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Modeling submillimeter galaxies in cosmological simulations: Contribution to the cosmic star formation density and predictions for future surveys

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read This paper claims that FLAMINGO, post-processed with a power-law flux recipe, reproduces observed submillimeter galaxy counts and redshift distributions, and forecasts that the TolTEC ultra-deep survey will detect about 80,000 sources at…

desk verdict Useful population-synthesis forecasts for SMGs from FLAMINGO, but the headline numbers hinge on a flux relation chosen against the simulation's own RT benchmark, so treat the quantitative claims as conditional. read the letter →

arxiv 2501.19327 v1 pith:JJ2CKEQQ submitted 2025-01-31 astro-ph.CO astro-ph.GA

Ankit Kumar , M. Celeste Artale , Antonio D. Montero-Dorta , Lucia Guaita , Kyoung-Soo Lee , Alexandra Pope , Joop Schaye , Matthieu Schaller
show 9 more authors
Eric Gawiser Ho Seong Hwang Woong-Seob Jeong Jaehyun Lee Nelson Padilla Changbom Park Vandana Ramakrishnan Akriti Singh Yujin Yang
This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords submillimetergalaxiescosmologicalsimulationsFLAMINGOstarformationratedensitydust-to-metalratioTolTECsourcenumbercountsredshiftdistribution
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

Bright submillimeter galaxies are the most intensely star-forming objects at high redshift, but cosmological models have struggled to reproduce their observed number counts and redshift distributions. This paper shows that the large-volume FLAMINGO simulation, when each galaxy's 850-micron flux is assigned from a simple power-law in star formation rate and dust mass, reproduces the observed abundance and redshift distribution of submillimeter galaxies without invoking a top-heavy stellar initial mass function. On that basis the paper estimates that galaxies brighter than 1 mJy at 850 microns contributed up to about 27% of the cosmic star formation rate density at $z\approx 2.6$, and forecasts that the TolTEC Ultra Deep Survey at 1.1 mm will detect roughly 80,000 sources in 0.8 square degrees, capturing about half of the star formation at $z\approx 2.5$. A reader should care because this turns the FLAMINGO volume into a quantitative bridge between the unresolved dusty star-forming population and upcoming wide-area submillimeter surveys.

What carries the argument

The central machinery is the power-law flux recipe of Hayward et al. (2013): $S_{850} = a\,(\mathrm{SFR}/100\,M_\odot\,\mathrm{yr}^{-1})^b\,(M_{\mathrm{dust}}/10^8\,M_\odot)^c$ with $(a,b,c)=(0.81,0.43,0.54)$. It converts each simulated galaxy's instantaneous star formation rate and dust mass, the latter taken from cold star-forming gas metal mass times a constant dust-to-metal ratio of 0.4, into an 850-micron flux density. This makes population synthesis possible over the 1 Gpc/h FLAMINGO volume where full dust radiative transfer is computationally unfeasible. The paper benchmarks the H13, L21, and C23 recipes against EAGLE radiative-transfer fluxes and against 892 observed SMGs, then selects H13 for its forecasts.

What would settle it

Compare the predicted 1.1-mm cumulative number counts from FLAMINGO+H13 against the first TolTEC Ultra Deep Survey catalog across $S_{1.1\,\mathrm{mm}} \approx 0.1$ to 10 mJy; a deviation larger than the 1$\sigma$ realization scatter shown in the paper's Fig. 10 would rule out the calibration. A more direct test measures star formation rates and dust masses of a flux-limited SMG sample and checks whether the observed pairs satisfy the H13 relation with the adopted dust-to-metal ratio.

Watch

Extended reading notes

Core claim

The central claim is that FLAMINGO simultaneously reproduces the observed cumulative number counts and redshift distribution of submillimeter galaxies (SMGs) when the 850-micron flux density is computed with the parametric relation of Hayward et al. (2013), $S_{850} = a\,(\mathrm{SFR}/100\,M_\odot\,\mathrm{yr}^{-1})^b\,(M_{\mathrm{dust}}/10^8\,M_\odot)^c$ with $(a,b,c)=(0.81,0.43,0.54)$, and dust mass derived from cold-gas metal mass at a fixed dust-to-metal ratio of 0.4. The same post-processing applied to EAGLE and IllustrisTNG underproduces bright SMGs, which the paper attributes to the smaller volumes of those simulations. The discovery is that a sufficiently large cosmological volume combined with this flux calibration removes the historical need for a top-heavy initial mass function in modeling the bright submillimeter population, and it identifies galaxies with $S_{850} > 1$ mJy as carrying up to 27% of the cosmic star formation rate density at $z=2.6$.

