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Tuning seven simulation dials reproduces low gas in galaxy clusters

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2026-08-02 06:02 UTC pith:YMQHOW7H

load-bearing objection Solid calibration study with an overstrong punchline: MTNG can be pushed toward low eROSITA gas fractions by retuning seven subgrid parameters, but the confirmatory simulation is never compared to the data, so the 'cannot be ruled out' claim needs direct support. the 3 major comments →

arxiv 2607.13151 v1 pith:YMQHOW7H submitted 2026-07-14 astro-ph.GA astro-ph.CO

Evaluating the flexibility of the MillenniumTNG galaxy formation model with multi-zoom re-simulations

Francisco Maion , Raul E. Angulo , Volker Springel , Shy Genel , Greg L. Bryan This is my paper
classification astro-ph.GA astro-ph.CO
keywords galaxy formationcosmological simulationssubgrid physicsAGN feedbackgalaxy stellar mass functiongas fractions in clustersGaussian process emulationmulti-zoom simulations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper asks whether the MillenniumTNG galaxy formation model can be made consistent with recent X-ray observations of very low gas fractions in galaxy groups and clusters, without adding new physical mechanisms. Using a new multi-zoom simulation technique, the authors vary seven subgrid parameters controlling star formation, stellar winds, and AGN feedback, and build Gaussian-process emulators of the galaxy stellar mass function and the gas fractions. They find a parameter combination—weaker stellar feedback and stronger, rarer kinetic AGN feedback—that fits both datasets, and they verify it with a direct simulation. The paper concludes that the model is flexible enough that these observations do not rule it out.

Core claim

The central claim is that the MillenniumTNG model 'cannot be ruled out at high significance' by current measurements of the galaxy stellar mass function and cluster gas fractions. A point in its seven-dimensional subgrid parameter space produces a qualitatively good simultaneous fit, with the key changes being less energetic stellar winds and more energetic, rarer kinetic AGN feedback events. This best-fit point is confirmed by a dedicated re-simulation, and the residual disagreement at the most massive clusters is substantially reduced, though not eliminated.

What carries the argument

Gaussian-process emulators trained on 31 multi-zoom re-simulations drawn from a Latin-hypercube design over a seven-dimensional subspace of the MillenniumTNG subgrid model. The emulators interpolate the galaxy stellar mass function and gas fractions across parameter space with roughly 0.1 dex and 10% precision, allowing an MCMC search for a parameter set consistent with both datasets; the multi-zoom technique is what makes 31 variations affordable, at about forty times lower cost than combined individual zoom-in runs.

Load-bearing premise

The claimed consistency with low gas fractions holds only if the seven varied parameters span a range wide enough to represent what the model can actually do; the best-fit values pressing against the prior edges suggest this may not be fully guaranteed.

What would settle it

Run the best-fit parameter set at a higher mass resolution than MillenniumTNG's native resolution. The paper itself shows that the galaxy stellar mass function shifts with resolution; if the simultaneous fit to the stellar mass function and the low gas fractions degrades beyond the emulator's stated errors, the claim that the model cannot be ruled out would not survive.

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

If this is right

  • The MillenniumTNG model does not require new physical mechanisms such as AGN jets or hot-gas bubbles to approach the low gas fractions reported by recent X-ray surveys; recalibrating existing parameters suffices.
  • The best-fit regime—weaker stellar feedback with stronger, rarer kinetic AGN feedback—offers a concrete target for interpreting strong-feedback scenarios in galaxy formation.
  • The most massive clusters (M500,c above about 10^14 solar masses) remain in tension, so the model still cannot match the most extreme low-gas measurements; the gap is reduced but not closed.
  • The multi-zoom technique makes systematic parameter exploration of large-volume simulations economically feasible, well below the cost of uniform boxes or individual zoom suites.
  • The explicit validation run at the best-fit point confirms that the emulator-based conclusion reflects a real property of the simulation model, not an interpolation artifact.

