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A hydrodynamical simulations-based model that connects the FRB DM--redshift relation to suppression of the matter power spectrum via feedback

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper argues that the sightline-to-sightline scatter in fast radio burst dispersion measures, modeled as a log-normal distribution whose moments are computed from the feedback-dependent electron power spectrum, can constrain baryonic…

desk verdict A useful, honest replacement for the F-parameter that ties FRB DM scatter to the electron power spectrum; the headline forecasts are self-consistency checks, but the framework is worth refereeing. read the letter →

arxiv 2504.18745 v1 pith:SEXA3E3E submitted 2025-04-25 astro-ph.CO astro-ph.GAastro-ph.HE

Kritti Sharma , Elisabeth Krause , Vikram Ravi , Robert Reischke , Pranjal R. S. , Liam Connor This is my paper · ORCID
classification astro-ph.COastro-ph.GAastro-ph.HE
keywords fastradioburstsdispersionmeasurebaryonicfeedbackmatterpowerspectrumsuppressionlog-normaldistributionelectroncosmologicalinferencehydrodynamicalsimulations
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

The paper tries to establish that fast radio bursts (FRBs) can be turned into a precision probe of how baryonic feedback reshapes cosmic structure. It replaces the standard ad hoc feedback parameter $F$ with a log-normal model of the cosmic dispersion-measure distribution, $p(\mathrm{DM}_{\rm cosmic}|z)$, whose mean and variance are computed from cosmology and from the feedback-dependent electron power spectrum $P_{ee}(k,z)$. The electron power spectrum is calibrated against a suite of 1,000 hydrodynamical simulations spanning a wide range of feedback strengths, which also builds in the correct redshift evolution of the DM scatter. If the model is right, a sample of $10^4$ FRBs would pin the baryonic suppression of the matter power spectrum to percent-level accuracy on large scales and roughly ten percent accuracy at $k\sim10\,h\,\mathrm{Mpc}^{-1}$, the small scales that upcoming weak-lensing surveys need to understand.

What carries the argument

The load-bearing object is the log-normal ansatz for $p(\mathrm{DM}_{\rm cosmic}|z)$, specified by two analytically computed moments. The mean is $\langle\mathrm{DM}_{\rm cosmic}(z_s)\rangle = \int_0^{z_s} \frac{3c\chi_e\Omega_b H_0}{8\pi G m_p} \frac{f_d(z)(1+z)\,dz}{\sqrt{\Omega_m(1+z)^3+\Omega_\Lambda}}$, and the variance is $\sigma^2[\mathrm{DM}_{\rm cosmic}(z_s)] = \int_0^{\chi_s} d\chi\, W_{\rm DM}^2(\chi) \int_0^\infty \frac{k\,dk}{2\pi} P_{ee}(k,z(\chi))$, where $P_{ee}$ is the electron power spectrum, the Fourier-space clustering of the free-electron density contrast. The paper calibrates $P_{ee}(k,z)$ as a function of two cosmological and four feedback parameters using nearest-neighbor interpolation on 1,000 hydrodynamical simulations, then feeds the moments into a per-FRB likelihood that is sampled with MCMC to constrain the parameters and the suppression ratio.

What would settle it

Take many mock FRB sightlines from a strong-feedback hydrodynamical simulation and measure the third and fourth moments of $\mathrm{DM}_{\rm cosmic}$ directly; if they disagree with the log-normal values implied by the fitted mean and variance, or if a simulation-based likelihood gives different posteriors on $P_{\rm hydro}/P_{\rm gravity-only}$ than the log-normal likelihood, the central claim is falsified.

Watch

Extended reading notes

Core claim

The central claim is that the log-normal parameterization $p(\mathrm{DM}_{\rm cosmic}|z)$, fully defined by its mean and variance, describes simulated FRB dispersion measures better than the standard $F$-parameterization, and that the variance computed from $P_{ee}(k,z)$ carries the feedback signal. Because the variance integral is dominated by scales out to $k\sim10\,h\,\mathrm{Mpc}^{-1}$, the scatter between FRB sightlines at fixed redshift responds to the same baryonic feedback that suppresses the matter power spectrum. Using the calibrated model, the paper reports that 10,000 FRBs would recover the true suppression ratio $P_{\rm hydro}/P_{\rm gravity-only}$ within 0.6, 2, 8, and 15 percent at $k=0.5,1,10,$ and $50\,h\,\mathrm{Mpc}^{-1}$, and that the same model recovers the suppression in simulations with substantially different feedback physics.

