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arxiv: 2606.17270 · v1 · pith:5LK7DU2Jnew · submitted 2026-06-15 · 🌌 astro-ph.GA

Dust in the Average Galaxy: Attenuation, Emission, and Opacity from 0<z<7

Caitlin M. Casey , Hollis B. Akins , Andrew J. Battisti , Jed McKinney , Ezequiel Treister , Jorge A. Zavala , Hiddo Algera , Manuel Aravena
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Yingjie Cheng Nicole E. Drakos Andreas L. Faisst Maximilien Franco Seiji Fujimoto Ghassem Gozaliasl Ali Hadi Santosh Harish Michaela Hirschmann Olivier Ilbert Kohei Inayoshi Jeyhan S. Kartaltepe Anton M. Koekemoer Claudia del P. Lagos Erini Lambrides Ronaldo Laishram Daizhong Liu Arianna S. Long Georgios E. Magdis Sinclaire M. Manning Crystal L. Martin Felix Martinez III Richard Massey Jacqueline E. McCleary Henry Joy McCracken Lauro Moscardini Desika Narayanan Louise Paquereau Jason Rhodes Brant E. Robertson Rasha M. Samir Claudia Scarlata Marko Shuntov Laura Sommovigo Aswin P. Vijayan Wuji Wang Can Xu Dhruv Zimmerman
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Pith reviewed 2026-06-27 02:22 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords dust attenuationfar-infrared emissionhigh-redshift galaxiesdust grain propertiesstar formation obscurationgalaxy dust masscosmic dust evolution
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The pith

The ratio of UV to far-infrared dust absorption coefficients decreases by more than an order of magnitude from redshift 0 to 7.

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

The paper establishes that dust in galaxies absorbs UV light much less efficiently relative to its far-infrared emission at earlier cosmic times. By stacking deep FIR data on over 500,000 galaxies with JWST UV/optical constraints, it shows that attenuation measured in the UV underpredicts the total infrared luminosity by a factor of about three on average, and by up to ten for the most massive systems. This points to a change in the dust grains themselves, with fewer small grains present at high redshift. The ratio of the two absorption coefficients also tracks star-formation surface density in a way that stays the same at all redshifts. Most of the drop in dust-to-stellar mass ratio occurs below redshift one.

Core claim

We measure over an order of magnitude decrease in κ_UV/κ_FIR—the ratio of dust mass absorption coefficients in the UV at 1600Å and FIR at 500μm—from z~0 to z~7, consistent with a deficit of small dust grains. UV/optical attenuation systematically underpredicts IR luminosity by a factor of ~3x at 0.5<z<7 and up to an order of magnitude for M⋆>10^10.5 M⊙. We derive empirical relationships for effective attenuation, dust temperature, unobscured star-formation fraction, and dust-to-stellar mass ratio as functions of redshift and stellar mass. A redshift-invariant inverse relationship exists between κ_UV/κ_FIR and Σ_SFR. Most evolution in the dust-to-stellar ratio occurs at z<1.

What carries the argument

κ_UV/κ_FIR, the ratio of dust mass absorption coefficients at 1600Å and 500μm, which separates the effects of star-dust geometry from intrinsic grain properties when combined with IR SED shape and dust mass surface density.

If this is right

  • UV/optical attenuation underpredicts true IR luminosity by factors of three to ten depending on redshift and stellar mass.
  • Empirical scaling relations exist for attenuation, dust temperature, unobscured star-formation fraction, and dust-to-stellar mass ratio versus redshift and mass.
  • κ_UV/κ_FIR maintains an inverse correlation with star-formation rate surface density that does not change with redshift.
  • The bulk of the decline in dust-to-stellar mass ratio is finished by redshift one, resulting from combined changes in gas fraction and dust-to-gas ratio.

Where Pith is reading between the lines

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

  • Dust formation models must include radiation-field strength as a driver of grain-size distribution if the deficit of small grains at high redshift is real.
  • Simulations assuming fixed dust optical properties will systematically understate the obscured fraction of star formation above redshift three.
  • Higher-sensitivity FIR observations could determine whether the trend in κ_UV/κ_FIR continues past redshift seven or levels off.

Load-bearing premise

The stacked FIR SEDs from the combined Spitzer/Herschel/SCUBA-2/NIKA-2/ALMA data accurately represent the average dust emission properties of the full 500,000-galaxy sample without significant biases from detection limits or sample selection at each redshift and mass bin.

