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CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- II. Constraining cosmology and astrophysical processes with early- and late-type galaxies

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that no CAMELS IllustrisTNG simulation can jointly reproduce the observed scaling relations of early- and late-type galaxies, so the inferred cosmological and feedback parameters depend on which dataset is fitted.

desk verdict A careful, transparent extension of the CASCO method to ETGs, but the headline no-single-simulation claim depends on an un-forward-modeled comparison between 3D simulated quantities and projected observables. read the letter →

arxiv 2412.00217 v1 pith:ORBG6YFJ submitted 2024-11-29 astro-ph.GA

Valerio Busillo , Crescenzo Tortora , Giovanni Covone , Leon V. E. Koopmans , Michela Silvestrini , Nicola R. Napolitano This is my paper · ORCID
classification astro-ph.GA
keywords galaxyscalingrelationsearly-typegalaxieslate-typedarkmatterfractioncosmologicalparameterssupernovafeedbackCAMELSsimulationsIllustrisTNG
open problems Dark Matter
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

The paper extends the CASCO comparison between cosmological simulations and galaxy observations from late-type galaxies to early-type galaxies. It asks whether one CAMELS IllustrisTNG simulation can reproduce the observed relations between stellar mass and stellar half-mass radius, dark-matter fraction within the half-mass radius, and dark-matter mass within the half-mass radius, for early-type galaxies in SPIDER, ATLAS3D, and MaNGA DynPop. The best-fit simulation changes from sample to sample: SPIDER favors a high supernova feedback strength $A_{\rm SN1}$, ATLAS3D and MaNGA favor low $A_{\rm SN1}$, and MaNGA requires extreme values of $\Omega_m$ and $\sigma_8$. When the full MaNGA DynPop sample is used, so that early- and late-type galaxies come from the same survey and analysis, no simulation reproduces both trends. The paper reads this as evidence that current simulations, and the six varied parameters explored here, do not capture the full diversity of real galaxies.

What carries the argument

The ranking machinery is the cumulative reduced chi-squared. For each of the three scaling relations, each simulated galaxy is compared with the observed median trend interpolated at its stellar mass, with the observed 16th-to-84th percentile scatter used as the uncertainty, and a Gaussian draw from that scatter replaces the fixed observed value in the numerator. Simulations are first filtered to require at least one simulated galaxy in each stellar-mass bin, then ranked by the sum of reduced chi-squared over the three relations. The parameter constraints come from 100 bootstrap resamplings of the observed and simulated samples, with the best-fit simulation selected each time; smoothing the empirical cumulative distributions of the selected parameters gives the reported 16th, 50th, and 84th percentiles.

What would settle it

Apply the same projection and mass-modeling pipeline used for SPIDER, ATLAS3D, and MaNGA DynPop to the simulated galaxies and rerun the bootstrap ranking; if one simulation then fits all three datasets and reproduces the early/late dichotomy, the paper's central negative result is an artifact of comparing unlike quantities, whereas if no simulation still fits, the claim that current simulations miss real galaxy diversity stands.

Watch

Extended reading notes

Core claim

The central claim is that the CAMELS IllustrisTNG simulations cannot jointly reproduce the scaling relations of early- and late-type galaxies, and that the inferred cosmological and feedback parameters depend on the chosen observational trends. For early-type galaxies alone, the best-fit simulations are LH_523 for SPIDER, LH_797 for ATLAS3D, and LH_586 for MaNGA DynPop, with $\Omega_m$ between $0.16$ and $0.25$, $\sigma_8$ between $0.77$ and $0.97$, and the supernova feedback parameters $A_{\rm SN1}$ and $A_{\rm SN2}$ reversing between SPIDER (high $A_{\rm SN1}$, low $A_{\rm SN2}$) and ATLAS3D or MaNGA (low $A_{\rm SN1}$, high $A_{\rm SN2}$). Constraining one simulation for the full MaNGA DynPop sample, early- and late-type together, gives a best fit with cumulative reduced $\tilde{\chi}^2 = 8.77$, still a poor representation; the observed early- and late-type relations have different slopes and normalizations, while the simulated populations of both types follow roughly the same trend. The AGN feedback parameters $A_{\rm AGN1}$ and $A_{\rm AGN2}$ are effectively unconstrained, because varying them has almost no effect on the simulated scaling relations.