Load-bearing premise

Everything rests on the assumption that a single power-law relation between star formation rate, dust mass, and 850-micron flux, with a constant dust-to-metal ratio of 0.4, holds for every galaxy in FLAMINGO; if that relation drifts with redshift or galaxy type, the counts, the SFRD contribution, and the survey forecasts all shift.

Editorial extensions

If this is right

  • The FLAMINGO simulation reproduces the observed SMG redshift distribution and source number counts above $S_{850} > 1$ mJy without a top-heavy initial mass function, removing a long-standing tension in galaxy formation modeling.
  • Bright SMGs ($S_{850} > 1$ mJy) carry up to 27% of the cosmic star formation rate density at $z \approx 2.6$, making them a major and quantifiable channel of obscured star formation.
  • The TolTEC Ultra Deep Survey will detect roughly 80,000 sources over 0.8 deg$^2$ at 1.1 mm at the 4$\sigma$ limit, and these sources trace about 50% of the cosmic star formation rate density at $z \approx 2.5$.
  • EAGLE and IllustrisTNG underproduce bright SMGs because their volumes are too small to sample the bright end, demonstrating that large-volume simulations are required for SMG statistics.
  • The flux density function grows from $z=6$ to $z=2.5$ and then declines sharply at the bright end, with Schechter-function parameters tracking this evolution.

Reading between the lines

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

  • If the H13 calibration is correct, the TolTEC number counts at 1.1 mm provide a direct independent test of the adopted dust-to-metal ratio and flux recipe; the measured counts will either confirm or rule out the assumption behind the 80,000-source forecast.
  • The validation hints at an internal tension: the L21 recipe matches EAGLE's radiative-transfer fluxes while the H13 recipe matches the observed fluxes of SMGs at given SFR and dust mass, suggesting the discrepancy lies in the simulations' dust properties or sample selection rather than in the recipe choice alone.
  • The forecast that about half of the cosmic SFRD at $z\approx2.5$ appears in the TolTEC 1.1-mm sample implies that the submillimeter-selected population is the dominant obscured channel at that epoch, and that combining TolTEC with the ODIN Ly$\alpha$ survey could trace the same large-scale structures in both obscured and unobscured tracers.
  • A testable extension would apply the same recipe to higher-resolution volumes, such as TNG50, to separate the volume effect from a resolution effect in the bright-end deficit of EAGLE and IllustrisTNG.
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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 / 5 minor

Summary. The paper models 850 µm fluxes of simulated galaxies by applying three published parametric S850(SFR, Mdust) relations (H13, L21, C23; Eq. 1 and Table 2) to EAGLE, IllustrisTNG, and FLAMINGO. After comparing against EAGLE radiative-transfer fluxes and against observed SFRs/dust masses of 892 SMGs, the authors adopt H13 as the preferred relation while also showing L21 throughout. With the FLAMINGO L1_m8 box they derive redshift distributions, cumulative source counts, the SMG contribution to the cosmic star formation rate density, flux density functions, and TolTEC UDS/LSS forecasts. The headline results are that FLAMINGO with a Chabrier IMF reproduces the observed SMG counts and redshift distribution; SMGs with S850>1 mJy contribute up to about 27% of the cosmic SFRD at z≈2.6; and the TolTEC UDS will detect about 80,000 sources at 1.1 mm, tracing about 50% of the SFRD at z≈2.5. Appendices test box size, subgrid prescriptions, and cosmology.

Significance. The large statistical volume of FLAMINGO is a genuine strength: it avoids the small-volume fluctuations that affect EAGLE and TNG100 at the bright end, and the appendix tests of box size, feedback variations, and cosmology provide a useful map of systematic sensitivity. The paper is also honest in showing both H13 and L21 results. If the H13 calibration is accepted, the conclusions are important: they suggest that no top-heavy IMF is required, give a quantitative SMG contribution to the cosmic SFRD, and provide concrete TolTEC survey forecasts. The use of public simulation data and explicit sample definitions additionally aids reproducibility. However, the central claim rests entirely on the choice of H13, and the manuscript's own EAGLE radiative-transfer test contradicts that choice; until this contradiction is resolved, the quantitative conclusions (27%, 80,000 sources, 50% SFRD) are not robust.