What would settle it

Individual ALMA detections of high-redshift galaxies that show no evolution in κ_UV/κ_FIR when their own FIR SEDs are measured directly, rather than relying on stacks.

Figures

Figures reproduced from arXiv: 2606.17270 by Ali Hadi, Andreas L. Faisst, Andrew J. Battisti, Anton M. Koekemoer, Arianna S. Long, Aswin P. Vijayan, Brant E. Robertson, Caitlin M. Casey, Can Xu, Claudia del P. Lagos, Claudia Scarlata, Crystal L. Martin, Daizhong Liu, Desika Narayanan, Dhruv Zimmerman, Erini Lambrides, Ezequiel Treister, Felix Martinez III, Georgios E. Magdis, Ghassem Gozaliasl, Henry Joy McCracken, Hiddo Algera, Hollis B. Akins, Jacqueline E. McCleary, Jason Rhodes, Jed McKinney, Jeyhan S. Kartaltepe, Jorge A. Zavala, Kohei Inayoshi, Laura Sommovigo, Lauro Moscardini, Louise Paquereau, Manuel Aravena, Marko Shuntov, Maximilien Franco, Michaela Hirschmann, Nicole E. Drakos, Olivier Ilbert, Rasha M. Samir, Richard Massey, Ronaldo Laishram, Santosh Harish, Seiji Fujimoto, Sinclaire M. Manning, Wuji Wang, Yingjie Cheng.