Where Pith is reading between the lines

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

  • If the roughly 28 unvaried subgrid parameters were also allowed to vary, the fit would likely improve further, but the model's 'flexibility' would become harder to distinguish from an ability to accommodate nearly any observation; the total of about 35 adjustable parameters is worth remembering when interpreting flexibility claims.
  • The paper notes the galaxy stellar mass function shifts with mass resolution; a higher-resolution counterpart of the best-fit simulation would test whether the recalibrated parameters are genuinely viable or an artifact of MillenniumTNG's resolution.
  • The same emulator-plus-validation pipeline could map which regions of parameter space remain consistent with other strong-feedback observations, such as thermal Sunyaev-Zeldovich profiles or dispersion measures, turning flexibility into a quantifiable property of different simulation codes.
  • The fact that two best-fit parameters press against the prior bounds hints that the true flexibility of the model may lie partly outside the probed ranges; extending the priors on the kinetic AGN efficiency and burstiness axes would clarify the robustness of the found solution.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces a suite of multi-zoom re-simulations of halos selected from MillenniumTNG (MTNG), running 31 variations of seven subgrid parameters controlling stellar feedback, star formation, and AGN feedback. The authors validate a reweighting procedure for reconstructing population statistics from the selected halos, measure the GSMF and gas fractions, and train Gaussian-process emulators for these quantities. The emulators are then fit to recent measurements of the GSMF and to two gas-fraction datasets: the K23 compilation ("high" gas fractions) and the P26 eROSITA measurements ("low" gas fractions). A joint best-fit parameter combination is found for the GSMF + P26 data, and the authors run one additional simulation at that best-fit point to validate the emulator prediction. They conclude that the MTNG/TNG model is flexible enough to provide plausible simultaneous fits to the GSMF and gas fractions and therefore cannot be ruled out by these data at high significance, although the fit still leaves tension at the highest cluster masses.

Significance. If substantiated, this is a valuable result. The multi-zoom technique appears to reduce computational cost by roughly an order of magnitude relative to individual zooms, and the reweighting validation on 100 random halo selections is a solid, constructive test. The GP emulators are cross-validated, and the paper includes an explicit direct simulation at the best-fit parameter point, which is a strong and commendable check that the emulator is not extrapolating nonsense. The astrophysical conclusion would also be significant: it would suggest that the MTNG model does not necessarily require additional physical mechanisms such as jets or bubbles to approach the low eROSITA gas fractions, and that recalibrating seven subgrid parameters substantially reduces the tension. However, the central statistical claim is currently stronger than what the validation supports, because the direct simulation is compared to the emulator, not to the observations.

major comments (3)
  1. [§6.3, Fig. 15; §7] The direct 'Joint Best-Fit Run' in Fig. 15 is compared only against the GP emulator prediction, not against the P26 gas-fraction or GSMF data points. The calibration likelihood (Eqs. 32–34) includes the GP predictive covariance K_pred(θ) as part of the model uncertainty, so the object that maximizes the likelihood is the emulator-smoothed prediction. The statement in §7 that the model 'cannot be ruled out at high significance' therefore requires a goodness-of-fit test of the actual simulated quantities against the observations, using the reconstruction covariance (Section 2.4) plus observational errors, without K_pred. Such a chi-squared/p-value is not provided and is needed to support the central claim.
  2. [Table 2 vs Table 4] The best-fit value A_AGN,1 = 1.06 exceeds the stated prior maximum of 1.0, and f_re = 35.8 lies within 10% of its maximum 40. The authors acknowledge in §6.2.2 that these parameters are pushed to the edge of their prior. Combined with the fact that only 7 of the ~35 model parameters are varied (as noted in §7), this weakens the generalization that the MTNG model 'cannot be ruled out.' The fit may exist only at or beyond the probed boundary, and other unvaried parameters could change the flexibility. A sensitivity test extending the A_AGN,1 range, or a more cautious phrasing of the conclusion, is required.
  3. [§6.2.2, Fig. 14] The emulator at the joint MLP provides a good fit to P26 below M500,c ~ 10^13.5 Msun, but the authors themselves state that the model cannot produce similarly low gas fractions for M500,c ≳ 10^14 Msun. The phrase 'significantly reducing the tension' is not quantified, and the central claim is only qualitative ('qualitatively good fit'). Because 'cannot be ruled out at high significance' is a statistical statement, the paper needs a quantitative measure of the residual discrepancy, not just a visual comparison.
minor comments (5)
  1. [§6.3] Typo: 'GSFM' should be 'GSMF' in the sentence 'while maintaining consistency with the GSFM.'
  2. [Table A1] The column headers for the simulation parameters are difficult to parse; consider adding a separate header row with units and the parameter symbols used in Table 2.
  3. [Section 2.4, Eq. (20)] The set S500,c is used before it is defined; reorder the definition for clarity.
  4. [Eq. (13) and Table 2] The parameter f_re is called a 'reorientation parameter' in Eq. (13) but is described as 'burstiness/reorientation' in Table 2. A short precise definition of what f_re controls in the implementation would help.
  5. [Abstract / Fig. 8] The abstract claims emulator precision of ~0.1 dex and ~10%, but the cross-validation section does not quote these numbers explicitly. Adding a quantitative statement in Section 5.2 would make the claim easier to verify.