major comments (3)
  1. [Section 3.2.1 / Fig. 1(a)] The H13 relation, which underlies the paper's central claims, overproduces the comoving number density of S850>1 mJy galaxies in EAGLE by a factor of about 5 at z≈2 relative to the radiative-transfer calculation, while L21 and C23 are consistent with the RT results. The motivation for adopting H13 in Section 3.2.2 is its agreement with observed fluxes derived from MAGPHYS SFRs and dust masses, but no physical argument is given for why H13 should be preferred for FLAMINGO's lower-resolution galaxies when it fails the direct RT test. Because all of the headline quantities (source counts, 27% SFRD contribution, TolTEC source numbers) are computed with H13, the paper needs to resolve this contradiction, for example by showing that resolution or differences between EAGLE and FLAMINGO galaxy properties can account for the factor of 5, or by demonstrating that the main conclusions are preserved with L21/C23 applied to the same FLAMINGO sample.
  2. [Section 3.2.2 vs Sections 4.1-4.2] The model selection and the subsequent validation are not fully independent. The 892-galaxy sample used to prefer H13 (da Cunha et al. 2015; Dudzeviciute et al. 2020; Hyun et al. 2023) is drawn from the same submillimeter-selected population that enters the redshift-distribution and number-count comparisons in Figs. 3 and 5; in particular, Dudzeviciute et al. (2020) contributes both to Fig. 2 and to the observed redshift distribution and counts. The manuscript should state explicitly which observational points are independent of the selection sample and quantify how the agreement in Figs. 3 and 5 depends on the Section 3.2.2 choice. Without that, the agreement is partly a restatement of the selection criterion.
  3. [Sections 3.1 and 5, Eqs. (1) and (6)] The default choices DTM=0.4 and β=1.8 are treated as fixed, but the headline forecasts depend on them. The DTM uncertainty is not negligible: the EAGLE RT comparison is run with DTM=0.3 while the FLAMINGO population synthesis uses DTM=0.4, which through the c exponent in Eq. (1) changes S850 by about 17%; and the S850 to 1.1/1.4/2.0 mm conversions in Eq. (6) assume a single modified-blackbody spectrum, so dust-temperature variations are ignored. The TolTEC number counts (80,000 at 1.1 mm) and SFRD fractions should be quoted with a systematic band obtained by varying DTM, β, and the observed flux conversion, or the authors should argue that these variations do not affect the ranking of models.
minor comments (5)
  1. [Fig. 10 / Section 5] The text describes the total SFRD curve in the bottom-left panel as yellow dashed, while the figure caption calls it green dotted; please reconcile the color and line-style description.
  2. [Section 3.2.2] The sentence 'following equation (6) in Section 4.2' refers to a relation defined later in the paper; reorder the presentation or give the conversion factor at first use.
  3. [Section 3.1] The phrase 'observational estimates form Dwek (1998)' contains a typo ('form' should be 'from').
  4. [Fig. 3 / Section 4.1] Because the simulated redshift distributions are rescaled to the height of the Dudzeviciute et al. (2020) histogram, the reader should be reminded that Fig. 3 tests only the shape; the absolute normalization is tested in Fig. 5. Adding the scaling factors to the caption would help avoid misinterpretation.
  5. [Table B.1] The identifier 'L1_M9' is used in Table B.1, while the main text refers to 'L1_m9' for the same simulation; unify the naming convention.

Circularity Check

1 steps flagged · score 4.0 of 10

Model selection and validation overlap: H13 is chosen using the same observed SMG samples (da Cunha+15, Dud+20, Hyun+23) that later serve as the observed redshift distributions and number counts, so the headline agreement is partly selection-driven rather than a fully independent prediction.

  1. fitted input called prediction [Section 3.2.2 (choice of H13) and Section 4.2 (source number counts)]
    "It is clear that the H13 model better represents the observations than the L21 model for the whole flux density range. ... In Fig 5, we show predicted source number counts ... The gray color open circles and open boxes represent observed source number counts compiled from the literature ... Hyun et al. 2023; Zeng et al. 2024."

    H13 is selected because, applied to the 892 observed SMGs (da Cunha+15, Dudzeviciute+20, Hyun+23), it reproduces their observed fluxes best (Fig. 2). The same publications then supply the observational redshift distribution (Dud+20 in Fig. 3) and number counts (Hyun+23 and other JCMT surveys in Fig. 5) against which FLAMINGO is declared to 'reproduce the observed number counts.' Choosing the highest-normalization relation on the very samples used as validation targets makes part of the later agreement a selection effect: L21 and C23, which fail the counts comparison, were rejected in the same step. The simulated counts are not computed from the observed SFR/Mdust values, so the FLAMINGO population adds independent content; but the headline agreement is not a fully independent prediction.

