sugahara , Y. 2023, , 521, 2962, 10.1093/mnras/stad743

    work page doi:10.1093/mnras/stad743 2023

  48. [49]

    A., Brammer , G

    Gim \'e nez-Arteaga , C., Oesch , P. A., Brammer , G. B., et al. 2023, , 948, 126, 10.3847/1538-4357/acc5ea

    doi:10.3847/1538-4357/acc5ea 2023

  49. [50]

    2024, , 686, A63, 10.1051/0004-6361/202349135

    Gim \'e nez-Arteaga , C., Fujimoto , S., Valentino , F., et al. 2024, , 686, A63, 10.1051/0004-6361/202349135

    doi:10.1051/0004-6361/202349135 2024

  50. [51]

    D., Meurer , G., Heckman , T

    Goldader , J. D., Meurer , G., Heckman , T. M., et al. 2002, , 568, 651, 10.1086/339165

    doi:10.1086/339165 2002

  51. [52]

    D., Clayton , G

    Gordon , K. D., Clayton , G. C., Misselt , K. A., Landolt , A. U., & Wolff , M. J. 2003, , 594, 279, 10.1086/376774

    doi:10.1086/376774 2003

  52. [53]

    2020, , 494, 1071, 10.1093/mnras/staa796

    Graziani , L., Schneider , R., Ginolfi , M., et al. 2020, , 494, 1071, 10.1093/mnras/staa796

    doi:10.1093/mnras/staa796 2020

  53. [54]

    1996, , 461, 20, 10.1086/177035

    Haardt , F., & Madau , P. 1996, , 461, 20, 10.1086/177035

    doi:10.1086/177035 1996

  54. [56]

    N., Johnson , B

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

    doi:10.3847/1538-4357/ad1ee4 2024

  55. [57]

    K., Ellis , R

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

    doi:10.3847/1538-4357/ad9b2c 2025

  56. [58]

    H., Armus , L., Mazzarella , J

    Howell , J. H., Armus , L., Mazzarella , J. M., et al. 2010, , 715, 572, 10.1088/0004-637X/715/1/572

    doi:10.1088/0004-637x/715/1/572 2010

  57. [59]

    Inami , H., Algera , H. S. B., Schouws , S., et al. 2022, , 515, 3126, 10.1093/mnras/stac1779

    doi:10.1093/mnras/stac1779 2022

  58. [60]

    Inoue , A. K. 2011, , 415, 2920, 10.1111/j.1365-2966.2011.18906.x

    arXiv 2011

  59. [61]

    K., Buat , V., Burgarella , D., et al

    Inoue , A. K., Buat , V., Burgarella , D., et al. 2006, , 370, 380, 10.1111/j.1365-2966.2006.10499.x

    arXiv 2006

  60. [62]

    K., Hashimoto , T., Chihara , H., & Koike , C

    Inoue , A. K., Hashimoto , T., Chihara , H., & Koike , C. 2020, , 495, 1577, 10.1093/mnras/staa1203

    doi:10.1093/mnras/staa1203 2020

  61. [63]

    L., Shen , Y., et al

    Ishikawa , Y., Zakamska , N. L., Shen , Y., et al. 2025, , 982, 22, 10.3847/1538-4357/adb4ee

    work page doi:10.3847/1538-4357/adb4ee 2025

  62. [64]

    2025, arXiv e-prints, arXiv:2502.20437, 10.48550/arXiv.2502.20437

    Kannan , R., Puchwein , E., Smith , A., et al. 2025, arXiv e-prints, arXiv:2502.20437, 10.48550/arXiv.2502.20437

    doi:10.48550/arxiv.2502.20437 2025

  63. [65]

    J., Saxena , A., et al

    Katz , H., Cameron , A. J., Saxena , A., et al. 2025, The Open Journal of Astrophysics, 8, 104, 10.33232/001c.142570

    doi:10.33232/001c.142570 2025

  64. [66]

    1998, , 36, 189, 10.1146/annurev.astro.36.1.189

    Kennicutt , Robert C., J. 1998, , 36, 189, 10.1146/annurev.astro.36.1.189

    doi:10.1146/annurev.astro.36.1.189 1998

  65. [67]

