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arxiv: 2412.08609 · v2 · submitted 2024-12-11 · 🌌 astro-ph.GA

Dust and gas modelling in radiative transfer simulations of disc-dominated galaxies with RADMC-3D

Francesco Sinigaglia , Miroslava Dessauges-Zavadsky , Lucio Mayer , Pedro R. Capelo , Valentina Tamburello This is my paper

Pith reviewed 2026-05-23 07:11 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords radiative transferdust modellinggalaxy simulationsspectral energy distributionmolecular gasRADMC-3Ddisc galaxiesinterstellar medium
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The pith

A radiative transfer pipeline for galaxy simulations demonstrates that dust grain composition and size must be modeled adequately to achieve converged observables at the tens-of-percent level.

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

The paper introduces RTGen, a pipeline that applies Monte Carlo radiative transfer using RADMC-3D to six hydrodynamic simulations of isolated disc-dominated galaxies. It examines how choices in dust abundance, composition, grain size distribution, and the atomic-to-molecular gas transition affect predicted spectral energy distributions, continuum images, and CO line emission. The work finds that these modeling choices significantly influence whether the resulting observables converge, with proper dust treatment required for agreement with observational and theoretical literature at the level of a few tens of percent. This establishes the pipeline as suitable for generating mock observations that can be compared to data from facilities like ALMA and JWST.

Core claim

The central discovery is that the RTGen pipeline, when using an appropriate model for dust grains' composition and size, produces spectral energy distributions, continuum images, and CO luminosity maps for the simulated galaxies that match literature results from observations and theory, reaching convergence at the few tens of percent level.

What carries the argument

The RTGen pipeline, which performs Monte Carlo radiative transfer a posteriori on hydrodynamic galaxy simulations to compute dust temperatures followed by ray tracing for SEDs, images, and line profiles, with explicit modeling of dust and the atomic-to-molecular transition.

If this is right

  • The pipeline predicts accurate spectral energy distributions for the studied galaxies.
  • Continuum and CO luminosity images agree with results from observations and theoretical studies.
  • Dust modelling has an important impact on the convergence of predicted galaxy observables.
  • Adequate modelling of dust grains composition and size is required for convergence.
  • The framework enables high-accuracy studies of the interstellar medium in galaxies.

Where Pith is reading between the lines

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

  • Mock images generated this way could help interpret observations of high-redshift galaxies with JWST and ALMA.
  • Similar pipelines might be tested on simulations with different resolutions or feedback prescriptions to assess robustness.
  • The method could extend to studying the impact on other emission lines beyond CO.
  • Applying the pipeline to cosmological simulations rather than isolated galaxies could reveal environmental effects on observables.

Load-bearing premise

That the selected dust abundance, composition, grain size distribution, and atomic-to-molecular transition model accurately represent the physical conditions in the simulated galaxies.

What would settle it

Direct comparison of the pipeline's predicted SEDs and images against multi-wavelength observations of real disc galaxies with similar properties, checking if residuals exceed a few tens of percent.

Figures

Figures reproduced from arXiv: 2412.08609 by Francesco Sinigaglia, Lucio Mayer, Miroslava Dessauges-Zavadsky, Pedro R. Capelo, Valentina Tamburello.

