Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog

Can-Min Deng; Min Meng

REVIEW 5 major objections 5 minor 1 cited by

Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that, once CHIME's beam response and selection effects are modeled in detail, FRB sources can still be born in lockstep with cosmic star formation, while the implied local source rate is too high for core-collapse…

desk verdict A genuinely more careful CHIME forward model, but the headline "SFH not ruled out" rests on an uncalibrated p>=0.01 acceptance rule and an early beam model; worth reviewing, but it needs revision. read the letter →

arxiv 2506.04986 v2 pith:MSPJT4W4 submitted 2025-06-05 astro-ph.HE

Min Meng , Can-Min Deng This is my paper

classification astro-ph.HE

keywords fastradioburstsFRBpopulationsynthesisstarformationhistoryCHIME/FRBcatalogselectioneffectsmagnetarprogenitorsvolumetricrateredshiftdistribution

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 asks whether the bursts in the first CHIME/FRB catalog can discriminate between FRB sources born in lockstep with cosmic star formation and sources that only ignite after a delay. Using Monte Carlo mock catalogs that mimic CHIME's beam pattern, its radio-frequency-interference filtering, and the fact that catalog fluences are lower limits, the authors test both scenarios against 223 detected bursts. They find that a population whose birth rate simply follows the star formation history cannot be excluded: a sizeable region of parameter space reproduces the observed fluence and dispersion-measure distributions, even though a delay of about a gigayear fits the data somewhat better. The same simulations imply a local volumetric rate of roughly $2.3^{+2.4}_{-1.2}\times10^5$ events per $\mathrm{Gpc}^3$ per year above $10^{38}$ erg, consistent with earlier estimates but high enough to strain the idea that every source is a young magnetar from a core-collapse supernova. If true, this would mean earlier rejections of the no-delay model were driven by selection-effect modeling rather than by the data.

What carries the argument

The load-bearing machinery is a Monte Carlo population-synthesis pipeline that turns a hypothetical FRB redshift distribution and energy function into a mock CHIME sample. The key identities are the detection criterion $\mathrm{S/N} = G F_\nu / \sqrt{B N_p (T_\mathrm{rec}+T_\mathrm{sky}) \sqrt{w}} \times s(\mathrm{DM})^{2/3}$ and the fluence-to-boresight relation $F_\nu^\mathrm{measured} = G(x,y)/G(0,y)\,F_\nu^\mathrm{true}$, where $G(x,y)$ is the beam-corrected gain at the burst's sky position. The measured fluence is always a lower limit because the gain peaks at the meridian, which lets the simulation use the catalog's lower-limit fluences honestly. A DM-dependent selection function $s(\mathrm{DM})$ encodes the telescope's strong incompleteness at low dispersion measures, and mock bursts are kept only if their S/N exceeds 12 and their fluence is below 100 Jy ms. Simulated and observed samples are then compared with 1D and 2D Kolmogorov-Smirnov tests on $F_\nu$ and $\mathrm{DM}_E$, with bootstrap p-values.

What would settle it

Use CHIME's calibration or injection system to measure the true off-meridian beam gain and check whether recovered fluences obey $F_\nu^\mathrm{measured}=G(x,y)/G(0,y)F_\nu^\mathrm{true}$, or repeat the KS analysis on the subset of bursts with independently measured beam-corrected fluences; if the no-delay model is rejected with corrected fluences, the acceptance here was a selection-modeling artifact, and if it survives, earlier rejections were artifacts of using lower limits.

Watch

Extended reading notes

Core claim

The central claim is that the observed redshift distribution of CHIME FRBs does not force a delay relative to the cosmic star formation history once the telescope's selection effects are modeled in detail. The authors show that the no-delay, SFH-tracking model passes Kolmogorov-Smirnov tests against the catalog's fluence and extragalactic DM distributions over a broad range of energy-function parameters; the delayed model with a typical delay of about 1 Gyr fits a bit better but is not required. They also derive a local event-rate density of $2.3^{+2.4}_{-1.2}\times10^5\,\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$ for sources with isotropic energy above $10^{38}$ erg. Because the core-collapse supernova rate is of the same order, the channel that makes young magnetars cannot by itself supply the required number of independent FRB sources; the paper concludes that additional or alternative progenitor channels are needed even though prompt, star-formation-associated progenitors remain viable.

