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arxiv: 2606.24061 · v1 · pith:6MXVIHGKnew · submitted 2026-06-23 · 🌌 astro-ph.CO

Probing Baryonic Feedback Effect with CSST Weak Lensing and Future FRB Measurements

Shuai Feng , Yan Gong , Qi Xiong , Xiaohui Liu , Hengjie Lin , Dingao Hu , Xuelei Chen This is my paper

Pith reviewed 2026-06-25 23:53 UTC · model grok-4.3

classification 🌌 astro-ph.CO
keywords baryonic feedbackweak lensingfast radio burstsdispersion measureneutrino massCSSTSKA
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The pith

Joint CSST weak lensing and FRB dispersion measures constrain the AGN feedback parameter to 0.4 percent accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper explores combining weak lensing measurements from the upcoming CSST photometric survey with dispersion measure statistics from future FRB observations by SKA and DSA-2000. Using a baryonic halo model to generate mock matter, electron, and matter-electron power spectra that include realistic noise, the authors run MCMC fits and find that lensing data alone reaches 3.1 percent precision on log10 T_AGN while the sum of neutrino masses is bounded below 0.53 eV. Adding the FRB data improves the feedback parameter precision to 0.4 percent and tightens the neutrino mass upper limit to 0.47 eV by breaking degeneracies between baryonic effects and other cosmological parameters.

Core claim

The joint analysis of CSST weak lensing and future FRB DM measurements, modeled with the baryonic halo model, improves the constraint on the baryonic feedback parameter log10 T_AGN from 3.1 percent to 0.4 percent and yields a tighter upper limit on the neutrino mass sum.

What carries the argument

The baryonic halo model, used to compute the matter, electron, and matter-electron power spectra that encode AGN feedback effects on the distributions.

If this is right

  • The joint probe supplies tighter bounds on the sum of neutrino masses by reducing uncertainty from baryonic feedback.
  • The 3x2pt analysis incorporating FRB data breaks the degeneracy between feedback strength and neutrino mass that appears in lensing alone.
  • Similar joint constraints become feasible for other cosmological parameters once the feedback effect is better controlled.

Where Pith is reading between the lines

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

  • Electron density information from FRBs supplies an independent handle on feedback that lensing shear alone cannot isolate.
  • Survey planners could prioritize overlapping sky coverage between CSST and next-generation radio arrays to maximize the gain shown in the mocks.
  • If the improvement holds in real data, the same combination might be applied to other large-scale structure observables sensitive to baryon distribution.

Load-bearing premise

The baryonic halo model used both to generate the mock power spectra and to fit the simulated data accurately captures the real effects of AGN feedback on matter and electron distributions.

What would settle it

Real CSST and SKA observations that produce a matter-electron power spectrum inconsistent with the baryonic halo model predictions at the relevant scales and redshifts would falsify the claimed improvement in parameter constraints.

Figures

Figures reproduced from arXiv: 2606.24061 by Dingao Hu, Hengjie Lin, Qi Xiong, Shuai Feng, Xiaohui Liu, Xuelei Chen, Yan Gong.

