pith. machine review for the scientific record. sign in

arxiv: 2605.08410 · v1 · submitted 2026-05-08 · 🌌 astro-ph.HE

Recognition: no theorem link

Discovery of 30 Repeating Fast Radio Burst Sources and Uniform Population Statistics of 80 Repeating Sources from CHIME/FRB

Amanda M. Cook (1 , 2 , 3) , Kaitlyn Shin (4) , Ziggy Pleunis (3 , 5) , Maxwell Fine (1 , 2)
show 156 more authors
Naman Jain (1 Derek Bingham (6) Alice P. Curtin (1 Gwendolyn Eadie (7 8 9) B. M. Gaensler (10 11 7) Jason W. T. Hessels (1 3 Calvin Leung (12 13) Robert Main (1 Nicole Mulyk (1 Ayush Pandhi (1 Paul Scholz (14) Seth R. Siegel (15 16 1 David C. Stenning (6) Thomas C. Abbott (1 Bridget C. Andersen (10) Mohit Bhardwaj (17) Alice Cai (18 19) Shami Chatterjee (20) Fengqiu Adam Dong (14) Emmanuel Fonseca (21 22) Dant\'e M. Hewitt (3) Ronniy C. Joseph (23) Lordrick Kahinga (10 24) Mattias Lazda (7 11) Victoria M. Kaspi (1 25) Afrokk Khan (1 Bikash Kharel (21 Lluis Mas-Ribas (10) Kiyoshi W. Masui (26 27) Kyle McGregor (1 Daniele Michilli (28) Ryan Mckinven (1 Mason Ng (1 Kenzie Nimmo (19) Swarali Shivraj Patil (21 Aaron B. Pearlman (26 27 Mawson W. Sammons (1 Ketan R. Sand (1 Aylar Sedaei Oghani (14) Vishwangi Shah (1 Kendrick Smith (16) Ingrid Stairs (29) Tarik J. Zegmott (1 2) ((1) Department of Physics McGill University Montr\'eal QC Canada (2) Trottier Space Institute (3) Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam The Netherlands (4) Cahill Center for Astronomy Astrophysics California Institute of Technology Pasadena CA USA (5) ASTRON Netherlands Institute for Radio Astronomy Dwingeloo (6) Department of Statistics Actuarial Science Simon Fraser University Burnaby BC (7) David A. Dunlap Department of Astronomy University of Toronto Toronto ON (8) Department of Statistical Sciences (9) Data Sciences Institute (10) Department of Astronomy University of California Santa Cruz (11) Dunlap Institute for Astronomy (12) Miller Institute for Basic Research Berkeley (13) Department of Astronomy (14) Department of Physics Astronomy York University (15) SKAO Science Operations Centre CSIRO ARRC Kensington WA Australia (16) Perimeter Institute for Theoretical Physics Waterloo (17) Department of Space Planetary Astronomical Sciences Engineering Indian Institute of Technology Kanpur Uttar Pradesh India (18) Department of Physics Northwestern University Evanston IL (19) Center for Interdisciplinary Exploration Research in Astronomy (20) Cornell Center for Astrophysics Planetary Science Cornell University Ithaca NY (21) Department of Physics West Virginia University Morgantown WV (22) Center for Gravitational Waves Cosmology (23) S&T Netherlands Delft (24) Department of Physics College of Natural Mathematical Sciences University of Dodoma Dodoma Tanzania (25) School of Physics Tel Aviv University Tel Aviv Israel (26) MIT Kavli Institute for Astrophysics Space Research MIT Cambridge MA (27) Department of Physics (28) Laboratoire d'Astrophysique de Marseille Aix-Marseille Univ. CNRS CNES Marseille France (29) Department of Physics University of British Columbia Vancouver Canada)
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:07 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords fast radio burstsrepeating FRBsCHIME/FRB catalogburst rate distributionpopulation statisticsdispersion measure variationspower-law model
0
0 comments X

The pith

CHIME/FRB data indicate repeating and one-off fast radio bursts come from one population whose repeat rates follow a power law, with 50 to 100 percent of sources repeating.

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

The paper reports 30 new repeating fast radio burst sources detected by CHIME, raising the catalog total to 80. Only 2.4 percent of all sources have been seen to repeat so far, yet the upper limits on repeat rates for the remaining sources lie entirely inside the range measured for confirmed repeaters. A single power-law model for the distribution of repeat rates fits the combined data from both groups equally well and requires that half to all of the population repeats. This unification matters because it removes the need for two separate source classes and instead treats non-detections as the expected tail of low-rate objects. The analysis also notes linear dispersion-measure changes in four repeaters over years.

Core claim

The observations of repeating and yet-one-off FRBs are equally well fit assuming a power-law distribution of repeat rates with 50-100% of the population repeating. No substantial evidence appears for bimodal populations in burst-rate distributions, and the fraction of sources observed to repeat shows no significant evolution over the five-year span of the experiment.

What carries the argument

Power-law distribution of repeat rates, which places the observed repeater rates and the upper limits from non-repeaters on the same continuous curve.