Load-bearing premise

The result rests on assuming that the early CHIME beam model and the adopted DM-dependent selection function correctly convert true fluences and burst positions into catalog fluences and detection probabilities; if the true beam response or incompleteness differs, the non-rejection of the star-formation-history model could be an artifact of the selection modeling.

Editorial extensions

If this is right

If the no-delay model is actually viable, FRB sources could be young, short-lived objects formed in star-forming regions, so a star-forming host galaxy does not by itself exclude such progenitors.
A delay of about 1 Gyr being preferred, though not required, leaves room for a mix of prompt and delayed channels, including old stellar populations such as those in globular clusters.
Treating CHIME catalog fluences as true values biases redshift-distribution conclusions, so future analyses should use beam-corrected fluences or marginalize over the beam model.
The local source rate above $10^{38}$ erg is too high for core-collapse magnetars to be the sole source population, so theoretical effort should go into additional channels or into mechanisms that boost the apparent source rate.
The inferred all-sky rate above 5 Jy ms, $216^{+21}_{-19}\,\mathrm{sky}^{-1}\,\mathrm{day}^{-1}$, is within a factor of two of the CHIME/FRB team's estimate, meaning the sample normalization is roughly consistent.

Reading between the lines

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

Beyond the paper, a clean test is to repeat this analysis on the CHIME/FRB catalog's beam-corrected fluence subset: if the no-delay model survives, earlier rejections were selection artifacts; if it fails, the early beam model used here is the weak link.
The claimed insensitivity of the energy-function slope ($\alpha\approx-1.8$) to the redshift model suggests that the energy function and the delay time can be constrained separately, which a future hierarchical fit could exploit to break the degeneracy between $\alpha$ and $\log E_c$.
If repeating FRB sources are numerous and each source bursts many times, the distinction between a source-rate density and a burst-rate density becomes essential; a survey counting first bursts only may be measuring the density of active sources times their burst rate rather than a formation rate, and this needs to be checked with repetition statistics.
A natural extension is to allow the spectral index $\gamma$ to float per source instead of fixing it at $-0.65$; the paper's robustness check with $\gamma=-1.0$ and $-1.5$ suggests the qualitative conclusion would hold, but a free $\gamma$ would also test whether the slight preference for a delay is driven by the assumed spectral correction.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is the most physically complete forward model yet applied to CHIME Catalog 1, and it correctly shows that earlier SFH rejections leaned on simple selection corrections. But the central non-rejection claim is statistically softer than the abstract implies.

What is actually new: they explicitly include the CHIME beam gain map, the DM-dependent selection function, and the fact that catalog fluences are lower limits. That is a real step beyond the papers they criticize, and their treatment of the fluence issue is fair. The parallel code and the systematic parameter exploration also go beyond the sparse grids used in several prior Monte Carlo studies. I also credit the honest engagement with the literature, including the independent Wang & van Leeuwen (2024) result and the careful note that the derived local rate challenges the core-collapse magnetar channel.

The soft spot is the acceptance rule. In Section 3 they accept parameter combinations with p2DKS >= 0.01 and then say the SFH model cannot be rejected because accepted draws occupy a "considerable" region. That is not calibrated: for a false model, a fraction of random draws will clear any threshold, and the paper never reports total trials or the fraction accepted. Their own footnote 12 concedes the "artificial uncertainty"; that is a real admission, not a minor caveat. The 95% ranges quoted for alpha and DM_host are histograms of accepted draws, not intervals with frequentist or Bayesian meaning, and the unconstrained log Ec shows the prior boundary problem. The beam model is also explicitly preliminary, and the DM selection function and spectral index are fixed inputs from other work. None of these alone sinks the paper, but together they mean the headline result "SFH not ruled out" does not yet distinguish a true preference for SFH from a test that would fail to reject many false models. The rate estimate is useful and roughly consistent with prior work, but it inherits the same selection-modeling caveats.

Missing code is a real reproducibility cost. The sample file and the beam model are public, but the simulation pipeline is not, so a referee cannot check the KS procedure or the acceptance calculation without reimplementing it.

Who this is for: FRB population theorists and anyone working with CHIME Catalog 1. It deserves a serious referee, not a desk reject. My recommendation: send it to review, but ask for (1) calibration of the acceptance threshold, ideally reporting total draws and the accepted fraction, (2) sensitivity tests to the s(DM) function and gamma, (3) an updated beam model when available, and (4) release of the simulation code. With those, the non-rejection claim could become solid; without them, it stays conditional.

