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Current hydrodynamical simulations overpredict the thermal pressure of galaxy groups by roughly a factor of two, pointing to missing non-thermal pressure support or departures from hydrostatic equilibrium.

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2026-08-04 01:06 UTC pith:CUKG2HXO

load-bearing objection Credible, carefully guarded evidence for a ~2x thermal pressure deficit at group masses, but the SED separation at 1.6' needs a broader dust/radio model before the headline is bulletproof. the 4 major comments →

arxiv 2608.00164 v1 pith:CUKG2HXO submitted 2026-07-31 astro-ph.CO astro-ph.GA

Evidence for a thermal pressure deficit in galaxy groups from the tSZ effect and weak lensing

Jared Siegel , Alexandra Amon , Jenny E. Greene , Ian G. McCarthy , Eliot Quataert , William Coulton This is my paper
classification astro-ph.CO astro-ph.GA
keywords thermal Sunyaev-Zel'dovich effectgalaxy groupsintracluster mediumgalaxy-galaxy lensinghydrodynamical simulationsbaryon feedbacknon-thermal pressure supportCompton y parameter
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.

This paper stacks the thermal Sunyaev-Zel'dovich (tSZ) signal around a large sample of DESI Luminous Red Galaxies to measure the thermal pressure of gas in galaxy groups, and compares these measurements to state-of-the-art hydrodynamical simulations (FLAMINGO). By also measuring galaxy-galaxy lensing for the same galaxy samples, the authors calibrate the halo masses and satellite fractions in the simulations like-with-like, removing a major source of ambiguity. They find that the fiducial simulation overpredicts the stacked tSZ signal at small scales, and even the simulation variant with the strongest gas expulsion—which already reproduces the gas density inferred from kinetic SZ measurements—overpredicts the thermal pressure by about a factor of two. Because the strong-feedback simulation already matches the gas density, the authors argue the remaining deficit is not due to gas depletion but points to the simulated gas being too hot, requiring either significant non-thermal pressure support or departures from hydrostatic equilibrium.

Core claim

The authors measure the tSZ effect around DESI LRGs using ACT single-channel maps, modeling the stacked SED as a combination of tSZ, dust, and radio emission, and compare the resulting Compton y profiles with forward-modeled predictions from the FLAMINGO simulations. After calibrating the simulated galaxy selection with galaxy-galaxy lensing, they find that the fiducial FLAMINGO simulation overpredicts the tSZ signal by roughly a factor of two or more across 10^13–10^14 Msun and z=0.4–1, and the strongest feedback variant, which best matches the kSZ-inferred gas density, still overpredicts it by about 2x at 1.6' apertures. The paper concludes that the deficit in thermal pressure cannot be ex

What carries the argument

The central observable is the Compton y parameter, which is proportional to the line-of-sight integral of electron pressure (n_e T_e). The measurement pipeline uses compensated aperture photometry (CAP) to stack ACT single-channel temperature maps, and a multi-frequency SED model with three components—tSZ, a modified black-body dust emission (T_dust=24 K, beta=1.7), and a power-law radio component—to isolate the tSZ signal. For the simulation comparison, galaxy-galaxy lensing (GGL) profiles are used to select a sample of simulated galaxies that matches the observed halo mass distribution and satellite fraction, and the tSZ and GGL profiles are forward-modeled from the simulations, accounting

Load-bearing premise

The three-channel SED decomposition assumes the stacked signal is exactly tSZ plus a single modified-black-body dust component (T_dust=24 K, beta=1.7) plus a single power-law radio component; if the true dust or radio SEDs differ—especially flat-spectrum radio sources not detected by VLASS—the inferred Compton y, and thus the pressure deficit, could shift.

What would settle it

Measure the tSZ signal around the same DESI LRG sample with a CMB experiment that has more frequency channels (e.g., six channels from 30 to 280 GHz) and check whether the derived Compton y at 1.6' changes by more than the quoted statistical uncertainty; alternatively, obtain spatially resolved X-ray spectra of the same groups to directly measure gas temperature and non-thermal pressure, and see whether the observed temperature is indeed about half of the simulated prediction.

