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REVIEW 4 major objections 5 minor 286 references

This paper contends that current AGN feedback models fail because they cannot simultaneously reproduce the observed hot-gas content of halos and the observed distribution of galaxies across the SFR–stellar mass plane.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 08:19 UTC pith:ZUBQWARY

load-bearing objection A genuinely useful three-simulation comparison showing a real gas-galaxy trade-off, but the instantaneous-vs-averaged SFR mismatch could shrink the overquenching gap. the 4 major comments →

arxiv 2607.21140 v1 pith:ZUBQWARY submitted 2026-07-23 astro-ph.GA

What's Missing in AGN Feedback? Lessons learnt from Magneticum, IllustrisTNG and Simba

D. T. Mazengo , P. Popesso , I. Marini , L. M. Valenzuela , N. de Is\'idio , V. Toptun , Mirjana Povi\'c , Petri V\"ais\"anen
show 15 more authors
J. M. Sunzu R.-S. Remus V. Biffi K. Dolag R. Dav\'e A. Fraser-McKelvie A. Dev S. Vladutescu-Zopp A. Merloni G. Ponti C. Aydar S. Shreeram L. A. Kahinga J. O. Chibueze P. Privatus
This is my paper
classification astro-ph.GA
keywords AGN feedbackgalaxy quenchinghot gas fraction–halo mass relationcosmological hydrodynamical simulationsMaNGAeROSITASFR–stellar mass planesubgrid feedback prescriptions
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 tries to establish that current AGN feedback in cosmological simulations is structurally incomplete: no single model can simultaneously match the observed hot-gas content of dark-matter halos and the observed distribution of galaxies in the star-formation-rate–stellar-mass plane. Using eROSITA and Sunyaev-Zel'dovich constraints on the hot gas fraction–halo mass relation and MaNGA as the galaxy benchmark, it shows that feedback strong enough to deplete group-scale gas also quenches too many galaxies, while weaker feedback preserves galaxies but retains too much halo gas. The paper concludes that what is missing is not just the amount of injected energy but when, where, and how that energy couples to surrounding gas. A sympathetic reader would care because this identifies a specific, testable failure mode in all three major simulation paradigms and sets up a direct test of feedback-strength variants in a companion study.

Core claim

The central claim is the existence of a trade-off between matching hot-halo gas and matching galaxy demographics: Magneticum and Simba, which reproduce the observed fgas–Mh relation, overproduce quenched galaxies and distort the SFR–M* plane (Magneticum red-sequence fractions exceed 93% at M* > 10^11 solar masses), while IllustrisTNG, which matches the star-forming main sequence and quenched fractions, systematically overpredicts hot gas masses in massive groups and poor clusters. The paper also finds that in MaNGA, AGN accretion rate varies vertically across the SFR axis—high-accretion AGN in star-forming hosts, low-accretion radio AGN in quenched hosts—whereas all three simulations tie acc

What carries the argument

The observational anchor is the hot-gas mass fraction–halo mass relation (fgas–Mh) from eROSITA X-ray stacking and SZ measurements, which quantifies how depleted group-scale halos are of baryons; the galaxy-side anchor is the SFR–M* plane, classified by offset from the Popesso et al. main sequence into starburst, main-sequence, green-valley, and red-sequence loci. The three simulations—Magneticum, Simba, and IllustrisTNG—serve as a controlled comparison of AGN feedback implementations: highly efficient thermal feedback, jet/X-ray feedback, and dual-mode kinetic/thermal feedback, respectively. Their differing success on gas versus galaxy observables is the mechanism that isolates the missing

Load-bearing premise

The comparison treats MaNGA's SSP-averaged star-formation rates, which have a detection floor, as directly comparable to the instantaneous SFRs recorded in the simulations; the paper itself says the observed 'quiescent peak' can be attributed to systematic uncertainties in SFR diagnostics (Sect. 3.2, App. B).

What would settle it

Forward-model the three simulations into synthetic MaNGA-like observations (same SSP fits, same SFR pipeline, same detection floor) and re-measure quiescent fractions; if the floor inflates the observed quiescent peak, the Magneticum/Simba overquenching gap shrinks and the inferred required feedback strength drops.

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

If this is right

  • Calibrating AGN feedback to the eROSITA/SZ fgas–Mh relation drives Magneticum and Simba to quench more than 90% of massive galaxies, so hot-gas constraints alone cannot set feedback parameters.
  • Calibrating to galaxy demographics alone (as in IllustrisTNG) leaves too much hot gas in groups and poor clusters, so galaxy statistics alone are also insufficient.
  • No current simulation reproduces the observed vertical gradient in AGN accretion along the SFR axis; simulations tie accretion to stellar mass, not to cold gas supply.
  • The ranking of the simulations by quenching strength is stable under different galaxy-classification schemes and across resolution/volume choices (Magneticum Box2 vs Box4; TNG100 vs TNG300).
  • A self-consistent feedback model must regulate star formation and halo gas thermodynamics at the same time; the tension points to timing, location, and coupling of feedback energy rather than total energy alone.

