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REVIEW 4 major objections 5 minor 130 references

The dispersion measure and scattering of Fast Radio Bursts: contributions from multi-components, and clues for the intrinsic properties

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper argues that the observed dispersion and scattering of fast radio bursts in the CHIME/FRB catalog can be reproduced by a mixed population of young and old progenitors, and it uses that model to estimate FRB redshifts to about 0.12.

desk verdict A useful FRB population-synthesis paper with real external checks, but the central MCMC likelihood uses an undefined covariance matrix and the scattering model fails its own KS test, so the quoted parameter constraints are not yet statistically grounded. read the letter →

arxiv 2502.05838 v1 pith:WCJSJLBH submitted 2025-02-09 astro-ph.HE astro-ph.COastro-ph.GA

Jian-Feng Mo , Weishan Zhu , Long-Long Feng This is my paper · ORCID
classification astro-ph.HEastro-ph.COastro-ph.GA
keywords fastradioburstsdispersionmeasurescatteringtimesourcepopulationSchechterenergyfunctionIllustrisTNGsimulationredshiftestimationCHIME/FRBcatalog
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 show that the joint distribution of dispersion measure (DM) and scattering time ($\tau$) observed for fast radio bursts in the CHIME/FRB catalog carries enough information to pin down the FRB source population and the environments along the line of sight. Using a mixed-population model in which a fraction $f_{\rm PSFR}$ of bursts trace cosmic star formation and the rest trace stellar mass, plus a Schechter-like intrinsic energy distribution, the authors find that $f_{\rm PSFR}=0.58^{+0.16}_{-0.27}$, $\gamma=-1.60^{+0.11}_{-0.13}$, and $\log_{10}E_*[\mathrm{erg}]=42.27^{+1.17}_{-1.18}$ reproduce the DM distribution and broadly the $\tau$ distribution. They also find that scattering is dominated by the circumburst medium or the host galaxy's ISM and CGM, which contribute only about $10\,\mathrm{pc\,cm^{-3}}$ of DM, and that a DM-only redshift estimator reaches an RMS error of $0.11$--$0.12$ on 68 localized FRBs. The reason this matters is that most FRBs lack redshifts, so a model-based estimator of distance from DM (and optionally $\tau$) would unlock FRBs as cosmological probes.

What carries the argument

The load-bearing mechanism is a multi-component decomposition of the observed DM and scattering time: $\mathrm{DM}_{\rm obs}$ is the sum of Milky Way ISM and halo, IGM, foreground, host, and local contributions, while $\tau_{\rm obs}$ is the analogous sum with factors of 3 and 6 for extragalactic host and local screens and a frequency scaling $\tau(\nu)\propto\nu^{-4}$. Host and foreground contributions are computed from the electron density, star formation, and stellar mass distributions of simulated galaxies, with unresolved gas assumed to follow Kolmogorov turbulence between inner scale $10^6$ m and outer scale 5 pc. Mock catalogs are then compared with the selection-corrected CHIME/FRB sample through a log-likelihood built on binned DM and $\tau$ distributions, and the free parameters are constrained by Markov chain Monte Carlo sampling.

What would settle it

Measure the electron-density fluctuation spectrum in a foreground galaxy's circumgalactic medium by imaging angular broadening of a background radio source on sub-parsec scales; a spectral index or inner scale that deviates from the assumed turbulence model would change the computed host and foreground scattering and invalidate the fitted Fmax and source-population fractions.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that a single, relatively simple model of FRB sources and their environments can explain the CHIME/FRB data. The model places mock bursts at redshifts drawn from a mixed young/old progenitor population, gives them energies from a Schechter function, and assigns DM and $\tau$ by summing contributions from the Milky Way ISM, the Milky Way halo, the IGM, foreground halos and galaxies, host galaxies and halos, and a local circumburst medium. MCMC fitting of four parameters yields $f_{\rm PSFR}=0.58^{+0.16}_{-0.27}$, $\gamma=-1.60^{+0.11}_{-0.13}$, $\log_{10}E_*=42.27^{+1.17}_{-1.18}$, and $F_{\max}=6.46^{+2.47}_{-2.11}$. The best-fit model reproduces the selection-corrected DM distribution (KS $p=0.42$) and broadly matches the $\tau$ distribution below 10 ms (KS $p\approx2\times10^{-4}$, better than the prior favored model), and it predicts that scattering is dominated by local/host contributions with small associated DM. The same model, applied as a redshift estimator, gives RMS residuals of about 0.11--0.12 on 68 localized FRBs once the extreme event FRB190520B is excluded.

