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Probing the Baryon Distribution with Fast Radio Bursts

T0 review · 3 major / 5 minor · reviewed 2026-07-14 · grok-4.5

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Pith's one-line read SKA FRB dispersion measures can pin down baryonic feedback and tighten Stage IV cosmology by a factor of two to five.

desk verdict Solid SKA science-case forecasts for FRB DM correlations; the 2–5 imes feedback gains are real under optimistic rates, not guaranteed otherwise. read the letter →

arxiv 2606.29388 v2 pith:PW4CSNIX submitted 2026-06-28 astro-ph.CO astro-ph.GA

Manisha Caleb , Jéferson A. S. Fortunato , Steffen Hagstotz , Clancy W. James , Joscha N. Jahns-Schindler , Dylan L. Jow , Evan F. Keane , Koustav Konar
show 7 more authors
Yin-Zhe Ma Daniele Michilli Robert Reischke Amit Seta Priyanka Singh Laura G. Spitler Yidan Wang
This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords fastradioburstsdispersionmeasurebaryonicfeedbackSquareKilometreArraycosmicsheargalaxyclusteringMacquartrelationcircumgalacticmedium
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

Stage IV galaxy surveys lose power on small scales because baryonic feedback moves gas and dark matter in ways that are hard to model. Fast radio bursts measure the integrated free-electron density along each line of sight through their dispersion measure, giving a direct tracer of ionised baryons without assuming temperature or pressure. This chapter forecasts that five years of SKA FRB detections, used both as the scatter around the Macquart relation and as the two-point statistics of the DM field cross-correlated with cosmic shear and galaxy clustering, can constrain feedback parameters tightly enough to restore much of the cosmological information those surveys would otherwise lose. The same data also open routes to cool gas in the circumgalactic medium via scattering timescales and to the timing of reionisation at high redshift. The practical payoff is that SKA FRBs become a calibration tool that lets optical Stage IV surveys reach their design precision on dark energy and neutrino mass.

What carries the argument

The angular power spectrum of the dispersion-measure field (and its cross-spectra with shear and galaxies), whose weight function is proportional to the ionised electron density times the FRB redshift distribution; the same electron density also sets the width of the Macquart DM–z relation under different feedback strengths.

What would settle it

After five years of SKA FRB operations, measure whether the joint 6 imes2pt Fisher constraints on log10 TAGN (and the associated small-scale power-spectrum parameters) actually tighten by a factor of ~2–5 relative to a pure Stage IV 3 imes2pt analysis, using the realised FRB number counts and redshift distribution.

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Extended reading notes

Core claim

With optimistic five-year SKA FRB samples, adding the DM auto-spectrum and its cross-spectra with galaxy clustering and cosmic shear to a Stage IV 3 imes2pt analysis improves constraints on baryonic feedback (parameterised by log10 TAGN) and related shape parameters by a factor of roughly two to five, so that SKA FRBs can pin down feedback models and thereby strengthen the cosmological reach of Euclid- and Rubin-class surveys.

Load-bearing premise

The forecasts rely on the most optimistic synthetic FRB detection rates and redshift distributions for the planned SKA configurations; if real rates, host contributions, or scattering are substantially worse, the claimed gains shrink.

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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

3 major / 5 minor

Summary. This SKA Science Book chapter forecasts how FRBs detected by SKA-Mid and SKA-Low can constrain the ionised baryon distribution from galactic to cosmological scales. It examines DM scatter about the Macquart relation under weak/strong feedback (Figs. 1–2), a simulation-based inference pipeline on GLASS log-normal shells for cosmological and host parameters (Fig. 3), and Fisher forecasts that add DM auto- and cross-spectra (DMDM, g–DM, κ–DM) to a Stage-IV 3×2pt analysis (cosmic shear + galaxy clustering). With five years of optimistic AA4/AA* counts the forecasts show factor ~2–5 improvements on log10 TAGN and related parameters (Figs. 4–5). Complementary sections discuss CGM cool-gas scattering (Eq. 15, Fig. 7), halo cross-matching/stacking, and speculative EoR/HeII applications.