Where Pith is reading between the lines

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

  • Beyond the paper: if MaNGA's quiescent peak is largely an SFR-diagnostic floor, the observed-vs-simulated gap could narrow; this is testable by running the same measurement pipeline on mock data cubes from each simulation.
  • Beyond the paper: the paper's own logic implies that the next generation of subgrid models should decouple feedback strength from feedback timing and location—for example, delayed or spatially offset energy injection may expel group-scale gas without dragging galaxies off the main sequence.
  • Beyond the paper: the observed vertical AGN accretion gradient could be reproduced in simulations only if black hole growth responds to local cold-gas availability rather than host stellar mass; a direct test is to compare accretion-rate scatter at fixed stellar mass against halo gas content.

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

4 major / 5 minor

Summary. This paper compares the SFR–M* plane from MaNGA (N=6709) with Magneticum Box2/Box4, IllustrisTNG100, and SIMBA, and combines this with the hot gas fraction–halo mass relation inferred from eROSITA/SZ data. It reports a trade-off: Magneticum and SIMBA reproduce the low f_gas values at group scales but overproduce quiescent galaxies, with red-sequence fractions as high as 93–95% in Table A.2, whereas TNG100 matches the MaNGA galaxy demographics better but retains too much hot gas in groups and clusters. The authors conclude that current AGN feedback implementations are incomplete not only in total energy but also in the timing, location, and coupling of the energy injection, and they announce a companion paper using FLAMINGO to test this trade-off.

Significance. The claimed trade-off is a timely and potentially important result for the development of subgrid feedback models in cosmological simulations. The paper's strengths are its use of three public simulation suites, a representative IFU sample, an explicit robustness test against a fixed sSFR classification (App. A.3.1), and a resolution check via Magneticum Box2/Box4. If established, the work sharpens the practical targets for feedback prescriptions. However, the quantitative overquenching claims rest on a likely mismatch between instantaneous simulated SFRs and MaNGA's effective SFR timescale, and the adopted f_gas benchmark is largely from the same team as several coauthors. Both issues need to be addressed before the central conclusion can be regarded as fully supported.

major comments (4)
  1. [§2.1, §2.2, Table A.2] The central ranking of overquenching compares Pipe3D SFRs that are SSP-averaged over 10, 32, and 100 Myr with simulation catalogue SFRs described as instantaneous. A galaxy whose star formation ceased 50 Myr ago has SFR=0 in the simulation and is assigned the artificial Gaussian at Δlog(SFR)=−3, placing it in the red sequence, whereas MaNGA's averaged SFR would place it above the RS boundary. The fixed-sSFR robustness test in App. A.3.1 does not address this because it uses the same observed SFRs. The numbers in Table A.2 (e.g., 93.46% versus 55.11% for M*>10^11 M_sun) cannot support the overquenching claim until the simulations are forward-modeled to the MaNGA SFR definition, or the induced shift in MS/GV/RS fractions is otherwise quantified.
  2. [§3.2, App. B] The manuscript states that the MaNGA quiescent peak at Δlog(SFR)≈−1.5 'likely reflects a physical or instrumental floor' and that the observed quiescent peak 'primarily reflects methodological limitations rather than ongoing star formation,' yet these galaxies are nevertheless counted as RS. At the same time, simulated SFR=0 galaxies are forced into the quiescent region via the artificial Gaussian. These choices can bias observed and simulated quenched fractions in different directions, and the net effect is not estimated. A test that separates the MaNGA SFR measurement floor from true quiescence (e.g., using SFR_Halpha or D_n4000 as an independent classifier) is needed before claiming that Magneticum and SIMBA overquench by the reported factors.
  3. [§2.1.1, App. A.1] The conclusion that Magneticum and SIMBA reproduce the observed f_gas–M_h relation while TNG100 does not is adopted from Popesso et al. (2024, 2026) and Siegel et al. (2026); the first two references are from the same research group as several coauthors of this paper, and Magneticum team members are also coauthors. This is not automatically circular, but it makes the central trade-off rest on a same-team benchmark. Please add an independent comparison or a quantitative sensitivity analysis (e.g., hydrostatic bias, SZ mass calibration, or alternative observational gas-mass estimates) so that the simulation ranking is not tied to a single group's reduction.
  4. [Table A.2, App. A.3] Population fractions are reported as point values without propagated uncertainties or cosmic-variance estimates, while sample sizes vary from N=166 (Box4) to N=400080 (Box2). Some differences are very large, but claims such as the 10–20% sensitivity of quenched fractions to the classification scheme, and the ranking of simulations, need bootstrap or jackknife errors. Without these, it is difficult to assess, for example, whether the 61.45% versus 44.50% RS fractions in the highest-mass bin represent a meaningful difference between Magneticum Box4 and TNG100.
minor comments (5)
  1. [Throughout] Typographical errors: 'Guassian' in §3.1, 'accreation' in §6, 'conclusively highlighting' in §1, and 'This test provides a direct test' in §5.2.
  2. [§2.1.1, Table A.1] The text says the final multi-wavelength catalogue contains 386 unique AGN, but Table A.1 lists 77+172+87=336 unique AGN after deduplication. Please reconcile the count.
  3. [App. A.2] Equation A.2 has incorrect units: log(SFR/M_sun s−1) should be log(SFR/M_sun yr−1).
  4. [Table A.2] The header 'N Total' is ambiguous; it presumably gives the number of galaxies in each stellar-mass bin. Consider renaming and adding a column with statistical uncertainties.
  5. [Fig. 3 caption] The comment that the row order 'reflects decreasing agreement with the observed f_gas–M_h relation' is a visual ordering choice based on a specific benchmark. If this ordering is intended to be substantive, provide the quantitative ranking with uncertainties; otherwise label it as illustrative.