Circularity Check

0 steps flagged

No significant circularity: the paper performs an explicit calibration fit, not a prediction, and its central flexibility claim rests on a genuinely independent validation simulation.

full rationale

The derivation chain is self-contained. The paper runs 31 multi-zoom simulations at sampled subgrid parameters, measures the GSMF and gas fractions, trains GP emulators on those simulation outputs, cross-validates them against held-out simulations (Figure 8), and then performs an explicit MCMC calibration of the seven varied parameters to external observational data (Eqs. 32-34). The 'good fit' is the product of a fitting procedure, not a disguised first-principles prediction; the abstract and Section 6 consistently say 'fit' and 'calibrate.' The central flexibility claim is therefore not circular: it is an existence statement obtained by optimizing a likelihood. The explicit validation run at the best-fit point is a genuinely new simulation, independent of the GP training set, and its agreement with the GP prediction (Figure 15) checks the emulator interpolation. The paper itself flags the remaining caveat that it has not examined many other observables, and the statistical support for 'cannot be ruled out' would be stronger if the direct simulation were compared to the observations rather than only to the emulator; that is a completeness/statistical-support limitation, not a circular reduction. The only overlapping-author citation (Genel et al. 2026 for the count of 35 TNG parameters) is ancillary and not load-bearing for the fitted result, so it does not raise the circularity score.

Axiom & Free-Parameter Ledger

9 free parameters · 6 axioms · 0 invented entities

The central claim depends on 7 subgrid parameters plus 2 nuisance parameters being sufficient to span the model's response to the two observables. The paper itself flags that ~35 parameters exist, that the best-fit presses against the prior boundaries, and that other observables (clustering, kSZ, power spectrum) have not been checked. These are the main unresolved burdens.

free parameters (9)
  • e_w (wind energy) = 1.41 (fiducial 3.6, range 0.9-14.4)
    Stellar wind energy per SNII; fitted to GSMF and gas fractions, drives much of the low-mass GSMF boost and gas-fraction suppression.
  • kappa_w (wind velocity) = 10.0 (fiducial 7.4, range 3.7-14.8)
    Controls wind mass-loading and speed; fitted.
  • rho_rec (recoupling density) = 0.13 (fiducial 0.05)
    Fraction of SF threshold at which wind particles recouple; fitted; units inconsistent between Tables 2, A1, and 4 (linear vs log).
  • t0_SFR (star-formation timescale) = 2.2 Gyr (fiducial 2.27, range 1.135-4.54)
    Timescale for star formation; fitted.
  • A_AGN,1 (kinetic AGN efficiency) = 1.06 (fiducial 0.1, stated max 1)
    Efficiency of low-accretion-mode AGN kinetic feedback; best-fit value exceeds the stated maximum in Table 2, indicating the true optimum may be beyond the prior.
  • eps_f,high (quasar-mode thermal coupling) = 0.081 (fiducial 0.1, range 0.05-0.2)
    Thermal coupling efficiency in high-accretion mode; fitted; nearly no effect on gas fractions due to self-regulation.
  • f_re (AGN reorientation/burstiness) = 35.8 (fiducial 20, range 10-40)
    Controls burstiness of kinetic AGN events; best-fit near maximum; drives rare, energetic, anisotropic feedback events.
  • b_* (stellar mass bias) = marginalized with prior log10 b_* ~ N(0,0.14)
    Nuisance parameter for systematic stellar-mass biases in the GSMF; fitted with a Gaussian prior in the likelihood.
  • b_cv (cosmic variance amplitude) = marginalized with prior b_cv ~ N(1,0.06)
    Nuisance parameter for cosmic variance in GAMA GSMF amplitude; fitted with a Gaussian prior.
axioms (6)
  • domain assumption The IllustrisTNG/MTNG subgrid model (star formation, SNII winds, AGN kinetic and thermal feedback) is an adequate representation of galaxy formation for this purpose.
    Assumed throughout; based on Vogelsberger+14, Pillepich+17, Weinberger+17, Pakmor+23. If the subgrid form is wrong, the fitted parameters have no physical meaning.
  • domain assumption The multi-zoom N-GENIC initial conditions correctly reproduce the large-scale tidal field and the evolution of high-resolution regions.
    Section 2.1; relies on Burger et al. (2025). Contamination analysis (Section 2.5) provides partial validation.
  • domain assumption A Gaussian process with RBF kernel and MLE hyperparameters faithfully interpolates the 7-dimensional simulation response surface.
    Section 5.1-5.2; cross-validation shows consistency, but 31 training points in 7D is sparse and the GP could still be biased in unsampled regions.
  • standard math The reweighting approximation that conditional GSMF and gas fractions are constant within each fine halo-mass bin is accurate.
    Section 2.4, Eq. 16-20; tested with 100 random selections; gas-fraction reconstruction is biased high by ~20% at 2-sigma below M500c~1e13.5, but the authors argue this is within emulator uncertainty.
  • domain assumption The observational systematics models adopted (HSE bias correction from K23, Eddington bias sigma=min(0.070+0.071z,0.3), stellar-mass-bias prior, cosmic-variance prior) are correct and complete.
    Section 4; these are taken from the literature (Kugel+23, Behroozi+19, Driver+22) without independent verification here.
  • standard math Planck 2016 cosmological parameters are fixed and correct.
    Table 1; standard assumption, not varied.