    2025, arXiv e-prints, arXiv:2511.10743, 10.48550/arXiv.2511.10743

    Komarova , L., Stefanon , M., Laza-Ramos , A., et al. 2025, arXiv e-prints, arXiv:2511.10743, 10.48550/arXiv.2511.10743

    doi:10.48550/arxiv.2511.10743 2025

  66. [68]

    Kravtsov , A. V. 2003, , 590, L1, 10.1086/376674

    work page doi:10.1086/376674 2003

  67. [69]

    V., Klypin , A

    Kravtsov , A. V., Klypin , A. A., & Khokhlov , A. M. 1997, , 111, 73, 10.1086/313015

    doi:10.1086/313015 1997

  68. [70]

    2020, , 494, 1988, 10.1093/mnras/staa880

    Langan , I., Ceverino , D., & Finlator , K. 2020, , 494, 1988, 10.1093/mnras/staa880

    doi:10.1093/mnras/staa880 2020

  69. [71]

    J., & Li , A

    Li , Q., Yang , X. J., & Li , A. 2024, , 535, L58, 10.1093/mnrasl/slae095

    work page doi:10.1093/mnrasl/slae095 2024

  70. [73]

    2021 b , , 502, 3210, 10.1093/mnras/stab096

    ---. 2021 b , , 502, 3210, 10.1093/mnras/stab096

    doi:10.1093/mnras/stab096 2021

  71. [74]

    2019, , 489, 1397, 10.1093/mnras/stz2134

    Liang , L., Feldmann , R., Kere s , D., et al. 2019, , 489, 1397, 10.1093/mnras/stz2134

    doi:10.1093/mnras/stz2134 2019

  72. [75]

    2025, , 694, A84, 10.1051/0004-6361/202452372

    Lin , Q., Yang , X., Li , A., & Witstok , J. 2025, , 694, A84, 10.1051/0004-6361/202452372

    work page doi:10.1051/0004-6361/202452372 2025

  73. [76]

    2021, , 507, 2755, 10.1093/mnras/stab2242

    Lin , Y.-H., Hirashita , H., Camps , P., & Baes , M. 2021, , 507, 2755, 10.1093/mnras/stab2242

    doi:10.1093/mnras/stab2242 2021

  74. [77]

    Lines , N. E. P., Bowler , R. A. A., Adams , N. J., et al. 2025, , 539, 2685, 10.1093/mnras/staf627

    doi:10.1093/mnras/staf627 2025

  75. [78]

    2017, , 472, 1372, 10.1093/mnras/stx1901

    Lo Faro , B., Buat , V., Roehlly , Y., et al. 2017, , 472, 1372, 10.1093/mnras/stx1901

    doi:10.1093/mnras/stx1901 2017

  76. [79]

    C., Geach , J

    Lovell , C. C., Geach , J. E., Dav \'e , R., Narayanan , D., & Li , Q. 2021, , 502, 772, 10.1093/mnras/staa4043

    doi:10.1093/mnras/staa4043 2021

  77. [80]

    2022, , 517, 2076, 10.1093/mnras/stac2762

    Makiya , R., & Hirashita , H. 2022, , 517, 2076, 10.1093/mnras/stac2762

    work page doi:10.1093/mnras/stac2762 2022

  78. [81]

    2015, , 451, L70, 10.1093/mnrasl/slv070

    Mancini , M., Schneider , R., Graziani , L., et al. 2015, , 451, L70, 10.1093/mnrasl/slv070

    doi:10.1093/mnrasl/slv070 2015

  79. [82]

    2024, , 533, 2488, 10.1093/mnras/stae1971

    Marconcini , C., D'Eugenio , F., Maiolino , R., et al. 2024, , 533, 2488, 10.1093/mnras/stae1971

    doi:10.1093/mnras/stae1971 2024

  80. [83]

    2025, Nature Astronomy, 9, 458, 10.1038/s41550-024-02426-1

    Markov , V., Gallerani , S., Ferrara , A., et al. 2025, Nature Astronomy, 9, 458, 10.1038/s41550-024-02426-1

    doi:10.1038/s41550-024-02426-1 2025

Showing first 80 references.