Referee Report

5 major / 5 minor

Summary. The paper uses Monte Carlo simulations that forward-model FRB populations through CHIME/FRB selection effects (beam response, DM-dependent selection, S/N threshold) to test whether the redshift distribution of the first CHIME/FRB catalog bursts tracks the cosmic star formation history directly or with a delay. The authors find that the SFH model cannot be rejected within a considerable parameter region, that a delay of about 1 Gyr is preferred though not required, and they derive a local volumetric rate of approximately 2.3e5 Gpc^-3 yr^-1 above 1e38 erg, which they argue challenges core-collapse magnetars as the sole FRB progenitor channel.

Significance. The question addressed is central to FRB progenitor physics, and the paper is unusually explicit about selection effects, using the CHIME beam model and a DM-dependent selection function. The non-rejection result, if confirmed, would overturn several earlier claims that the SFH model is excluded, and the local rate estimate provides a concrete falsifiable constraint on progenitor models. The main weakness is that the central non-rejection claim rests on an uncalibrated p-value acceptance rule and on preliminary instrument models; these issues are correctable but load-bearing.

major comments (5)

[Section 3, Figs. 3 and 5, footnote 12] The claim that the SFH model 'cannot be rejected' rests entirely on selecting parameter draws with p_2DKS >= 0.01 and on observing that accepted draws occupy a 'considerable' region. The paper does not report the total number of parameter draws, the fraction of the prior volume accepted, or the distribution of p-values under a known false model. With uncalibrated thresholding, a false model can produce p >= 0.01 in some draws by chance, and footnote 12 itself concedes that the resulting parameter ranges carry 'artificial uncertainty.' The authors should calibrate the acceptance rule, for example by running the same pipeline on simulated populations with a deliberately wrong redshift distribution and reporting the acceptance fraction as a function of sample size, and they should show the actual p-value distribution rather than only accepted contours. Without this, the headline result cannot be distinguished from low test power.
[Section 3, Table 2 and text near Fig. 3] The quoted '95% confidence' ranges alpha = -2.1(+0.5,-0.4) and DM_host < 239 pc cm^-3 are histograms of accepted draws with p_2DKS >= 0.05, not confidence intervals in a frequentist or Bayesian sense. They depend on the arbitrary p-value threshold and on the prior boundaries, as the unconstrained log(E_c) demonstrates. These quantities should be re-labeled as summaries of the accepted region or replaced with a properly calibrated inference procedure.
[Section 2.4, Eq. (16), and Section 5] The beam model is described in Section 5 as 'an early version,' yet Eq. (16) uses it to convert every true fluence to a measured lower-limit fluence and Eq. (15) uses the same gain G to set detection. If the gain pattern or the boresight normalization is inaccurate, the simulated F_nu and S/N distributions are systematically biased, and the non-rejection of the SFH model could be an artifact of the beam model rather than a property of the FRB population. A robustness check against the released CHIME beam-corrected fluences or against variations of the beam model is needed to support the central claim.
[Section 2.4, Eq. (15) and Table 1] The DM selection function s(DM) is imported from James (2023) as a fourth-order polynomial and applied through the relation S/N_bias ~ s^{2/3}(DM). The paper does not test the sensitivity of the accepted parameter region to this function, even though low-DM selection strongly shapes the DM_E distribution and hence the inferred redshift distribution. The authors should vary the polynomial parameters or compare with CHIME's injection-based selection function to demonstrate that the non-rejection of the SFH model is robust to this modeling choice.
[Section 2.1, Eq. (7), and footnote 5] The spectral index is fixed to gamma = -0.65, and footnote 5 states that tests with gamma = -1.0 and -1.5 show 'only minor differences' without presenting those results. Given the known degeneracy between spectral index and source evolution discussed in Shin et al. (2023) and Hoffmann et al. (2025), the redshift-distribution conclusion requires those tests to be shown, at least as an appendix figure or table.

minor comments (5)

[Section 2, first paragraph] The phrase 'a parallel-computable code hat runs on a thread-ripper CPU' should read 'that runs on a thread-ripper CPU.'
[Table 3 caption] The caption says 'Accepted range of free parameters for SFH model,' but the table refers to the delayed SFH model; the caption should be corrected.
[Section 2.2, Eq. (8)] The notation 'DM_WM' should be 'DM_MW' for the Milky Way contribution, for consistency with the text.
[Section 2.2, Eq. (10)] The dispersion parameter is denoted sigma_DM in the surrounding text but appears as sigma_IGM in the equation; the notation should be made consistent.
[Section 5 and footnote 13] The explanation of counting only the first burst of repeating sources is repeated nearly verbatim in the Conclusions and in footnote 13; one of these repetitions should be removed or condensed.