Figure 1
Figure 1. Figure 1: The rescaled intrinsic FRB source redshift distri￾bution, ns(z), observed redshift distribution after accounting for selection effects for SKA1-Mid and DSA-2000, nf(z), and corresponding radial kernel of the FRB DM field, WD(z). The all-sky FRB event rate Nsky above a fluence thresh￾old Fν is assumed as (Macquart & Ekers 2018) Nsky(> Fν) = N ′ sky  Fν F′ ν λ , (12) where the power-law index λ = −1.5, as … view at source ↗
Figure 2
Figure 2. Figure 2: Impact of baryonic feedback parameterized by TAGN on Pmm, Pme, and Pee at redshift z = 0. Each power spectrum computed with a different TAGN is divided by the fiducial spectrum with log10 TAGN = 7.8 to illustrate the sensitivity to baryonic feedback strength. is treated as a delta function in the two-halo term and neglected in the one-halo term (Mead et al. 2020). The stellar fraction, f∗(M), takes the for… view at source ↗
Figure 3
Figure 3. Figure 3: The theoretical predictions and mock data for the angular power spectrum of the FRB DM field. The black solid curve shows the fiducial theoretical model, C˜DD = CDD + ND, while the green dashed and blue dash– dotted curves represent the signal term, CDD, and the noise term, ND, respectively. The red data points denote the mock data used in this work. The data points in the shaded region at ℓ < 50 are exclu… view at source ↗
Figure 4
Figure 4. Figure 4: The CSST redshift distributions of galaxies (left panel), n i g(z), and the weak lensing kernel (right panel), Wi κ(z), for the i-th tomographic bin. Under the Limber and flat-sky approximation, the convergence power spectrum P ij κ (ℓ) can be derived as P ij κ (ℓ) = Z χH 0 dχ Wi κ (χ)Wj κ (χ) r 2(χ) Pmm  k = ℓ + 1/2 r(χ) , z , (28) where Wi κ (χ) is the lensing kernel for the i-th tomo￾graphic bin, defi… view at source ↗
Figure 5
Figure 5. Figure 5: The theoretical predictions and mock data for the angular power spectra of weak lensing between the i-th and j-th tomographic bins, C˜ij γγ. The black solid curve represents the fiducial theoretical model, while the green dashed curve shows the signal component, C ij γγ, and the blue dash-dotted curve denotes the noise term, δijσ 2 γ/n¯i +Nadd. The red data points correspond to the mock data used in this w… view at source ↗
Figure 6
Figure 6. Figure 6: The theoretical predictions and mock data for the angular cross-power spectra between the FRB DM field and weak lensing in each tomographic bin, C˜i γD. The black solid curves represent the fiducial theoretical model, while the red data points denote the mock data used in this work. The data points in the shaded region at ℓ < 50 are excluded from the analysis, where the flat-sky and Limber approximations a… view at source ↗
Figure 7
Figure 7. Figure 7: The covariance matrix for the FRB DM au￾to-power spectrum C˜DD, the weak lensing auto-power spec￾tra C˜γγ, and their cross-power spectra C˜γD, where the cor￾relation coefficients are defined as C˜ij/ q C˜iiC˜jj , with i and j denoting the indices of the data points. they can be expressed as (Berghaus et al. 2025) C i κD(ℓ) = Z χH 0 dχ Wi κ (χ)WD(χ) r 2(χ) Pme  ℓ + 1/2 r(χ) , z (36) and C i ID(ℓ) = Z χH 0… view at source ↗
Figure 8
Figure 8. Figure 8: The predicted contour maps (68% and 95% CL) and 1D PDFs of the seven cosmological parameters and the baryonic feedback parameter log10 TAGN derived from the FRB observables (gray), CSST weak lensing measurements (red), and the joint 3 × 2pt analysis (blue). The vertical and horizontal dashed lines indicate the fiducial values of these parameters. 30% of the samples are discarded as burn-in, and the remaini… view at source ↗
Figure 9
Figure 9. Figure 9: The predicted contour maps (68% and 95% CL) and 1D PDFs of the systematic parameters, including the photo-z calibration parameters ∆zi and σ i z, shear calibration parameters mi, intrinsic alignment parameters AIA and ηIA, additive error parameter Nadd and DM field noise parameter ND, derived from the FRB observables (gray), CSST weak lensing measurements (red), and 3 × 2pt analysis (blue). The vertical an… view at source ↗
read the original abstract

We explore the joint probe on the baryonic feedback effect using the weak lensing measurement from the upcoming Chinese Space-station Survey Telescope (CSST) photometric survey and the dispersion measure (DM) statistics of the fast radio bursts (FRBs) from next-generation radio telescopes, i.e., the Square Kilometre Array (SKA) and the Deep Synoptic Array (DSA-2000). By employing the baryonic halo model, we compute the matter, electron, and matter-electron power spectra, and generate mock data considering realistic noise and systematic effects based on the designs of the telescopes. These mock data are then analyzed using the Markov Chain Monte Carlo (MCMC) method to investigate the parameter constraints. We find that CSST weak lensing alone can constrain the baryonic feedback parameter $\log_{10} T_{\text{AGN}}$ to an accuracy of $3.1\%$, with the sum of neutrino mass bound $\sum m_{\nu} < 0.53\,\mathrm{eV}$. When performing the $3\times2$pt analysis, the inclusion of FRB DM measurements can significantly improve the precision of $\log_{10} T_{\text{AGN}}$ to $0.4\%$, and will lead to a better constraint on $\sum m_{\nu}$ with an upper limit $< 0.47\,\mathrm{eV}$ by effectively breaking the degeneracy. Our results demonstrate that the joint observation of future FRB DM and weak lensing surveys is a powerful tool for probing the baryonic feedback effect, which is helpful in obtaining robust constraints on the neutrino mass and other important cosmological parameters.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper forecasts cosmological constraints from joint CSST weak-lensing (WL) and future FRB dispersion-measure (DM) data. Using a baryonic halo model, it computes matter, electron, and matter-electron power spectra, generates mock 3×2pt data with realistic noise, and runs MCMC to constrain log10 T_AGN and Σmν. CSST WL alone yields 3.1% precision on log10 T_AGN and Σmν < 0.53 eV; adding FRB DM tightens these to 0.4% and Σmν < 0.47 eV by breaking degeneracies. The central claim is that the joint probe is a powerful tool for baryonic feedback and yields robust neutrino-mass constraints.