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

If this is right

  • If the deficit is real, the thermal pressure of gas in galaxy groups is about half of what current simulations predict, so the gas is either supported by non-thermal pressure (turbulence, cosmic rays, magnetic fields) or is not in hydrostatic equilibrium.
  • Additional gas depletion alone cannot resolve the discrepancy because the strongest feedback simulation already matches the kSZ-inferred gas density, so future simulation development must include additional physics rather than merely stronger feedback.
  • The tSZ power spectrum at small scales is expected to be suppressed relative to simulation predictions, potentially explaining observed deficits at high multipoles.
  • X-ray measurements of the same galaxy groups could directly test the predicted temperature deficit by measuring gas temperature and non-thermal pressure support.
  • Mass calibration of groups and clusters using X-ray or SZ observables may be biased if non-thermal pressure support is significant and unaccounted for in hydrostatic equilibrium analyses.

Where Pith is reading between the lines

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

  • If non-thermal pressure support is the cause, this would affect not only group gas fractions but also cluster mass calibration and the interpretation of cluster counts and tSZ power spectrum measurements in cosmological analyses.
  • The galaxy-informed SED modeling approach used here could be extended to other galaxy samples and redshift ranges, especially with upcoming multi-frequency CMB experiments (e.g., Simons Observatory) that can better break the tSZ-dust-radio degeneracy.
  • The factor-of-two temperature overprediction in simulations may also imply that the gas in groups is more multiphase or clumpy than simulated, or that the energy injection from feedback is more efficiently converted into non-thermal forms than currently modeled.

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 / 4 minor

Summary. The paper presents new stacked thermal Sunyaev-Zel'dovich (tSZ) measurements around DESI photometric LRGs using ACT DR6 single-channel maps, together with galaxy-galaxy lensing (GGL) measurements from HSC. The authors model the stacked 90/150/220 GHz photometry as tSZ plus dust plus radio emission, and compare the inferred Compton-y signal with forward-modeled predictions from the FLAMINGO simulations, with simulated galaxy selections calibrated to match the observed GGL profiles. The central claim is that current hydrodynamical simulations overpredict the tSZ signal of group-mass halos by roughly a factor of two at apertures ≲3′, and that even the strongest-feedback FLAMINGO variant, which reproduces the kSZ-inferred gas density, still overpredicts the thermal pressure. The authors interpret this as evidence for missing non-thermal pressure support or significant departures from hydrostatic equilibrium in the simulations.

Significance. If the central result holds, this is a significant step toward a coherent observational picture of the thermodynamic state of gas in galaxy groups: it combines a new foreground-modeled tSZ measurement with GGL-calibrated simulation comparisons, and it connects the tSZ deficit to the existing kSZ-based evidence for gas depletion. The paper is careful in several respects: the tSZ signal is isolated via galaxy-informed SED modeling rather than blind map-level component separation; the simulation comparison is like-with-like, forward-modeling miscentering, satellites, masking, and two-halo contributions; and many robustness tests are reported (free β, radio omitted, VLASS prior, cluster-mask variants, extreme simulation selections). The authors also make conservative choices, e.g., focusing on the upper end of the Compton-y posterior and using a stricter cluster mask in the simulations. These strengths make the claimed deficit credible within the adopted model family. However, as detailed below, the central claim depends on the assumed dust/radio SED family being sufficiently broad, and the paper does not yet provide a fully quantitative significance for the factor-of-two deficit.