Load-bearing premise

The inference depends on the assumption that the cosmic dispersion measure at a fixed redshift is exactly log-normal, which the paper concedes has no rigorous derivation; if the true distribution has different skewness or a heavier tail, the feedback constraints could be biased.

Editorial extensions

If this is right

  • With $10^4$ localized FRBs, the baryonic suppression ratio $P_{\rm hydro}/P_{\rm gravity-only}$ can be constrained to 0.6% at $k=0.5\,h\,\mathrm{Mpc}^{-1}$ and about 8% at $k=10\,h\,\mathrm{Mpc}^{-1}$, reaching the accuracy needed for upcoming weak-lensing cosmology.
  • Even today's roughly $10^2$ localized FRBs would constrain the suppression to 1-3% at large scales and 12-20% at $k\sim10$-$50\,h\,\mathrm{Mpc}^{-1}$.
  • The redshift evolution of the DM variance is included by construction, so the model avoids the assumptions behind the $F$-parameter (Poisson halo statistics and $\sigma_{\rm DM}=F z^{-1/2}$) that can bias feedback estimates by 20-40%.
  • The same calibrated model recovers the suppression in simulations with substantially different subgrid feedback physics, so it is not tied to one feedback prescription.
  • Constrained feedback parameters translate directly into predictions for baryon fractions in galaxy groups and clusters and for halo gas profiles within standard halo-model analyses.

Reading between the lines

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

  • Because the DM variance is an integral over $P_{ee}(k,z)$, it carries limited scale information; the paper's shape recovery relies on the correlation between feedback parameters and $P_{ee}$. An extension the authors do not make is to use the angular correlation of DM across neighboring sightlines, which would measure $P_{ee}(k,z)$ by scale and could break remaining degeneracies.
  • The paper reports strong degeneracies between $H_0$, $\Omega_m$, and the feedback parameters; a natural next step, not taken here, is a joint FRB plus lensing plus kinematic/thermal Sunyaev-Zel'dovich analysis that could separate cosmological from feedback effects.
  • The authors note that roughly 10% of the variance comes from scales larger than their simulation box; re-running the calibration on a larger-volume suite would likely tighten the large-scale suppression constraints and test whether the reported percent-level numbers are optimistic.
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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. The paper presents a simulation-based formalism for connecting the FRB dispersion-measure–redshift relation to baryonic feedback and the suppression of the matter power spectrum. The authors parameterize p(DMcosmic|z) as a log-normal whose mean is computed from the cosmological mean electron density (Eq. 5) and whose variance is computed from the electron power spectrum Pee(k,z) via Eq. (11). They calibrate Pee(k,z) and the matter-power-spectrum suppression Phydro/Pgravity-only to the CAMELS IllustrisTNG Latin-hypercube suite as functions of four feedback parameters and two cosmological parameters, using nearest-neighbor interpolation. They compare the log-normal form against the Macquart F-parameterization, argue that the F-parameter misestimates the DM variance and introduces biases in Pmm, and then perform MCMC forecasts with mock FRB samples drawn from the model itself. They report that 10^4 FRBs can constrain Phydro/Pgravity-only to percent-level precision at large scales and about 10% precision at k ~ 10 h/Mpc, with a prior-to-posterior width ratio of about 20. They also apply the IllustrisTNG-calibrated model to Astrid and SIMBA fiducial runs and claim that it recovers the true Pmm suppression. The paper includes a detailed caveats section covering large-scale variance, parameter degeneracies, sparse interpolation, selection effects, and the lack of a rigorous derivation of log-normality.