Load-bearing premise

The whole calculation rests on assuming that the fine-grained lumpiness of gas in and around galaxies, which the simulation cannot see, follows a standard turbulence law with a particular inner and outer scale; if real gas is lumpier or smoother on those scales, every fitted parameter and redshift error bar shifts.

Editorial extensions

If this is right

  • A mixed source population is sufficient: if the fitted parameters are correct, no exotic single-population model is required to explain the CHIME DM distribution.
  • Tau is not a useful distance indicator in this model: because the dominant scattering component carries only about $10\,\mathrm{pc\,cm^{-3}}$ of DM, adding tau to a DM-only estimator changes redshift accuracy by only a few percent.
  • Host-galaxy demographics become a model test: the authors predict 54% star-forming and 51% disk hosts at $z<0.1$, and 68% and 54% at $z<1.1$, broadly consistent with current localized FRBs.
  • The result sets priors for FRB cosmology: the $\mathrm{DM}_{\rm IGM}(z)$ relation with redshift-dependent baryon fraction and foreground halo contributions can be used to convert future DM catalogs into redshift estimates.
  • Resolution of the host and foreground scattering is a leading systematic: switching from TNG100 to TNG50 changes $f_{\rm PSFR}$ and $\gamma$ enough to matter, so higher-resolution simulations are the next step.

Reading between the lines

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

  • Because the local scattering screen in this model can absorb unresolved electron clumps up to 100 kpc away, the fitted $F_{\max}$ is not a direct measure of circumburst turbulence unless the host ISM and CGM clump model is trusted.
  • A sharper test of the assumed Kolmogorov spectrum would come from angular-broadening measurements of background sources viewed through foreground CGM, which would directly constrain the small-scale electron-density power spectrum that sets $\tau_{\rm Host}$ and $\tau_{\rm Fore}$.
  • The paper's host-galaxy prediction implies that a complete sample of roughly 40 local ($z<0.1$) localized FRBs with morphology classifications could constrain $f_{\rm PSFR}$ independently of the CHIME DM fit; the current 18-of-21 disk-host rate is already in tension with the 51% prediction.
  • The same mock-catalog machinery could be rerun for other telescopes with different bandwidths and sensitivity curves to predict their DM and tau distributions and optimize future surveys.
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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

4 major / 5 minor

Summary. The paper presents a forward model for the joint distribution of dispersion measure (DM) and scattering time (tau) of fast radio bursts (FRBs), combining a mixed young/old progenitor population, a Schechter energy function, and multi-component estimates of DM and tau contributions from the Milky Way, IGM, foreground halos, host galaxies/halos, and local environments. The host and foreground contributions are taken from IllustrisTNG simulations, while other terms use empirical models. Model parameters are fit to selection-corrected CHIME/FRB Catalog 1 data via MCMC, using either DM only or DM and tau. The paper then constructs four redshift estimators and tests them on 71 localized FRBs, reporting RMS errors of order 0.11-0.12 after excluding extreme cases. It also compares predicted host-galaxy type fractions with observations.

Significance. If the statistical inference were sound, this would be a valuable contribution: it provides a simulation-informed, multi-component framework for interpreting FRB DM and tau distributions, gives quantitative constraints on the young-progenitor fraction fPSFR and the energy-distribution parameters, and offers a redshift estimator validated against localized FRBs. The paper deserves credit for using TNG100 and TNG50 to inform host and foreground contributions, for checking predictions against 71 localized FRBs, for exploring multiple scenarios (TNG50, extended-local-scattering, larger tau range), and for compiling a substantial table of localized FRB properties. The host-galaxy fraction comparison is a useful external cross-check. However, the central MCMC likelihood is not fully specified because the covariance matrix in Eq. (18) is never defined, and one fitted parameter (log10 E*) is explicitly reported as non-converged. These issues affect the quoted parameter values and error bars, so the quantitative claims are not yet statistically grounded.