If this is right

  • The repeating fraction of the overall FRB population lies between 50 and 100 percent.
  • The observed 2.4 percent detection rate of repetition is consistent with the low-rate tail of a single power-law distribution.
  • Four repeaters exhibit linear dispersion-measure changes on multi-year timescales.
  • Burst rates in the sample range from 10 to the -5.7 to 10 to the -0.5 per hour when scaled to a 5 Jy ms threshold.

Where Pith is reading between the lines

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

  • Longer monitoring campaigns should convert many current one-offs into repeaters at the low-rate end of the same distribution.
  • A single progenitor class could produce all FRBs if the power-law index and normalization are set by source age or environment.
  • Future catalogs can test the model by checking whether the repeating fraction remains stable as sensitivity improves.

Load-bearing premise

Non-detections of repetition supply reliable upper limits on repeat rates without large selection biases from incomplete survey coverage or varying detection thresholds.

What would settle it

Deeper monitoring that reveals a clear gap between the lowest measured repeater rates and the highest upper limits from one-offs, or a statistically significant bimodal split in the combined rate distribution.

Figures

Figures reproduced from arXiv: 2605.08410 by 1, (10) Department of Astronomy, 11, 11), (11) Dunlap Institute for Astronomy, (12) Miller Institute for Basic Research, 13), (13) Department of Astronomy, (14) Department of Physics, (15) SKAO, 16, (16) Perimeter Institute for Theoretical Physics, (17) Department of Space Planetary Astronomical Sciences, (18) Department of Physics, 19), (19) Center for Interdisciplinary Exploration, 2, 2), (20) Cornell Center for Astrophysics, (21) Department of Physics, 2) ((1) Department of Physics, 22), (22) Center for Gravitational Waves, (23) S&T Netherlands, 24), (24) Department of Physics, 25), (25) School of Physics, (26) MIT Kavli Institute for Astrophysics, 27, 27), (27) Department of Physics, (28) Laboratoire d'Astrophysique de Marseille, (29) Department of Physics, (2) Trottier Space Institute, 3, 3), (3) Anton Pannekoek Institute for Astronomy, (4) Cahill Center for Astronomy, 5), (5) ASTRON, (6) Department of Statistics, 7), (7) David A. Dunlap Department of Astronomy, 8, (8) Department of Statistical Sciences, 9), (9) Data Sciences Institute, Aaron B. Pearlman (26, Actuarial Science, Afrokk Khan (1, Aix-Marseille Univ., Alice Cai (18, Alice P. Curtin (1, Amanda M. Cook (1, Amsterdam, Astronomy, Astrophysics, Australia, Aylar Sedaei Oghani (14), Ayush Pandhi (1, BC, Berkeley, Bikash Kharel (21, B. M. Gaensler (10, Bridget C. Andersen (10), Burnaby, CA, California Institute of Technology, Calvin Leung (12, Cambridge, Canada, Canada), CNES, CNRS, College of Natural, Cornell University, Cosmology, CSIRO ARRC, Daniele Michilli (28), Dant\'e M. Hewitt (3), David C. Stenning (6), Delft, Derek Bingham (6), Dodoma, Dwingeloo, Emmanuel Fonseca (21, Engineering, Evanston, Fengqiu Adam Dong (14), France, Gwendolyn Eadie (7, IL, India, Indian Institute of Technology Kanpur, Ingrid Stairs (29), Israel, Ithaca, Jason W. T. Hessels (1, Kaitlyn Shin (4), Kendrick Smith (16), Kensington, Kenzie Nimmo (19), Ketan R. Sand (1, Kiyoshi W. Masui (26, Kyle McGregor (1, Lluis Mas-Ribas (10), Lordrick Kahinga (10, MA, Marseille, Mason Ng (1, Mathematical Sciences, Mattias Lazda (7, Mawson W. Sammons (1, Maxwell Fine (1, McGill University, MIT, Mohit Bhardwaj (17), Montr\'eal, Morgantown, Naman Jain (1, Netherlands Institute for Radio Astronomy, Nicole Mulyk (1, Northwestern University, NY, ON, Pasadena, Paul Scholz (14), Planetary Science, QC, Research in Astronomy, Robert Main (1, Ronniy C. Joseph (23), Ryan Mckinven (1, Santa Cruz, Science Operations Centre, Seth R. Siegel (15, Shami Chatterjee (20), Simon Fraser University, Space Research, Swarali Shivraj Patil (21, Tanzania, Tarik J. Zegmott (1, Tel Aviv, Tel Aviv University, The Netherlands, Thomas C. Abbott (1, Toronto, University of Amsterdam, University of British Columbia, University of California, University of Dodoma, University of Toronto, USA, Uttar Pradesh, Vancouver, Victoria M. Kaspi (1, Vishwangi Shah (1, WA, Waterloo, West Virginia University, WV, York University, Ziggy Pleunis (3.