pith-pipeline@v1.3.0-alltime-deepseek · 33135 in / 14180 out tokens · 125803 ms · 2026-08-02T06:02:42.770481+00:00 · methodology

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read the original abstract

In this study we introduce a new simulation campaign designed to understand how parameters that control star-formation and AGN feedback processes in cosmological hydrodynamical simulations impact observables such as the galaxy stellar-mass function (GSMF) and the gas fractions in large dark matter halos. These simulations are zoom-ins to halos selected from the MillenniumTNG (MTNG) simulation, and are run employing a novel multi-zoom approach which simultaneously re-simulates several sub-regions of a given large volume at a higher resolution than the background, thus reducing computational cost and imbalances in parallelization. We measure the GSMF and gas-fractions in halos for each of the re-simulations, and train Gaussian-process emulators on these quantities. The resulting emulators predict the GSMF and gas-fractions in halos with $\sim0.1\,\mathrm{dex}$ and $\sim 10\%$ precision respectively. Using the emulators we can simultaneously fit recent measurements of both quantities, in particular the lower gas fractions now observed even for comparatively massive clusters. Interestingly, we find a combination of parameters of the MTNG galaxy formation model that provides a qualitatively good fit to both the measured GSMF and gas fractions. This combination of parameters differs from the fiducial one mainly by requiring that stellar-feedback is significantly less energetic, and that kinetic AGN feedback events are significantly more energetic and rare. This finding implies that the MTNG model can be consistent with scenarios of strong feedback that remove large amounts of gas from groups and clusters, albeit we caution that we have not extensively examined the effect of these new parameters on many quantities for which MTNG made successful predictions.

Figures

Figures reproduced from arXiv: 2607.13151 by Francisco Maion, Greg L. Bryan, Raul E. Angulo, Shy Genel, Volker Springel.