Significance. If the forecasts hold under realistic rates, the work supplies a concrete, multi-probe case that SKA FRBs can calibrate baryonic feedback that otherwise limits Stage-IV weak-lensing and clustering cosmology, while also opening CGM morphology and (more cautiously) reionisation science. Strengths include standard Fisher machinery with explicit multipole cuts, noise models (Eq. 10) and Gaussian covariance, an SBI treatment of non-Gaussian DM likelihoods, and clear synergy with Rubin/Euclid. The chapter is a useful planning document for the SKA Transients SWG and the broader Stage-IV community.

major comments (3)
  1. [§4, Figs. 4–5, Eq. (10)] §4 (and abstract): the headline claim that SKA FRBs improve Stage-IV constraints on log10 TAGN (and related parameters) by a factor ~2–5 rests on “the most optimistic FRB count from the synthetic redshift distribution” of the companion Caleb et al. (2026) for five years of AA4/AA* Band 2 and Low. Because the DM noise term in Eq. (10) scales as 1/n_FRB and the feedback signal lives on small-scale power, a factor-of-several drop in usable events (or stronger host/scattering losses) would shrink the reported gains below the “crucial role” threshold. At minimum the Fisher matrices should be re-run for a conservative or intermediate rate, or the abstract/§4 language should be explicitly conditioned on the optimistic counts.
  2. [§4, Figs. 4–5] §4: baryonic feedback is compressed into a single free parameter log10 TAGN whose effect on the electron power spectrum is taken from a fixed simulation suite. Real feedback models are multi-parameter (mass- and redshift-dependent gas profiles, AGN vs SN, etc.). The paper should either demonstrate that the single-parameter compression does not artificially inflate the gain relative to a more flexible model, or clearly state that the factor 2–5 is an upper bound under this simplification.
  3. [§3.2, Fig. 3] §3.2 / Fig. 3: the SBI Macquart analysis inherits the same optimistic one-year localised counts and a simple log-normal host-DM model. The text already notes that σ8 is only constrained once Nside ≥ 1024 (AA4 Low). A short robustness check with reduced n_FRB or a more flexible host model would strengthen the claim that SBI becomes “the gold standard”.
minor comments (5)
  1. [§6.1, abstract] §6.1: high-z (z>6) FRB rates are extrapolated from z<5 simulations; the paper correctly flags this as speculative, but the abstract’s phrasing on EoR should be softened to match the body.
  2. [§5] Eq. (15) / Fig. 7: the CGM scattering forecast is useful, but the primary challenge of disentangling MW ISM + host scattering is only mentioned in one sentence; a brief quantitative estimate of residual contamination would help.
  3. [§2] Notation: χe (electrons per baryon) and f(z) (ionised fraction) appear without a single consolidated definition table; a short glossary or early equation block would aid non-FRB readers.
  4. [§4] Heavy self-citation of Reischke/Hagstotz methods is appropriate given the authors, but a few additional independent FRB–LSS forecast papers already in the literature could be cited for balance.
  5. [§4.2] Figure 6 caption and surrounding text: clarify whether the Magneticum lightcone matching is purely illustrative or is used quantitatively in any forecast.

Circularity Check

1 steps flagged · score 1.0 of 10

No significant circularity: SKA forecasts are standard Fisher/SBI exercises on assumed rates and models, not tautological redefinitions of their inputs.

  1. self citation load bearing [§4 (Fisher setup) and §3.2 (SBI)]
    "For SKA, we use 5 years’ worth of commensal observations and adopt the most optimistic FRB count from the synthetic redshift distribution (see Section 3 in Caleb et al., 2026). ... As the SBI framework requires forward-simulated data, we implement the prescription outlined in Konar et al. (2025)."