full rationale

The derivation chain is: Eq. (1) assigns 850 micron fluxes from SFR and dust mass using external RT-calibrated constants (H13/L21/C23); dust mass is obtained from cold-gas metal mass assuming a constant dust-to-metal ratio; the model is then tested on EAGLE RT and on 892 observed SMGs; H13 is adopted because it best matches the observed fluxes; all subsequent results (redshift distributions, number counts, 27% SFRD contribution, TolTEC forecasts) are computed with H13. The main circularity concern is that H13 is selected using the same observational samples that later appear as the 'observed' comparison points for the redshift distribution and counts (Dud+20, Hyun+23). Because H13 has the highest normalization of the three relations, choosing it on the observed fluxes and then reporting agreement with overlapping observed counts is partly a model-selection effect rather than an independent confirmation. This is not full circularity: the FLAMINGO simulated SFR and dust-mass distributions are independent of the observed galaxy sample, so the match in Fig. 5 has real content, and the H13 parameters themselves are not fitted in this paper. The paper is also transparent that it proceeds 'based on the model that best reproduces observations.' The factor-of-five discrepancy between H13 and EAGLE radiative transfer (Fig. 1) is a correctness/robustness concern, not a circularity per se. No load-bearing self-citation chain or uniqueness argument is present; the central claim retains independent content, so the score is 4 rather than 6-8.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central results rest on published parametric flux relations (especially H13), a constant dust-to-metal ratio, and a single power-law SED conversion for band-shifting. The paper does not introduce new physical entities or new fitted constants beyond the descriptive Schechter fits in Section 4.5. The flux-relation coefficients and DTM ratio are the quantities most worth auditing because they directly control which simulated galaxies appear as SMGs.

free parameters (3)
  • H13 parametric model coefficients (a=0.81, b=0.43, c=0.54) = 0.81, 0.43, 0.54
    Fitted in Hayward et al. (2013) to radiative transfer calculations on idealized galaxies; adopted here as the fiducial flux model. The normalization a sets the flux scale and thus which galaxies exceed the 1 mJy threshold; the paper shows H13 produces fluxes about 75% higher than L21 on average.
  • Dust-to-metal ratio (DTM) = 0.4
    Chosen from Dwek (1998) and James et al. (2002); the paper notes DTM = 0.5 would raise fluxes by 11-13%. It directly sets Mdust from metal mass and enters Eq. 1.
  • Spectral index beta for band conversion = 1.8
    Adopted from Planck Collaboration et al. (2011); used in Eq. 6 to scale 850 micron fluxes to 870, 1100, 1200, 1300, and 2000 microns. This assumption propagates into observed count compilations and the TolTEC forecasts.
assumptions (5)
  • standard math Comoving number counts are computed with standard flat LCDM cosmology (Eqs. 2-5).
    Background equations for converting simulation snapshots into projected source counts; no alternative cosmologies are needed.
  • domain assumption The parametric flux relation (Eq. 1) calibrated on idealized/SIMBA/FIRE-2 galaxies applies to all galaxies in EAGLE, TNG, and FLAMINGO at all redshifts, independent of ISM geometry and dust temperature distribution.
    Invoked in Section 3; the paper partially tests it on EAGLE RT (Fig. 1) but still relies on it when applying the models to simulations without RT.
  • domain assumption A constant dust-to-metal ratio of 0.4 holds for cold star-forming gas in all simulated galaxies; dust in hot gas is treated as destroyed.
    Section 3.1; the paper only considers cold star-forming cells for metal mass, following Draine & Salpeter (1979) and McKinnon et al. (2016).
  • domain assumption Observed SFRs and dust masses from MAGPHYS SED fitting (da Cunha+08) are accurate and comparable to the simulation's 30 pkpc aperture quantities.
    Section 3.2.2; the model selection in Fig. 2 uses these SED-derived quantities as ground truth.
  • domain assumption The modified black body conversion (Eq. 6) with beta = 1.8 correctly maps fluxes among 850, 870, 1100, 1200, 1300, and 2000 microns for all SMGs.
    Used to homogenize observed number counts and to scale FLAMINGO fluxes to TolTEC bands; ignores dust temperature diversity.