major comments (4)
  1. [§3.3, Fig. 4, Appendix B] The load-bearing SED separation is underdetermined: three ACT channels are fit with four free parameters (log10 y, log10 A_dust, log10 A_radio, α) plus fixed T_dust=24 K and β=1.7. The robustness tests in Appendix B vary β with T fixed, omit radio, or add a VLASS prior, but they do not jointly vary T_dust and β, include a second dust temperature component, or add a flat-spectrum radio population. Since dust contributes >60% of the 150 GHz signal in the lowest-mass bin (Fig. 4), the posterior upper envelope of y—which the paper deliberately adopts—may be set by the fixed SED family rather than by the data. Warmer dust or flat-spectrum radio below the VLASS threshold could partially fill the tSZ decrement and raise the allowed y, shrinking the claimed factor-of-two deficit. Please quantify this: e.g., fit with T_dust in a physically plausible range (say 20–40 K) with β free, and/or with an
  2. [§5, Fig. 6] The central claim is that the strongest-feedback FLAMINGO simulation overpredicts the observed tSZ signal by about a factor of two, yet the paper refrains from quoting a formal significance. Because the statement is the headline result, the manuscript should provide a quantitative summary of the deficit—e.g., per-bin posterior distributions of y_data/y_sim, a combined probability that y_data < y_sim, and an explicit accounting of the systematic contributions (SED, GGL mass calibration, cluster masking) that dominate over statistical errors. Currently the reader cannot assess whether the factor of two is a 2σ effect or a 10σ effect, nor how the systematic uncertainties are folded into the claim.
  3. [§4.3, Appendix D] It is not fully clear whether the GGL measurements and the tSZ stacking are performed on the same galaxy samples. GGL uses HSC Y3, whose footprint is smaller than the ACT-DESI overlap; the tSZ stacking appears to use the full ACT-DESI overlap. If the GGL-calibrated simulation selection is derived from a subset of galaxies that differs from the tSZ sample (e.g., in stellar mass distribution, depth, or environment), the like-with-like comparison could be biased. Please state explicitly whether the tSZ analysis is restricted to the HSC overlap for the GGL-calibrated bins, or demonstrate that sample differences between the footprints are negligible for the tSZ signal.
  4. [§6.1] The interpretation that the tSZ deficit reflects an overpredicted gas temperature (rather than gas density) relies on the external kSZ result that the strongest-feedback FLAMINGO simulation reproduces the gas density of the same galaxy sample. The paper should propagate the uncertainty of this kSZ anchor into the temperature-deficit claim. For example, if the kSZ normalization is uncertain at the 10–20% level, does this change the required non-thermal pressure fraction? A quantitative propagation would strengthen the physical conclusion and separate it from the robust-but-less-interpreted statement that simulations overpredict tSZ.
minor comments (4)
  1. [§3.2] The sentence 'We therefore fix T_dust = 24 K throughout this work, without loss of model flexibility' is too strong: β and T_dust are degenerate but not perfectly so, and the Appendix B results show that the inferred y can shift when the dust model is changed. Please rephrase to 'without substantial loss of model flexibility within the adopted dust family' or similar.
  2. [Fig. 3 / Fig. 5] The gray bands for the CIB+dβ deprojected maps are described in the caption but are visually dense and sometimes confused with statistical error bars. Consider labeling the band directly in the figure or using a hatched region, and clarify in the caption that the band spans the range of assumed dust SEDs, not a statistical uncertainty.
  3. [General] There is no explicit data/code availability statement. Given the reproducibility value of the SED-fitting and forward-modeling pipeline, please add one (or state that code will be released upon acceptance).
  4. [§4.4] The notation 'f_gas −8σ' is used without defining whether σ is the observational uncertainty on the mean gas fraction relation. It is clear later, but a one-line definition at first use would help.

Circularity Check

0 steps flagged

No significant circularity: the tSZ measurement and the GGL-calibrated simulation predictions are independent, and the kSZ anchor rests on external observational results.