Significance. If the central claims hold, this is a valuable contribution: it provides a physically interpretable replacement for the ad hoc F-parameter, exploits the redshift-dependent DM variance that is automatically built into simulations, and links FRB observables to baryonic feedback at scales k ~ 10 h/Mpc, which are relevant for upcoming weak-lensing surveys. The analytic moment formalism in Eqs. (5) and (11) is standard and is applied in a novel way, and the use of the CAMELS suite to span diverse feedback scenarios is appropriate. The paper also gives a clear and honest caveats section, and it demonstrates concrete gains over the Macquart parameterization, which is a useful result in itself. However, as detailed below, the main forecast precision is currently a self-consistency result, and the log-normal shape assumption is not directly validated against simulated sightlines. The significance of the paper will be substantially higher if the authors add a quantitative distributional validation and reframe or robustify the forecast claims.

major comments (3)
  1. The log-normal shape of p(DMcosmic|z) is load-bearing for the central forecast, but the validation path is circular. In §5.2 and Fig. 9, mock DMcosmic samples are drawn from the analytically constructed log-normal p(DMcosmic|z), and the likelihood in Eqs. (15)–(16) uses exactly the same log-normal form. The MCMC recovery tests in §5.3 and §5.4 therefore verify only that parameters are recovered under the assumed likelihood, not that the log-normal assumption is adequate. The only direct comparison with simulated sightline DM distributions is Fig. 2, which is qualitative, limited to IllustrisTNG, and does not include a quantitative goodness-of-fit or tail comparison; no such check is shown for Astrid or SIMBA. Since the reported Pmm-suppression constraints in Table 1 and Fig. 10 could be biased if the true distribution has heavier tails or different skewness, the paper should add a quantitative distributional validation (e.g., quantile–quantile plots, Kolmogorov–Smirnov or Anderson–Darling tests, or tail-index comparisons) against actual sightline DM in IllustrisTNG, Astrid, and SIMBA at several redshifts, and/or a robustness test with an alternative likelihood form to bound the potential bias.
  2. [§5.5.1, §5.5.3, Table 1] The forecast precision quoted in the abstract and Table 1 is presented as the expected constraining power with 10^4 FRBs, but it is a self-consistency check that does not include the systematic uncertainties acknowledged in §5.5. The 25 h^-1 Mpc CAMELS box misses about 4–10% of the DM variance from large scales (§5.5.1), and nearest-neighbor interpolation in a 6-dimensional space with 1000 calibration points is sparse (§5.5.3). Because the mock catalogs are generated from the same calibrated model that is fitted, the 68% widths in Table 1 and Fig. 10 do not propagate these errors. The claims such as 'percent-level precision at large scales and ~10% precision at k ≳ 10 h/Mpc' should either be explicitly reframed as conditional on the model and its known limitations, or the identified systematic uncertainties should be propagated into the quoted errors. As written, the forecast overstates the current state of the model.
  3. [§5.4, Fig. 10] The cross-simulation validation for Astrid and SIMBA is a useful transfer test, but it inherits the same log-normal assumption: the mock samples for these simulations are drawn from the analytically constructed p(DMcosmic|z) in Fig. 9, not from actual sightline distributions. The test therefore demonstrates that the IllustrisTNG-calibrated Pee interpolation can reproduce the Pmm suppression of other feedback implementations under the assumed likelihood, but it does not test whether the log-normal shape holds for Astrid or SIMBA. In addition, the reported recovery accuracy for SIMBA at large scales (2.5% at k=0.5 h/Mpc) and Astrid at k=50 h/Mpc (32%) is noticeably worse than the IllustrisTNG values; the attribution of the large-scale discrepancy to the CAMELS box size is plausible but should be quantified, e.g., by comparing against a larger-volume simulation or by estimating the variance contributed by modes larger than the box.
minor comments (5)
  1. [Abstract] The abstract contains a typo: 'upto' should be 'up to'.
  2. [Fig. 3 caption] The caption contains a typo: 'feedack strength' should be 'feedback strength'.
  3. [§3.1.1] The discussion of Zhang et al. (2021) is slightly confusing: the text says 'in contrast to the findings of Zhang et al. (2021), we find that this functional form provides a poor fit', but then says 'we reanalyze their data and provide the updated fits'. Please clarify whether the reanalysis reproduces their data or uses a different fitting procedure, and state the normalization condition explicitly relative to their Table 1 and Figure 2.
  4. [Table 1] The 68% intervals for SP(k=50) and for logASN2 at N_FRB=10^4 are asymmetric and in some cases the upper or lower error bar is much smaller than the other; a note on how the percentiles are computed (highest posterior density interval versus equal-tailed interval) would aid interpretation.
  5. [Fig. 10] In the bottom panels of Fig. 10, the legend distinguishes 'Best Fit' from 'Posterior', but the text does not define what 'Best Fit' refers to (maximum a posteriori value?); please define this in the caption or text.