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Pith. "Pith review of Modeling submillimeter galaxies in cosmological simulations: Contribution to the cosmic star formation density and predictions for future surveys." pith.science (2026). https://pith.science/paper/JJ2CKEQQ

@misc{pith2026250119327,
  author       = {Pith},
  title        = {Pith review of: Modeling submillimeter galaxies in cosmological simulations: Contribution to the cosmic star formation density and predictions for future surveys},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JJ2CKEQQ}},
  note         = {Machine review of arXiv:2501.19327}
}
read the original abstract

Submillimeter galaxies (SMGs) constitute a key population of bright star-forming galaxies at high redshift. These galaxies challenge galaxy formation models, particularly in reproducing their observed number counts and redshift distributions. Furthermore, although SMGs contribute significantly to the cosmic star formation rate density (SFRD), their precise role remains uncertain. Upcoming surveys, such as the Ultra Deep Survey with the TolTEC camera, are expected to offer valuable insights into SMG properties and their broader impact. Robust modeling of SMGs in a cosmologically representative volume is necessary to investigate their nature in preparation for next-generation submillimeter surveys. We implement and test parametric relations derived from radiative transfer calculations across three cosmological simulations: EAGLE, IllustrisTNG, and FLAMINGO. Particular emphasis is placed on the FLAMINGO due to their large volume and robust statistical sampling of SMGs. Based on the model that best reproduces observations, we forecast submillimeter fluxes within the simulations, analyze the properties of SMGs, and evaluate their evolution over cosmic time. Our results show that the FLAMINGO reproduces the observed redshift distribution and source number counts of SMGs without requiring a top-heavy initial mass function. On the other hand, the EAGLE and IllustrisTNG show a deficit of bright SMGs. We find that SMGs with S850 > 1 mJy contribute up to 27% of the SFRD at z=2.6 in the FLAMINGO, consistent with recent observations. Flux density functions reveal a rise in SMG abundance from z = 6 to 2.5, followed by a sharp decline in the number of brighter SMGs from z = 2.5 to 0. Leveraging the SMG population in FLAMINGO, we forecast that the TolTEC UDS will detect 80,000 sources over 0.8 deg^2 at 1.1 mm (at the 4{\sigma} detection limit), capturing about 50% of the cosmic SFRD at z=2.5.

Figures

Figures reproduced from arXiv: 2501.19327 by the authors.

Figure 1
Figure 1. , the orange color shows the Hayward et al. (2013) rela￾tion (H13), green shows the Lovell et al. (2021) relation (L21), and pink shows the Cochrane et al. (2023) relation (C23). The RT calculations from the EAGLE data are shown in blue. The shaded regions around the measurements from three parametric relations represent 1σ uncertainties around the mean of 100 re￾alizations of the flux densities. In [PITH_FULL_IMAG… view at source ↗
Figure 2
Figure 2. Comparison of observed flux densities with flux densities pre￾dicted using the parametric relations listed in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. The median of the redshift distributions of submm galaxies as a function of the flux density cut in FLAMINGO simulation. The solid blue curve represents the prediction using the L21 relation, and the dot￾ted blue curve shows the prediction assuming the H13 relation. Gray color points represent observational estimates obtained from Chapman et al. (2005); Pope et al. (2005); Wardlow et al. (2011); Casey et al. (2013);… view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: Redshift distribution of submm-bright galaxies with S850 > 3 mJy in the FLAMINGO (blue), TNG300 (orange), TNG100 (green) simulations. gray color histograms represent recent observational esti￾mates from Dudzeviciˇ ut¯ e et al. ˙ (2020) [Dud+20], and Simpson et al. (202…
Figure 5
Figure 5. Figure 5: Source number counts of submm galaxies in the FLAMINGO (solid blue), TNG300 (dashed orange), TNG100 (dash-dotted green), and EAGLE (dotted pink) simulations. Shaded regions show 1σ errors for 100 realizations of the modeled submm galaxy population, as dis￾cussed in Sec…
Figure 6
Figure 6. Figure 6: Left column: Contribution of submm galaxies with S850 > 1 mJy to the cosmic star formation rate density in the FLAMINGO (solid blue) TNG300 (dashed orange), and TNG100 (dash-dotted green) simulations. Dotted curves represent the total SFRD in corresponding colors. The …
Figure 7
Figure 7. Figure 7: The evolution of the median star formation rate − stellar mass relation in the FLAMINGO simulation from z = 6 to z = 0.5 for SMGs grouped into four flux density ranges shown with yellow (1 ≤ S850/mJy < 2), orange (2 ≤ S850/mJy < 3), green (3 ≤ S850/mJy < 5), and pink (…
Figure 9
Figure 9. Figure 9: Redshift evolution of the Schechter function fitting parameters for the flux density function shown in [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: TolTEC UDS survey predictions for redshift distributions (top-left panel), source number counts (top-right panel), SFRDs (bottom-left panel), and ratio of submm SFRDs to the total cosmic SFRDs (bottom-right panel) using the H13 relation and the FLAMINGO simulation. Th…

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