full rationale

I walked the paper's derivation chain and found no step where a prediction reduces, by definition or by fitting, to its own input. The tSZ measurement (Section 3.3) treats Compton y as a free parameter in a three-component SED fit to ACT photometry; y is not derived from the simulations. The fit is acknowledged to be degenerate (Section 3.3), but the paper conservatively adopts the upper end of the y posterior and demonstrates robustness in Appendix B to freeing beta, omitting radio, and imposing VLASS priors. This is an ordinary fitting degeneracy, not circularity. The GGL calibration (Section 4.4) selects simulated galaxies by matching an independent observable (lensing) and then forward-predicts a different observable (tSZ); the strong mass scaling of tSZ does not make this circular. The interpretation step (Section 6.1) uses the prior kSZ result that the strongest-feedback FLAMINGO simulation reproduces the observed gas density. That claim is supported by citations to overlapping authors, but the underlying kSZ measurements are external DESI+ACT data, and the FLAMINGO comparison is a forward model, not a fit to the present tSZ measurement. The factor-of-two thermal-pressure deficit is therefore a comparison of an independently measured y with forward-modeled simulation predictions, not an identity. The residual concern is that the SED family (fixed T_dust = 24 K, beta = 1.7) may not be exhaustive, which is a systematic uncertainty, not a circular step.

Axiom & Free-Parameter Ledger

8 free parameters · 7 axioms · 0 invented entities

No new particles or forces are introduced. The free parameters are either nuisance parameters in the SED fit, the target y amplitude, or analysis choices for the simulation selection. The main domain assumptions are the single-SED dust model and the representativeness of FLAMINGO, both acknowledged in the paper.

free parameters (8)
  • Dust temperature T_dust = 24 K (fixed)
    Fixed from literature because β and T_dust are degenerate in the ACT bands; the inferred y can shift if T_dust differs.
  • Dust spectral index β = 1.7 ± 0.5
    Fitted to the radio-quiet LRG subsample and then fixed; controls how much of the 220 GHz signal is attributed to dust rather than tSZ.
  • Radio spectral index α = uniform prior [-2, 0], posterior constrained
    Free parameter in the SED fit; the radio component is small but shifts y slightly.
  • Dust and radio amplitudes A_dust, A_radio = per sample and radial bin
    Nuisance amplitudes in the joint SED fit; degenerate with y given only three ACT channels.
  • Compton y amplitude (log10 y) = posteriors per sample and radial bin
    The target measurement of the paper; fitted jointly with dust and radio in each bin.
  • Log-normal stellar-mass center M0 = optimized to GGL per sample
    Determines the mean halo mass and satellite fraction of the simulated sample; fitted to GGL, not to tSZ.
  • Stellar-mass width σ = 0.2 dex (fixed)
    Fixed based on stellar-mass systematic uncertainty; the width affects the high-mass tail that drives tSZ.
  • Cluster masking threshold M500,max = 10^14.3 Msun (fixed)
    Chosen to mirror ACT cluster masking; deliberately stricter than the data mask, making simulated tSZ predictions conservatively low.
axioms (7)
  • standard math Non-relativistic tSZ spectral shape with negligible relativistic corrections
    Equation 6; group-mass halos have low enough temperatures that relativistic corrections are subdominant to foreground systematics.
  • domain assumption Dust emission of LRGs is a single modified blackbody with T_dust = 24 K and β = 1.7
    Section 3.2; β is inferred from radio-quiet galaxies, but dust SEDs may vary with redshift and environment.
  • domain assumption Background-subtracted aperture photometry removes all uncorrelated line-of-sight CIB and radio emission
    Section 3.1; residual contamination is assumed to originate only in the targeted galaxies or their associated structure.
  • domain assumption The GGL-calibrated log-normal stellar-mass selection with σ = 0.2 dex reproduces the true halo mass distribution of DESI LRGs
    Section 4.4; the selection is fit to GGL, but the high-mass tail that drives tSZ is not directly constrained by the lensing data.
  • domain assumption FLAMINGO simulations are representative of 'current hydrodynamical simulations' for thermal pressure
    Only FLAMINGO fiducial and fgas -8σ runs are forward-modeled; the broad generalization in the abstract relies on this.
  • domain assumption The kSZ-inferred gas density, which the fgas -8σ run reproduces, is accurate
    Used to break the density-temperature degeneracy; this is an external result from Hadzhiyska et al. 2025 and companion papers, not re-derived here.
  • domain assumption Hydrostatic equilibrium is a reasonable starting assumption when interpreting the deficit as missing non-thermal pressure
    Section 6.1; the authors explicitly note that departures from hydrostatic equilibrium could also explain the deficit.