Circularity Check

1 steps flagged · score 6.0 of 10

Validation and forecast mocks are drawn from the same log-normal likelihood being fitted, so the reported constraints and cross-simulation recovery are partly self-consistency results.

  1. fitted input called prediction [§5.2 Mock FRB Samples Generation; §5.3 Likelihood Analysis and Model Validation; Eqs. 15–16; Fig. 9–10; Table 1.]
    "The DM cosmic−z relations for the fiducial runs of these simulations, constructed using our methodology of analytically computing the ⟨DMcosmic(z)⟩ and the σ2[DMcosmic(z)] of the p(DMcosmic|z) distribution with log-normal parameterization are shown in Figure 9. ... For our MCMC analysis, we generate mock samples of DM cosmic by sampling from the p(DMcosmic|z) distribution."

    The mock 'data' are drawn from the very log-normal p(DMcosmic|z) that the likelihood in Eqs. 15–16 assumes and that is built from the same mean (Eq. 5) and variance (Eq. 11) model being calibrated. The MCMC recovery, Table 1, and Figure 10 therefore verify parameter retrieval under the exact assumed likelihood, not whether the model describes actual simulated sightline DMs. For SIMBA and Astrid the same analytic log-normal construction is used, so the reported 'recovery' of Phydro/Pgravity−only inherits the log-normal shape by construction. Section 5.5.5 concedes 'the absence of a rigorous theoretical derivation' and defers likelihood-free tests to future work, confirming that the shape assumption is load-bearing rather than independently validated.

full rationale

The analytical derivation of the DM moments is self-contained: Eq. 5 follows from the electron-density integral and Eq. 11 from the Limber-approximated angular power spectrum, with neither reducing to the target result. The Pee(k,z) calibration on the CAMELS LH dataset and the transfer of variances to SIMBA/Astrid use genuinely external simulation power spectra, and Fig. 2 provides an out-of-sample comparison of the log-normal shape against actual IllustrisTNG sightline DMs from Zhang et al. (2021). The circular component is the validation/forecast pipeline: mocks in §5.2 are sampled from the analytically constructed log-normal p(DMcosmic|z), and the likelihood in Eqs. 15–16 uses the same log-normal form. Consequently, the reported constraints and the cross-simulation recovery demonstrate self-consistency of the assumed likelihood rather than an independent test against simulated sightline distributions. The paper's own §5.5.5 admits that log-normality has no rigorous derivation, making the shape assumption load-bearing for the Pmm-suppression forecasts; if the true distribution has heavier tails or different skewness, the quoted precision could be biased. The partial independence provided by Fig. 2 and the use of real simulation power spectra for Pee prevent this from being a fully circular derivation, but the central validation claims are substantially self-referential.

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

The central model rests on simulation-derived calibrations rather than first-principles derivations. The free parameters are the four CAMELS feedback parameters, the fixed diffuse baryon fraction, and the two host-galaxy DM parameters. The key axioms are the log-normal form of p(DMcosmic|z), the representativeness of the CAMELS feedback parameter space, the independence of FRB sightlines, and the adopted host DM distribution.