Figure 1
Figure 1. Figure 1: — The distribution of the stackable 501,656 sources from the COSMOS field used in this work. Contours indicate con￾centrations of sources exceeding >5 per unit ∆z = 0.05 and ∆ log(M) = 0.05 (contours mark densities of 5, 10, 20, 50, 100, ... sources per same binning). The bins used for stacking are shown with green and orange gridlines, every ∆z = 0.5 in redshift and ∆ log(M⋆/M⊙) = 0.25. Bins with converge… view at source ↗
Figure 2
Figure 2. Figure 2: — The best-fit dust SEDs to stacked (sub)millimeter photometry for stellar mass and redshift selected samples. Each panel shows a schematic of the dust SED (blue, with light blue SEDs sampling the uncertainty) superimposed on the stacked photometry (black with gray error bars). Panels with gray backgrounds have photometric constraints of too poor quality to fit a converged dust SED (fewer than two points a… view at source ↗
Figure 3
Figure 3. Figure 3: — A comparison of derived properties from LePhare (Shuntov et al. 2025) and our bagpipes-derived quantities based on the same model-based photometry. At left, LePhare photometric redshifts and their uncertainties were used as input to bagpipes; the resulting agreement is tight with σ∆z/(1+z) = 0.04. Middle, the stellar masses agree within uncertainties. At right, the derived AV from bagpipes agrees with th… view at source ↗
Figure 4
Figure 4. Figure 4: — A comparison of our binned AUV-M⋆ relation (shades of green/cyan) compared to literature compilations spanning 0 < z < 1.5 (left) and 1.5 < z < 3.5 (right). Our points represent the median AUV per mass and redshift bin with shaded regions showing the inner 68%-ile on the spread of measured AUVin the corresponding bin; the errors on the median are of order the size of each data point. At z ∼ 0 we compare … view at source ↗
Figure 5
Figure 5. Figure 5: — Our mass- and redshift-binned AUV-M⋆ relation (left) and AV-M⋆relation (right) from 0 < z < 8. Note bins shown on [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: — The redshift evolution of δ, the parameter describ￾ing the rest-frame UV slope of the attenuation curve. δ describes the deviation from a Calzetti attenuation curve, where δ=0 re￾covers Calzetti, and a negative δ produces a steeper attenuation curve. The SMC attenuation curve roughly follows δ=-0.4, which is consistent with the average attenuation curve recovered by Salim et al. (2018, red square) and Co… view at source ↗
Figure 7
Figure 7. Figure 7: — The redshift evolution of LIR (left) and Mdust (right) in fixed stellar mass bins. Dashed lines at left denote the total IR luminosity that would be expected if 100% of star-formation were obscured, and the SFR is taken from the main-sequence of star formation, or the M⋆-SFR relation (Speagle et al. 2014). Between 0 < z < 2, we see a 100× increase in the average LIR and 5-10× increase in Mdust per mass b… view at source ↗
Figure 8
Figure 8. Figure 8: — The redshift evolution of dust temperature derived for stacked SEDs in fixed stellar mass bins ranging 8.6 < log(M⋆/M⊙) < 11.6. The floor on dust temperature is set by the temperature of the CMB which evolves as TCMB = 2.73(1 + z) (gray filled region). Three literature relations are overplotted. From Drew and Casey (2022) we show the expectation of the evolution of Tdust at fixed stellar mass given the e… view at source ↗
Figure 9
Figure 9. Figure 9: — The distribution in the mid-infrared slope, αMIR, and emissivity spectral index, β, for redshift-stellar mass bins where sufficient photometric constraints allow for direct fitting. The ver￾tical lines mark the adopted values for stacked SED bins where direct fits of αMIR and β are not possible. high stellar masses and high redshifts (z > 7), some masses unphysically large. These bins in the Viero et al.… view at source ↗
Figure 10
Figure 10. Figure 10: — The star-formation rate, stellar mass relation (SFR-M⋆) shown in dz = 0.5 bins broken down into the UV (blue) and IR (red) components. The coaddition of the UV and IR together are shown as black points, while the average SFR taken directly from the COSMOS2025 catalog LePhare SED fits are shown as gray points. In the four panels 0.5 < z < 2.5 we overplot the measurements of Whitaker et al. (2017) which u… view at source ↗
Figure 11
Figure 11. Figure 11: — The stellar mass and redshift dependence of funobs. Broadly consistent with prior findings at z < 3 from Whitaker et al. (2017), we find a very strong stellar mass dependence in funobs, such that >90% of star formation is obscured above a stellar mass of 1010 M⊙ at all redshifts beyond z ∼ 0.5. The diminishingly low fraction of unobscured star formation that is seen at high masses is even smaller at z ∼… view at source ↗
Figure 12
Figure 12. Figure 12: — A literature comparison of the dust-to-stellar ratio, DTS, with our data. The redshift evolution is shown at left and stellar mass dependence at right. HATLAS-detected galaxies show strong evolution in the DTS from z = 0 to z ∼ 0.5 (blue stars; Dunne et al. 2011). Work from the PEP+HerMES surveys is shown in gray boxes (Santini et al. 2014); lighter gray boxes correspond to lower stellar mass bins. We a… view at source ↗
Figure 13
Figure 13. Figure 13: — The redshift (left) and stellar mass (right) dependence of the dust-to-stellar ratio, DTS, measured via stacking. The redshift evolution of the DTS is predominantly flat over all individual mass bins (light green to purple color) as well as averaged over all masses (black points). The most significant variance is seen in the lowest redshift bin, z < 0.5. In contrast, the stellar mass dependence of the D… view at source ↗
Figure 14
Figure 14. Figure 14: — At top, we show GUV, the star/dust geometry prefactor that relates the magnitudes of attenuation using SED-based techniques, AUV, anchored to rest-frame UV/optical constraints, to AUV,direct, a directly-inferred AUV measured from IRX as given in Eq. 8. GUV = 1 corresponds to a foreground dust screen and GUV < 1 corresponds to mixed star/dust geometry that serves to lessen the perceived attenuation for a… view at source ↗
Figure 15
Figure 15. Figure 15: — The relationship between AV and Σdust as a func￾tion of redshift, with color indicating different redshift bins. The thick light blue line corresponds to a foreground screen of dust cal￾ibrated to the properties of Milky Way dust, with CV=0.74, where CV is defined in Eq. 7. With increasing redshift, a clear trend is seen where fixed AV corresponds to a higher and higher dust mass surface density Σdust. … view at source ↗
Figure 16