pith-pipeline@v1.3.0-alltime-deepseek · 35252 in / 12181 out tokens · 137352 ms · 2026-08-04T01:06:01.998914+00:00 · methodology

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read the original abstract

Measurements of the thermal Sunyaev-Zel'dovich (tSZ) effect have yet to form a consistent picture of the thermodynamic state of the gas in the intracluster medium: their interpretation is complicated by foreground contamination and uncertain halo masses. We present new measurements of the tSZ effect around the Dark Energy Spectroscopic Instrument (DESI) Luminous Red Galaxy (LRG) sample, together with galaxy-galaxy lensing (GGL) measurements that enable a like-with-like comparison to state-of-the-art hydrodynamical simulations. We robustly isolate the tSZ signal by directly modeling the dust and radio emission of the target galaxies using the Atacama Cosmology Telescope (ACT) single-channel temperature maps, substantially reducing uncertainties from astrophysical foregrounds. Across halo masses $M_{500}=10^{13}-10^{14}~M_\odot$ and redshifts $0.4<z<1$, we find that the fiducial 1 Gpc$^3$ FLAMINGO simulation significantly overpredicts the observed tSZ signal at $\lesssim3'$ (i.e., $\lesssim 4\,R_{500}$ at $z=0.7$). Even the simulation with the strongest gas expulsion---which successfully reproduces the gas density inferred from kinetic SZ measurements of the same galaxy sample---overpredicts the thermal pressure. Because the strongest feedback model already reproduces the observed gas density, the remaining discrepancy is difficult to explain with additional gas depletion alone. Instead, current hydrodynamical simulations appear to overpredict the thermal pressure of galaxy groups by a factor of two, pointing toward missing non-thermal pressure support or significant departures from hydrostatic equilibrium.

Figures

Figures reproduced from arXiv: 2608.00164 by Alexandra Amon, Eliot Quataert, Ian G. McCarthy, Jared Siegel, Jenny E. Greene, William Coulton.