    The quantitative gains rest on rate and forward-model inputs taken from companion/prior papers by overlapping authors. This is ordinary methodological reuse, not a reduction of the forecasted constraints to those inputs by definition; the Fisher/SBI machinery still produces independent numerical posteriors relative to a Stage-IV-only baseline. Flagged only as minor self-citation density, not as a circular step that forces the result.

full rationale

The paper is a forecasting chapter. Its central claims (DM scatter vs feedback, 2–5× gains on log10 TAGN and related parameters from adding DMDM/g–DM/κ–DM to Stage-IV 3×2pt, CGM scattering reach of SKA-Low, HeII reionisation S/N) are obtained by feeding synthetic FRB counts, redshift distributions, host-DM log-normals, and a single-parameter feedback model into ordinary Fisher matrices (Eqs. 6–14, Figs. 4–5) and SBI on GLASS log-normal mocks (Eq. 5, Fig. 3). These are not forced by construction: the Stage-IV-only baseline is computed independently and then compared; the noise term (Eq. 10) and power spectra are evaluated under the stated survey specs rather than being fitted to the same data they later “predict.” Self-citations (Reischke & Hagstotz methods, Konar et al. 2025 forward model, companion Caleb et al. 2026 rates) supply the tools and the optimistic n_FRB inputs; they do not redefine the output constraints as identical to those inputs. Optimistic rates and the single-parameter TAGN ansatz are load-bearing modelling choices (and are flagged as such for high-z), but modelling assumptions are not circularity under the stated criteria. No self-definitional identity, no fitted-then-predicted quantity, no uniqueness theorem imported from the authors, and no renaming of a known result appear in the derivation chain. Score 1 only for the minor, non-load-bearing self-citation density typical of a multi-author methods chapter.

Assumptions & free parameters 5 free parameters · 7 assumptions · 0 invented entities

The central SKA-feedback claim rests on standard cosmological projection formulae plus several domain modelling choices (optimistic FRB n(z), host DM log-normal, single-parameter AGN feedback, Limber+Gaussian covariance) and no new physical entities. Free parameters are those varied or fixed in the mocks/Fishers; axioms are the usual LSS and FRB modelling assumptions invoked in §§2–4.

free parameters (5)
  • log10 TAGN (baryonic feedback strength)
    Single-parameter feedback model whose constraint improvement is a headline result of the Fisher analysis (Figs. 4–5); value and mapping to power-spectrum suppression are model choices, not measured here.
  • Host DM median and σ_host (log-normal)
    Free parameters in SBI mocks (Eq. 5; priors DM_host ∈ [10,1500], σ_host ∈ [10,800]); host contribution is a dominant systematic for Macquart and power-spectrum analyses.
  • Optimistic SKA FRB detection counts / n_FRB(z)
    Taken as the most optimistic synthetic distribution for 5-year commensal observations; sets shot noise and redshift leverage of all DM spectra (§4).
  • Galaxy bias b_i per tomographic bin
    Ten free linear bias parameters in the Stage IV clustering model (Euclid optimistic settings).
  • F̃_l (CGM turbulence fluctuation parameter)
    Benchmark ~0.5×10^{-3} (pc^2 km)^{-1/3} from Ocker et al. assumptions; controls whether SKA-Low reaches τ>1 ms scattering (Eq. 15, Fig. 7).
assumptions (7)
  • domain assumption Limber approximation for angular power spectra C_AB(ℓ)
    Used for all DM–shear–clustering forecasts (Eq. 6, §4.1).
  • domain assumption Gaussian covariance of multipole-binned spectra with uncoupled ℓ modes
    Fisher matrix built from Eq. 13; ignores non-Gaussian covariance that can matter on small scales where feedback lives.
  • domain assumption Log-normal realisations of the matter field (GLASS) suffice for DM SBI
    Forward model in §3.2; true electron field may be more non-Gaussian.
  • domain assumption Ionised baryon fraction f(z) and electron-per-baryon χ_e known well enough for DM mean and weights
    Enters Macquart mean (Eq. 3) and W_DM (Eq. 7).
  • domain assumption Linear galaxy bias valid for clustering multipoles ℓ≤500
    Explicit cut in §4.1 Fisher setup.
  • ad hoc to paper High-z FRB rate can be extrapolated from z<5 simulations for EoR forecasts
    §6.1 admits this is highly speculative; still used to quote ~1–10 FRBs/yr at z>6.
  • standard math Standard ΛCDM background and electron DM integral (Eqs. 1–3)
    Baseline cosmology of the entire chapter.

