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Replacing galaxies with weak-lensing convergence in a kSZ template yields a cross-correlation directly sensitive to the electron–matter power spectrum, giving few-percent to sub-percent constraints on baryonic feedback without modeling the

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2026-08-03 13:48 UTC pith:XG3IZWJT

load-bearing objection New estimator, clean and honest, but the headline precision claim rests on an unvalidated P_em≈P_bm mapping. the 4 major comments →

arxiv 2607.29091 v1 pith:XG3IZWJT submitted 2026-07-31 astro-ph.CO

Direct shear times kSZ correlation: controlling baryons without modeling galaxies

Anoma Ganguly , Emmanuel Schaan , Elisabeth Krause , Tim Eifler This is my paper
classification astro-ph.CO
keywords kinetic Sunyaev-Zeldovich effectweak lensing convergencebaryonic feedbackmatter power spectrumvelocity reconstructionnulling transformcosmic shearCMB cross-correlation
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 proposes a new way to measure how baryonic feedback suppresses the small-scale matter power spectrum, the leading unknown for upcoming weak-lensing surveys. Instead of stacking the kSZ signal on galaxies, which requires modeling how galaxies trace dark matter, the authors multiply tomographic weak-lensing convergence maps by radial velocity maps reconstructed from galaxy surveys to build a kSZ template, then cross-correlate it with the CMB temperature. The resulting signal is directly proportional to the electron–matter power spectrum, so a clean measurement yields the baryonic contribution to cosmic shear without a galaxy–matter model. Forecasting for two weak-lensing surveys with three CMB experiments, they find signal-to-noise of 5–15 for current and upcoming CMB data and above 100 for a future CMB-HD survey, corresponding to few-percent and sub-percent constraints, respectively, on the baryonic suppression of the matter power spectrum.

Core claim

The central claim is that the cross-correlation between the CMB temperature and a template \(\hat{T}_{i,\alpha}=K_i V_\alpha\), formed by multiplying the weak-lensing convergence in redshift bin \(i\) with a radial-velocity map reconstructed in bin \(\alpha\), isolates the kinetic Sunyaev-Zeldovich signal and measures the electron–matter power spectrum \(P_{em}(k,z)\) nearly directly (Eq. 4.3). Because the template is linear in the line-of-sight velocity and in the observed temperature, primary CMB and foregrounds average away; because the convergence field traces total matter rather than galaxies, no galaxy–halo connection needs to be modeled. Applying the BNT nulling transform localizes th

What carries the argument

The central object is the template kSZ estimator \(\hat{T}_{i,\alpha}=K_i V_\alpha\), the product of a tomographic weak-lensing convergence map and a radial-velocity map reconstructed from galaxy positions via the linearized continuity equation. Cross-correlating this template with the observed CMB temperature yields a signal whose leading term is \(P_{em}\) multiplied by the velocity power spectrum (Eq. 4.3). The BNT nulling transform, a linear combination of three adjacent lensing kernels that cancels low-redshift contributions, localizes the measurement in comoving distance. An important secondary mechanism is the decomposition of the matter power spectrum \(P_{mm}\) into dark-matter and

Load-bearing premise

The conversion of the measured electron–matter power spectrum into a constraint on baryonic suppression assumes that free electrons faithfully trace all baryons and that the neglected baryon–baryon term stays at its \(O((\Omega_b/\Omega_m)^2)\approx 1.7\%\) level; on the small scales where feedback is strongest, the ionized gas distribution may depart from the total baryon distribution.

What would settle it

In a hydrodynamical simulation box where the electron–matter and baryon–matter power spectra are known, compute the ratio \(P_{em}/P_{bm}\) at \(z\approx 0.5\) and \(k\approx 1\,h/\mathrm{Mpc}\). If this ratio deviates from unity by more than the claimed few-percent precision, the method's translation from \(P_{em}\) to the baryonic suppression of the matter power spectrum is invalid.