free parameters (7)
  • A_SN1 = 0 (fiducial), prior [-0.6, 0.6]
    Supernova feedback wind energy parameter in CAMELS, calibrated to the LH dataset and inferred from the DM variance.
  • A_SN2 = 0 (fiducial), prior [-0.3, 0.3]
    Supernova feedback wind speed parameter, affects small-scale gas power and DM variance.
  • A_AGN1 = 0 (fiducial), prior [-0.6, 0.6]
    AGN feedback energy parameter, suppresses gas power across scales and reduces DM variance.
  • A_AGN2 = 0 (fiducial), prior [-0.3, 0.3]
    AGN ejection speed and burstiness parameter, affects gas distribution around halos.
  • f_d = 0.9 (fixed by hand)
    Diffuse baryon fraction sets the mean DM amplitude in Equation 5; fixed for mock generation, with degeneracy with Omega_b H0 noted.
  • mu_host = 5 (fiducial), prior [4, 6]
    Mean of the log-normal host galaxy DM distribution, inferred jointly as a nuisance parameter.
  • sigma_host = 0.5 (fiducial), prior [0.2, 1]
    Width of the log-normal host galaxy DM distribution, inferred jointly as a nuisance parameter.
assumptions (5)
  • domain assumption p(DMcosmic|z) is log-normal with moments given by Equations 5 and 11.
    Adopted as an approximation without rigorous derivation, stated in Sections 3.2 and 5.5.5. If violated, the likelihood is misspecified.
  • domain assumption The electron power spectrum P_ee(k,z) from the CAMELS IllustrisTNG LH simulations spans the real range of baryonic feedback.
    The calibration dataset is 1000 simulations with varied subgrid parameters, but there is no guarantee that real feedback lies within this range.
  • domain assumption FRB sightlines are statistically independent and covariance induced by large-scale structure is negligible.
    The paper states in Section 2.2 that accounting for covariance between sightlines will be crucial for unbiased inference with large samples.
  • domain assumption Diffuse baryon fraction f_d is fixed to 0.9 for mock generation and analysis.
    Equation 5 gives the mean DM proportional to f_d(z); the paper fixes f_d=0.9 and notes degeneracy with Omega_b H0.
  • domain assumption Host galaxy DM follows a log-normal distribution with mu_host=5 and sigma_host=0.5.
    Adopted from Connor et al. 2024 and used in the likelihood in Equation 15.

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Cite this review

Pith. "Pith review of A hydrodynamical simulations-based model that connects the FRB DM--redshift relation to suppression of the matter power spectrum via feedback." pith.science (2026). https://pith.science/paper/SEXA3E3E

@misc{pith2026250418745,
  author       = {Pith},
  title        = {Pith review of: A hydrodynamical simulations-based model that connects the FRB DM--redshift relation to suppression of the matter power spectrum via feedback},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SEXA3E3E}},
  note         = {Machine review of arXiv:2504.18745}
}
abstract

Understanding the impact of baryonic feedback on the small-scale ($k \gtrsim 1\,h\,$Mpc$^{-1}$) matter power spectrum is a key astrophysical challenge, and essential for interpreting data from upcoming weak-lensing surveys, which require percent-level accuracy to fully harness their potential. Astrophysical probes, such as the kinematic and thermal Sunyaev-Zel'dovich effects, have been used to constrain feedback at large scales ($k \lesssim 5\,h\,$Mpc$^{-1}$). The sightline-to-sightline variance in the fast radio bursts (FRBs) dispersion measure (DM) correlates with the strength of baryonic feedback and offers unique sensitivity at scales upto $k \sim 10\,h\,$Mpc$^{-1}$. We develop a new simulation-based formalism in which we parameterize the distribution of DM at a given redshift, $p(\mathrm{DM}|z)$, as a log-normal with its first two moments computed analytically in terms of cosmological parameters and the feedback-dependent electron power spectrum $P_\mathrm{ee}(k, z)$. We find that the log-normal parameterization provides an improved description of the $p(\mathrm{DM}|z)$ distribution observed in hydrodynamical simulations as compared to the standard $F$-parameterization. Our model robustly captures the baryonic feedback effects across a wide range of baryonic feedback prescriptions in hydrodynamical simulations, including IllustrisTNG, SIMBA and Astrid. Leveraging simulations incorporates the redshift evolution of the DM variance by construction and facilitates the translation of constrained feedback parameters to the suppression of matter power spectrum relative to gravity-only simulations. We show that with $10^4$ FRBs, the suppression can be constrained to percent-level precision at large scales and $\sim 10$\% precision at scales $k \gtrsim 10\,h\,$Mpc$^{-1}$ with prior-to-posterior $1\sigma$ constraint width ratio $\gtrsim 20$.

Figures

Figures reproduced from arXiv: 2504.18745 by the authors.