Figure 16. Figure 16: — The redshift and stellar mass dependence of CUV (top) and CV (bottom); Cλ is the ratio relating Aλ to Σdust, i.e. it captures the evolution seen [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: — Measurements of the variation in κUV/κFIR (see Eq. 10) in redshift and stellar mass. κUV/κFIR is a galaxy-integrated indicator of dust grain properties in the ISM – the grain size distribution, composition, and the morphology of dust grains. First-order effects of star/dust geometry do not impact the measurement of κUV/κFIR. There is a precipitous fall in κUV/κFIR with increasing redshift, over an order… view at source ↗
Figure 18
Figure 18. Figure 18: — The relationship between SFR surface density and κUV/κFIR for all redshift and stellar mass bins shown on [PITH_FULL_IMAGE:figures/full_fig_p024_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: compares measurements to this Stefan￾Boltzmann expectation at fixed stellar mass intervals. All mass bins show increasing temperatures over the 0 < z < 2 mass range, consistent with the dominant evolution in the SFRs of galaxies on the main sequence between those epochs. At higher redshifts, we note that the high mass bins (> 1010.5 M⊙) show remarkably good agreement with the Stefan-Boltzmann model; those… view at source ↗
Figure 20
Figure 20. Figure 20: — A comparison of the averaged binned dependence of the DTS from our data (black points, taken from [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: — An illustration of the estimated relative contribu￾tions to the evolution of the dust-to-stellar (DTS) mass ratio. Here we show a single mass bin and its evolution in redshift (log(M⋆) = 9.625, left) and a single redshift bin and the stellar mass dependence of the DTS ratio (z = 3.75, right). By anchoring to literature measurements of the mass metallicity relation (Zahid et al. 2014; Jain et al. 2026) a… view at source ↗
Figure 22
Figure 22. Figure 22: — Redshift evolution of the AUV-M⋆ relation as measured in our dataset (blue points with 68% confidence intervals on the sample distribution). Orange curves show the best-fit derived relation for AUV(z,M) (Eq. 11) fit jointly to all data points shown [PITH_FULL_IMAGE:figures/full_fig_p030_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: — Stellar mass bins showing the redshift evolution of AUV (blue points). Orange curves show best-fit model as given in Eq 11 [PITH_FULL_IMAGE:figures/full_fig_p030_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: — The evolution in dust temperature, as given by log(λpeak), in stellar mass bins. Blue points show our data while orange shows the best-fit model from Eq. 14. The clustered model builds on the SIDES framework (B´ethermin et al. 2017) and uses the same 2 deg2 light cone pro￾duced from the Bolshoi-Planck cosmological simulation (Klypin et al. 2016; Rodr´ıguez-Puebla et al. 2016). B´ethermin et al. (2017) p… view at source ↗
Figure 25
Figure 25. Figure 25: — The fraction of star formation that is unobscured as a function of M⋆ in different redshift bins. Blue show our data and orange show the model fit jointly across all z, M bins simultaneously. Green points are drawn from hydrodynamic simulations in Zimmerman et al. (2024), showing clear redshift evolution that is more pronounced in higher redshift bins than lower redshift bins, and in agreement with the … view at source ↗
Figure 26
Figure 26. Figure 26: — The fraction of star formation that is unobscured as a function of redshift. Blue show our data and orange show the best-fit model; one notes that the general trend is, at fixed stellar mass, that funobs drops precipitously from 0 < z < 2 but then rises modestly at higher redshifts [PITH_FULL_IMAGE:figures/full_fig_p033_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: — The evolution of the dust-to-stellar mass ratio assuming a fixed κFIR. Blue points show our data while orange shows the best-fit model from Eq. 18 [PITH_FULL_IMAGE:figures/full_fig_p033_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: — The stellar mass dependence of the dust-to-stellar mass ratio assuming a fixed κFIR. to the differences between our SED model and that of SIDES: with greater dynamic range of SED shapes (ie. input dust temperatures thus input flux density distributions in a given mass and redshift bin), there is naturally far larger scatter in the resulting output stack measurement and associated uncertainty, as well as… view at source ↗
Figure 29
Figure 29. Figure 29: — The measured stacked flux density in the real maps, simulated unclustered, and simulated clustered maps when sampling the same number of sources in the real data with scrambled, random positions. Median stacking on a median-subtracted map results in median flux density measurements of zero across all flux densities, redshifts and mass bins. What is important to gather from this plot is that the random s… view at source ↗
Figure 30
Figure 30. Figure 30: — A comparison of output stacked flux density in redshift and mass bins relative to input flux density (median of the population in the same bin) across the unclustered (top row) and clustered (bottom row) simulations. Solid lines denote a regime (redshift and stellar masses) where the significance of the stacked result in the real maps is >3σ and dashed lines indicate stacked flux densities <3σ. Within m… view at source ↗
Figure 31
Figure 31. Figure 31: — Two-dimensional stacked MIPS 24 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p042_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: — Two-dimensional stacked PACS 100 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p042_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: — Two-dimensional stacked PACS 160 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p043_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: — Two-dimensional stacked SPIRE 250 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p043_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: — Two-dimensional stacked SPIRE 350 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p044_35.png] view at source ↗
Figure 36
Figure 36. Figure 36: — Two-dimensional stacked SPIRE 500 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p044_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: — Two-dimensional stacked SCUBA-2 850 µm cutouts [PITH_FULL_IMAGE:figures/full_fig_p045_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: — Two-dimensional stacked CHAMPS 1.2 mm cutouts [PITH_FULL_IMAGE:figures/full_fig_p045_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: — Two-dimensional stacked NIKA-2 1.2 mm cutouts [PITH_FULL_IMAGE:figures/full_fig_p046_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: — Two-dimensional stacked Ex-MORA 2.1 mm cutouts [PITH_FULL_IMAGE:figures/full_fig_p046_40.png] view at source ↗
Figure 41
Figure 41. Figure 41: — Two-dimensional stacked NIKA-2 2 mm cutouts [PITH_FULL_IMAGE:figures/full_fig_p047_41.png] view at source ↗
read the original abstract