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

If this is right

  • A detection is predicted at about 5σ for Rubin with ACT and 10σ for Roman with ACT, so the signal should be measurable with data already being collected.
  • With the Simons Observatory the same method reaches SNR around 8–15, enough to constrain baryonic feedback at the few-percent level required for Rubin-era cosmic shear.
  • With CMB-HD the SNR exceeds 100, giving sub-percent constraints on the baryonic suppression of the matter power spectrum.
  • Because the estimator uses convergence instead of a galaxy overdensity field, it sidesteps the galaxy–matter connection, assembly bias, and halo miscentering that complicate existing kSZ–galaxy analyses.
  • The nulling transform makes the measurement redshift-localized, so the redshift evolution of feedback can in principle be mapped.

Where Pith is reading between the lines

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

  • The few-percent calibration implicitly assumes free electrons trace baryons; a useful cross-check would be comparing the shear–kSZ template signal with pairwise kSZ measurements around galaxy clusters, where the gas profile is independently known.
  • The same template construction could be extended to other gas tracers, such as fast radio burst dispersion measures or X-ray gas maps, potentially providing independent handles on the same baryonic suppression.
  • A practical refinement would be to use a dedicated low-redshift spectroscopic sample for velocity reconstruction, which the paper suggests but does not quantify; this would recover the missing leverage for Roman at z<0.5.
  • The correlation coefficient between template and true kSZ is only about 0.04–0.06, so this estimator is not suited to cleaning kSZ from CMB maps; the paper's own forecasts do not require such cleaning.

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 / 6 minor

Summary. This paper proposes a new estimator for the baryonic contribution to weak-lensing signals. The kSZ template \hat{T}_{i,\alpha} = K_i V_\alpha is formed by multiplying tomographic weak-lensing convergence maps with reconstructed radial-velocity maps, then cross-correlated with CMB temperature maps. The signal is shown to be proportional to the electron–matter power spectrum P_em, and BNT nulling is used to localize the measurement in redshift. Forecasts are computed for Rubin LSST Y10 and Roman kinematic-lensing samples combined with ACT, SO, and CMB-HD, using a Battaglia AGN-feedback model for P_em and Gaussian covariance. The paper reports SNR ≈ 5–15 for current/upcoming CMB experiments and ≳100 for CMB-HD, and interprets these as few-percent and sub-percent constraints on the baryonic suppression of the matter power spectrum. Detailed derivations are provided in appendices.

Significance. If the central assumption P_em ≈ P_bm can be validated, this is a novel and valuable probe: it avoids modeling the galaxy–matter connection, it is linear in the CMB temperature and therefore less sensitive to foregrounds than ⟨T²g⟩ estimators, and the BNT nulling enables redshift-resolved measurements. The analytic derivation in App. A is coherent and the appendices provide enough detail for the forecast to be reproduced. The authors are also transparent about several limitations, including shape noise, velocity-reconstruction noise, and the modest correlation coefficient for CMB map cleaning. However, the headline precision claim is statistical and conditional on a physical mapping that is not demonstrated. The paper would be substantially strengthened by quantifying P_em/P_bm and the neglected ⟨δ_e v_r⟩ term.