Significance. If the baryonic halo model is an adequate description of AGN feedback on both matter and electron fields, the work demonstrates that FRB DM statistics can meaningfully tighten WL constraints on feedback and neutrino mass. The quantitative improvement (factor ~8 in log10 T_AGN precision) is a concrete illustration of the value of cross-correlating lensing with electron-tracing observables. The manuscript does not, however, provide evidence that the quoted precisions survive model misspecification.

major comments (1)
  1. [Abstract] Abstract: the headline claim that the joint analysis yields 'robust constraints on the neutrino mass' rests on the assumption that the baryonic halo model used to generate the mock power spectra is also the correct model for inference. Because the same parametric form is employed for both forward modeling and fitting, the reported gains (3.1% → 0.4% on log10 T_AGN; 0.53 eV → 0.47 eV on Σmν) demonstrate internal consistency within that model family but do not address bias or loss of constraining power if the true feedback deviates from the halo-model parametrization (different heating profiles, scale-dependent suppression, or non-parametric effects seen in hydrodynamical simulations). This single shared assumption is load-bearing for the robustness statement.
minor comments (1)
  1. [Abstract] The abstract and methods description should explicitly state that all forecasts are conditional on the baryonic halo model being correct, rather than presenting the results as model-independent robustness tests.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive comment. We agree that the language in the abstract regarding 'robust constraints' requires clarification to accurately reflect the scope of our forecast analysis.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that the joint analysis yields 'robust constraints on the neutrino mass' rests on the assumption that the baryonic halo model used to generate the mock power spectra is also the correct model for inference. Because the same parametric form is employed for both forward modeling and fitting, the reported gains (3.1% → 0.4% on log10 T_AGN; 0.53 eV → 0.47 eV on Σmν) demonstrate internal consistency within that model family but do not address bias or loss of constraining power if the true feedback deviates from the halo-model parametrization (different heating profiles, scale-dependent suppression, or non-parametric effects seen in hydrodynamical simulations). This single shared assumption is load-bearing for the robustness statement.

    Authors: We appreciate the referee's observation. Our analysis is a forecast performed entirely within the baryonic halo model: the same parametrization generates the mock power spectra and is used for MCMC inference. The quoted improvements therefore illustrate the gain from adding FRB DM data under consistent model assumptions, specifically the breaking of degeneracies between log10 T_AGN and Σmν. We acknowledge that this does not constitute a test of robustness against model misspecification (e.g., different heating profiles or hydrodynamical effects). We will revise the abstract to replace the phrase 'robust constraints on the neutrino mass' with 'improved constraints on the neutrino mass' and insert a brief clarifying sentence noting that results are obtained within the adopted halo-model framework. This change will be implemented in the revised manuscript. revision: yes

Circularity Check

1 steps flagged

Baryonic halo model generates mocks and performs the fit; constraints are internal to the model

specific steps
  1. fitted input called prediction [Abstract]
    "By employing the baryonic halo model, we compute the matter, electron, and matter-electron power spectra, and generate mock data considering realistic noise and systematic effects based on the designs of the telescopes. These mock data are then analyzed using the Markov Chain Monte Carlo (MCMC) method to investigate the parameter constraints."

    The same baryonic halo model (with parameters such as log10 T_AGN) is used to forward-model the mock CSST WL and FRB DM data and is subsequently fitted to those mocks. The reported improvements (log10 T_AGN precision from 3.1% to 0.4%, neutrino mass limit from 0.53 eV to 0.47 eV) are therefore statistically forced by the shared parametric form rather than arising from independent data.

full rationale

The paper's forecast uses the baryonic halo model both to compute the power spectra and generate mock data, then fits the identical parametric model via MCMC to those mocks. This produces the quoted precision gains on log10 T_AGN and neutrino mass as internal consistency within the shared assumption, matching the fitted_input_called_prediction pattern. No alternative hydrodynamical simulations or different feedback parametrizations are used for robustness, so the central claim reduces to the input model by construction. The derivation remains self-contained only within that model family, warranting a moderate circularity score.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim depends on the baryonic halo model being an adequate description of feedback and on the mock noise models matching actual telescope performance; both are domain assumptions rather than derived quantities.

free parameters (1)
  • log10 T_AGN
    Central parameter of the baryonic feedback model whose fiducial value is used to generate mocks and whose recovery precision is reported.
axioms (2)
  • domain assumption The baryonic halo model correctly captures AGN feedback effects on matter and electron power spectra.
    Invoked to compute the spectra used for both mock generation and parameter fitting.
  • domain assumption Noise and systematic models for CSST and SKA/DSA-2000 match the assumed levels in the mocks.
    Required to produce the 'realistic' mock data whose analysis yields the quoted constraints.

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discussion (0)

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