Figure 1
Figure 1. Figure 1: Probability of chance coincidence (PCC) versus declination of repeater candidates from CHIME/FRB’s Second catalog. Candidates are colored according to the number of bursts in their clusters, which were identified via an in-house implementation of a DBSCAN clustering algorithm (see §2.2.1). The error bars on the data points correspond to the candi￾dates’ 95% credible regions, where uncertainties have arisen… view at source ↗
Figure 2
Figure 2. Figure 2: Left panel: Burst rate above 5 Jy ms for repeating FRBs from CHIME/FRB vs. source declination. Previously published repeaters (A. P. Curtin et al. 2025; CHIME/FRB Collaboration et al. 2026) are plotted in red and repeaters newly reported in this work are plotted as gold dots. In both cases, their error bars correspond to 95% uncertainty regions assuming Poisson statistics in number of detections and the ap… view at source ↗
Figure 3
Figure 3. Figure 3: Correlation plots between burst rate, fluence, burst duration, and the extragalactic DM for the repeating FRBs in our sample. Burst duration and extragalactic DM are given in the observer’s frame. Following the approach outlined in §3.4. Only repeaters with at least two bursts detected are included. The quoted Spearman rank correlation coefficients and corresponding p-values represent the median values obt… view at source ↗
Figure 4
Figure 4. Figure 4: DM versus burst time of arrival for sources (black data points) for which the best-fit linear model (red solid line) is strongly preferred to the best-fit constant model (dashed grey line) and corresponding simulation-based p-value is smaller than 0.001 (which is equal to a 50% family-wise error rate across the sample given our trials factor of 40 searched repeaters) and DM variation is not dominated by a … view at source ↗
Figure 5
Figure 5. Figure 5: Burst detection time versus declination of all of CHIME/FRB’s discovered repeaters. Marker size indicates the S/N of the burst detections. Strong evidence of clustered burst detections, or evidence of enhanced activity, is apparent for many sources, for example FRB 20220912A, FRB 20201124A, and FRB 20201130A (near Decl. ∼48, 26, and 8 degrees, respectively). Individual sources are identified with a gray ho… view at source ↗
Figure 6
Figure 6. Figure 6: Bottom panel: Extragalactic DM histograms for the apparent one-off population (dark blue histogram), the repeaters newly reported in this work (gold histogram), and all previously published repeaters from CHIME/FRB (red histogram). Extragalactic DMs are reported in the ob￾server’s frame. The medians of each sample are shown in dotted vertical lines of their respective colors, to demonstrate consistent dist… view at source ↗
Figure 7
Figure 7. Figure 7: Repeating FRB fraction (number of FRB sources that have been observed to repeat divided by the total number of observed sources) as a function of average total exposure in different declination bins (grey line). Declination bins are indicated in the top right corner, with U/L separating the upper and lower transit of the top bin, respectively. In each pair of panels, the first represents the fraction of FR… view at source ↗
Figure 8
Figure 8. Figure 8: The maximum probability of the J23 repeat￾ing population model as a function of the fraction of as-yet one-off FRBs observed by CHIME/FRB that are assumed to be attributable to repeaters, Fsingle. The dotted line shows the original values found when applied to the Cat1 sample by J23. The solid line shows the results for the model fit to Cat2 data. In both cases, fractions between 0.5–1.0 have higher probab… view at source ↗
read the original abstract

We present 30 newly discovered repeating fast radio burst (FRB) sources from the second catalog of bursts detected by the FRB backend on the Canadian Hydrogen Intensity Mapping Experiment (CHIME/FRB). These repeaters have extragalactic dispersion measures (DMs) spanning $99.4-1446.0\ \text{pc cm}^{-3}$ and burst rates between $10^{-5.7}$ and $10^{-0.5}$ hr$^{-1}$ scaled to a fluence threshold of 5 Jy ms. We report evidence of monotonic, linear DM variations in four repeaters on years-long timescales. The newly discovered sources bring CHIME/FRB's total number of observed repeating FRBs to 80, 79 of which were discovered by CHIME/FRB, between 2018 July 25 and 2023 September 15. In the full CHIME/FRB sample, only 2.4$\pm 0.4\%$ of sources have been observed to repeat, and we do not find evidence for significant evolution of this value over the duration of the experiment. We find no substantial evidence for bimodal populations of one-off and repeating FRBs in their burst rate distributions; the distribution of upper limits on repeat rates implied from observations of as-yet one-offs is entirely contained within the observed range of repeater burst rates and the distributions do not appear inconsistent. Similarly, using the population analysis framework of C. W. James (2023), we find that our observations of repeating and yet-one-off FRBs are equally well fit assuming a power-law distribution of repeat rates with 50$-$100% of the population repeating.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The manuscript reports the discovery of 30 new repeating FRB sources from CHIME/FRB, bringing the total to 80 repeaters (79 discovered by CHIME/FRB) out of ~3300 sources observed between 2018 and 2023. Only 2.4±0.4% of sources have been seen to repeat, with no significant evolution in this fraction. The burst-rate distribution of the 80 repeaters (rates 10^{-5.7} to 10^{-0.5} hr^{-1} scaled to 5 Jy ms) and the upper limits from one-off sources show no evidence of bimodality; using the James (2023) framework, the combined data are equally well fit by a single power-law repeat-rate distribution implying 50-100% of the population repeats. Monotonic linear DM variations on year-long timescales are reported in four repeaters.