Circularity Check

0 steps flagged · score 0.0 of 10

No material circularity: the model comparison and rate estimate are anchored in external CHIME data, external beam and selection models, and independent prior parameterizations.

full rationale

The central test compares simulated FRB populations against the first CHIME/FRB catalog, using redshift distributions from Eqs. (1)-(2), energy distributions from Eq. (13), and instrumental selection effects from the external beam model of Merryfield et al. (2023) and the DM-dependent selection function of James (2023). The simulated observable quantities, fluence and DME, are derived through Eqs. (14)-(16) rather than being set equal to the catalog values. The headline statement that the SFH model is not ruled out is a statistical inference from bootstrap KS p-values, not a quantity that reduces by construction to an input. The local volumetric rate of 2.3e5 Gpc^-3 yr^-1 is computed from the simulation's detection efficiency and the fitted energy function, so it is a model-derived estimate rather than a re-labeled fitted parameter. Self-citations such as Deng et al. (2019) and Deng et al. (2021) are contextual literature references and are not load-bearing for the main derivation. The paper's own caveats about the early beam model and the p-value threshold in footnote 12 are limitations or calibration concerns, not evidence that any claimed prediction is equivalent to its inputs. Overall, the derivation chain is self-contained and externally anchored.

Assumptions & free parameters 7 free parameters · 11 assumptions · 0 invented entities

The paper's central claim depends on a long chain of adopted astrophysical and instrumental models that it does not derive. The genuinely fitted parameters (alpha, DM_host, tau_bar, Ec) are few, and the energy cutoff Ec is unconstrained, which weakens the rate estimate. The main risk is the preliminary beam model and the adopted DM selection function, which the authors acknowledge.

free parameters (7)

alpha (energy function power-law index) = -1.8 (+0.4, -0.7) for delayed SFH; -2.1 (+0.5, -0.4) for SFH
Slope of the cutoff power-law energy distribution (Eq. 13); constrained by the 2D KS test to a fairly narrow range.
log(Ec/erg) (energy cutoff) = unconstrained within [41.5, 43]
Exponential cutoff energy in the energy function; the data provide no constraint within the prior.
DM_host (mean of lognormal host DM) = < 363 pc cm^-3 (delayed SFH), < 239 (SFH)
Mean of the rest-frame host-galaxy DM distribution; only an upper limit is obtained from the marginalized p-value distributions.
tau_bar (mean delay time) = unconstrained within [0.01, 10] Gyr; preferred ~1 Gyr
Mean of the lognormal delay distribution in the delayed SFH model; not constrained by the data, with a weak peak near 1 Gyr.
sigma_host (host DM width) = 0.4 (fixed)
Width of the lognormal host DM distribution; fixed because the authors find it is not constrained in [0.2, 0.8].
gamma (spectral index, rate interpretation) = -0.65 (fixed)
Spectral index with a +0.85 bias correction from James et al. (2022b); chosen for computational feasibility after tests with -1.0 and -1.5 showed minor differences.
DM selection function coefficients (a0..a4, N) = N=0.90, a4=0.22, a3=-2.55, a2=10.25, a1=-16.31, a0=8.99
Fourth-order polynomial fit to CHIME's DM-dependent detection efficiency, adopted from James (2023); enters the S/N via s^{2/3}(DM) in Eq. (15).

assumptions (11)