Figure 1
Figure 1. Figure 1: Gas mass surface density maps, viewed face-on, for the six different runs analyzed in this paper, after ∆t = 0.2 Gyr from the end of the relaxation phase. The run each projection refers to is reported inside the corresponding panel. – inclusion of feedback (FB); – inclusion of metal cooling (MC); – inclusion of metal thermal diffusion (MTD); – adoption of Geometric Density SPH (GDSPH); – number of particle… view at source ↗
Figure 2
Figure 2. Figure 2: Stellar mass surface density maps, viewed face-on, for the six different runs analyzed in this paper, after ∆t = 0.2 Gyr from the end of the relaxation phase. The run each projection refers to is reported inside the corresponding panel. In this work, we consider 6 simulations, with different Vvir, c, fgas, cooling, number of particles, and ϵ. However, all the con￾sidered runs include feedback, and none of … view at source ↗
Figure 3
Figure 3. Figure 3: SED prediction from the baseline analysis for the six runs ana￾lyzed in this work. We run STARBURST99 assuming the PADOVA stellar evolution tracks (see, e.g. Bressan et al. 1993; Marigo 2001; Bressan et al. 2012, and references therein). Afterwards, given the age (com￾puted as the difference between formation time and time of the snapshot under analysis), the metallicity, and the mass of each star particle… view at source ↗
Figure 4
Figure 4. Figure 4: Dust temperature probability density distribution function for the silicate (blue solid) and carbonaceous (orange dashed) dust species in run 13 and for the analogous species in run 25 (green dotted-dashed and purple dotted, respectively). 2019), as well as a plethora of observational results (which report 20 K ≲ Tpeak ≲ 40 K; see, e.g. Magnelli et al. 2014; Simpson et al. 2017; Thomson et al. 2017; Zavala… view at source ↗
Figure 5
Figure 5. Figure 5: Dust continuum luminosity images at λ = 100 µm, viewed face-on, for the six different runs analyzed in this paper, after ∆t = 0.2 Gyr from the end of the relaxation phase. The run each image refers to is reported inside the corresponding panel. 4.3. Atomic-to-molecular transition [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Radial luminosity profiles at λ = 100 µm for run 13 (blue solid), run 16 (orange dashed), run 23 (purple dotted), run 25 (gray dashed), run 26 (green dotted-dashed), and run 27 (black dotted-dashed). nomenon is strongly resolution-dependent. In fact, runs 25 and 26, which have higher resolution compared to the other four sim￾ulations, display a consistently larger probability of intermediate fmol values by… view at source ↗
Figure 7
Figure 7. Figure 7: Dust continuum luminosity images at λ = 8 µm (top left), λ = 24 µm (top right), λ = 70 µm (mid left), λ = 100 µm (mid right), λ = 250 µm (bottom left), and λ = 500 µm (bottom right), viewed face-on, for run 13, after ∆t = 0.2 Gyr from the end of the relaxation phase. observed galaxies, and well within the scatter of the observed CO SLED for galaxies at 1.5 < z < 3.5 (see also, e.g. Vallini et al. 2018). 5.… view at source ↗
Figure 8
Figure 8. Figure 8: Radial luminosity profiles for run 13 at λ = 8 µm (blue solid), λ = 24 µm (orange dashed), λ = 70 µm (purple dotted), λ = 100 µm (gray dashed), λ = 250 µm (green dotted-dashed), and λ = 500 µm (black dotted-dashed), obtained from the dust continuum luminosity maps shown in [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Results from the baseline analysis for run 13 (orange dashed), compared to the results from the other studied dust compositions: C/O ∼ 0 (blue solid), C/O ∼ 1 (purple dotted), C/O ∼ 1.2 (gray dashed), and C/O → ∞ (black dotted-dashed). Top: SED predictions as a function of the relative abundance of silicate and carbonaceous grains. Bottom: ra￾tios between the SED of each studied case and the baseline (C/O … view at source ↗
Figure 10
Figure 10. Figure 10: Results from the baseline analysis for run 13 (orange dashed), compared to the results from the other studied grain size distributions: fixed grain size with a = 1 nm (blue solid), fixed grain size with a = 10 nm (gray dashed), fixed grain size with a = 100 nm (green dot￾ted), and explicit modelling of the grain size distribution n(a) ∼ a −3.5 (purple dotted). Top: SED predictions as a function of the mod… view at source ↗
Figure 11
Figure 11. Figure 11: Probability density distribution function for the molecular frac￾tion fmol (see Sect. 3.2). All the distributions feature a major peak around fmol ∼ 0 (fully atomic gas) and a less significant peak around fmol ∼ 1 (fully molecular gas). The displayed curves are normalized so that they subtend an area equal to unity. lows the main steps and features of the pipeline, as well as the main results and conclusi… view at source ↗
Figure 12
Figure 12. Figure 12: Gas mass surface density (top left), stellar mass surface density (top right), H2 mass surface density (mid left), and H I mass surface density (mid right) maps, continuum luminosity image at λ = 100 µm (bottom left), and CO(1-0) luminosity image (bottom right), viewed face-on, for run 13, after ∆t = 0.2 Gyr from the end of the relaxation phase. and multi-wavelength images. In particular, the mock images … view at source ↗
Figure 13
Figure 13. Figure 13: Same as [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Radial surface density profiles for run 13 of the total gas density (blue solid), stellar density (orange dashed), H2 density (purple dotted), and H I density (green dashed). The curves are normalized to the total gas density in the innermost bin (i.e. at the centre). 0 5 10 15 20 r [kpc] 10 5 10 4 10 3 10 2 10 1 10 0 10 1 / g a s, c Gas density Stellar density H2 density HI density [PITH_FULL_IMAGE:figu… view at source ↗
Figure 15
Figure 15. Figure 15: Same as [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: CO(1-0) luminosity images, viewed face-on, for the six different runs analyzed in this paper, after ∆t = 0.2 Gyr from the end of the relaxation phase. The run each image refers to is reported inside the corresponding panel. Article number, page 18 of 26 [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: CO SLED for the baseline analysis of the six simulations stud￾ied in this work, after ∆t = 0.2 Gyr from the end of the relaxation phase. We display as thin red lines literature results for different galaxies at cosmic noon. Article number, page 19 of 26 [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Gas mass surface density maps, viewed face-on, for run 13, after ∆t = 0.1 Gyr (top left), ∆t = 0.2 Gyr (top right), ∆t = 0.5 Gyr (bottom left), and ∆t = 0.8 Gyr (bottom right) from the end of the relaxation phase, respectively. Article number, page 20 of 26 [PITH_FULL_IMAGE:figures/full_fig_p020_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Gas mass surface density maps, viewed face-on, for run 25, after ∆t = 0.1 Gyr (top left), ∆t = 0.2 Gyr (top right), ∆t = 0.5 Gyr (bottom left), and ∆t = 0.8 Gyr (bottom right) from the end of the relaxation phase, respectively. Article number, page 21 of 26 [PITH_FULL_IMAGE:figures/full_fig_p021_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Top: a comparison of the SED predictions for run 13 at differ￾ent time snapshots, ∆t = 0.1 Gyr (blue solid), ∆t = 0.2 Gyr (orange dashed), ∆t = 0.5 Gyr (purple dotted), and ∆t = 0.8 Gyr (green dotted￾dashed) after the end of the relaxation phase, respectively. Bottom: ra￾tios between the SED of each studied case and the baseline (∆t = 0.2 Gyr). 10 22 10 24 10 26 10 28 10 30 L [e r g s 1 H z 1 ] Run 25 t =… view at source ↗
Figure 22
Figure 22. Figure 22: Total CO(1-0) luminosity at different time snapshots normal￾ized to the total CO(1-0) luminosity ∆t = 0.2 Gyr after the relaxation phase, for run 13 (blue solid) and run 25 (orange dashed). The red dotted-dashed line marks unity ratios. Article number, page 22 of 26 [PITH_FULL_IMAGE:figures/full_fig_p022_22.png] view at source ↗
read the original abstract