Significance. If the population analysis is robust, the result supports a unified FRB population with a broad continuum of repeat rates rather than distinct one-off and repeating classes. This would simplify progenitor models, revise all-sky rate estimates, and guide future survey cadence choices. The large homogeneous sample and direct use of an established statistical framework are strengths, but the claim's weight depends on explicit validation of completeness corrections.

major comments (1)
  1. [Population analysis (James 2023 framework)] In the population analysis section applying the James (2023) framework: the central claim that the repeater and one-off data are equally well fit by a power-law repeat-rate distribution with 50-100% repeaters rests on treating non-detections as meaningful upper limits. The text states that the upper-limit distribution lies within the observed repeater range, but does not provide the per-source exposure map, total on-source times, fluence thresholds, or Monte-Carlo injection tests that would confirm the survey selection function has been propagated through the likelihood without bias. If longer-exposed sources are preferentially classified as repeaters, the inferred repeater fraction can be biased even under a pure power-law parent distribution.
minor comments (1)
  1. [Abstract and rate scaling description] The abstract states burst rates are scaled to a 5 Jy ms fluence threshold; the main text should explicitly confirm that the same scaling and any associated uncertainties are applied uniformly to both the repeater sample and the one-off upper limits used in the power-law fit.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We address the major comment below and outline revisions that will strengthen the clarity of the population analysis without altering our scientific conclusions.

read point-by-point responses

  1. Referee: In the population analysis section applying the James (2023) framework: the central claim that the repeater and one-off data are equally well fit by a power-law repeat-rate distribution with 50-100% repeaters rests on treating non-detections as meaningful upper limits. The text states that the upper-limit distribution lies within the observed repeater range, but does not provide the per-source exposure map, total on-source times, fluence thresholds, or Monte-Carlo injection tests that would confirm the survey selection function has been propagated through the likelihood without bias. If longer-exposed sources are preferentially classified as repeaters, the inferred repeater fraction can be biased even under a pure power-law parent distribution.

    Authors: We appreciate the referee highlighting the need for explicit documentation of the selection function in our application of the James (2023) framework. That framework is constructed to incorporate per-source exposure times, fluence thresholds, and the resulting upper limits on repeat rates for non-repeating sources, thereby treating non-detections as meaningful constraints and mitigating bias from inhomogeneous coverage. Our upper limits are computed from the actual CHIME/FRB observation histories for each source. To address the concern directly, we will revise the population analysis section (and add a short appendix if space permits) to summarize the exposure maps, total on-source times, and fluence thresholds used, and to explain how these quantities enter the likelihood. We will also add an explicit reference to the Monte Carlo validation tests already performed in James (2023) that demonstrate the framework recovers unbiased repeater fractions even when exposure times vary. These additions will allow readers to confirm that the 50-100% repeater fraction is robust. We therefore agree that greater transparency is warranted, but maintain that the underlying analysis already follows a validated procedure that accounts for the described selection effects. revision: partial

Circularity Check

0 steps flagged

No significant circularity in population statistics derivation

full rationale

The paper's central claim—that a power-law repeat-rate distribution with 50-100% repeaters fits the combined repeater and one-off data equally well—is obtained by applying the external James (2023) framework to directly compare observed burst rates against upper limits from non-detections. This is a statistical model fit to the data, not a derivation in which any predicted quantity reduces by construction to the fitted parameters or to a self-citation chain. No self-definitional, fitted-input-as-prediction, or load-bearing self-citation steps appear in the reported chain; the result remains falsifiable against the raw rate measurements and exposure assumptions.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The statistical claim depends on the assumption that repeat rates follow a power-law distribution and that survey selection effects are adequately modeled; these are standard domain assumptions rather than new postulates.

free parameters (1)
  • power-law index and normalization for repeat-rate distribution
    Parameters fitted to the combined repeater rates and one-off upper limits using the cited James 2023 framework.
axioms (2)
  • domain assumption FRB repeat rates are drawn from a single underlying power-law distribution across the population
    Invoked when stating that the observations are equally well fit by this model.
  • domain assumption Non-detection of repetition provides a valid upper limit on intrinsic repeat rate after accounting for observing time and sensitivity
    Required to place the one-off sources on the same distribution as the detected repeaters.

pith-pipeline@v0.9.0 · 6454 in / 1349 out tokens · 57033 ms · 2026-05-12T01:07:48.097959+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

131 extracted references · 131 canonical work pages · 2 internal anchors

  1. [1]

    D., Acernese, F., et al

    Abbott, R., Abbott, T. D., Acernese, F., et al. 2023, ApJ, 955, 155, doi: 10.3847/1538-4357/acd770

    work page doi:10.3847/1538-4357/acd770 2023

  2. [2]

    2024, The Astronomer’s Telegram, 16734, 1

    Abbott, T., & CHIME/FRB Collaboration. 2024, The Astronomer’s Telegram, 16734, 1

    work page 2024

  3. [3]

    C., Pearlman, A

    Abbott, T. C., Pearlman, A. B., Kaspi, V. M., & Pandhi, A. 2026, in prep

    work page 2026

  4. [4]

    C., Fonseca, E., McKee, J

    Andersen, B. C., Fonseca, E., McKee, J. W., et al. 2023, ApJ, 943, 57, doi: 10.3847/1538-4357/aca485

    work page doi:10.3847/1538-4357/aca485 2023

  5. [5]

    2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0310287

    Beck, R. 2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0310287

    work page doi:10.48550/arxiv.astro-ph/0310287 2003

  6. [6]