domain assumption SFH functional form and coefficients (Eq. 1) from Yüksel et al. (2008)
The assumed cosmic star formation history with a=3.4, b=-0.3, c=-3.5, eta=-10, B=5000, C=9 is taken from the literature.
domain assumption Lognormal delay distribution with sigma_tau=0.8 (Eq. 3)
Delay distribution adopted from Wanderman & Piran (2015) and Zhang & Zhang (2022); the width is fixed, not fitted.
domain assumption Macquart DM-IGM fluctuation distribution (Eq. 10) with alpha=3, beta=3 and C0, sigma_IGM from Zhang et al. (2021b)
The probability distribution of DM_IGM around the mean is adopted from the standard Macquart relation.
domain assumption Mean DM_IGM(z) with f_IGM=0.84, f_e=7/8 (Eq. 9)
Standard cosmological baryon content and ionization fraction are assumed for the IGM contribution.
domain assumption Fixed MW halo DM of 30 pc cm^-3
Following Zhang & Zhang (2022); a single value is used for all lines of sight.
domain assumption NE2001 model for the Milky Way disk DM
MW electron density model from Cordes & Lazio (2002) is used to subtract the disk contribution.
domain assumption CHIME beam model from Merryfield et al. (2023), preliminary version
The beam-corrected gain G(x,y) at 600 MHz, used in Eq. (15) and Eq. (16), comes from an early public beam model; the paper states this is an early version.
ad hoc to paper Uniform, isotropic sky distribution with x in [-0.4, 0.8] and y in [-60, 60] degrees
The allowed positions are limited to the range spanned by the observed sample, which may not represent the full CHIME visibility region.
domain assumption Rate interpretation of the spectral index with gamma=-0.65 (Eq. 7)
The factor (1+z)^gamma is absorbed into the rate evolution, with a bias-corrected gamma; this choice affects the redshift distribution.
domain assumption S/N detection threshold of 12 and fluence cap of 100 Jy ms
Instrumental detection criterion and the high-fluence cut motivated by RFI rejection, taken from CHIME/FRB papers.
domain assumption Pulse width model from CHIME/FRB Collaboration (2021) Table 4
Intrinsic and scattering widths are sampled from fiducial distributions rather than derived from first principles.

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Cite this review

Pith. "Pith review of Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog." pith.science (2026). https://pith.science/paper/MSPJT4W4

@misc{pith2026250604986,
  author       = {Pith},
  title        = {Pith review of: Constraints on the progenitor models of fast radio bursts from population synthesis with the first CHIME/FRB catalog},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MSPJT4W4}},
  note         = {Machine review of arXiv:2506.04986}
}

abstract

Fast radio bursts (FRBs) are enigmatic extragalactic radio transients with unknown origins. We performed comprehensive Monte Carlo simulations based on the first CHIME/FRB catalog to test whether the FRB population tracks the cosmic star formation history directly or requires a delay. By fully considering CHIME's complex selection effects and beam response, we find that the hypothesis that the FRB population tracks the SFH is not ruled out by the current data, although a small delay is preferred. This is consistent with the scenario in which young magnetars formed through core-collapse supernovae serve as the progenitors of FRBs. However, we estimate the local volumetric rate of FRB sources with energy above $10^{38}$ erg to be $2.3^{+2.4}_{-1.2} \times 10^5~\rm{Gpc}^{-3}~\rm{yr}^{-1}$, which is consistent with previous results. This high volumetric rate means the core-collapse magnetar scenario alone cannot fully account for the observed population. Further theoretical efforts are required to explore alternative or additional progenitor channels for FRBs.

Figures

Figures reproduced from arXiv: 2506.04986 by the authors.

**Figure 1.** Beam-corrected gain (G) as a function of the angle from zenith along the meridian (y) at 600 MHz for x = −0.4, 0, 0.4, and 0.8. the location (x, y) in the topocentric coordinate system where (x, y) = (0, 0) is the zenith. With the zenith as the origin, y is degrees north from the zenith and x is degrees west from the meridian. In this work, we assumed that y ranges from −60 to 60 based on the fact that the y coordin… view at source ↗

**Figure 3.** Contours of p2DKS in the planes of α − Ec and α − log(DMhost) for the SFH model. The color regions indicate parameters with p2DKS ⩾ 0.01, with the color intensity representing the value of p2DKS. The contour lines corresponding to p2DKS = 0.01 and 0.05 are marked. The marginalized 1D distribution of each parameter with p2DKS ⩾ 0.05 is also plotted as histograms. 0.0 0.5 1.0 1.5 2.0 log(F / [Jy ms]) 0 50 100 150 200… view at source ↗

**Figure 4.** KS tests of the delayed SFH model against the data with ¯τ = 2.2 Gyr (similar to [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Contours of p2DKS in the planes of α − Ec , α − τ¯, and α − log(DMhost). The colored regions within the contours represent parameter spaces where p2DKS ⩾ 0.01. The contour lines corresponding to p2DKS = 0.01 and 0.05 are marked. The marginalized 1D distribution of the…

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Forward citations

Cited by 1 Pith paper

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

Fast Radio Bursts Trace Cosmic Star Formation with Little Delay
astro-ph.HE 2026-07 conditional novelty 6.0 of 10

Hierarchical Bayesian analysis of CHIME/FRB finds the FRB volumetric rate peaks with the cosmic star-formation history at mean delays of 0.1–0.3 Gyr, consistent with zero delay and ruling out multi-Gyr merger-like delays.

