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Mapping the gas density with the kinematic Sunyaev-Zel'dovich and patchy screening effects: a self-consistent comparison

T0 review · 1 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The patchy-screening signal around DESI luminous red galaxies is dominated by CMB lensing, not gas.

desk verdict A useful paper that makes the first same-sample kSZ/patchy screening comparison and shows CMB lensing dominates the stacked patchy estimator; the lensing subtraction needs a data-side check before the tau upper limit is trusted. read the letter →

arxiv 2506.17379 v1 pith:KH73HIOA submitted 2025-06-20 astro-ph.CO astro-ph.GA

Boryana Hadzhiyska , Noah Sailer , Simone Ferraro This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords kinematicSunyaev-Zel'dovicheffectpatchyscreeningCMBlensingcontaminationopticaldepthDESIluminousredgalaxiesACTDR5baryonicfeedbacksecondaryanisotropies
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

This paper attempts to put the kinematic Sunyaev-Zel'dovich (kSZ) effect and the patchy screening effect on the same footing: both are imprints of free electrons on the cosmic microwave background, and both should measure the same optical depth $\tau$ of gas around galaxies. Stacking ACT temperature maps on DESI luminous red galaxies at $z\approx0.7$, the authors detect kSZ at $7.2\sigma$ and a patchy screening signal at $4.1\sigma$, and argue that about two thirds of the patchy signal is gravitational lensing of the CMB rather than gas. After subtracting a lensing template built from $N$-body simulations, the two probes agree, and the data yield a 95% upper limit $\tau<2.5\times10^{-4}$ for the sample. The kSZ effect requires a reconstructed velocity field, whereas patchy screening does not, so their agreement is a check on systematics and on the gas density itself. If the lensing correction holds, comparing the two breaks the degeneracy between optical depth and velocity, allowing tests of baryonic feedback and velocity-sensitive cosmological models.

What carries the argument

The load-bearing machinery is a pair of stacked estimators applied to the same filtered CMB maps. The kSZ estimator (Eq. 12) averages the small-scale temperature decrement around each galaxy weighted by the reconstructed line-of-sight velocity, normalized by the velocity rms and a cross-correlation coefficient $r$; the patchy screening estimator (Eq. 13) averages the small-scale temperature decrement weighted by the sign of the large-scale primary CMB temperature, normalized by the mean absolute large-scale temperature. Non-overlapping high-pass ($\ell\gtrsim2350$) and low-pass ($\ell\lesssim2000$) filters separate the relevant scales. The lensing contamination is isolated by stacking a convergence map generated from $N$-body simulations at the same halo positions, and a transfer-function method paints gas onto the $N$-body density field to predict the pure optical depth profile.

What would settle it

Stack the same patchy screening estimator around the same DESI LRGs, but subtract a lensing convergence map reconstructed directly from CMB observations instead of the simulated template; if the residual profile changes significantly or no longer agrees with the kSZ profile, the simulated lensing template is biased, and the $\tau<2.5\times10^{-4}$ bound would need revision.

Watch

Extended reading notes

Core claim

The central claim is that the apparent patchy screening signal around DESI luminous red galaxies is not primarily gas damping the primary CMB: it is dominated by CMB lensing. The stacked estimator averages a small-scale temperature decrement weighted by the sign of a large-scale temperature fluctuation, and gravitational lensing couples those two scales, producing a profile that mimics screening. Simulated lensing maps stacked at the same halo positions show that this contamination makes up roughly two thirds of the measured $4.1\sigma$ signal. Once the lensing contribution is removed, the residual optical depth profile agrees with the kSZ-derived profile, so the paper quotes $\tau<2.5\times10^{-4}$ at 95% confidence for a sample with mean $\tau\approx1.6\times10^{-4}$. The comparison with hydrodynamical simulations also indicates that baryonic feedback around these galaxies is stronger than fiducial models predict, and closer to a simulation with aggressive feedback.

Load-bearing premise

The argument stands on the assumption that the lensing template built from $N$-body simulations matches the true CMB lensing contamination of the stacked patchy screening signal in both amplitude and shape, since that contaminant makes up about two thirds of the measured signal and is subtracted from it.