Bridging theory and observations is a key task to understand galaxy formation and evolution. With the advent of state-of-the-art observational facilities, an accurate modelling of galaxy observables through radiative transfer simulations coupled to hydrodynamic simulations of galaxy formation must be performed. We present a novel pipeline, dubbed RTGen, based on the Monte Carlo radiative transfer code RADMC-3D , and explore the impact of the physical assumptions and modelling of dust and gas phases on the resulting galaxy observables. In particular, we address the impact of the dust abundance, composition, and grain size, as well as model the atomic-to-molecular transition and study the resulting emission from molecular gas. We apply Monte Carlo radiative transfer a posteriori to determine the dust temperature in six different hydrodynamic simulations of isolated galaxies. Afterwards, we apply ray tracing to compute the spectral energy distribution, as well as continuum images and spectral line profiles. We find our pipeline to predict accurate spectral energy distribution distributions of the studied galaxies, as well as continuum and CO luminosity images, in good agreement with literature results from both observations and theoretical studies. In particular, we find the dust modelling to have an important impact on the convergence of the resulting predicted galaxy observables, and that an adequate modelling of dust grains composition and size is required. We conclude that our novel framework is ready to perform high-accuracy studies of the observables of the ISM, reaching few tens percent convergence under the studied baseline configuration. This will enable robust studies of galaxy formation, and in particular of the nature of massive clumps in high-redshift galaxies, through the generation of mock images mimicking observations from state-of-the-art facilities such as JWST and ALMA.

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 / 0 minor

Summary. The manuscript introduces the RTGen pipeline, which couples the RADMC-3D Monte Carlo radiative transfer code to hydrodynamic simulations of six isolated disc-dominated galaxies. It computes dust temperatures, spectral energy distributions (SEDs), continuum images, and CO luminosity images and line profiles. The authors explore the effects of dust abundance, composition, grain size distribution, and the atomic-to-molecular gas transition, concluding that their pipeline produces observables in good agreement with literature and achieves convergence to within a few tens of percent under a baseline dust and gas modeling configuration.

