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REVIEW 3 major objections 5 minor 37 references

BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit -- Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Standard full-band single-pulse searches for fast radio bursts are incomplete for narrow-band pulses, and BASSET—by averaging only the frequency channels a burst occupies—recovers 79 previously missed pulses from FRB 20190520B.

desk verdict BASSET is a practical, likely-useful adaptive sub-band search tool with real new pulse candidates, but its headline sensitivity numbers are inflated by a missing trials correction and an uneven comparison to the previous catalog. read the letter →

arxiv 2501.05875 v1 pith:ZBC3R5GG submitted 2025-01-10 astro-ph.IM

J.-H. Cao , P. Wang , D. Li , Q.-H. Pan , K. Mao , C.-H. Niu , Y.-K. Zhang , Q.-Y. Qu
show 24 more authors
W.-J. Lu J.-S. Zhang Y.-H. Zhu Y.-D. Wang H.-X. Chen X.-L. Chen E. Gügercinoğlu J.-H. Fang Y. Feng H. Gao Y.-F. Huang J. Li C.-C. Miao C.-W. Tsai J.-M. Yao S.-P. You R.-S. Zhao Q.-Z. Liu S.-M. Weng S.-H. Yew J. Zhang L. Zhang D.-K. Zhou W.-W. Zhu
This is my paper · ORCID
classification astro-ph.IM
keywords fastradioburstssingle-pulsesearchnarrow-bandpulsesadaptivefiltersignal-to-noiseratioFRB20190520BpulsedetectioncompletenesswaveletRFIexcision
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

This paper argues that standard full-band single-pulse searches for fast radio bursts are systematically incomplete for faint, narrow-band pulses, because the time series is formed by averaging over the whole receiver band, including frequency channels where the burst has no emission. The authors introduce BASSET, a toolkit that estimates each candidate's occupied bandwidth and central frequency, then builds the time series from only those channels, raising the pulse's signal-to-noise ratio by the fraction of the band that is pure noise. Applied to 18.5 hours of observations of the repeating burst FRB 20190520B, BASSET recovered 79 additional pulses, doubling the known sample from 75 to 154, with 29 of the new pulses below a signal-to-noise of 5 in the standard pipeline. Injected-pulse simulations set the 90% completeness fluence threshold at 6.0 rather than 7.5. This claim matters because low-energy pulse censuses shape the inferred luminosity function of repeating FRBs, and this result indicates the census is incomplete at the faint end.

What carries the argument

The load-bearing object is the adaptive filter, a three-stage modification to the extraction of the time series: Triggering (matched filtering the bandpass with boxcar trial bandwidths and removing false candidates whose autocorrelation width is small), Searching Time-Frequency Range (estimating the central frequency as the peak of the matched-filter output and extending the time range while adjacent 1 ms intervals keep consistent autocorrelation width and center), and Enhancement (averaging the data over the localised band and reporting an SNR boosted by beta = SNR_local / SNR_full). The autocorrelation function is fit with a Gaussian to measure W_ACF = 2 sigma as the bandwidth estimate. The paper models the ideal pulse spectrum as a boxcar or Gaussian of constant bandwidth, and the argument is that excluding channels outside this band removes only noise, so the reported SNR rises by the fraction of the band that was empty.

What would settle it

Design a search with mock pulses that include linear frequency drifting or scintillation-induced spectral modulation, inject them into real noise, and compare BASSET's recovered bandwidth to the injected bandwidth and its SNR gain to the predicted beta ratio. If the recovered bandwidth is biased beyond the quoted uncertainties or the SNR gain falls well short of the predicted beta for a substantial fraction of injections, the boxcar/Gaussian model fails for realistic narrow-band pulses.

Watch

Extended reading notes

Core claim

The paper's central discovery is that a search which first localises the burst in frequency and then averages only over that band recovers narrow-band FRB pulses that a full-band pipeline misses. The authors call this bandpass-adaptive filtering: a matched-filter trigger finds candidate times, an autocorrelation of the candidate's bandpass estimates its bandwidth, and an enhancement step builds the time series over the localised frequency range. This raises the signal-to-noise ratio by roughly the fraction of the band that contains no burst, and on the FRB 20190520B data it doubles the number of known pulses, from 75 to 154. The authors' interpretation is that the standard full-band averaging step, not the underlying burst rate, is the limiting factor in the previous census.

Load-bearing premise

The whole gain rests on the assumption that a narrow-band FRB pulse looks like a boxcar or Gaussian bump of roughly constant bandwidth, so every frequency channel outside that bump contains only independent Gaussian noise; real pulses that drift in frequency or scintillate violate this, and the paper itself notes the Triggering step fails for faint pulses when the spectral intensity is contaminated by noise.

Editorial extensions

If this is right

  • BASSET lowers the 90% completeness threshold from fluence SNR 7.5 to 6.0, so surveys using it can claim complete detection of fainter narrow-band pulses than full-band pipelines.
  • Reprocessing FRB 20190520B raises the known pulse count from 75 to 154, indicating that the burst rate of this repeater at low energies is roughly twice what the standard pipeline census suggested.
  • The energy distribution of FRB 20190520B remains single-modal after adding the 79 new pulses, so the shape of the luminosity function is not an artefact introduced by the new sample.
  • The GPU/OpenMP accelerated version achieves a 5.98x speedup on a 140-second test segment, making the adaptive search practical for large survey datasets.
  • The 90.91% of mock pulses detected by both methods show SNR enhancement, with the median enhancement ratio of about 2.28 consistent with the injected 150 MHz bandwidth of the 500 MHz band.