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Cite this review

Pith. "Pith review of Probing the Baryon Distribution with Fast Radio Bursts." pith.science (2026). https://pith.science/paper/PW4CSNIX

@misc{pith2026260629388,
  author       = {Pith},
  title        = {Pith review of: Probing the Baryon Distribution with Fast Radio Bursts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PW4CSNIX}},
  note         = {Machine review of arXiv:2606.29388}
}
read the original abstract

Baryonic feedback redistributes matter on small to mid cosmological scales, ultimately limiting inferences from Stage IV galaxy surveys. Direct baryon tracers are crucial for recovering cosmological signals masked by astrophysical effects, and vice versa: galaxy formation and other astrophysical processes must be interpreted cosmologically. Fast radio bursts (FRBs) serve as such tracers: their dispersion measure (DM) records the line-of-sight integrated ionised electron density. The Square Kilometre Array (SKA) will be the only radio telescope capable of detecting many FRBs in the southern hemisphere, significantly enhancing synergy with surveys such as Rubin Observatory. This chapter completes the FRB trilogy by forecasting the SKA's potential to constrain the baryon distribution from cosmological to galactic scales and across cosmic time. We tackle this question by investigating the DM scatter as a function of redshift. We also study the statistical properties of the DM field and its cross-correlation with Stage IV galaxy surveys. Our focus is on cosmic shear and galaxy clustering. This shows that the SKA can play a crucial role in pinpointing baryonic feedback models, thereby greatly enhancing the cosmological constraining power of Stage IV galaxy surveys. Furthermore, we show that the SKA will be able to measure the properties of the circumgalactic medium using the scattering timescale of FRBs. Lastly, the large redshift range of FRB detections with the SKA can improve our understanding of the epoch of reionisation. It may also clarify the mechanism behind FRBs.

Figures

Figures reproduced from arXiv: 2606.29388 by the authors.

Figure 1
Figure 1. Mock observations of the Macquart relation for 100 FRBs with varying feedback strength for illustration. Stronger baryonic feedback (red) redistributes baryons and leads to a smoother electron distribution, leading to decreased scatter in the Macquart relation. For clarity, we also show the contour level where the corresponding likelihood has dropped to 5% of its peak value. the comoving electron density contrast, 𝐸… view at source ↗
Figure 2
Figure 2. Shown is the scaling of the different components (their probability density function in different line-styles) with redshift from Equation (1) depicted as a colour gradient. Red-coloured lines show a scenario with strong feedback (a fairly smooth electron distribution), and blue-coloured lines show one with weak feedback (a very clustered electron distribution). the likelihood spreads more towards large DM values du… view at source ↗
Figure 3
Figure 3. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Marginal constraints on cosmological parameters. All contours show the one and 2𝜎 constraints. Grey contours denote a 3 × 2 analysis with a Stage IV galaxy survey, such as Rubin-LSST or Euclid. The blue and red contours show the improvement achieved by adding 5 years o…
Figure 5
Figure 5. Figure 5: Marginal constraints on cosmological parameters. Same colour scheme as [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Left: Mass and redshift distribution of halos from Magneticum lightcone (25 square-degrees). Right: FRBs (from AA* predictions) matched to simulated halos. prior information on Ωb or ℎ from CMB measurements will further enhance the gain from SKA due to the degeneracy b…
Figure 7
Figure 7. Figure 7: CGM scattering parameters, 𝐹˜ 𝑙DM2 𝑙 and 𝐺scatt(1 + 𝑧𝑙) −3 , that will produce a 𝜏 > 1 ms scattering tail for BURSTT, CHIME, CHORD, DSA-2000, and SKA-Low. The grey, dashed line represents the fiducial value of the fluctuations expected for cool gas in the CGM, 𝐹˜ 𝑙 = 0…

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Pith tools

Reviewed July 14, 2026 · model on record in the stance chip above.