Figure 1
Figure 1. Halo mass sensitivity of various baryons tracers, including X-ray observations, thermal Sunyaev-Zel’dovich (tSZ) effect, kinematic Sunyaev-Zel’dovich (kSZ) effect, cos￾mic shear and FRB dispersion measure (DM). While the mean DM is sensitive to ≳ 108 M⊙ halos (neglecting feed￾back effects that may evacuate small halos), the DM vari￾ance depends on the degree of clustering of baryons in halos and is sensitive to more… view at source ↗
Figure 2
Figure 2. Evaluating the efficacy of the F-parameter p(DMcosmic|z) parameterization introduced by Macquart et al. (2020) in comparison to the log-normal parameteriza￾tion proposed in this work. The comparison is done using the p(DMcosmic|z) distributions derived from the IllustrisTNG simulation within a 205 h −1 cMpc box (solid lines), as com￾puted by Zhang et al. (2021). The log-normal distribution (dotted lines) provides a … view at source ↗
Figure 3
Figure 3. The influence of the F-parametrization on the estimation of feedback. On the left, we show the relative error introduced by the F-parametrization on the sightline-to-sightline variance of the cosmological dispersion measure. If the F￾parametrization was accurate, all lines should coincide with zero. The right side propagates this error into the matter power spectrum using Equation 14. The errors introduced by the F-… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The dispersion measure (DM) maps of the redshift z = 0.00 snapshots for the fiducial runs of Astrid (first column), IllustrisTNG (second column), and SIMBA (third column) simulation suites in the CAMELS project (Villaescusa-Navarro et al. 2023; Ni et al. 2023). Each si…
Figure 5
Figure 5. Figure 5: The power spectra (first column) and correlation function (second column) for the matter, cold dark matter (CDM) and gas field components in the fiducial run of IllustrisTNG (normalized by the mean density of the corresponding component). The dotted line indicates the …
Figure 6
Figure 6. Figure 6: The electron power spectrum (first column), suppression in the matter power spectrum (second column), and variance in DMcosmic (third column), as measured from the 1P dataset of simulations in IllustrisTNG, are analyzed as functions of the supernova and AGN feedback pa…
Figure 7
Figure 7. Figure 7: The observed range of the electron power spectrum (top panel), suppression in the matter power spectrum (middle panel), and variance in DMcosmic (bottom panel) for the LH set, which comprises variations of the cosmological (Ωm, σ8) and astrophysical feedback (ASN1, ASN…
Figure 8
Figure 8. Figure 8: The electron power spectrum (left panel), suppression in the matter power spectrum (middle panel), and variance in DMcosmic (right panel) examined across the IllustrisTNG (dashed line), SIMBA (dotted line), and Astrid (dash-dotted line) simulation suites for their fidu…
Figure 9
Figure 9. Figure 9: The normalized p(DMcosmic|z) distribution for the fiducial runs of Astrid (first column), IllustrisTNG (second column) and SIMBA (third column) simulations. The mean (solid line) and mode (dashed line) of the distributions are shown for reference. While the mean of the…
Figure 10
Figure 10. Figure 10: Constraints on the electron power spectrum (top panels), variance in DMcosmic (middle panels) and suppression of the matter power spectrum (bottom panels) at redshift z = 0.00 for the fiducial runs of the Astrid (first column), IllustrisTNG (second column), and SIMBA …
Figure 11
Figure 11. Figure 11: The correlation among cosmological and feed￾back parameters that characterize the variance in DMcosmic is evident, thus supporting the necessity of parameterizing the variance in terms of physical quantities, such as the electron power spectrum, which directly encodes…
Figure 12
Figure 12. Figure 12: The halo-model predictions for matter, cold dark matter (CDM), and gas power spectra (Mead et al. 2020, 2021). We rescale the halo model predictions (which normalizes the density using the mean matter density) to normalize with respect to the mean density in the respe…
Figure 13
Figure 13. Figure 13: The power spectra (left panel) in Fourier space and the autocorrelation function in configuration space for different fields, including matter, cold dark matter (CDM) and gas (similar to [PITH_FULL_IMAGE:figures/full_fig_p021_13.png]

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A Fisher forecast shows that combining FRB dispersion-measure correlations with galaxy clustering can constrain baryonic feedback to about 3% and cosmological parameters to 10-18% with 10^4 FRBs.

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  6. Measurement of the Dispersion$\unicode{x2013}$Galaxy Cross-Power Spectrum with the Second CHIME/FRB Catalog

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    FRB dispersion and foreground galaxy density are spatially correlated at 5.1 sigma, with a fitted plasma clustering cutoff near 0.9 Mpc, measured from 2,873 CHIME FRBs and about 6 million DESI galaxies.

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