Circularity Check

0 steps flagged

No construction-level circularity; only minor same-author citations for the observational f_gas benchmark.

full rationale

The paper's derivation is an empirical comparison, not a fitted prediction. The central claim that no simulation simultaneously reproduces hot-halo gas fractions and galaxy demographics rests on two comparisons: (1) observed vs. simulated f_gas–M_h, and (2) MaNGA vs. simulated SFR–M_star demographics. The f_gas benchmark (Popesso et al. 2026) is same-author but is an observational eROSITA/SZ stacking measurement, and the paper also cites the independent Siegel et al. (2026) kSZ constraints; the simulated gas fractions come from the simulation suites themselves, not from a fit in this paper. The same-author citation is therefore not load-bearing in a circular sense. The SFR–M_star comparison uses the Po19 main-sequence parameterization from the same group, but it is applied symmetrically to observations and simulations, and the robustness test with a fixed sSFR threshold (App. A.3.1) reproduces the same simulation ranking, so the ranking is not an artifact of that choice. The paper itself flags the main potential confound: MaNGA SFRs are SSP-averaged while simulation SFRs are instantaneous, and the observed 'quiescent peak' 'can be attributed to systematic uncertainties in SFR diagnostics' (Sect. 3.2); Appendix B further calls the quenched/star-forming distinction 'inherently ambiguous and arbitrary.' These are validity/robustness concerns about the comparison, not circularity: the claimed trade-off does not reduce by construction to any fitted parameter or to a self-citation chain. Score 2 reflects only the presence of repeated same-author citations (Popesso et al. 2024, 2026; Dolag et al. 2025) for the f_gas data and ranking, without treating them as circular evidence.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities. However, several choices - the fitted SFR-L144 relation, the artificial distribution for unresolved SFR, and the Delta-log(SFR) boundaries - function as hand-set inputs that affect population fractions. The dominant domain assumptions are that the eROSITA/SZ gas-fraction benchmark is correct and that observed and simulated SFRs are commensurable.

free parameters (3)
  • SFR-L144 relation (slope, intercept) = slope=1.105+/-0.026; intercept=21.985+/-0.044 (log units)
    Fit to the MaNGA+LoTSS cross-match (Eq. A.2) and used to select radio AGN as >3sigma excess; this selection underlies the observed AGN demographics in Sect. 4.1.
  • Artificial Gaussian for simulated SFR=0 galaxies = peak at Delta-log(SFR) = -3 dex, dispersion 0.3 dex
    Introduced in Sect. 3.1/App. B for visualization and population counts; the text states the intrinsic distribution is physically unconstrained, so this choice directly shapes the quenched tail in Fig. 3.
  • Delta-log(SFR) population boundaries = SB > +0.6, MS -0.3 to +0.6, GV -1.1 to -0.3, RS < -1.1
    Chosen from Bluck et al. (2020) in Sect. 3.1. Robustness to a fixed sSFR cut is shown, so the impact is limited, but the exact fractions in Table A.2 depend on these boundaries.
axioms (4)
  • domain assumption The adopted fgas-Mh relations (Popesso et al. 2026; Siegel et al. 2026) correctly measure hot gas fractions in halos.
    Used to rank Magneticum/SIMBA/TNG (Appendix A.1). Popesso et al. 2026 overlaps with this paper's authors; if the relation is biased, the central tension is misstated.
  • domain assumption MaNGA SFRs from SSP fits are directly comparable to logged instantaneous SFRs from simulations after IMF rescaling.
    Underlies every SFR-M* comparison (Sect. 2.1, 3.2). The paper itself notes the observed quiescent peak may be a diagnostic floor, so comparability is not fully established.
  • domain assumption Differences in subgrid AGN feedback prescriptions dominate the residual differences between simulations and observations, over resolution, volume, and cosmology effects.
    The conclusion that feedback is incomplete in timing/location/coupling (Sect. 6) assumes the compared codes differ chiefly in feedback and that Box2/Box4 agreement rules out numerical effects.
  • domain assumption The Yang et al. (2007) group catalogue and Behroozi SHMR give reliable halo masses for MaNGA centrals.
    Used for environment and SHMR analysis (Sect. 2.1.2, 5.2). Misclassification or halo mass errors would alter the environmental comparisons.