major comments (4)
  1. [Section 3, Eq. (18)] The log-likelihood is written as -0.5*(Ni - ni)^T Cov^{-1}(Ni - ni), but the covariance matrix Cov is never defined anywhere in the text or appendices. It is not stated whether Cov is diagonal with Poisson variances, a bootstrap covariance, or something else, and its dimensions are not specified. All posterior distributions and uncertainties in Figures 9-10 and Table 1 depend on this choice, so the quoted parameter constraints are not reproducible and the error bars are not statistically justified. This affects both the DM-only and the combined DM-tau fits and is therefore load-bearing for the paper's central claim of identifying optimal model parameters.
  2. [Section 3.1, Figure 9, Table 1] The paper states that log10(E*) 'shows signs of non-convergence toward the end of the MCMC process, displaying a broad range of values from 42 to 44' (Section 3.1). Despite this, the abstract and Table 1 quote log10(E*) = 42.27^{+1.17}_{-1.18} as a meaningful constraint. A non-converged posterior for E* means that the reported median and 1-sigma interval for this parameter are not reliable; the parameter should be reported as poorly constrained or the chains extended until convergence is actually achieved. Because E* is one of the three headline parameters describing the energy distribution, this undermines a central quantitative conclusion.
  3. [Table 2 and Abstract] The two-sided KS test on the tau distribution gives p = 2e-4, and the AD test gives p = 10^-3 or lower, meaning the model does not reproduce the tau distribution at the 5% significance level. The abstract's phrase 'broadly reproduce the tau distribution' overstates this result. The authors acknowledge the difficulty in Section 3.2 and discuss possible causes, including selection-function uncertainties for highly scattered events, but the discrepancy should be stated with the same prominence as the DM success, and the conclusion should explicitly note that the tau distribution is not statistically reproduced. This is important because the paper's title and framing emphasize the joint DM-tau distribution.
  4. [Section 2.4.3, Eq. (15)] The variance of DMIGM is modeled as sigma_DMIGM(z) = 0.623 * (-234.3 exp(1.0 z) + 237.2), with the factor 0.623 introduced to 'exclude the effect of baryons in intervening halos based on the results in Zhu & Feng (2021)'. No derivation or quantitative justification is given for this specific factor, and no test is shown that the scaling correctly removes halo variance without double-counting. Since sigma_DMIGM directly enters the likelihood and also affects the redshift estimator, this assumption should either be validated directly against simulation sightlines at multiple redshifts or treated as an additional systematic uncertainty in the fitted parameters.
minor comments (5)
  1. [Abstract and Section 1] There are several typographical errors, including 'F ast Radio Bursts' in the title area, 'dectected events' in the introduction, and 'the the extragalactic DM' in Section 4. These should be corrected.
  2. [Section 2.4.3, Eq. (13) usage] The log-normal variance parameter is written as sigma = {ln[1 + (sigma_DMIGM/<DMIGM>)^2]}^2, which appears to be missing a square root; the standard relation is sigma^2 = ln[1 + (sigma_DMIGM/<DMIGM>)^2]. Please check and correct the formula.
  3. [Section 6, first bullet] The sentence 'More events with robust measurements of tau are needed to to further refine the constraints' contains a duplicated 'to'.
  4. [Table 2 and Section 5.2] For the Ext-local scenario, the text states that F is drawn from a uniform distribution between 0.5 and 2, while Table 2 lists Fmax = 2; clarify in the table caption or text that the entry 'Fmax' for this scenario is the fixed upper bound of the uniform prior rather than a fitted parameter.
  5. [General reproducibility] To make the MCMC results reproducible, the paper should state the full likelihood specification, including the definition of Cov, the number of bins used, and the treatment of tau upper limits in the likelihood; currently the treatment of upper limits is described only in the context of sample generation, not in the likelihood itself.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's MCMC parameter fits and redshift estimators are standard fitting with external validation; self-citations provide simulation-based inputs rather than circular reductions.

