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Interpreting the stacked kinetic SZ signal II: the degeneracy between satellite galaxies and baryonic feedback

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Lurdes Ondaro-Mallea, Boryana Hadzhiyska, Raul E. Angulo

Cosmology and Nongalactic Astrophysics · Submitted 12 Oct 2026 · 213 references

Pith's verdict: Solid quantification of the satellite-feedback degeneracy in stacked kSZ, though the factor-of-two bias claim rests on a fixed-mass assumption that real analyses might relax.

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arXiv ID2610.17200 (version 1)
Pith ID77NLWL5T, Pith's permanent record of this paper and its review
CitationBoryana Hadzhiyska, Lurdes Ondaro-Mallea, Raul E. Angulo. "Interpreting the stacked kinetic SZ signal II: the degeneracy between satellite galaxies and baryonic feedback." arXiv:2610.17200 (2026). https://pith.science/paper/77NLWL5T
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Claim paper

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The paper

The paper claims

Satellite galaxies in stacked kSZ samples produce an effect degenerate with strong baryonic feedback, biasing inferred gas fractions low by up to a factor of two at the precision of current DESI×ACT data.

The reading

This paper uses the FLAMINGO hydrodynamical simulations to quantify how satellite galaxies corrupt stacked kinetic Sunyaev-Zel'dovich (kSZ) measurements of halo gas. The key insight: satellite galaxies, though only 20–30% of DESI-like galaxy samples, contribute 50–80% of the kSZ signal because they occupy more massive halos than centrals. Their radial miscentering within host halos suppresses the small-scale profile in exactly the same way baryonic feedback does. When fitting real DESI×ACT data with a central-only model, the inferred halo gas fractions are biased low by up to a factor of two and the inferred feedback strength is overestimated. To avoid this bias, the satellite fraction must be known to better than 10% from lensing or clustering data, or the analysis must marginalize over an unconstrained satellite distribution, which greatly weakens constraints.

What carries the argument

The radial miscentering of satellite galaxies within their host halos: the offset between the satellite position and the halo centre washes out the high-density gas core and spreads signal to larger radii, mimicking an extended gas distribution produced by baryonic feedback. The paper decomposes the satellite optical depth into an isotropic (radial) component, controlled by the satellite distance from the halo centre, and an anisotropic component, which traces the filamentary gas distribution around the halo. The anisotropic piece contributes about 10% of the signal and is largely independent of feedback model.

Extended reading notes

Core claim

The central claim is that the stacked kSZ signal from realistic galaxy samples is dominated by satellite galaxies, not centrals, and that ignoring this fact leads to a severe misinterpretation of the data. Stacking on satellites suppresses the small-scale optical depth profile because the gas density peaks at the halo centre, not at the satellite position; this suppression is quantitatively similar to the suppression caused by strong AGN feedback. The degeneracy is most acute for low-redshift, high-mass samples (DESI BGS and LRG), where neglecting satellites biases the inferred halo gas fractions low by 40–50% and points toward feedback models that are too strong. For emission-line galaxies (ELG), no significant bias is found at current precision. The paper proves this by constructing mock kSZ catalogues from FLAMINGO, fixing the mean halo mass via external CMB lensing, and showing that a central-only model gives a poor fit to the full data that includes satellites, and that the best-fitting feedback model when satellites are ignored is systematically more extreme than the true feedback.

Load-bearing premise

The analysis fixes the mean halo mass of each galaxy sample to values derived from external CMB lensing measurements and assumes those lensing-derived masses are unbiased; if the lensing masses have significant systematic errors, the inferred satellite bias could be different.

Editorial extensions

If this is right

  • Current and past stacked kSZ analyses that assume the galaxy sample is entirely central likely underestimate the true halo gas mass and overestimate the required feedback strength, especially for samples with mean halo masses around 10^13 solar masses.
  • Future kSZ measurements with higher signal-to-noise will require simulation-based models that capture the anisotropic gas distribution around halos traced by satellites, because this anisotropic component will become statistically significant.
  • External datasets such as CMB lensing and galaxy clustering must constrain the satellite fraction to better than 10% to avoid systematic shifts in the inferred kSZ profile and gas fraction posteriors.
  • Analyzing the ratio of kSZ to lensing signals can largely cancel the satellite bias, but this cancellation is less effective for tSZ and X-ray stacks because of their steeper halo-mass scaling.