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Works this paper leans on

66 extracted references · 37 canonical work pages · cited by 1 Pith paper

[1]

C., Patel, C., Brar, C., et al

Andersen, B. C., Patel, C., Brar, C., et al. 2023, AJ, 166, 138

work page 2023
[2]

R., Macquart, J

Arcus, W. R., Macquart, J. P., Sammons, M. W., James, C. W., & Ekers, R. D. 2021, MNRAS, 501, 5319

work page 2021
[3]

Beloborodov, A. M. 2017, ApJ, 843, L26

2017
[4]

E., Aggarwal, K., et al

Bhandari, S., Heintz, K. E., Aggarwal, K., et al. 2022, AJ, 163, 69

2022
[5]

D., Ravi, V ., Belov, K

Bochenek, C. D., Ravi, V ., Belov, K. V ., et al. 2020, Nature, 587, 59

2020
[6]

2018, ApJ, 858, 89

Cao, X.-F., Yu, Y .-W., & Zhou, X. 2018, ApJ, 858, 89

work page 2018
[7]

H., Jia, X

Chen, J. H., Jia, X. D., Dong, X. F., & Wang, F. Y . 2024, ApJ, 973, L54 CHIME/FRB Collaboration, Amiri, M., Andersen, B. C., et al. 2024, ApJ, 969, 145 CHIME/FRB Collaboration, Amiri, M., Andersen, B. C., et al. 2021, ApJS, 257, 59 Chime/Frb Collaboration, Amiri, M., Andersen, B. C., et al. 2023, ApJS, 264, 53 Chime/Frb Collaboration, Amiri, M., Andersen...

work page 2024
[8]

Cordes, J. M. & Chatterjee, S. 2019, ARA&A, 57, 417

2019

Show all 66 references

[9]

Cordes, J. M. & Lazio, T. J. W. 2002, arXiv: astro-ph/0207156

2002 arXiv
[10]

M., Wharton, R

Cordes, J. M., Wharton, R. S., Spitler, L. G., Chatterjee, S., & Wasserman, I. 2016, arXiv e-prints, arXiv:1605.05890

2016 arXiv
[11]

2019, Journal of High Energy Astrophysics, 23, 1

Deng, C.-M., Wei, J.-J., & Wu, X.-F. 2019, Journal of High Energy Astrophysics, 23, 1

2019
[12]

2021, ApJ, 922, 98

Deng, C.-M., Zhong, S.-Q., & Dai, Z.-G. 2021, ApJ, 922, 98

2021
[13]

W., Connor, L., van Leeuwen, J., Oostrum, L

Gardenier, D. W., Connor, L., van Leeuwen, J., Oostrum, L. C., & Petroff, E. 2021, A&A, 647, A30

2021
[14]

Gardenier, D. W. & van Leeuwen, J. 2021, A&A, 651, A63

2021
[15]

W., van Leeuwen, J., Connor, L., & Petroff, E

Gardenier, D. W., van Leeuwen, J., Connor, L., & Petroff, E. 2019, A&A, 632, A125 13 This is because in the sample used in this work, we only count the first detected burst from each repeating source, and thus each FRB cor- responds to an independent source. Furthermore, in ou...

2019
[16]

2016, ApJ, 823, L28

Gu, W.-M., Dong, Y .-Z., Liu, T., Ma, R., & Wang, J. 2016, ApJ, 823, L28

2016
[17]

2020, MNRAS, 497, 1543

Gu, W.-M., Yi, T., & Liu, T. 2020, MNRAS, 497, 1543

2020
[18]

H., et al

Hashimoto, T., Goto, T., Chen, B. H., et al. 2022, MNRAS, 511, 1961

2022
[19]

Hashimoto, T., Goto, T., On, A. Y . L., et al. 2020, MNRAS, 498, 3927

2020
[20]

Haslam, C. G. T., Salter, C. J., Stoffel, H., & Wilson, W. E. 1982, A&AS, 47, 1

1982
[21]