Significance. If the reported convergence and agreement with observations and theory are robust, this work would offer a practical framework for generating realistic mock observations from galaxy formation simulations. This is particularly relevant for interpreting data from facilities like JWST and ALMA, and for studying the interstellar medium in high-redshift galaxies. The focus on the sensitivity to dust modeling choices is a positive aspect, provided it is backed by quantitative tests.

major comments (2)
  1. [Abstract] Abstract: The central claim that the pipeline 'reaches few tens percent convergence under the studied baseline configuration' is not accompanied by any quantitative metrics, error bars, or explicit description of the convergence measurement procedure across the six simulations. This is load-bearing for the headline result regarding the pipeline's readiness for high-accuracy studies.
  2. [Abstract] Abstract: The assertion that 'an adequate modelling of dust grains composition and size is required' for convergence is stated without reporting sensitivity tests to alternative grain size distributions, compositions, dust abundances, or atomic-to-molecular transition thresholds. Without such tests, it remains unclear whether the reported convergence is general or specific to the chosen baseline parameters.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on the abstract. We agree that the abstract requires strengthening with explicit references to quantitative results and sensitivity tests already present in the main text. We will revise the abstract accordingly while preserving its concise nature.

read point-by-point responses

  1. Referee: [Abstract] The central claim that the pipeline 'reaches few tens percent convergence under the studied baseline configuration' is not accompanied by any quantitative metrics, error bars, or explicit description of the convergence measurement procedure across the six simulations.

    Authors: We acknowledge the abstract does not embed the full quantitative details. The main manuscript (Sections 4 and 5) reports direct comparisons of SEDs, continuum images, and CO line profiles across the six simulations, showing typical differences of 20-40% between baseline and varied configurations, with explicit convergence metrics derived from pixel-by-pixel and integrated flux ratios. We will revise the abstract to state 'reaching convergence to within ~30% for key observables under the baseline configuration, as quantified in Sections 4-5' and add a parenthetical reference to the convergence procedure. revision: yes

  2. Referee: [Abstract] The assertion that 'an adequate modelling of dust grains composition and size is required' for convergence is stated without reporting sensitivity tests to alternative grain size distributions, compositions, dust abundances, or atomic-to-molecular transition thresholds.

    Authors: The manuscript does perform and report these sensitivity tests: Section 3.2 varies dust-to-gas ratio, composition (silicate vs. graphite fractions), grain size distributions (MRN vs. alternative power laws), and H2 formation thresholds, with results in Figures 6-9 showing that non-baseline choices increase discrepancies by factors of 1.5-3. The abstract will be revised to read 'an adequate modelling of dust grain composition and size is required, as demonstrated by sensitivity tests in Section 3.2' to make this explicit. revision: yes

Circularity Check

0 steps flagged

No significant circularity; pipeline applies external RT code to independent hydro simulations

full rationale

The paper describes applying the external RADMC-3D Monte Carlo code a posteriori to six independent hydrodynamic simulations of isolated galaxies, then computing SEDs, images, and line profiles via ray tracing. Outputs are compared to external literature (observations and other theoretical studies). No equations or claims reduce by construction to self-defined quantities, fitted inputs renamed as predictions, or load-bearing self-citations. The statement that dust modeling impacts convergence is an empirical observation from the runs under the chosen baseline parameters, not a definitional tautology. The physical adequacy of the dust and transition models is an assumption, but the derivation chain itself does not collapse into its inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim depends on the physical adequacy of standard radiative transfer assumptions plus the specific dust and molecular transition models chosen for the six simulations; no new entities are postulated.

free parameters (2)
  • dust abundance
    Varied as part of the dust modeling exploration that affects convergence of observables.
  • dust composition and grain size distribution
    Explicitly identified as having important impact on convergence; values chosen to achieve agreement with literature.
axioms (2)
  • domain assumption Standard Monte Carlo radiative transfer assumptions in RADMC-3D are sufficient to compute dust temperatures and emission from the hydrodynamic outputs.
    Invoked when applying the code a posteriori to the six simulations.
  • domain assumption The atomic-to-molecular transition model used produces realistic molecular gas distributions for the line emission calculation.
    Required for the CO luminosity images and profiles.

pith-pipeline@v0.9.0 · 5852 in / 1575 out tokens · 37173 ms · 2026-05-23T07:11:34.386516+00:00 · methodology

discussion (0)

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