    2013, in Planets, Stars and Stellar Systems

    Beck, R., & Wielebinski, R. 2013, in Planets, Stars and Stellar Systems. Volume 5: Galactic Structure and Stellar Populations, ed. T. D. Oswalt & G. Gilmore, Vol. 5 (Springer Nature), 641, doi: 10.1007/978-94-007-5612-0 13

    work page doi:10.1007/978-94-007-5612-0 2013

  7. [7]

    2025, ApJ, 993, 37, doi: 10.3847/1538-4357/ae0712

    Beniamini, P., & Kumar, P. 2025, ApJ, 993, 37, doi: 10.3847/1538-4357/ae0712

    work page doi:10.3847/1538-4357/ae0712 2025

  8. [8]

    Wharton, R. S. 2023, MNRAS, 524, 3303, doi: 10.1093/mnras/stad2009

    work page doi:10.1093/mnras/stad2009 2023

  9. [9]

    M., Kaspi, V

    Bhardwaj, M., Gaensler, B. M., Kaspi, V. M., et al. 2021, ApJL, 910, L18, doi: 10.3847/2041-8213/abeaa6

    work page doi:10.3847/2041-8213/abeaa6 2021

  10. [10]

    Y., et al

    Bhardwaj, M., Michilli, D., Kirichenko, A. Y., et al. 2024, ApJL, 971, L51, doi: 10.3847/2041-8213/ad64d1

    work page doi:10.3847/2041-8213/ad64d1 2024

  11. [11]

    D., Ravi, V., Belov, K

    Bochenek, C. D., Ravi, V., Belov, K. V., et al. 2020, Nature, 587, 59, doi: 10.1038/s41586-020-2872-x

    work page doi:10.1038/s41586-020-2872-x 2020

  12. [12]

    L., Frail, D

    Brogan, C. L., Frail, D. A., Goss, W. M., & Troland, T. H. 2000, ApJ, 537, 875, doi: 10.1086/309068

    work page doi:10.1086/309068 2000

  13. [13]

    2025, A&A, 695, L12, doi: 10.1051/0004-6361/202453233

    Bruni, G., Piro, L., Yang, Y.-P., et al. 2025, A&A, 695, L12, doi: 10.1051/0004-6361/202453233

    work page doi:10.1051/0004-6361/202453233 2025

  14. [14]

    J., Petroff, E., Kramer, M., et al

    Champion, D. J., Petroff, E., Kramer, M., et al. 2016, Monthly Notices of the Royal Astronomical Society: Letters, 460, L30, doi: 10.1093/mnrasl/slw069 CHIME Collaboration, Amiri, M., Bandura, K., et al. 2022, ApJS, 261, 29, doi: 10.3847/1538-4365/ac6fd9 CHIME/FRB Collaboration, Amiri, M., Bandura, K., et al. 2019a, Nature, 566, 235, doi: 10.1038/s41586-0...

    work page doi:10.1093/mnrasl/slw069 2016

  15. [15]

    2019, MNRAS, 487, 5753, doi: 10.1093/mnras/stz1666

    Connor, L. 2019, MNRAS, 487, 5753, doi: 10.1093/mnras/stz1666

    work page doi:10.1093/mnras/stz1666 2019

  16. [16]

    C., & Gardenier, D

    Connor, L., Miller, M. C., & Gardenier, D. W. 2020, MNRAS, 497, 3076, doi: 10.1093/mnras/staa2074

    work page doi:10.1093/mnras/staa2074 2020

  17. [17]

    2018, ApJL, 861, L1, doi: 10.3847/2041-8213/aacd02

    Connor, L., & Petroff, E. 2018, ApJL, 861, L1, doi: 10.3847/2041-8213/aacd02

    work page doi:10.3847/2041-8213/aacd02 2018

  18. [18]

    M., Bhardwaj, M., Gaensler, B

    Cook, A. M., Bhardwaj, M., Gaensler, B. M., et al. 2023, ApJ, 946, 58, doi: 10.3847/1538-4357/acbbd0

    work page doi:10.3847/1538-4357/acbbd0 2023

  19. [19]

    M., Scholz, P., Pearlman, A

    Cook, A. M., Scholz, P., Pearlman, A. B., et al. 2024, ApJ, 974, 170, doi: 10.3847/1538-4357/ad6a13

    work page doi:10.3847/1538-4357/ad6a13 2024

  20. [20]

    M., Li, D., Eadie, G

    Cook, A. M., Li, D., Eadie, G. M., et al. 2026, The Annals of Applied Statistics, in press. https://arxiv.org/abs/2410.12146

    work page arXiv 2026

  21. [21]

    NE2001.I. A New Model for the Galactic Distribution of Free Electrons and its Fluctuations

    Cordes, J. M., & Lazio, T. J. W. 2002, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0207156

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/0207156 2002

  22. [22]

    G., Scholz, P., et al

    Cruces, M., Spitler, L. G., Scholz, P., et al. 2020, Monthly Notices of the Royal Astronomical Society, 500, 448, doi: 10.1093/mnras/staa3223

    work page doi:10.1093/mnras/staa3223 2020

  23. [23]

    P., Tendulkar, S

    Curtin, A. P., Tendulkar, S. P., Josephy, A., et al. 2023, ApJ, 954, 154, doi: 10.3847/1538-4357/ace52f

    work page doi:10.3847/1538-4357/ace52f 2023

  24. [24]