Load-bearing premise

The whole argument relies on simulated galaxies and observed galaxies being measured in the same way: simulations give three-dimensional stellar half-mass radii and dark-matter masses, while observations infer these from projected light using a standard radius conversion and simplified spherical mass models, and the paper does not forward-model the simulations through those observational steps.

Editorial extensions

If this is right

  • Single-sample constraints are not universal: a simulation that fits SPIDER early-type galaxies does not fit ATLAS3D or MaNGA, and the SPARC late-type best fit fails for early types.
  • Supernova feedback constraints flip sign with sample: SPIDER wants stronger $A_{\rm SN1}$ and weaker $A_{\rm SN2}$, while ATLAS3D and MaNGA want the reverse, so feedback parameters should be reported together with the galaxy type and sample definition.
  • The AGN feedback parameters $A_{\rm AGN1}$ and $A_{\rm AGN2}$ cannot be constrained from these scaling relations, because varying them leaves the trends nearly unchanged.
  • Resolution is not the fix: the higher-resolution original IllustrisTNG runs also fail to reproduce the observed early/late dichotomy, and lower resolution sometimes matches the data better.
  • The observed early/late dichotomy in MaNGA DynPop, with different slopes and normalizations for the two galaxy types, is a sharper test for simulations than any single scaling relation.

Reading between the lines

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

  • A natural next step, not done by the paper, is to forward-model the simulated galaxies through the same projection and mass-modeling pipelines as the observed samples; if the galaxy-type dichotomy survives that test, the conclusion points to baryon physics in the simulations rather than to the comparison space.
  • The paper's appendix test, which adds gas mass to the simulated dark-matter mass for the MaNGA comparison, shifts the best-fit supernova parameters toward the SPARC-based values; this suggests the exact definition of “dark matter within the half-mass radius” should be made identical on both sides before drawing physical conclusions.
  • Because the six varied parameters omit other AGN feedback parameters available in newer CAMELS runs, the null result is a statement about this parameter set, not about all possible feedback implementations.
  • The strong $\Omega_m$-$\sigma_8$ degeneracy shown in the chi-squared heat maps implies that these scaling relations alone cannot rank cosmological models on the $S_8$ tension; a combined probe with other observables would be needed.
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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

4 major / 4 minor

Summary. This paper extends the CASCO framework of Busillo et al. (2023) from late-type galaxies to early-type galaxies, comparing CAMELS IllustrisTNG simulations with three observational datasets (SPIDER, ATLAS3D, and MaNGA DynPop). The authors examine the size--stellar mass, dark-matter-fraction--stellar mass, and dark-matter-mass--stellar mass relations of simulated ETGs, rank the 1061 CAMELS runs with a cumulative reduced chi-squared statistic, and derive constraints on Omega_m, sigma_8, and the feedback parameters ASN1, ASN2, AAGN1, and AAGN2 via bootstrap resampling. They find that the best-fit simulations and the resulting parameter constraints differ between datasets, and that no single simulation reproduces the full MaNGA DynPop sample containing both ETGs and LTGs, because the observed ETG and LTG trends are dichotomous while simulated ETGs and LTGs follow similar trends. The paper also compares with the original IllustrisTNG simulations at different resolutions to test for resolution effects, and includes appendices on observational biases, raw CDF constraints, chi-squared degeneracy maps, and a selection-bias check.