Editorial extensions

If this is right

  • The measured $4.1\sigma$ patchy screening signal should not be read as a gas detection until lensing is subtracted; after subtraction it is consistent with the kSZ-derived profile, but the gas-only significance drops below a detection.
  • For the Extended DESI LRG sample, the optical depth is bounded as $\tau<2.5\times10^{-4}$ at 95% confidence, so current data cannot distinguish the predicted mean $\tau\approx1.6\times10^{-4}$ from a more gas-poor population.
  • Running both estimators on identical maps and the same galaxy sample gives a direct systematics cross-check, since additive foregrounds such as the cosmic infrared background and thermal SZ cancel differently in the two estimators.
  • If both signals are measured at high signal-to-noise, their amplitude ratio is proportional to the root-mean-square line-of-sight velocity of the host halos, which can constrain velocity-sensitive models such as modified gravity and phantom dark energy.

Reading between the lines

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

  • A direct test of the paper's lensing interpretation would replace the simulated lensing template with a lensing convergence map reconstructed from the CMB itself; if the residual $\tau$ profile is template-dependent, the two-thirds lensing attribution is not yet settled.
  • Because the high-pass filter erases most profile-shape information, the feedback comparison rests almost entirely on the amplitude of the stacked profile; a matched or multi-band filter that preserves shape could separately constrain halo mass, satellite fraction, and feedback strength.
  • The paper's upper bound is only about 1.6 times the predicted mean optical depth, so a modest increase in survey area or depth should either detect the gas or force a substantial revision of the predicted halo gas content.
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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

1 major / 6 minor

Summary. This paper presents a joint measurement of the kinematic Sunyaev-Zel'dovich (kSZ) and 'patchy screening' (anisotropic screening) effects on the same sample of DESI Extended LRGs at z~0.7, using ACT DR5 temperature maps. The authors detect kSZ at 7.2 sigma and a patchy-screening signal at 4.1 sigma, finding the latter in excess relative to kSZ. They attribute approximately two-thirds of the patchy signal to CMB lensing contamination estimated from AbacusSummit N-body simulations, and after subtracting this lensing template they place a 95% upper bound of tau < 2.5e-4 on the sample's mean optical depth. They also compare the residual signal with IllustrisTNG and Illustris simulations, inferring strong baryonic feedback, and discuss prospects for measuring the rms line-of-sight velocity from the ratio of the two effects.

Significance. If the lensing attribution is correct, this is a useful demonstration that real-space patchy-screening estimators are strongly contaminated by CMB lensing and that the contamination can be quantified with simulations; the first self-consistent kSZ-plus-patchy measurement on the same galaxy sample also provides a path toward breaking the optical-depth/velocity degeneracy. Strengths of the paper include the use of public ACT DR5 and DESI DR9 data, the ThumbStack pipeline, and state-of-the-art simulations (AbacusSummit, IllustrisTNG, Illustris), as well as explicit caveats about transfer-function limitations and covariance underestimation. The central scientific conclusions, however, rest on a simulation-based lensing template that is not validated against observed lensing maps, and the tau upper bound and feedback comparison inherit that template's systematic uncertainty.