    P., Sirota, S., Kaspi, V

    Curtin, A. P., Sirota, S., Kaspi, V. M., et al. 2024, ApJ, 972, 125, doi: 10.3847/1538-4357/ad5c65 CHIME/FRB’s Fourth Repeater Catalog27 T able A.Properties of 14 New Candidate Repeaters (Our Silver Sample) SourceP CC R.A.a Decl.a DM DM NE2001 DMYMW16 N b Exposure Sensitivity c Rate≥5 Jy ms d (deg) (deg) (pc cm −3) (pc cm −3) (pc cm −3) (U/L, hr) (U/L, Jy...

    work page doi:10.3847/1538-4357/ad5c65 2024

  25. [25]

    P., Sand, K

    Curtin, A. P., Sand, K. R., Pleunis, Z., et al. 2025, ApJ, 992, 206, doi: 10.3847/1538-4357/adf844

    work page doi:10.3847/1538-4357/adf844 2025

  26. [26]

    , archivePrefix = "arXiv", eprint =

    Desvignes, G., Eatough, R. P., Pen, U. L., et al. 2018, ApJL, 852, L12, doi: 10.3847/2041-8213/aaa2f8

    work page doi:10.3847/2041-8213/aaa2f8 2018

  27. [27]

    M., Beck, A

    Dolag, K., Gaensler, B. M., Beck, A. M., & Beck, M. C. 2015, MNRAS, 451, 4277, doi: 10.1093/mnras/stv1190

    work page doi:10.1093/mnras/stv1190 2015

  28. [28]

    A., et al., 2023, @doi [ ] 10.1093/mnras/stad2012 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.5132D 524, 5132

    Dong, F. A., Crowter, K., Meyers, B. W., et al. 2023, MNRAS, 524, 5132, doi: 10.1093/mnras/stad2012

    work page doi:10.1093/mnras/stad2012 2023

  29. [29]

    D., Fong, W., et al

    Dong, Y., Kilpatrick, C. D., Fong, W., et al. 2025, ApJ, 991, 199, doi: 10.3847/1538-4357/adfb74

    work page doi:10.3847/1538-4357/adfb74 2025

  30. [30]

    Y., Verbiest, J

    Donner, J. Y., Verbiest, J. P. W., Tiburzi, C., et al. 2020, A&A, 644, A153, doi: 10.1051/0004-6361/202039517

    work page doi:10.1051/0004-6361/202039517 2020

  31. [31]

    2025, The Astrophysical Journal Letters, 979, L22, doi: 10.3847/2041-8213/ad9de2

    Eftekhari, T., Dong, Y., Fong, W., et al. 2025, The Astrophysical Journal Letters, 979, L22, doi: 10.3847/2041-8213/ad9de2

    work page doi:10.3847/2041-8213/ad9de2 2025

  32. [32]

    2025, A&A, 695, L10, doi: 10.1051/0004-6361/202453563

    Eppel, F., Krumpe, M., Limaye, P., et al. 2025, A&A, 695, L10, doi: 10.1051/0004-6361/202453563

    work page doi:10.1051/0004-6361/202453563 2025

  33. [33]

    C., Cruces, M., Cassanelli, T., et al

    Espinoza-Dupouy, M. C., Cruces, M., Cassanelli, T., et al. 2025, arXiv e-prints, arXiv:2507.04609, doi: 10.48550/arXiv.2507.04609

    work page doi:10.48550/arxiv.2507.04609 2025

  34. [34]

    1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 (AAAI Press), 226–231

    Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. 1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 (AAAI Press), 226–231

    work page 1996

  35. [35]

    C., Bhardwaj, M., et al

    Fonseca, E., Andersen, B. C., Bhardwaj, M., et al. 2020, ApJL, 891, L6, doi: 10.3847/2041-8213/ab7208

    work page doi:10.3847/2041-8213/ab7208 2020

  36. [36]

    2024, ApJS, 271, 49, doi: 10.3847/1538-4365/ad27d6

    Fonseca, E., Pleunis, Z., Breitman, D., et al. 2024, ApJS, 271, 49, doi: 10.3847/1538-4365/ad27d6

    work page doi:10.3847/1538-4365/ad27d6 2024

  37. [37]

    C., Kramer, M., Lyne, A

    Freire, P. C., Kramer, M., Lyne, A. G., et al. 2001, ApJL, 557, L105, doi: 10.1086/323248

    work page doi:10.1086/323248 2001

  38. [38]

    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, doi: 10.1051/0004-6361/202039626

    work page doi:10.1051/0004-6361/202039626 2021

  39. [39]

    C., Chawla, P., Fonseca, E., et al

    Good, D. C., Chawla, P., Fonseca, E., et al. 2023, ApJ, 944, 70, doi: 10.3847/1538-4357/acb139

    work page doi:10.3847/1538-4357/acb139 2023

  40. [40]

    C., Fong, W.-f., Kilpatrick, C

    Gordon, A. C., Fong, W.-f., Kilpatrick, C. D., et al. 2023, ApJ, 954, 80, doi: 10.3847/1538-4357/ace5aa

    work page doi:10.3847/1538-4357/ace5aa 2023

  41. [41]