major comments (4)
  1. [§3.1, Eq. (3.3)] The headline conversion from the measured cross-spectrum to the baryonic suppression of P_mm rests on the one-line assumption that free electrons trace all baryons, P_em ≈ P_bm. This is not tested. On the scales where feedback matters (k ≳ 1 h/Mpc), a non-negligible baryon fraction resides in stars and cold neutral gas, which do not contribute to P_em, and the hot ionized gas is spatially segregated from these condensed phases. If P_em/P_bm deviates from unity by a few percent, the inferred suppression is biased by an amount comparable to the claimed 2–5% (ACT/SO) or <1% (CMB-HD) statistical precision. The paper should quantify this ratio using hydrodynamical simulations (e.g., FLAMINGO, IllustrisTNG, or at least a two-phase model) and propagate it as a systematic, or explicitly reframe the claim as a measurement of P_em rather than of the baryonic suppression of P_mm.
  2. [App. A.3, Eq. (A.4)–(A.5)] The Wick contraction yields three terms; the second (and third) is dropped with the argument 'P_evr ∼ 1/k P_em.' This argument is incomplete: the retained term contains P_em P_vrvr, and P_vrvr ∼ P_mm/k², so the ratio of the dropped term to the retained term is of order P_em/P_mm, not 1/k. Unless the k_∥/K_∥ integrations cause cancellation, the dropped term can be comparable to the signal. The manuscript does not quantify it. Given that the forecast SNR is a central result, this term should be estimated (e.g., with a simple perturbative model or a simulation-based power spectrum) and shown to be subdominant, or included in the signal.
  3. [§5.1, §5.3, Fig. 2] The translation of total SNR into 'precision on the matter power spectrum' is too schematic. It uses ≈2(Ω_b/Ω_m)/SNR, which assumes P_bm/P_mm ≈ 1 and a single effective scale, while the SNR is integrated over ℓ and over all bin pairs, i.e., over a range of k and z. The resulting constraint on the baryonic suppression is a weighted average with a model-dependent shape (here Battaglia AGN). Please express the forecast as a constraint on P_em bandpowers (or on a specified amplitude of the suppression in k bins), and state clearly that the percent-level numbers are statistical only, before systematic terms such as P_em/P_bm are added.
  4. [§4.3, §5.1, App. A] The forecast relies on the snapshot approximation: P_em and P_vrvr are evaluated at the kernel peak redshifts, while the nulled kernels have widths 355–977 Mpc. The appendix itself notes that this assumption 'may not be accurate on non-linear scales if the redshift bin is very wide.' The covariance is also Gaussian and drops non-Gaussian contributions and the K–V cross-term. None of these approximations is tested or assigned an uncertainty. For a paper claiming sub-percent precision with CMB-HD, these choices need validation (e.g., comparing to a light-cone computation or using non-Gaussian covariance from simulations), or the precision claims should be softened to reflect the systematic uncertainty.
minor comments (6)
  1. [§1, first paragraph] There is a typo: 'The kSZ effect is one such probe that traces the electron distribution directly is the kSZ effect.' The duplicated phrase should be removed.
  2. [Fig. 2] Please clarify in the caption that the right-axis precision is the statistical precision under the P_em = P_bm and P_bm/P_mm = 1 assumptions; as written, it may be misread as an unconditional forecast.
  3. [Eq. (4.1)] The notation H^v_α(χ) uses χ′ in the velocity integral but is written as a function of χ; align the variables to avoid confusion.
  4. [§5.1] The section is titled 'Full covariance' but the covariance is Gaussian and explicitly ignores the K–V cross-term; rename or caveat to avoid overstating the completeness.
  5. [§1, references to [73]] The sentence about [73] appearing 'the same week' is better placed in a footnote or removed; in a refereed paper, contemporaneous related work is usually cited without the submission-time remark.
  6. [App. B.1] The toy model assumes ⟨δ1δ2⟩ = 0 for Δχ > 100 Mpc; this is a strong assumption and should be stated as such in the main text when using the optimum width.

Circularity Check

0 steps flagged

No significant circularity: the forecast SNR is a genuine prediction from external survey specifications and a Battaglia gas profile; the P_em≈P_bm mapping is a physical modeling assumption, not a circular reduction.

full rationale

The derivation chain is self-contained rather than circular. The kSZ signal is defined in Eq. (2.2) as a projection of δ_e v_r, and the template in Eq. (4.1) is a product of the lensing convergence (δ_m) and reconstructed radial velocity (v_r). The cross-correlation derived in Eq. (4.3) therefore isolates ⟨δ_e δ_m⟩⟨v_r v_r⟩ = P_em P_vr; this is exactly what the estimator is constructed to measure, not a pre-existing result renamed as a prediction. The SNR forecast in Eq. (5.9) uses the signal model (Eq. 4.3, with P_em from the external Battaglia [76] AGN profile) and the covariance (Eq. 5.1, with survey noise from DESC SRD, Roman KL, and CMB experiment specifications). No parameter is fitted to the headline 'baryonic suppression of the matter power spectrum'; the conversion in Eq. (3.3) is an explicit algebraic decomposition together with the assumption P_em≈P_bm. The BNT nulling coefficients are linear and, as the paper states, the method does not rely crucially on nulling because the transformation is invertible. Self-citations to prior velocity-reconstruction and Roman-sample papers supply practical methodology and survey definitions, not a load-bearing uniqueness theorem. The unvalidated electron-baryon mapping (P_em≈P_bm) is a genuine modeling uncertainty that could bias the physical interpretation at the claimed precision, but that is a correctness risk, not a circularity. No specific step in the paper reduces a prediction to a fitted input by construction, so the circularity score is 0.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The forecast rests on external inputs (Battaglia 2016 profile, survey specs from [69,75,78]) and on modeling choices (snapshot approximation, Gaussian covariance, linear velocity reconstruction, P_em≈P_bm). None are fitted to the target result in this paper, so the circularity burden is low, but the headline precision inherits their uncertainties.