E., Prochaska, J

Heintz, K. E., Prochaska, J. X., Simha, S., et al. 2020, ApJ, 903, 152

2020
[22]

L., James, C., Glowacki, M., et al

Hoffmann, J. L., James, C., Glowacki, M., et al. 2025, PASA, 42, e017

2025
[23]

& Zhang, B

Ioka, K. & Zhang, B. 2020, ApJ, 893, L26

2020
[24]

James, C. W. 2023, PASA, 40, e057

2023
[25]

Keane, E. F. & Kramer, M. 2008, MNRAS, 391, 2009

2008
[26]

Kelly, P. L. & Kirshner, R. P. 2012, ApJ, 759, 107

2012
[27]

2022, Nature, 602, 585

Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585

2022
[28]

2018, ApJ, 852, 54

Kothes, R., Sun, X., Gaensler, B., & Reich, W. 2018, ApJ, 852, 54

2018
[29]

A., Mitra, D., Naidu, A., Joshi, B

Krishnakumar, M. A., Mitra, D., Naidu, A., Joshi, B. C., & Manoharan, P. K. 2015, ApJ, 804, 23

2015
[30]

2024, ApJ, 969, 123

Lin, H.-N., Li, X.-Y ., & Zou, R. 2024, ApJ, 969, 123

2024
[31]

& Zou, R

Lin, H.-N. & Zou, R. 2024, ApJ, 962, 73

2024
[32]

2019, A&A, 625, A109

Locatelli, N., Ronchi, M., Ghirlanda, G., & Ghisellini, G. 2019, A&A, 625, A109

2019
[33]

R., Bailes, M., McLaughlin, M

Lorimer, D. R., Bailes, M., McLaughlin, M. A., Narkevic, D. J., & Crawford, F. 2007, Science, 318, 777

2007
[34]

2020, MNRAS, 498, 1397

Lu, W., Kumar, P., & Zhang, B. 2020, MNRAS, 498, 1397

2020
[35]

2014, MNRAS, 442, L9

Lyubarsky, Y . 2014, MNRAS, 442, L9

2014
[36]

P., Prochaska, J

Macquart, J. P., Prochaska, J. X., McQuinn, M., et al. 2020, Nature, 581, 391

2020
[37]

P., Shannon, R

Macquart, J. P., Shannon, R. M., Bannister, K. W., et al. 2019, ApJ, 872, L19

2019
[38]

Marcote, B., Nimmo, K., Hessels, J. W. T., et al. 2020, Nature, 577, 190

2020
[39]

2014, ApJ, 780, L33

McQuinn, M. 2014, ApJ, 780, L33

2014
[40]

2020, ApJ, 898, L29

Mereghetti, S., Savchenko, V ., Ferrigno, C., et al. 2020, ApJ, 898, L29

2020
[41]

P., Shin, K., et al

Merryfield, M., Tendulkar, S. P., Shin, K., et al. 2023, AJ, 165, 152

2023
[42]

D., Margalit, B., & Sironi, L

Metzger, B. D., Margalit, B., & Sironi, L. 2019, MNRAS, 485, 4091

2019
[43]

H., Aggarwal, K., Li, D., et al

Niu, C. H., Aggarwal, K., Li, D., et al. 2022, Nature, 606, 873

2022
[44]

Petroff, E., Hessels, J. W. T., & Lorimer, D. R. 2019, A&A Rev., 27, 4 Planck Collaboration, Aghanim, N., Akrami, Y ., et al. 2020, A&A, 641, A6

2019
[45]

2019, Phys

Platts, E., Weltman, A., Walters, A., et al. 2019, Phys. Rep., 821, 1

2019
[46]

C., Kaspi, V

Pleunis, Z., Good, D. C., Kaspi, V . M., et al. 2021, ApJ, 923, 1

2021
[47]

Qiang, D.-C., Li, S.-L., & Wei, H. 2022, J. Cosmology Astropart. Phys., 2022, 040

2022
[48]

M., Mickaliger, M

Rajwade, K. M., Mickaliger, M. B., Stappers, B. W., et al. 2020, MNRAS, 495, 3551

2020
[49]

2019, Nature Astronomy, 3, 928

Ravi, V . 2019, Nature Astronomy, 3, 928

2019
[50]