major comments (1)
  1. [Appendix A and Fig. 5] The paper itself notes that the transfer-function method used to paint gas onto N-body simulations breaks down on the smallest radial bins, where the cross-correlation coefficient r(k) becomes much smaller than unity; Fig. 5 shows that for the high-pass-filtered estimators most of the signal-to-noise is concentrated in the first one to two radial bins. Since the amplitude fit A and the feedback comparison in Figs. 1 and 2 rely on these small-scale bins, the analysis should either restrict the fit to scales where the transfer function is calibrated (r(k) > ~0.95) or include a model for the small-scale systematic error. As written, the central amplitude conclusions are partly supported by theory curves that are acknowledged to be unreliable in the regime where the data carry the most weight.
minor comments (6)
  1. [Section II B] The text contains a typo: 'single-drequency' should be 'single-frequency'.
  2. [Section III A] The cutoff at ell ~ 2000 for the low-pass filter is motivated by preserving the sign of the primary CMB, but the threshold |T_lo| > 40 micro-K in Section III C is introduced without justification; a brief explanation of how this threshold was chosen would help the reader assess foreground contamination.
  3. [Eq. (13)] The minus sign in the patchy estimator in Eq. (13) is stated without a derivation; since the sign determines whether the measured signal has the expected positive tau profile, a one-sentence explanation of the sign convention would improve clarity.
  4. [Section IV A] The statement that lensing makes up 'about two thirds' of the signal refers to the amplitude of the high-pass-filtered profile, but the profile is a function of radius; the paper should specify whether this fraction is evaluated at the peak bin, as an integrated quantity, or as an amplitude ratio.
  5. [Appendix A] The text says the CMB map is generated from a 'random Gaussian realization from the lensed C_l,' but does not specify whether this is the lensed primary CMB power spectrum computed with CAMB; please clarify the exact input spectrum and realization procedure.
  6. [References [91] and [99]] Two references that are central to the method (the analytic lensing treatment and the transfer-function validation) are marked 'in preparation'; the manuscript should either provide the essential results in an appendix or state explicitly which conclusions would need revision if those works change.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the lensing excess is a forward-modeled simulation prediction compared against independent ACT/DESI data, not a quantity defined by the fit.

full rationale

The paper's central attribution of the patchy-screening excess to CMB lensing is an externally anchored forward model: Sec. IV B constructs the lensing contribution from public AbacusSummit convergence maps and a random CAMB C_l realization, stacks on halos matched to the DESI LRG host mass, and adds this template to the kSZ measurement in Fig. 1. The agreement with the patchy data is therefore a non-trivial consistency check, not a fit of the lensing amplitude to the data. The tau upper bound (Eq. 20 and the conversion tau < 2.5e-4) is a standard amplitude calibration: a single amplitude A is fit to a simulation template whose mean optical depth (1.6e-4) comes from the external theory/simulation prediction of Ref. [89], and the bound is A + 1.64 sigma_A times that template normalization. The kSZ-to-tau conversion uses r=0.25 and v_rms=300 km/s from separate velocity-reconstruction simulations (Refs. [61,84]), not fitted to the ACT data being interpreted. The acknowledged limitations (transfer-function breakdown on sub-arcmin scales in App. A, and the companion paper [91] still in preparation) are model-uncertainty and provenance caveats, not cases where an output equals an input by construction. No equation in the paper reduces to its own input, and the comparison is self-contained against external data and simulations.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims rest on simulation-based calibrations rather than invented physics: velocity reconstruction assumes linear bias and a cross-correlation coefficient from N-body mocks, the lensing contamination is estimated from AbacusSummit convergence maps, and the tau template comes from hydro simulations via a transfer function. The only free amplitude is the scale A fitted to the data. No new particles, forces, or entities are introduced.