full rationale

The paper's central quantitative claims are obtained by an MCMC fit of a forward model to the selection-corrected CHIME/FRB DM and tau distributions. The parameters fPSFR, gamma, log10(E*), and Fmax are fitted, not derived from the target quantities by definition, so this is ordinary statistical inference rather than circularity. The redshift estimators are built from the mock catalog generated with the fitted model, but they are validated against 68 localized FRBs whose redshifts were not used in the fit, and the host-galaxy-type fractions are checked against independent localized-host catalogs; these are external tests. The self-citations to Mo et al. 2023 and Zhu & Feng 2021 provide TNG-simulation-based distributions of DMHost, tauHost, and turbulence assumptions; these are openly adopted modeling inputs from prior published simulation work with independent content, not uniqueness theorems or redefinitions of the present results. The undefined covariance matrix in Eq. 18 is a serious reproducibility and statistical-validity concern, but it does not make any prediction equivalent by construction to its inputs, so it is a correctness issue rather than circularity. Overall, the derivation chain is self-contained in the sense required by the circularity analysis, and no quoted reduction of a prediction to a fitted input or self-citation chain is present.

Assumptions & free parameters 8 free parameters · 8 assumptions · 0 invented entities

The model relies on a large number of hand-set astrophysical inputs: turbulence scales, local environment DM distributions, local screen distances, a variance scaling factor, and the assumed selection functions. The four MCMC parameters describe population and energy properties, and they are fit to the same CHIME data used for comparison, so the DM match is partly a demonstration of fitting flexibility. The external checks are the localized FRB redshifts and host galaxy types, which are not used in the fit.

free parameters (8)
  • fPSFR = 0.58+0.19/-0.27 (DM+tau); 0.45+0.25/-0.26 (DM only)
    Fraction of FRBs tracing cosmic star formation rate; one of the MCMC free parameters.
  • gamma = -1.60+0.11/-0.13
    Power-law index of the Schechter energy distribution; fitted in MCMC.
  • log10(E*/erg) = 42.27+1.17/-1.18
    Characteristic cutoff energy of the Schechter distribution; fitted in MCMC, but posterior is broad and not fully converged in the DM-only fit.
  • Fmax = 6.46+2.47/-2.11 (default); 0.17 in Extended-local-scattering; 12.30 for TNG50
    Maximum density fluctuation parameter of the local environment; fitted in the DM+tau MCMC.
  • sigma_DMIGM scale factor 0.623 = 0.623
    Hand-applied factor on the Batten et al. (2021) variance formula to remove intervening-halo contributions, based on Zhu & Feng (2021); directly affects the DMIGM scatter and the redshift estimator.
  • DMLocal lognormal mu, sigma = PStar: mu=1.8, sigma=0.8; PSFR: mu=2.8, sigma=0.8
    Hand-set parameters for the local environment DM distribution, following Chawla et al. and Cordes & Lazio; not fitted.
  • local screen Deff range = 0.01 to 1 kpc (default); 0.01 to 100 kpc (extended); Deff_max = 3.40+0.92/-0.77 in Ext-local fit
    Assumed effective distance to the scattering screen; range is hand-set except in the Ext-local scenario where Deff is fitted.
  • turbulence scales l0, L0 = l0 = 1e6 m, L0 = 5 pc
    Inner and outer turbulence scales for unresolved CGM and ISM, adopted from Zhu & Feng (2021); they control the amplitude of tauHost and tauFore.
assumptions (8)
  • domain assumption Unresolved ISM and CGM in TNG100 follow Kolmogorov turbulence down to 1e6 m, with outer scale 5 pc and density variance approximately n_e^2.
    Section 2.4.1 and Appendix A; scattering amplitudes for host and foreground halos depend directly on this.
  • domain assumption The CHIME selection functions for DM and tau from Hashimoto et al. (2022) are correct and complete.
    Equations 2 and 3 are applied to observed data before comparing with the model; wrong selection corrections would bias the fitted population parameters.
  • domain assumption FRB intrinsic energy distribution is a non-evolving Schechter function with Emin = 1e38 erg, Emax = 1e48 erg, spectral index alpha = 0, and fluence threshold 0.4 Jy ms.
    Section 2.3; this determines mock observability and the mapping from energy to fluence.
  • domain assumption FRB sources trace either the cosmic star formation rate (Madau-Dickinson) or the stellar mass density with return fraction R = 0.27.
    Section 2.3.1; defines the PSFR and PStar redshift distributions.
  • domain assumption TNG100 and TNG50 simulations realistically represent baryon distributions in galaxies and halos, and fb,IGM from excluding Rm200 is an appropriate IGM definition.
    Section 2.4.3; DMIGM normalization and host/foreground DM and tau contributions rest on this.
  • standard math The scattering relations of Macquart & Koay (2013) and Zhu & Feng (2021), Equations A3 to A6, apply to the media considered.
    Appendix A; the pulse broadening time and scattering measure conversion are taken from established literature.
  • ad hoc to paper The factor 0.623 reduction of sigma_DMIGM correctly removes foreground halo variance without double counting.
    Section 2.4.3, Equation 15; this is a hand-tuned scaling based on comparing two simulations, not a derived result.
  • domain assumption NE2001 and YMW16 Milky Way DM estimates, plus the lognormal DMMW,Halo fit from Mo23, are reliable.
    Section 2.4.2; the decomposition of observed DM into MW and extragalactic parts depends on these models.