Figure 1
Figure 1. Figure 1: Normalized frequency dependence of the tSZ effect (green line), dust emission (purple, dash-hatched), and radio point sources (blue, cross-hatched). The left (center) panel shows the induced flux (temperature) perturbation, relative to the primary CMB. The rightmost panel shows the apparent Compton y signal—i.e., converting the temperature fluctuations to y assuming the fluctuations are sourced by the tSZ … view at source ↗
Figure 2
Figure 2. Figure 2: The photometric LRG sample is well described by a joint tSZ, dust, and radio SED model. Left and middle columns: stacked temperature fluctuations within the 1.6 ′ aperture as a function of frequency for the radio-quiet LRG sample (left; yellow) and the full sample with no radio cut (middle; gray). Both samples are at z = 0.54–0.71 with log10 M⋆/M⊙ = 11.25–12.0. The shaded bands show the 1σ posterior constr… view at source ↗
Figure 3
Figure 3. Figure 3: The astrophysical contaminants significantly bias tSZ constraints at small angular scales (≲ 3 ′ ). The SED model consistently recovers higher Compton y values than existing deprojected ACT+Planck maps at radii where dust contamination is most important, while converging on large scales where the tSZ signal dominates. For a representative galaxy sample, the top panel presents the Compton y constraints and … view at source ↗
Figure 4
Figure 4. Figure 4: Fractional contribution of the SED model components—tSZ (green), dust (purple), and radio (blue)— to the stacked 150 GHz signal within the 1.6 ′ aperture (the FWHM of the beam) as a function of stellar mass. We present the results for the z = 0.54–0.71 redshift bin, which is representative of the LRG sample. The dust emission dom￾inates the lower mass bins, and the tSZ contribution rises steeply with stell… view at source ↗
Figure 5
Figure 5. Figure 5: Like-with-like comparison of the stacked Compton y profile with the FLAMINGO simulations, for the same sample of LRGs as [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Stacked Compton y amplitude at 1.6 ′ from the CAP filter as a function of mean halo mass. Each column presents a different redshift bin; each redshift bin is divided into four stellar mass bins. The posteriors on Compton y from the joint tSZ, dust, and radio model are shown in green. The gray bars represent the range of stacked Compton y from the CIB+dβ deprojected maps with Tdust = 10.7–24 K and β = 1.0–1… view at source ↗
Figure 7
Figure 7. Figure 7: The Compton y profiles (green) relative to the GGL-calibrated predictions of the fiducial (L1 m9; light blue) FLAMINGO simulation; the strongest feedback simulation (fgas − 8σ) is shown in dark blue. Each row corresponds to a different redshift bin, and each column presents a different stellar mass bin. The dashed vertical lines present R500 for each sample, and the vertical dotted lines mark 1.6 ′ : the b… view at source ↗
Figure 8
Figure 8. Figure 8: The Compton y profiles (green) relative to the prediction of the fiducial (L1 m9; solid light blue) FLAMINGO simulation; the strongest feedback simulation (fgas −8σ) is shown in dark blue. The dotted and dashed lines present simulation predictions without satellite galaxies and with narrow stellar mass distributions (σ = 0.05 dex), respectively; the GGL calibration is repeated for each variant. Each row co… view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of the SED models for the radio-quiet and full LRG samples, assuming a joint tSZ and dust model; the full LRG sample favors extreme β of ≳ 2.5, likely reflecting the need for a radio component. Left and middle columns: stacked temperature fluctuations as a function of frequency for the radio-quiet LRG sample (left; yellow) and the full sample with no radio cut (middle; gray), shown for the 1.6 ′… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the SED models for the radio-quiet and full LRG samples, assuming the fiducial joint tSZ, dust, and radio model with β = 1.7 and Tdust = 24 K. Left and middle columns: stacked temperature fluctuations as a function of frequency for the radio-quiet LRG sample (left; yellow) and the full sample with no radio cut (middle; gray), shown for the 1.6 ′ aperture. Both samples are at z = 0.54–0.71 wi… view at source ↗
Figure 11
Figure 11. Figure 11: The Compton y constraints are robust to large variations in the SED modeling process. The stacked Compton y amplitude at 1.6 ′ is shown as a function of mean halo mass for the fiducial SED model (green circles) and three variants: (i) treating β as a free parameter (orange squares), (ii) imposing a VLASS-informed prior (purple crosses), and (iii) omitting the radio component (red diamonds). Where the tSZ … view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of the stacked Compton y amplitude at 1.6 ′ for the full LRG sample (green circles) and the radio-quiet subsample (yellow pluses). The radio-quiet and full samples are consistent at the 1σ level; however, the Compton y values for the full sample are systematically lower, potentially reflecting differences in the gas distribution or residual radio contamination. The Compton y constraints are der… view at source ↗
Figure 13
Figure 13. Figure 13: The distribution of ACT DR6 clusters (S/N > 6) in mass and redshift from Aguena et al. (2026); the halo masses are estimated from a weak lensing calibrated scaling relation. The contours represent the 50th and 95th percentiles. The dashed horizontal line presents the criterion applied to the simulated halos: M500 < 1014.3 M⊙. The excess surface density measurements are corrected for known systematics, inc… view at source ↗
Figure 14
Figure 14. Figure 14: Top: the footprints of the DESI photometric LRG sample (green) and ACT DR6 (gray) alongside the HSC (red) lensing survey; we also include the DES (blue) and KiDS (yellow) lensing surveys for context. The DESI and lensing survey footprints are limited to their regions of overlap with ACT. Bottom: The redshift distribution (left) and stellar mass distributions (right) of the photometric DESI LRG sample. The… view at source ↗
Figure 15
Figure 15. Figure 15: The excess surface density measurements (green) alongside the best-fitting FLAMINGO GGL profiles for the fiducial (L1 m9; light blue) and strongest feedback (fgas − 8σ; dark blue) simulations. The lower panels show the ratio relative to the fiducial FLAMINGO simulation and report the number of standard deviations by which each simulation deviates from the observations. Each row corresponds to a different … view at source ↗

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