Reading between the lines

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

  • The degeneracy identified here likely also affects other stacked gas probes such as thermal SZ and fast radio burst stacks, because the same miscentering mechanism operates. The magnitude of the bias may be larger for tSZ than for kSZ.
  • If the degeneracy is not resolved, upcoming high-S/N data could falsely 'detect' a stronger redshift or mass dependence of feedback than actually exists, because satellite fractions evolve with redshift.
  • A direct test: compare the gas fraction inferred from kSZ stacks of the same galaxies using different substructure finders; if the gas fraction posterior shifts by more than the statistical error, the analysis is sensitive to the satellite modelling choice.
Original abstract

The kinetic Sunyaev-Zel'dovich (kSZ) signal stacked around a galaxy sample traces the gas around halos and is a powerful probe of baryonic feedback. Its interpretation, however, depends on the satellite galaxies in the sample. Using the FLAMINGO hydrodynamical simulations and DESI-like samples of luminous red galaxies (LRGs), bright galaxies survey (BGS), and emission-line galaxies (ELGs), we show that satellites and feedback have similar effects: both suppress the small-scale signal. This degeneracy arises because stacking on satellites, which are offset from the centre of their host haloes, mimics the extended gas distribution produced by feedback. Although satellites make up only 20-30% of each sample, they account for 50-80% of the signal because they occupy more massive halos than centrals; thus their imprint on the stacked profiles can be as large as that of feedback. At the precision of current DESI$\times$ACT data, neglecting satellites in the modelling biases the inferred gas fractions low, by up to a factor of two, and overestimates the feedback strength, particularly for the LRG and BGS samples. Remaining agnostic about the satellite population avoids this bias but reduces the constraining power, so robust constraints would require knowing the satellite fraction to better than 10%, for instance from lensing and clustering data of the same galaxy samples. Finally, a further contribution from satellites will become statistically significant with upcoming surveys: satellites trace the anisotropic gas distribution around their host haloes, an effect that contributes 10% of the signal, and will likely require simulation-based models to capture.

The compiled record

The paper's own sentences, arranged, kept beside the review and separate from it. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-10-14: 1 request, 6,577 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.69 · human participants 0.01 · controlled comparison 0.16 · randomized assignment 0.01 · states quantitative result 0.98 · compares with prior results 0.39 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The kinetic Sunyaev-Zel'dovich (kSZ) signal stacked around a galaxy sample traces the gas around halos and is a powerful probe of baryonic feedback. Its interpretation, however, depends on the satellite galaxies in the sample. Using the FLAMINGO hydrodynamical simulations and DESI-like samples of luminous red galaxies (LRGs), bright galaxies survey (BGS), and emission-line galaxies (ELGs), we show that satellites and feedback have similar effects: both suppress the small-scale signal. This degeneracy arises because stacking on satellites, which are offset from the centre of their host haloes, mimics the extended gas distribution produced by feedback. Although satellites make up only 20-30% of each sample, they account for 50-80% of the signal because they occupy more massive halos than centrals; thus their imprint on the stacked profiles can be as large as that of feedback. At the precision of current DESI$\times$ACT data, neglecting satellites in the modelling biases the inferred gas fractions low, by up to a factor of two, and overestimates the feedback strength, particularly for the LRG and BGS samples. Remaining agnostic about the satellite population avoids this bias but reduces the constraining power, so robust constraints would require knowing the satellite fraction to better than 10%, for instance from lensing and clustering data of the same galaxy samples. Finally, a further contribution from satellites will become statistically significant with upcoming surveys: satellites trace the anisotropic gas distribution around their host haloes, an effect that contributes 10% of the signal, and will likely require simulation-based models to capture.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The kinetic Sunyaev-Zel'dovich (kSZ) signal stacked around a galaxy sample traces the gas around halos and is a powerful probe of baryonic feedback. background 1.00 0.08 0.04 asserted 1.00
s1 Its interpretation, however, depends on the satellite galaxies in the sample. background 0.59 0.09 0.04 asserted 1.00
s2 Using the FLAMINGO hydrodynamical simulations and DESI-like samples of luminous red galaxies (LRGs), bright galaxies survey (BGS), and emission-line galaxies (ELGs), we show that satellites and feedback have similar effects: both suppress the small-scale signal. result 0.66 0.94 0.40 asserted 1.00
s3 This degeneracy arises because stacking on satellites, which are offset from the centre of their host haloes, mimics the extended gas distribution produced by feedback. interpretation 0.98 0.69 0.22 asserted 1.00
s4 Although satellites make up only 20-30% of each sample, they account for 50-80% of the signal because they occupy more massive halos than centrals; thus their imprint on the stacked profiles can be as large as that of feedback. result 0.99 0.91 0.24 asserted 0.95
s5 At the precision of current DESI$\times$ACT data, neglecting satellites in the modelling biases the inferred gas fractions low, by up to a factor of two, and overestimates the feedback strength, particularly for the LRG and BGS samples. result 0.75 0.88 0.29 asserted 0.99
s6 Remaining agnostic about the satellite population avoids this bias but reduces the constraining power, so robust constraints would require knowing the satellite fraction to better than 10%, for instance from lensing and clustering data of the same galaxy samples. future work 0.49 0.46 0.21 asserted 0.80
s7 Finally, a further contribution from satellites will become statistically significant with upcoming surveys: satellites trace the anisotropic gas distribution around their host haloes, an effect that contributes 10% of the signal, and will likely require simulation-based models to capture. future work 0.53 0.71 0.05 hedged possibility 0.77