    C., Fong, W.-f., Deller, A

    Gordon, A. C., Fong, W.-f., Deller, A. T., et al. 2025, ApJ, 993, 119, doi: 10.3847/1538-4357/ae0298

    work page doi:10.3847/1538-4357/ae0298 2025

  42. [42]

    The DSA-2000 -- A Radio Survey Camera

    Hallinan, G., Ravi, V., Weinreb, S., et al. 2019, in Bulletin of the American Astronomical Society, Vol. 51, 255, doi: 10.48550/arXiv.1907.07648

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1907.07648 2019

  43. [43]

    Hessels, J. W. T., Spitler, L. G., Seymour, A. D., et al. 2019, ApJL, 876, L23, doi: 10.3847/2041-8213/ab13ae

    work page doi:10.3847/2041-8213/ab13ae 2019

  44. [44]

    M., Hessels, J

    Hewitt, D. M., Hessels, J. W. T., Ould-Boukattine, O. S., et al. 2023, MNRAS, 526, 2039, doi: 10.1093/mnras/stad2847

    work page doi:10.1093/mnras/stad2847 2023

  45. [45]

    M., Bhandari, S., Marcote, B., et al

    Hewitt, D. M., Bhandari, S., Marcote, B., et al. 2024, MNRAS, 529, 1814, doi: 10.1093/mnras/stae632

    work page doi:10.1093/mnras/stae632 2024

  46. [46]

    H., Michilli, D., Spitler, L

    Hilmarsson, G. H., Michilli, D., Spitler, L. G., et al. 2021, The Astrophysical Journal Letters, 908, L10, doi: 10.3847/2041-8213/abdec0

    work page doi:10.3847/2041-8213/abdec0 2021

  47. [47]

    James, C. W. 2023, PASA, 40, e057, doi: 10.1017/pasa.2023.51

    work page doi:10.1017/pasa.2023.51 2023

  48. [48]

    W., Prochaska, J

    James, C. W., Prochaska, J. X., Macquart, J.-P., et al. 2022a, MNRAS, 509, 4775, doi: 10.1093/mnras/stab3051

    work page doi:10.1093/mnras/stab3051

  49. [49]

    W., Prochaska, J

    James, C. W., Prochaska, J. X., Macquart, J.-P., et al. 2022b, MNRAS, 510, L18, doi: 10.1093/mnrasl/slab117

    work page doi:10.1093/mnrasl/slab117

  50. [50]

    C., Caleb, M., et al

    Jankowski, F., Bezuidenhout, M. C., Caleb, M., et al. 2023, MNRAS, 524, 4275, doi: 10.1093/mnras/stad2041

    work page doi:10.1093/mnras/stad2041 2023

  51. [51]

    N., Lyne, A

    Johnston, S., Manchester, R. N., Lyne, A. G., et al. 1996, Monthly Notices of the Royal Astronomical Society, 279, 1026, doi: 10.1093/mnras/279.3.1026

    work page doi:10.1093/mnras/279.3.1026 1996

  52. [52]

    L., McLaughlin, M

    Jones, M. L., McLaughlin, M. A., Lam, M. T., et al. 2017, ApJ, 841, 125, doi: 10.3847/1538-4357/aa73df

    work page doi:10.3847/1538-4357/aa73df 2017

  53. [53]

    2019, ApJL, 882, L18, doi: 10.3847/2041-8213/ab2c00

    Josephy, A., Chawla, P., Fonseca, E., et al. 2019, ApJL, 882, L18, doi: 10.3847/2041-8213/ab2c00

    work page doi:10.3847/2041-8213/ab2c00 2019

  54. [54]

    J., Johnston, S., Karastergiou, A., et al

    Keith, M. J., Johnston, S., Karastergiou, A., et al. 2024, MNRAS, 530, 1581, doi: 10.1093/mnras/stae937

    work page doi:10.1093/mnras/stae937 2024

  55. [55]

    D., Tejos, N., Andersen, B

    Kilpatrick, C. D., Tejos, N., Andersen, B. C., et al. 2024, ApJ, 964, 121, doi: 10.3847/1538-4357/ad2687

    work page doi:10.3847/1538-4357/ad2687 2024

  56. [56]

    2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w

    Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w

    work page doi:10.1038/s41586-021-04354-w 2022

  57. [57]

    S., Herrmann, W., et al

    Kirsten, F., Ould-Boukattine, O. S., Herrmann, W., et al. 2024, Nature Astronomy, 8, 337, doi: 10.1038/s41550-023-02153-z

    work page doi:10.1038/s41550-023-02153-z 2024

  58. [58]

    C., Hewitt, D

    Konijn, D. C., Hewitt, D. M., Hessels, J. W. T., et al. 2024, MNRAS, 534, 3331, doi: 10.1093/mnras/stae2296

    work page doi:10.1093/mnras/stae2296 2024

  59. [59]

    M., Os lowski, S., et al

    Kumar, P., Shannon, R. M., Os lowski, S., et al. 2019, ApJL, 887, L30, doi: 10.3847/2041-8213/ab5b08

    work page doi:10.3847/2041-8213/ab5b08 2019

  60. [60]

    M., Flynn, C., et al

    Kumar, P., Shannon, R. M., Flynn, C., et al. 2021, MNRAS, 500, 2525, doi: 10.1093/mnras/staa3436

    work page doi:10.1093/mnras/staa3436 2021

  61. [61]