free parameters (4)
  • Battaglia AGN-feedback generalized NFW parameters = AGN-feedback best-fit from Battaglia 2016
    The fiducial P_em signal used for the SNR forecast is computed from this profile; a different gas distribution changes the signal amplitude and hence the SNR.
  • Nulled-kernel width (Rubin) = 977 Mpc
    Chosen by maximizing forecast SNR (Sec. 5.3, Fig. 7); an optimization over a hyperparameter, not a fit to data, but the result depends on it.
  • Nulled-kernel width (Roman KL) = 355 Mpc
    Same as above.
  • Galaxy linear bias b(z) for velocity reconstruction = 0.95 for Rubin; 0.88z+0.49 / 0.98z+0.49 for Roman Hα/[OIII]
    Used in the velocity reconstruction noise (Eq. 5.5-5.6); adopted from [75] and [78].
axioms (5)
  • domain assumption Free electrons trace baryons: P_em ≈ P_bm
    Assumed in §3.1 before Eq. (3.2); the conversion of the kSZ measurement to the baryonic contribution to P_mm depends on it.
  • domain assumption Snapshot approximation: 3D power spectra evaluated at kernel-peak redshifts z_i,z_α
    Used in §4.3 and App. A to factor the signal into P_em × P_vrvr; App. A notes it may fail for wide bins (977 Mpc for Rubin).
  • domain assumption Gaussian covariance for the estimator
    Covariance in Eq. (5.1) ignores non-Gaussian contributions and the κ–v cross-term; the kSZ signal is inherently non-Gaussian.
  • domain assumption Linear continuity-equation velocity reconstruction with linear bias
    Reconstructed velocity field V_α (Eq. 2.8, Sec. 5.1) assumes galaxies trace matter linearly on large scales; used in signal and noise.
  • standard math Limber and shift-property evaluation of LoS integrals
    App. A.3/A.4 uses Limber approximation (ℓ+1/2)/χ̄ and Fourier shift property; standard but approximate at low ℓ.

pith-pipeline@v1.3.0-daily-deepseek · 26432 in / 18197 out tokens · 168724 ms · 2026-08-03T13:48:16.288479+00:00 · methodology

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read the original abstract

Baryonic feedback redistributes gas within and around dark matter haloes, suppressing the small-scale matter power spectrum at a level that is now the leading systematic for upcoming weak-lensing surveys. The kinetic Sunyaev-Zeldovich (kSZ) effect directly probes this redistributed gas, but existing measurements around galaxies are either tied to the properties of the chosen galaxy sample or are susceptible to biases from other extragalactic foregrounds. We address both by cross-correlating a kSZ template constructed from the tomographic weak-lensing convergence maps and the radial velocity maps reconstructed from galaxy surveys via the continuity equation, with the observed CMB temperature. We forecast the detectability for Rubin LSST Y10 and Roman kinematic lensing samples combined with ACT, SO, and CMB-HD, finding signal-to-noise ratios of $\sim$ 5-15 for current and upcoming CMB data and $\gtrsim 100$ for CMB-HD, corresponding to few-per-cent and sub-per-cent constraints, respectively, on the baryonic suppression of the matter power spectrum. This method should thus achieve sufficient statistical precision to model baryonic feedback effects for Rubin, without the systematic challenge of modeling any galaxy-matter connection.

discussion (0)

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