pith-pipeline@v1.3.0-alltime-deepseek · 23913 in / 12946 out tokens · 126381 ms · 2026-08-01T08:19:47.851503+00:00 · methodology

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read the original abstract

Accurately balancing gas reservoirs, star formation, and feedback across cosmic time remains a central challenge for galaxy formation models in modern hydrodynamical simulations. While different feedback prescriptions reproduce selected local galaxy properties with varying success, the most pronounced discrepancies emerge in predictions for the hot gas content of dark matter halos. We examine three state-of-the-art cosmological simulations: Magneticum, IllustrisTNG, and SIMBA, which struggle to simultaneously reproduce observed galaxy and halo gas properties in the local Universe. We confront their predictions with spatially resolved galaxy data from MaNGA and recent constraints on the hot gas mass fraction-halo mass (fgas-Mh) relation from eROSITA and Sunyaev-Zel'dovich (SZ) measurements. Reproducing the observed fgas-Mh relation requires strong active galactic nucleus (AGN) feedback. However, such feedback often leads to excessive quenching in simulated galaxy populations. Magneticum and SIMBA match the observed gas fraction relation but predict an overabundance of quenched galaxies. In contrast, IllustrisTNG implements weaker AGN feedback, yielding more realistic star-forming fractions but systematically overpredicting hot gas masses in massive groups and poor clusters. Overall, these tensions indicate current feedback models remain incomplete, not only in the total energy injected but also in the timing, location, and coupling of this energy to the surrounding gas. Our results therefore highlight the need to revisit subgrid feedback prescriptions and develop more self-consistent models capable of simultaneously regulating galaxy growth and the thermodynamic properties of halo gas. Motivated by this discrepancy, a companion study will explore whether the feedback strengths required to match halo gas constraints inevitably lead to overquenching and distorted galaxy demographics.

Figures

Figures reproduced from arXiv: 2607.21140 by A. Dev, A. Fraser-McKelvie, A. Merloni, C. Aydar, D. T. Mazengo, G. Ponti, I. Marini, J. M. Sunzu, J. O. Chibueze, K. Dolag, L. A. Kahinga, L. M. Valenzuela, Mirjana Povi\'c, N. de Is\'idio, Petri V\"ais\"anen, P. Popesso, P. Privatus, R. Dav\'e, R.-S. Remus, S. Shreeram, S. Vladutescu-Zopp, V. Biffi, V. Toptun.

Figure 1
Figure 1. Figure 1: Distribution of multi-wavelength AGN in the SFR– [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of central and satellite galaxies in our [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: SFR-M⋆ plane in simulations color-coded according to the specific normalized BH accretion, log(BHAR/MBH) in units of [yr−1 ]. From left, the first two panels are for Magneticum (Box2 and Box4, respectively), SIMBA (third panel), and TNG100 (fourth panel). The solid line indicates the location of the Po19 MS, while the dashed line indicates the 1σ scatter of the relation. marking the shift from radiatively … view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of MaNGA galaxies in the SFR– [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the galaxy fractions of centrals (top row) and satellites (bottom row) in the di [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The stellar-to-halo mass relation (M⋆/Mh vs. Mh) for MaNGA (first), Magneticum Box2 and Box4 (second and third, respectively), SIMBA (fourth) and IllustrisTNG (fifth). Points are color-coded by log(sSFR). Lines show semi-empirical models from Moster et al. (2010) (magenta), Moster et al. (2018) (yellow), and Moster et al. (2013) (cyan). White points and black curves indicate the median trend [PITH_FULL_IM… view at source ↗
Figure 8
Figure 8. Figure 8: Satellite fractions in the SFR–M⋆ plane as a function of halo mass. Galaxy loci are defined relative to the Po19 main se￾quence: SB (magenta), MS (blue), GV (green), and RS (red). Solid lines show MaNGA measurements and dashed lines show simulation predictions. et al. 2025). At higher halo masses, continued star formation sug￾gests that AGN feedback may not fully suppress cooling flows. SIMBA (fourth panel… view at source ↗

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