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Pith. "Pith review of The dispersion measure and scattering of Fast Radio Bursts: contributions from multi-components, and clues for the intrinsic properties." pith.science (2026). https://pith.science/paper/WCJSJLBH

@misc{pith2026250205838,
  author       = {Pith},
  title        = {Pith review of: The dispersion measure and scattering of Fast Radio Bursts: contributions from multi-components, and clues for the intrinsic properties},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WCJSJLBH}},
  note         = {Machine review of arXiv:2502.05838}
}
abstract

Fast radio bursts (FRBs) are luminous, millisecond-duration transients that offer great potential for probing the universe, yet their physical origins remain unclear. The dispersion measure (DM) and scattering time ($\tau$) distributions provide key insights into FRBs' properties, including source population, redshift, and energy distribution. We use a simplified model of FRB source population and intrinsic Schechter function-like energy distribution, coupled with a thorough assessment of various contributors to dispersion and scattering, to replicate the joint distribution of DM and $\tau$ in the CHIME/FRB catalog. A mixed FRB source population, including both young and old progenitors, is considered. Contributions to the DM and $\tau$ from interstellar medium (ISM), circumgalactic medium (CGM) within host and foreground halos are informed by the IllustrisTNG simulation, while contributions from the Milky Way, intergalactic medium (IGM), and local environmental are estimated by updated models. Using MCMC simulations, we identify optimal model that well reproduce the DM distribution and broadly reproduce the $\tau$ distribution in the CHIME/FRB catalog. Our model suggests that the fraction of FRBs tracing star-formation rate is $\rm{f_{PSFR}=0.58^{+0.16}_{-0.27}}$, while $\rm{log_{10}E_*[erg]=42.27^{+1.17}_{-1.18}}$ and $\gamma=-1.60^{+0.11}_{-0.13}$ in the energy distribution function. Scattering predominantly arises from the circumburst medium or the ISM and CGM of hosts, which cause a DM of $\sim 10\, \rm{pc\,cm^{-3}}$. Using our optimal model, we estimate FRB redshifts with two methods: DM-only and combined DM-$\tau$. Evaluation with 68 localized FRBs reveals an RMS error $0.11-0.12$, and incorporation of $\tau$ has a minor effect. We further argue that the host galaxy properties of localized FRBs could be a potential tool to validate our model in the future.

Figures

Figures reproduced from arXiv: 2502.05838 by the authors.