We present constraints on the dust emission and attenuation properties of galaxies across 0<z<7 using JWST imaging from the COSMOS-Web Survey combined with deep FIR/(sub)millimeter data from Spitzer, Herschel, SCUBA-2, NIKA-2 and ALMA. We analyze over 500,000 galaxies to independently constrain attenuation in the rest-frame UV/optical as well as dust emission from stacked FIR SEDs, enabling a direct comparison between the two. We find UV/optical attenuation systematically underpredicts IR luminosity by a factor of ~3x at 0.5<z<7 and up to an order of magnitude for $M_\star>10^{10.5}M_\odot$. We derive empirical relationships for the effective attenuation, dust temperature, fraction of star formation that is unobscured, and dust-to-stellar mass ratio as functions of redshift and stellar mass. We separate the first order effect of star/dust geometry from dust grain properties by combining constraints on the IR SED, UV SED, and dust mass surface density. Importantly, we measure over an order of magnitude decrease in $\kappa_{UV}/\kappa_{FIR}$--the ratio of dust mass absorption coefficients in the UV at 1600\AA\ and FIR at 500$\mu$m--from z~0 to z~7. A depressed $\kappa_{UV}/\kappa_{FIR}$ is consistent with a deficit of small dust grains, possibly attributable to the intense radiation fields of high-$z$ star formation; indeed, we find a redshift-invariant inverse relationship between $\kappa_{UV}/\kappa_{FIR}$ and $\Sigma_{SFR}$. Most evolution in the dust-to-stellar ratio is at $z<1$, the product of mild downward evolution in the dust-to-gas ratio combined with steep evolution in the gas-to-stellar ratio. The significant evolution and dynamic range of $\kappa_{UV}/\kappa_{FIR}$ and prevailing disconnect between the UV/optical and FIR regimes emphasize that direct dust constraints are irreplaceable for the majority of star-forming galaxies at z<7, not just the most extreme star-formers.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript analyzes dust properties in over 500,000 galaxies at 0<z<7 using JWST COSMOS-Web UV/optical data combined with stacked FIR/(sub)mm SEDs from Spitzer/Herschel/SCUBA-2/NIKA-2/ALMA. It reports that UV/optical attenuation underpredicts IR luminosity by a factor of ~3 (up to 10x at M⋆>10^10.5 M⊙), derives empirical relations for effective attenuation, T_dust, unobscured SF fraction, and M_dust/M⋆ as functions of z and M⋆, and measures an order-of-magnitude drop in κ_UV/κ_FIR (1600Å to 500μm) from z~0 to z~7. This is interpreted as evidence for evolving grain properties (deficit of small grains at high z), with an inverse relation to Σ_SFR; most M_dust/M⋆ evolution occurs at z<1.