free parameters (4)
  • A (amplitude rescaling of simulation template) = 0.54 for the tau upper bound; sigma_A = 0.61
    Single free parameter scaling the simulation curve to the data in Eq. 18, used to derive the 95% upper bound via Eq. 20.
  • r (velocity reconstruction cross-correlation) = 0.25
    Assumed from simulations for a photometric DESI-like LRG sample without photo-z cleaning; enters the kSZ-to-tau conversion in Eq. 19 and is not measured from the data.
  • v_rms (rms line-of-sight velocity) = 300 km/s
    Assumed typical value for LRG host halos in Eq. 19; directly scales the kSZ optical depth amplitude.
  • Low-pass temperature sign threshold = 40 microK
    Galaxies with |T_lo| below 40 microK are removed to avoid foreground sign flips (Section III C); this hand-chosen cut affects the effective galaxy sample and signal amplitude.
assumptions (6)
  • domain assumption Linear bias and the linearized continuity equation relate galaxy overdensity to the velocity field (Eq. 10).
    Used for velocity reconstruction; assumes a linear bias model and fails in highly nonlinear regimes.
  • domain assumption The reconstructed velocity field is correlated with true velocities with coefficient r=0.25, and the sample's rms line-of-sight velocity is 300 km/s.
    External simulation calibration, not measured in the ACT/DESI data; enters Eq. 19.
  • domain assumption AbacusSummit N-body convergence maps plus a Gaussian CMB realization accurately reproduce the CMB lensing contribution to the stacked patchy screening estimator.
    Underpins the claim that lensing makes up about two thirds of the screening signal and shapes the residual tau estimate; no direct comparison to observed lensing maps is shown.
  • domain assumption The transfer function method (Eq. A1) maps dark matter density to optical depth with sufficient fidelity at the scales used, with r(k) above 0.95 until about k = 1.25 h/Mpc.
    Used to construct tau templates from AbacusSummit and to compare with IllustrisTNG and Illustris; breaks down on the smallest radial bins.
  • domain assumption Hydro simulations (IllustrisTNG, Illustris) bracket the baryonic feedback of DESI LRG halos, and the mean optical depth of the sample is about 1.6e-4.
    The quoted upper bound tau < 2.5e-4 is expressed relative to this simulated mean optical depth (Section IV A).
  • domain assumption The patchy screening estimator is immune to CIB and tSZ foregrounds after sign downsampling and the |T_lo| cut.
    Relies on the sign of the large-scale CMB being uncorrelated with foreground emission; foreground sign flips near small |T_lo| are mitigated by the cut.

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Pith. "Pith review of Mapping the gas density with the kinematic Sunyaev-Zel'dovich and patchy screening effects: a self-consistent comparison." pith.science (2026). https://pith.science/paper/KH73HIOA

@misc{pith2026250617379,
  author       = {Pith},
  title        = {Pith review of: Mapping the gas density with the kinematic Sunyaev-Zel'dovich and patchy screening effects: a self-consistent comparison},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KH73HIOA}},
  note         = {Machine review of arXiv:2506.17379}
}
abstract

The secondary anisotropies of the cosmic microwave background (CMB) provide a wealth of astrophysical and cosmological information. Pairing measurements of the CMB temperature map obtained by DR5 of the Atacama Cosmology Telescope (ACT) with the large-scale structure imaging survey conducted by the Dark Energy Spectroscopic Instrument, DECaLS DR9, we investigate two effects that are sensitive to the gas density $\tau$: kinematic Sunyaev-Zel'dovich (kSZ) and `patchy screening' (also known as `anisotropic screening'). In particular, we measure the stacked profiles around Luminous Red Galaxies (LRGs) at a mean redshift of $z \approx 0.7$. We detect the kSZ signal at 7.2$\sigma$, and we find a signal at $\sim 4.1\sigma$ for the patchy screening estimator, which is in excess relative to the kSZ signal. We attribute this excess to contamination from CMB lensing. We demonstrate the effect of lensing using $N$-body simulations, and we show that the screening signal is dominated by it. Accounting for lensing, our measurement places a 95\% upper bound on the optical depth of the Extended DESI LRG sample of $\tau <$ 2.5 $10^{-4}$ for a mean value of the sample of $\tau \approx$ 1.6 $10^{-4}$. Furthermore, via hydro simulations, we show that the underlying optical depth signal measured by both effects (after removing the CMB lensing contribution) is in perfect agreement when adopting either a Compensated Aperture Photometry (CAP) filter or a high-pass filter. Consistent with previous measurements, we see evidence for excess baryonic feedback around DESI LRGs in the patchy screening measurement. Provided both effects are measured with high signal-to-noise, one can measure the amplitude ratio between them, which is proportional to the root-mean-square velocity of the host halo sample, and place constraints on velocity-sensitive models such as modified gravity and phantom dark energy.

Figures

Figures reproduced from arXiv: 2506.17379 by the authors.

Figure 1
Figure 1. FIG. 1. Estimates of the optical depth of DESI LRG groups using [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Optical depth signal in the ‘patchy screening’ measurement [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. CAP ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Simulated high-pass-filtered profiles of the ‘patchy screening’ [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Comparison of the signal-to-noise per radial bin for our [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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