2021, Nature Astronomy, 5, 372

Ridnaia, A., Svinkin, D., Frederiks, D., et al. 2021, Nature Astronomy, 5, 372

2021
[51]

1976, ApJ, 203, 297

Schechter, P. 1976, ApJ, 203, 297

1976
[52]

W., Bhardwaj, M., et al

Shin, K., Masui, K. W., Bhardwaj, M., et al. 2023, ApJ, 944, 105

2023
[53]

D., Beniamini, P., et al

Sridhar, N., Metzger, B. D., Beniamini, P., et al. 2021, ApJ, 917, 13

2021
[54]

2023, Chinese Physics C, 47, 085105

Tang, L., Lin, H.-N., & Li, X. 2023, Chinese Physics C, 47, 085105

2023
[55]

P., Bassa, C

Tendulkar, S. P., Bassa, C. G., Cordes, J. M., et al. 2017, ApJ, 834, L7

2017
[56]

& Nelson, R

Terrell, D. & Nelson, R. H. 2014, ApJ, 783, 35

2014
[57]

& Piran, T

Wanderman, D. & Piran, T. 2015, MNRAS, 448, 3026

2015
[58]

& van Leeuwen, J

Wang, Y . & van Leeuwen, J. 2024, A&A, 690, A377

2024
[59]

2021, Science China Physics, Mechanics, and Astronomy, 64, 249501

Xiao, D., Wang, F., & Dai, Z. 2021, Science China Physics, Mechanics, and Astronomy, 64, 249501

2021
[60]

& Zhang, B

Xu, S. & Zhang, B. 2016, ApJ, 832, 199

2016
[61]

M., Manchester, R

Yao, J. M., Manchester, R. N., & Wang, N. 2017, ApJ, 835, 29 Yüksel, H., Kistler, M. D., Beacom, J. F., & Hopkins, A. M. 2008, ApJ, 683, L5

2017
[62]

2023, Reviews of Modern Physics, 95, 035005

Zhang, B. 2023, Reviews of Modern Physics, 95, 035005

2023
[63]

J., Dong, X

Zhang, K. J., Dong, X. F., Rodin, A. E., et al. 2024, arXiv e-prints, arXiv:2406.00476

2024 arXiv
[64]

Zhang, R. C. & Zhang, B. 2022, ApJ, 924, L14

2022
[65]

2023, A&A, 675, A66

Zhang, Z.-L., Yu, Y .-W., & Cao, X.-F. 2023, A&A, 675, A66

2023
[66]

2020, ApJ, 898, L5 Article number, page 9 of 9

Zhong, S.-Q., Dai, Z.-G., Zhang, H.-M., & Deng, C.-M. 2020, ApJ, 898, L5 Article number, page 9 of 9

2020

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

C., Patel, C., Brar, C., et al

Andersen, B. C., Patel, C., Brar, C., et al. 2023, AJ, 166, 138

work page 2023

[2] [2]

R., Macquart, J

Arcus, W. R., Macquart, J. P., Sammons, M. W., James, C. W., & Ekers, R. D. 2021, MNRAS, 501, 5319

work page 2021

[3] [3]

Beloborodov, A. M. 2017, ApJ, 843, L26

2017

[4] [4]

E., Aggarwal, K., et al

Bhandari, S., Heintz, K. E., Aggarwal, K., et al. 2022, AJ, 163, 69

2022

[5] [5]

D., Ravi, V ., Belov, K

Bochenek, C. D., Ravi, V ., Belov, K. V ., et al. 2020, Nature, 587, 59

2020

[6] [6]

2018, ApJ, 858, 89

Cao, X.-F., Yu, Y .-W., & Zhou, X. 2018, ApJ, 858, 89

work page 2018

[7] [7]

H., Jia, X

Chen, J. H., Jia, X. D., Dong, X. F., & Wang, F. Y . 2024, ApJ, 973, L54 CHIME/FRB Collaboration, Amiri, M., Andersen, B. C., et al. 2024, ApJ, 969, 145 CHIME/FRB Collaboration, Amiri, M., Andersen, B. C., et al. 2021, ApJS, 257, 59 Chime/Frb Collaboration, Amiri, M., Andersen, B. C., et al. 2023, ApJS, 264, 53 Chime/Frb Collaboration, Amiri, M., Andersen...

work page 2024

[8] [8]

Cordes, J. M. & Chatterjee, S. 2019, ARA&A, 57, 417

2019