    C., et al

    Kumar, P., Luo, R., Price, D. C., et al. 2023, MNRAS, 526, 3652, doi: 10.1093/mnras/stad2969

    work page doi:10.1093/mnras/stad2969 2023

  62. [62]

    Y., Jordan, C

    Kuzmin, A., Losovsky, B. Y., Jordan, C. A., & Smith, F. G. 2008, A&A, 483, 13, doi: 10.1051/0004-6361:20079211

    work page doi:10.1051/0004-6361:20079211 2008

  63. [63]

    E., Andersen, B

    Lanman, A. E., Andersen, B. C., Chawla, P., et al. 2022, ApJ, 927, 59, doi: 10.3847/1538-4357/ac4bc7

    work page doi:10.3847/1538-4357/ac4bc7 2022

  64. [64]

    J., Sharma, K., Ravi, V., et al

    Law, C. J., Sharma, K., Ravi, V., et al. 2024, ApJ, 967, 29, doi: 10.3847/1538-4357/ad3736

    work page doi:10.3847/1538-4357/ad3736 2024

  65. [65]

    O., & Christiaan Bezuidenhout, M

    Letsele, T., Mfulwane, L., Venter, C., Chibueze, J. O., & Christiaan Bezuidenhout, M. 2025, arXiv e-prints, arXiv:2512.02693, doi: 10.48550/arXiv.2512.02693

    work page doi:10.48550/arxiv.2512.02693 2025

  66. [66]

    B., Yang, Y

    Li, Y., Zhang, S. B., Yang, Y. P., et al. 2026, Science, 391, 280, doi: 10.1126/science.adq3225

    work page doi:10.1126/science.adq3225 2026

  67. [67]

    2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500

    Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500

    work page doi:10.1093/mnras/stab3500 2022

  68. [68]

    J., Men, Y

    Luo, R., Wang, B. J., Men, Y. P., et al. 2020, Nature, 586, 693, doi: 10.1038/s41586-020-2827-2 30Cook, A.M., Shin, K., Pleunis, Z. et al

    work page doi:10.1038/s41586-020-2827-2 2020

  69. [69]

    P., Keane, E., Grainge, K., et al

    Macquart, J. P., Keane, E., Grainge, K., et al. 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14), 55, doi: 10.22323/1.215.0055

    work page doi:10.22323/1.215.0055 2015

  70. [70]

    G., Fong, W.-f., Simha, S., et al

    Mannings, A. G., Fong, W.-f., Simha, S., et al. 2021, ApJ, 917, 75, doi: 10.3847/1538-4357/abff56

    work page doi:10.3847/1538-4357/abff56 2021

  71. [71]

    Marcote, B., Paragi, Z., Hessels, J. W. T., et al. 2017, ApJL, 834, L8, doi: 10.3847/2041-8213/834/2/L8

    work page doi:10.3847/2041-8213/834/2/l8 2017

  72. [72]

    Marcote, B., Nimmo, K., Hessels, J. W. T., et al. 2020, Nature, 577, 190, doi: 10.1038/s41586-019-1866-z

    work page doi:10.1038/s41586-019-1866-z 2020

  73. [73]

    Margalit, B., Beniamini, P., Sridhar, N., & Metzger, B. D. 2020, ApJL, 899, L27, doi: 10.3847/2041-8213/abac57

    work page doi:10.3847/2041-8213/abac57 2020

  74. [74]

    McGregor, K., & Lorimer, D. R. 2024, ApJ, 961, 10, doi: 10.3847/1538-4357/ad1184

    work page doi:10.3847/1538-4357/ad1184 2024

  75. [75]

    W., Lyne, A

    McKee, J. W., Lyne, A. G., Stappers, B. W., Bassa, C. G., & Jordan, C. A. 2018, MNRAS, 479, 4216, doi: 10.1093/mnras/sty1727

    work page doi:10.1093/mnras/sty1727 2018

  76. [76]

    2022, The Astronomer’s Telegram, 15679, 1

    Mckinven, R., & CHIME/FRB Collaboration. 2022, The Astronomer’s Telegram, 15679, 1

    work page 2022

  77. [77]

    M., Michilli, D., et al

    Mckinven, R., Gaensler, B. M., Michilli, D., et al. 2023a, ApJ, 950, 12, doi: 10.3847/1538-4357/acc65f

    work page doi:10.3847/1538-4357/acc65f

  78. [78]

    M., Michilli, D., et al

    Mckinven, R., Gaensler, B. M., Michilli, D., et al. 2023b, ApJ, 951, 82, doi: 10.3847/1538-4357/acd188

    work page doi:10.3847/1538-4357/acd188

  79. [79]

    P., Shin, K., et al

    Merryfield, M., Tendulkar, S. P., Shin, K., et al. 2023, AJ, 165, 152, doi: 10.3847/1538-3881/ac9ab5

    work page doi:10.3847/1538-3881/ac9ab5 2023

  80. [80]

    Michilli, D., Seymour, A., Hessels, J. W. T., et al. 2018, Nature, 553, 182, doi: 10.1038/nature25149

    work page doi:10.1038/nature25149 2018

Showing first 80 references.