Significance. If the central negative result is robust, it is an important cautionary finding for simulation-based inference: it would show that the current CAMELS IllustrisTNG parameter space cannot jointly reproduce the structural and dark-matter scaling relations of both early- and late-type galaxies, and that inferred cosmological and feedback parameters depend on galaxy type and observational dataset. The paper is systematic in its coverage: it uses three ETG datasets, checks the LTG+ETG joint fit, tests gas-mass corrections in Appendix A, and compares with TNG300, TNG100, TNG50, and lower-resolution TNG100 runs. These resolution and gas-correction checks give credibility to the claim that the failure to reproduce the ETG/LTG dichotomy is not simply a resolution artifact. However, the headline claim is vulnerable to a comparison-space issue: simulated 3D subfind quantities are compared directly with observed quantities derived from projected light and dynamical modeling, and the paper explicitly does not forward-model the simulations into the observational space.

major comments (4)
  1. [Section 2.2 and Appendix A] The central claim that no CAMELS IllustrisTNG simulation reproduces the observed ETG/LTG dichotomy rests on a direct one-to-one comparison between simulated 3D quantities (stellar half-mass radius, DM fraction within that radius, DM mass within that radius) and observed quantities derived with R*=1.35 Re, M*,1/2=M*/2, and SIS- or JAM-based dynamical models. The paper lists these projection and modeling biases in Appendix A and states that full forward-modeling is outside the scope, but it does not quantify whether type-dependent biases could create or erase the apparent ETG/LTG dichotomy. Because the dichotomy is the load-bearing element of the paper's main conclusion, the authors should provide a sensitivity test, for example by forward-modeling mock observations of the TNG snapshots or by estimating how large a type-dependent M/L or mass-anisotropy bias would need to be to explain the observed separation. Without such a test, the headline negative result remains vulnerable to a comparison-space artifact.
  2. [Section 4.1, Eq. (2)] The reduced chi-squared in Eq. (2) is stochastic: the numerator subtracts a randomly drawn point from a Gaussian centered on the observed median, so repeated evaluations for the same simulation and the same data produce different chi-squared values. The bootstrap procedure resamples the datasets but does not propagate this additional Monte-Carlo noise into the reported parameter uncertainties. In addition, the Gaussian smoothing of the empirical CDFs with standard deviation sigma=1 is ad hoc, and the quoted 16th/50th/84th percentiles change when the smoothing is omitted (Tables 3 and B.1). The paper should quantify the sensitivity of the constraints to the smoothing scale and to the number of bootstrap resamples, and should either justify Eq. (2) or replace it with a deterministic ranking statistic.
  3. [Section 4.5 and Table 5] The full MaNGA DynPop ETG+LTG analysis yields a best-fit simulation, LH_531, with cumulative reduced chi-squared 8.77, and the bootstrap constraints in Table 5 have very large and asymmetric uncertainties (for example ASN1 = 1.83^{+0.74}_{-1.19}). The conclusion that no single simulation reproduces the full sample is therefore based on the absence of any good fit, but the paper does not provide a statistical test of whether the observed ETG/LTG dichotomy is significantly different from the simulated one. A quantitative comparison of the slopes or offsets between ETG and LTG median trends as a function of stellar mass would strengthen the central claim and would make the result less dependent on visual inspection of Figs. 8 and 9.
  4. [Section 2.1.2 and Section 4.6] The original IllustrisTNG model was calibrated partly against the z=0 stellar mass--stellar size relation and the gas mass content of groups, as stated in Section 2.1.2. Because the CAMELS fiducial run uses the same subgrid model and parameters, the size-mass and DM-fraction relations are not fully independent probes of cosmology or feedback. This does not invalidate the negative result, but it means the derived values for Omega_m and sigma_8 should be interpreted as conditional on the TNG subgrid implementation rather than as independent cosmological measurements. The paper should make this conditioning explicit in the abstract or in the discussion of the SPIDER/ATLAS3D/MaNGA constraints.
minor comments (4)
  1. [Section 4.1] The phrase 'mathematicaresource function' appears to be a formatting artifact; it should read 'Mathematica resource function'.
  2. [Section 3.1] There are typographical errors such as 'avaliable' in the discussion of the new CAMELS simulations; the manuscript would benefit from a careful proofreading pass.
  3. [Section 2.2.3] The selection criteria for MaNGA LTGs are listed as T-Type>=0, PLTG>=0.5, VC=3, and VF=0; it should be stated explicitly whether all four conditions are required simultaneously and how many galaxies are removed by each individual cut.
  4. [Figure 1] In the caption of Fig. 1, the labels 'ASN,1=0.25, ASN,1=1.00, ASN,1=2.30' appear to use 'ASN,1' three times where the second and third entries should refer to the other varied parameters; please correct the caption to match the column labels.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the parameter constraints and the no-dichotomy claim are outputs of an explicit ranking fit against independent observed scaling relations, not reductions to the inputs.