What kind of paper the classifier reports this is: simulation study 1.00 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
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  • relations to other papers
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

Figures
Figure 1. Halo occupation distributions of the three DESI-like mock samples: LRGs (blue), BGS (orange), and ELGs (green). The stellar-mass-selected samples (LRGs and BGS) exhibit pronounced high-halo-mass tails, whereas the ELG sample has a broader halo mass distribution. The high-mass end of each distribution is dominated by satellite galaxies. We use the halo properties computed with Spherical Overdensity and Aperture Processor (McGibbon et al. 2025). Throughout, we define the halo radius 𝑅200m as the radius enclosing a mean density 200 times the mean density of the Universe, and the host…
Figure 2. separates these two contributions from the total optical depth around satellite galaxies, 𝜏sat in black solid lines. In the upper panel we show the profiles, while in the two lower panels we show the ratio to the total satellite signal. In the bottom panel, we compare the isotropic and anisotropic contributions. The isotropic compo￾nent (orange solid line) dominates the satellite signal and provides a good description for 𝑟 ≳ 𝑅200m. At smaller scales, however, the anisotropic component (purple line) becomes increasingly impor￾tant: it contributes approximately 20 % of the total…
Figure 3. Impact of baryonic feedback on the mean optical depth around central (left panel) and satellite galaxies on our DESI LRG-like sample (
Figure 4. Contribution of stacks around central (orange) and satellite (green) galaxies to the total optical depth (blue) around DESI-like LRG (left), BGS (middle), and ELG (right) samples. Upper panels show the mean optical depth profiles, while lower panels show the fractional contribution of the central and satellite components to the total signal. The horizontal dashed line marks equal (50%) contributions, and the vertical dashed line the effective virial radius of each sample. Although the satellite fraction in all three samples is considerably smaller than 50%, they contribute at least…
Figure 5. Impact of baryonic feedback and satellite galaxies on the stacked optical depth profiles around DESI LRG, BGS and ELG-like samples. Solid coloured curves show the profiles for varying feedback strengths, stacked on central-only samples. The dotted points show the profile in a fiducial feedback scenario ( 𝑓gas), stacked in galaxy samples including satellites. The associated error bars correspond to the statistical uncertainty of DESI DR2 × ACT measurements (Qu et al. 2026; Hadzhiyska et al. 2026). The upper panels show the profiles, and the lower panels the ratio to the fiducial…
Figure 6. Inferred halo gas fractions obtained by fitting kSZ profiles stacked on centrals and satellites with a model calibrated on centrals only. The gas fractions of the mock data are shown in green, and the inferred ones in black shaded area and dashed line. Thin blue lines show the halo gas fractions of the other FLAMINGO feedback variations for reference. Each column corresponds to a DESI-like sample, with the vertical line indicating its mean halo mass. Ignoring satellites in the modelling biases the inferred gas fractions low, with the largest bias for BGS, followed by LRGs, while no…
Figure 7. Comparison of theoretical and statistical errors on the stacked kSZ profiles, for DESI LRG-like (left), DESI BGS-like (middle) and DESI ELG-like (right) samples at three angular distances from the galaxy positions. The theoretical errors are computed from a) fully agnostic model about the satellite distribution within the host haloes (orange), and b) the error done by neglecting the contribution from anisotropic gas traced by satellites (purple). The statistical errors come from DESI DR2 × ACT measurements (Qu et al. 2026; Hadzhiyska et al. 2026) (grey). The dominant theoretical…
Figure 8. Sensitivity of the stacked kSZ profiles to uncertainties in the satellite fraction for DESI LRG, BGS and ELG-like samples. We assess the impact of uncertainties on the satellite fraction by randomly removing increasing fractions of satellite galaxies while simultaneously removing low-mass central galaxies to keep the mean halo mass of the sample fixed. For each perturbed sample, we measure the deviation of the stacked kSZ profile from the profile obtained using the fiducial satellite fraction. We quantify this deviation as a function of the fractional error in the satellite fraction…

Figures reproduced from arXiv:2610.17200 by the authors.

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