Figure 1
Figure 1. Figure 1: Illustration of our simulation suite, depicting both a general view of how the zoomed-in halos are distributed in space, and a more detailed view of one individual halo, and how its stellar and gas densities vary with the feedback changes. Left Panel: Projected distribution of dark-matter density in a slab of width Δ𝑧 = 250 ℎ −1Mpc, in which the density of low-resolution particles is shown in a color-schem… view at source ↗
Figure 2
Figure 2. Figure 2: shows the GSMF of the original MTNG simulation, represented by a black solid line, compared to the ensemble mean of the GSMFs computed from different random selections of halos as a dashed black line. To compute the gray-shaded area shown in that figure we combine the standard deviation computed from the 100 samples with an additional 7% variability that is present in the observational estimates of the GSM… view at source ↗
Figure 4
Figure 4. Figure 4: Histogram of the contamination fraction of all halos simulated across the entire suite. Most of the halos have zero contamination fraction, and we add a constant offset of 10−6 to visualize them in the logarithmically￾scaled plot. The majority of halos, roughly 91%, have less than 1% of the mass in low-resolution particles. 10 10 10 11 10 12 10 13 10 14 10 15 Total Halo Mass [M /h] 10 7 10 6 10 5 10 4 10 3… view at source ↗
Figure 5
Figure 5. Figure 5: Scatter plot showing the contamination fraction as a function of total halo mass for all the selected halos across all physics variations. Most of the halos have zero contamination fraction, therefore we add a constant offset of 10−6 in order to visualize this population in the logarithmically-scaled plot. The blue line gives the median mass of low-resolution particles divided by halo mass, showing that th… view at source ↗
Figure 6
Figure 6. Figure 6: Top Panel: Computational time measured in CPU hours as a func￾tion of the total number of high-resolution particles. Red points show the computational expense obtained when using the multi-zooms algorithm to simulate a number of regions, while the blue points show the computational expense of running the same regions with individual zooms. We have re￾moved the cost of the repeated background in the individ… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison between GSMFs measured under several varying as￾sumptions by Bernardi et al. (2018), represented by colored lines, to the GAMA measurements (Driver et al. 2022), represented by blue diamonds. In the top panel we also show the mean of the four colored curves as a black line with error-bars corresponding to the spread of these lines. The bottom panel shows the difference between the mean GSMF and … view at source ↗
Figure 8
Figure 8. Figure 8: Generalization error of the GP emulators trained in a cross￾validation exercise. We have divided our 31 simulations into 10 sets, and then iterated over them, leaving one out at a time and training on the re￾maining 9, to then evaluate the prediction error with respect to the left-out simulations. Gray lines show the mean prediction compared to the true sim￾ulation, and the blue shaded region shows the 1𝜎 … view at source ↗
Figure 9
Figure 9. Figure 9: Changes to the GSMF caused by varying each of the 7 chosen subgrid parameters individually around their fiducial value, while maintaining the rest fixed. Denoting by 𝜃 the parameter being varied, we define Δ = 1 4 (max( 𝜃 ) − min( 𝜃 ) ), except for the parameters that are varied logarithmically, and for these our procedure is exactly analogous but in log-space. emulator at four values of the parameter that… view at source ↗
Figure 10
Figure 10. Figure 10: Changes to the gas-fraction in halos caused by varying each of the 7 chosen parameters individually around their fiducial value, while keeping the rest fixed. Denoting by 𝜃 the parameter being varied, we define Δ = 1 4 (max( 𝜃 ) − min( 𝜃 ) ), except for the parameters that are varied logarithmically, and for these our procedure is exactly analogous but in log-space. halos. We show the ratio between the ga… view at source ↗
Figure 11
Figure 11. Figure 11: Contours obtained from fitting our emulators to the GSMF in blue and to the gas-fractions data compiled in red. Stars indicate the MLP for each of the contours and gray dashed lines indicate the values of the fiducial parameters used in the MTNG. Left Panel: Contours obtained by fitting to the gas-fractions compilation of K23. Right Panel: Contours obtained by fitting to the gas-fractions of P26. of their… view at source ↗
Figure 12
Figure 12. Figure 12: Emulators for six summary statistics evaluated at the MLP for the fits to the GSMF (blue), to the gas-fractions compiled by K23 (red), and to a joint fit of both quantities (teal). Gray lines show the emulator evaluated at the fiducial parameters. Top Left: GSMF as a function of the galaxy stellar-mass contained in a 30 kpc radius around the center. Top Center: Black-hole mass-function. Top Right: Stellar… view at source ↗
Figure 13
Figure 13. Figure 13: Contours obtained from jointly fitting measurements of the GSMF and gas-fractions in halos. The small and large shaded regions denote the regions containing respectively 16% and 84% of the total marginalized probability in that 2-dimensional space. Stars indicate the MLP in each subspace, and the gray dashed lines indicate the fiducial parameters employed in MTNG. Left Panel: Contours obtained using the c… view at source ↗
Figure 14
Figure 14. Figure 14: Emulators for six summary statistics evaluated at the MLP for the fits to the GSMF (blue), to the gas-fractions measured by P26 (red), and to a joint fit of both quantities (teal). Gray lines show the emulator evaluated at the fiducial parameters. Top Left: GSMF as a function of the galaxy stellar-mass contained in a 30 kpc radius around the center. Top Center: Black-hole mass-function. Top Right: Stellar… view at source ↗
Figure 15
Figure 15. Figure 15: Comparison between results obtained from the direct simulation of our collection of halos at the MLP obtained via the joint fit to the GSMF and the low gas-fractions of P26 (black dashed lines), and the predictions obtained at that same point from the GP emulator (teal lines). The results are compatible within the GP emulator uncertainty regions. Bigwood L., Bourne M. A., Iršič V., Amon A., Sijacki D., 20… view at source ↗

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