full rationale

The paper is an explicit fitting and ranking exercise, not a derivation of predictions from first principles. The central claims—that best-fit CAMELS parameters differ across SPIDER, ATLAS3D, and MaNGA DynPop, and that no single simulation reproduces the observed ETG/LTG dichotomy—are outputs of the chi-squared ranking defined in Eqs. (1)-(2), applied to independent observed trends. The simulated quantities (3D half-mass radii, DM fractions from subfind particles) are not defined in terms of the observed quantities (projected effective radii, SIS/JAM dynamical masses); the paper candidly lists these as comparison-space biases in Appendix A and explicitly states that forward-modeling is out of scope, which is a validity limitation rather than a circular step. The IllustrisTNG calibration overlap with the size-mass relation is acknowledged (Section 2.1.2), but the fiducial simulation is not selected as best fit, the result is robust across TNG resolutions (Section 4.6), and the gas-mass correction in Appendix A preserves the dichotomy. Self-citations to Paper I are methodological and descriptive; the Paper I best-fit is used as an out-of-sample test in Section 4.3 and fails for ETGs, which is a genuine predictive check rather than a self-confirming premise. No fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The central negative claim is therefore self-contained with respect to the inputs, and any concerns about projection or modeling biases belong to correctness risk, not circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper does not introduce new physical entities. Its central claim rests on domain assumptions about the comparability of simulated 3D quantities with observed projected quantities, about the coverage of the CAMELS parameter space, and about the suitability of the chi-squared ranking statistic. The hand-chosen analysis settings (CDF smoothing width, bootstrap count, mass-bin filtering) are free parameters that influence the reported parameter uncertainties.

free parameters (4)
  • Gaussian kernel smoothing width for empirical CDFs = sigma = 1 (chosen)
    Applied to the empirical CDFs of best-fit parameter values from bootstrap resampling; the width is ad hoc and directly affects the reported 16th-84th percentile uncertainties in Tables 3 and 5.
  • Number of bootstrap resamplings = 100
    Chosen by hand; determines the granularity and stability of the empirical CDFs and the resulting parameter percentiles.
  • Stellar mass bin edges for simulation filtering = same edges as observational binning
    Simulations with fewer than one galaxy in any bin are excluded, with first and last bins ignored; this selection affects which simulations survive and can bias the parameter constraints.
  • sSFR threshold for ETG/LTG selection = log10(sSFR/yr^-1) = -10.5
    Used to split simulated galaxies into early- and late-type; the authors verify +/-0.5 dex insensitivity, so it is a tested but hand-chosen threshold.
assumptions (5)
  • domain assumption The six varied CAMELS parameters (Omega_m, sigma_8, ASN1, ASN2, AAGN1, AAGN2) span the model space relevant for galaxy scaling relations; Omega_b, n_s, h are fixed.
    Section 2.1.1 fixes Omega_b=0.049, n_s=0.9624, h=0.6711 and notes degeneracies with Omega_m and sigma_8; the conclusion that 'no single simulation matches' is restricted to this parameter space, and other AGN feedback parameters are not varied.
  • domain assumption Simulated 3D quantities are directly comparable to observed projected quantities via R* = 1.35 Re and SIS-based dynamical masses.
    Section 2.2 and Appendix A. The authors list biases (M/L gradients, projection, SIS assumption, gas neglect) but do not forward-model; the reported best-fit parameters assume these conversions are unbiased.
  • ad hoc to paper The reduced chi-squared with a random Gaussian draw (Eq. 2) is an appropriate ranking statistic.
    Section 4.1. The random draw N(frel, sigma_rel) adds stochasticity and an expected +1 contribution to each term; the resulting values are used for ranking without calibrating the statistic.
  • domain assumption The IllustrisTNG subgrid model is the correct underlying physical model.
    Section 2.1.2. Only CAMELS IllustrisTNG variants are considered, so the inferred feedback parameters are meaningful only if this feedback prescription captures the relevant physics.
  • domain assumption The observational classification of ETGs and LTGs matches the sSFR cut used for simulations.
    Sections 2.1.1 and 2.2.3. Observational samples use T-Type, PLTG, and visual classification, while simulations use sSFR <= -10.5; the authors check a photometric cut, but residual mismatch could bias the comparison.