Figure 1
Figure 1. The intrinsic probability density distribution of FRBs between redshift 0.0 − 3.0 for the PStar (blue), PSFR (red) and PMix (green) source population models. The blue, green, and red lines indicate the fraction of FRBs associated with young progenitors (i.e., tracing the cosmic star forma￾tion rate density), with fPSFR = 0.0, 0.5, 1.0, respectively. The intrinsic energy distribution of FRBs is commonly assumed to fo… view at source ↗
Figure 2
Figure 2. The probability density distribution (top row) and cumulative probability distribution (bottom row) of the scattering measure caused by the host galaxies and their ha￾los, SMhost, for PSFR(left column) and PStar(right column) model at different redshift values based on TNG100 simula￾tion. The blue, orange, green, red, purple, and brown lines are the distribution at z=0.0, 0.5, 1.0, 1.5, 2.0, 3.0, respec￾tively. The … view at source ↗
Figure 3
Figure 3. Similar to [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: The top panel shows the evolution of baryon fraction in the IGM, fb,IGM, at redshifts z=0.0,0.1,0.2,0.3,0.4,0.5,0.7,1.0,1.5,2.0,3.0, which are calcu￾lated from the TNG100 simulation. The second panel shows DMIGM as a function of redshift, in which the blue line is cal￾…
Figure 5
Figure 5. Figure 5: The blue, orange, red and purple lines are the evolution of σDMIGM according to the results in Zhu & Feng (2021), best fitting results of EAGLE simulation given by the eqn. 12 and eqn. 11 of Batten et al. (2021), and the simplified model used in Cordes et al. (2022), r…
Figure 6
Figure 6. Figure 6: The signals emitted by an FRB source at z ∼ 1 are expected to pass through around 7 halos within the mass range 1010 − 1015 M⊙, consistent with previous studies (e.g., Vedantham & Phinney 2019). The entire mass range is divided into 5 bins with an interval of dlog(Mh) …
Figure 8
Figure 8. Figure 8: The cumulative distribution function of the dis￾persion measure and scattering measure caused by the fore￾ground halos, i.e., DMFore (left) and SMFore (right), for halos with mass in the range 1010−1011 , 1012−1013 , 1014−1015 M⊙ (from top to bottom) respectively. Actu…
Figure 7
Figure 7. Figure 7: The upper panel is the 3D schematic dia￾gram which illustrates the L.O.S. to FRBs that have passed through a foreground halo. The red dashed line with an ar￾row shows the L.O.S. and corresponding direction. The blue dots are gas particles in simulation, which indicates…
Figure 9
Figure 9. Figure 9: The posterior distribution of the MCMC results for the parameters fPSFR, γ, log10(E∗) in DM-only model, thinned by a factor of 20 for the sake of clarity. The vertical and horizontal red lines denote the median(solid), 16th(dashed) and 84th(dashed) percentile of the di…
Figure 10
Figure 10. Figure 10: Similar to [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Comparison between the distribution of observed dispersion measure (left) and scattering time at 600 MHz (right) for the selection-corrected FRBs in CHIME/FRB catalog 1 (blue), and our mock FRBs, which is generated with parameter values given by the DM and τ combined …
Figure 12
Figure 12. Figure 12: The upper row shows the distributions of dispersion measure (left) and scattering time at 600 MHz (right) of our ∼ 106 mock FRBs with fluence ¿ 0.4 Jy ms, which are generated with parameter values given by the DM and τ combined MCMC simulation. The blue, orange, green…
Figure 14
Figure 14. Figure 14: The distribution of the observed scattering time at 600 MHz and total dispersion measure of our ∼ 106 mock FRBs with fluence ¿ 0.4 Jy ms (dots), and 33 localized FRBs (solid circles and downward triangles represent sources with a scattering time of true value and uppe…
Figure 13
Figure 13. Figure 13: The top panel shows the distribution of the red￾shift and total dispersion measure of our ∼ 106 mock FRBs with fluence above 0.4 Jy ms, superposed by 71 localized FRBs. The middle (bottom) panel shows the probability dis￾tribution of redshift (dispersion measure) of o…
Figure 15
Figure 15. Figure 15: Redshift estimated by our procedures, ˆz versus the true redshift, z, for 32 localized FRBs, of which both their DM and scattering time are available. The upper (lower) shows the results of the redshift estimator with combined DM and τ (DM-only) information. The left …
Figure 16
Figure 16. Figure 16: Similar to [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]
Figure 17
Figure 17. Figure 17: The probability (upper) and cumulative (lower) distribution of DMHost (left), SMHost (middle), τHost (right) based on TNG100 (blue) and TNG50 (green) simulation at z=0 for PSFR source population model. TNG100 simulation, requiring a higher fPSFR to com￾pensate. Based …
Figure 18
Figure 18. Figure 18: The left panel shows the redshift distribution of our ∼ 106 mock FRB with fluence larger than 0.4 Jy ms, which are divided further into two populations. One is PSFR, i.e., tracing the cosmic star formation rate (red histogram), and the other is PStar, i.e., tracing th…
Figure 19
Figure 19. Figure 19: Similar to [PITH_FULL_IMAGE:figures/full_fig_p035_19.png]
Figure 20
Figure 20. Figure 20: Similar to [PITH_FULL_IMAGE:figures/full_fig_p036_20.png]

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.