Significance. The large sample and direct UV-to-FIR comparison provide a broad empirical baseline for average-galaxy dust behavior across cosmic time. If the κ_UV/κ_FIR evolution is robust, the result would strengthen the case that grain-size distributions change with redshift and radiation-field intensity, with implications for dust models and the reliability of UV-based attenuation corrections. The work credits the scale of the JWST+FIR stacking approach for enabling population-level constraints beyond extreme starbursts.

major comments (2)
  1. [Abstract; FIR stacking procedure] Abstract and methods on FIR stacking: the central claim of an order-of-magnitude decrease in κ_UV/κ_FIR (and the separation of geometry from grain properties via IR SED + UV SED + dust mass surface density) assumes the stacked L_IR and T_dust values represent the full 500k-galaxy population in each z–M⋆ bin. No explicit completeness corrections, upper-limit handling, or detection-fraction statistics are described; if high-z stacks are dominated by the FIR-detectable tail, L_IR would be overestimated relative to the UV attenuation measured on the full sample, directly biasing the κ ratio downward and producing the reported trend.
  2. [Results on κ_UV/κ_FIR and Σ_SFR relation] Results on empirical relations: the reported redshift-invariant inverse relation between κ_UV/κ_FIR and Σ_SFR, and the claim that UV underpredicts IR by ~3× (10× at high mass), rest on post-hoc binning whose effect on the derived ratios is not quantified with error budgets or jackknife tests. This leaves open whether the dynamic range in κ_UV/κ_FIR is driven by the underlying data or by binning choices.
minor comments (2)
  1. [Abstract] The abstract states the factor-of-3 (and order-of-magnitude) discrepancies without accompanying uncertainties or sample-completeness notes; adding a one-sentence qualifier would improve clarity.
  2. [Methods on opacity derivation] Notation for κ_UV/κ_FIR is introduced without an explicit equation defining the absorption coefficients from the SED fits; a short derivation or reference to the fitting procedure would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and insightful comments on our manuscript. Their concerns about the FIR stacking representativeness and the robustness of the derived relations to binning choices are well-taken. We address each major comment in detail below and propose revisions to enhance the clarity and rigor of the presentation.

read point-by-point responses

  1. Referee: [Abstract; FIR stacking procedure] the central claim of an order-of-magnitude decrease in κ_UV/κ_FIR assumes the stacked L_IR and T_dust values represent the full 500k-galaxy population in each z–M⋆ bin. No explicit completeness corrections, upper-limit handling, or detection-fraction statistics are described; if high-z stacks are dominated by the FIR-detectable tail, L_IR would be overestimated relative to the UV attenuation measured on the full sample, directly biasing the κ ratio downward and producing the reported trend.

    Authors: The FIR stacking is performed by co-adding the FIR images of all galaxies in each redshift-stellar mass bin from the full sample of over 500,000 galaxies, including those not individually detected in the FIR. This median stacking approach provides the average emission properties for the entire population in the bin. We will update the methods section to explicitly describe this procedure, include detection fraction statistics per bin, and discuss how this mitigates the potential bias raised. revision: yes

  2. Referee: [Results on κ_UV/κ_FIR and Σ_SFR relation] the reported redshift-invariant inverse relation between κ_UV/κ_FIR and Σ_SFR, and the claim that UV underpredicts IR by ~3× (10× at high mass), rest on post-hoc binning whose effect on the derived ratios is not quantified with error budgets or jackknife tests. This leaves open whether the dynamic range in κ_UV/κ_FIR is driven by the underlying data or by binning choices.

    Authors: The binning in z and M⋆ is chosen based on achieving sufficient numbers for reliable stacking and is not post-hoc. The reported relations are robust to variations in binning as tested internally. We will add quantitative error budgets using bootstrap methods on the stacks and include jackknife resampling tests in the revised manuscript to explicitly quantify the impact of binning choices on the derived κ_UV/κ_FIR values and the UV-IR discrepancy. revision: partial

Circularity Check

0 steps flagged

No circularity: all results are direct empirical fits to stacked observational data

full rationale

The paper reports empirical measurements of attenuation, FIR SEDs, dust temperature, and the ratio κ_UV/κ_FIR derived from JWST UV/optical data combined with stacked Spitzer/Herschel/SCUBA-2/NIKA-2/ALMA FIR photometry across 500k galaxies. No equation or result is shown to reduce by construction to a parameter fitted from the same quantity; the reported order-of-magnitude evolution in κ_UV/κ_FIR is obtained by combining independent constraints on IR luminosity, UV attenuation, and dust mass surface density. Self-citations, if present, are not load-bearing for the central claims. The derivation chain remains self-contained against external data.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The central claims rest on empirical fits to stacked photometry whose functional forms and coefficients are not specified in the abstract; no new physical entities are introduced.

free parameters (1)
  • coefficients of empirical attenuation, T_dust, f_unobscured, and M_dust/M_star relations
    Fitted to binned stacked SEDs as functions of redshift and stellar mass

pith-pipeline@v0.9.1-grok · 6192 in / 1263 out tokens · 42840 ms · 2026-06-27T02:22:42.699051+00:00 · methodology

discussion (0)

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Reference graph

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