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Cite this review

Pith. "Pith review of CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- II. Constraining cosmology and astrophysical processes with early- and late-type galaxies." pith.science (2026). https://pith.science/paper/ORBG6YFJ

@misc{pith2026241200217,
  author       = {Pith},
  title        = {Pith review of: CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- II. Constraining cosmology and astrophysical processes with early- and late-type galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ORBG6YFJ}},
  note         = {Machine review of arXiv:2412.00217}
}
abstract

Physical processes impact galaxy formation and evolution in diverse ways, requiring validation of their implementation in cosmological simulations through comparisons with real data across various galaxy types and properties. In this second paper of the CASCO series, we compare the structural properties and dark matter (DM) content of early-type galaxies from the CAMELS IllustrisTNG simulations to three observational datasets (SPIDER, $\textrm{ATLAS}^{\textrm{3D}}$, and MaNGA DynPop), to constrain cosmological and astrophysical feedback parameters, contrasting these results with those obtained for late-type galaxies. We analyze the size-, internal DM fraction-, and DM mass-stellar mass relations, identifying the best-fit simulation for each dataset. For SPIDER, we find cosmological parameter values consistent with literature and results obtained from the comparison between simulations and late-type galaxies, with supernova feedback parameters differing from results derived for late-type galaxies. For $\textrm{ATLAS}^{\textrm{3D}}$, cosmological parameter results align with SPIDER, while supernova feedback parameters are more consistent with late-type galaxies results. MaNGA DynPop yields extreme cosmological parameter values but similar supernova feedback results to $\textrm{ATLAS}^{\textrm{3D}}$. However, no single simulation matches the full range of observational trends, especially when combining early- and late-type galaxies from MaNGA DynPop. These findings highlight the limitations of simulations in reproducing diverse galaxy properties, underscoring the challenge of capturing the complexity of galaxy formation across all types.

Figures

Figures reproduced from arXiv: 2412.00217 by the authors.

Figure 1
Figure 1. Comparison between SPIDER (grey region), ATLAS3D (red region), MaNGA DynPop (orange region) and the theoretical Mtot-M∗ relation from Moster et al. (2013) (pink region) with IllustrisTNG simulations having differing astrophysical feedback parameters. Each row in the plot corresponds to a different scaling relation, from top to bottom: stellar half-mass radius, R∗,1/2, DM fraction within R∗,1/2, fDM(< R∗,1/2), DM mas… view at source ↗
Figure 2
Figure 2. Same as [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Top row: Number of ETGs as a function of the cosmological/astrophysical parameters for each camels IllustrisTNG simulation. Bottom row: Same as top row, but for LTGs. Each column is associated to a different parameter. For each plot, for a fixed value of one of the parameters, the trends with respect to the other parameters are the same as those shown in the other panels. Parameters AAGN1 and AAGN2 are not shown for… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: shows the comparison between the simulated ETGs from the fiducial IllustrisTNG camels simulation, ‘1P_1_0’, and the observational trends. Quantitatively, the cumulative reduced chi-squared, ˜χ 2 , is χ˜ 2 = 4.67 with respect to SPIDER, ˜χ 2 = 7.65 with respect to ATLAS…
Figure 5
Figure 5. Figure 5: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Each column of this figure is the same as [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: From top to bottom: stellar half-mass radius, DM fraction within the stellar half-mass radius and DM mass within the stellar half-mass radius as a function of stellar mass, for fiducial CAMELS simulation (first column), TNG-300 (second column), TNG-100 (third column) a…

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Reviewed August 12, 2026 · model on record in the stance chip above.