Reading between the lines

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

  • If narrow emission is as common in other repeating FRBs as in FRB 20190520B, previously published repeater pulse counts and luminosity functions built from full-band searches are likely underestimates at the faint end, and the correction is not uniform across energy.
  • The bandpass-adaptive principle could be bolted onto other single-pulse search frameworks beyond the one BASSET extends, since their common step of full-band averaging creates the same incompleteness.
  • The per-event estimated bandwidth could become a useful observable in its own right: the distribution of burst bandwidths and its correlation with energy might constrain emission geometry or propagation effects, something the paper does not explore.
  • A direct test would be to run BASSET on archival data of other repeating FRBs observed with the same telescope and count how many sub-SNR=7 pulses appear; the paper's own 627-pulse sample suggests such a harvest is possible.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. BASSET is a modified PRESTO-based single-pulse search toolkit that removes RFI with a wavelet mask and, after bandpass removal, applies an 'adaptive filter' to estimate the spectral band occupied by a candidate via an autocorrelation width and then forms a time series only from that band. The paper tests the method on FAST data, reports 79 additional pulses from FRB 20190520B (increasing the sample from 75 to 154), updates the energy and luminosity distribution, provides a parallelized implementation, and uses 2000 injected Gaussian mock pulses to claim a 90% completeness threshold lowered from fluence SNR 7.5 to 6.0. The claimed gain is attributed to excluding zero-detection frequency bands from the time-series summation.

Significance. The algorithmic idea is relevant and the experimental setup is unusually grounded: the paper ships code, tests on real FAST data, manually vets the new detections, and compares the BASSET time-frequency box against a data-defined 'best location' residual statistic. If the sensitivity claims survive the missing trial-factor calibration, BASSET would be a practical contribution to FRB search completeness. At present, however, the quantitative claims are not fully supported: the SNR statistic is a maximum over many sub-band trials and the energy normalization in Section 5.1 contains an apparent inconsistency. The 79 new pulses themselves are plausible, so the central discovery claim is credible; what needs work is the calibration of the sensitivity gain.

major comments (3)
  1. [§2.3, Eq. (7); §5.3] Equation (7) defines SNR_Cand as max(TS_Cand)/STD(TS_Bg) and the Searching Time-Frequency Range step also tests adjacent 1-ms intervals until Eq. (6) fails; along with the L0..Lm matched-filter lengths in Eq. (2), the reported SNR_B is a maximum over a large family of sub-bands and trial intervals. No null distribution, trials factor, or false-positive calibration for this max statistic is given anywhere in the paper. Section 5.3 then compares BASSET and the standard pipeline at the same fixed SNR=5 threshold and quotes the detection counts (1379 vs 1005) and the 90% completeness threshold (7.5 to 6.0) without establishing equal false-alarm rates. Because SNR_B is expected to be biased upward relative to SNR_A by selection over many trials, the claimed sensitivity gain and completeness threshold are not yet established. A noise-only injection set measuring the false-positive rate as a function of SNR_B would resolve this; alternatively the completeness curves should be reported at matched false-alarm probabilities.
  2. [§5.1, Eq. (15); Table 4] The text gives DL = 1.218 Mpc for z = 0.241, which cannot be correct and is presumably a typo for Gpc. Even reading it as 1.218 Gpc, the tabulated energies do not follow from Eq. (15) with the fluences in Table 4: for pulse 1, Fν = 193.14 mJy ms = 0.19314 Jy ms, and Eq. (15) gives roughly 3.4e38 erg, whereas the table lists 4.19e37 erg. Since Fig. 9 and Section 5.1's updated luminosity function are built on these energies, the normalization and units need to be audited and corrected.
  3. [§3.3 and §4] Section 3.3 states that the Triggering step 'will fail for faint pulses' when the calibration-noise diode contaminates the spectral intensity, and the three modes tested use relatively high injected SNRs (60, 10, 10). Section 4, however, claims 29 new pulses with SNRA < 5, i.e., exactly the faint regime in which the trigger is acknowledged to fail. The paper should characterize the trigger's recovery fraction as a function of pulse SNR and RFI contamination so that the completeness claims in Section 5.3 can be extended to the faint narrow-band population.
minor comments (5)
  1. [Global] The text contains repeated typos and spacing artifacts ('T oolkit', 'F AST', 'T able'); 'trail lengths' in Eq. (2) should be 'trial lengths'. These should be cleaned up.
  2. [§3.1, Eq. (14)] The quantities 68.42% and 80.70% are labeled 'completeness' but appear to be the fraction of the 627-pulse sample satisfying a residual criterion; please define the denominator and explain why this is a completeness measure.
  3. [§5.3] The experiment draws 2000 parameter sets from specified distributions; this is a Monte Carlo injection study, not a Markov Chain Monte Carlo analysis, and the label 'MCMC simulated injection' should be changed.
  4. [§3.3] The percentages 79.13%, 58.57%, and 71.85% are ratios of SNR_B between RFI-contaminated and clean backgrounds, not detection-recovery fractions; the text should say so explicitly.
  5. [§4] The manual selection step should be described more fully (how many candidates exceeded SNR_B > 5, how many were rejected by eye, and the reproducibility criterion) so that the reprocessing is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the adaptive-filter sensitivity claim is supported by independent simulated injections and real-data reprocessing against an externally published baseline; the main caveat is a missing trials correction, which is a statistical correctness concern, not circularity.

full rationale

The derivation chain is: define a sub-band adaptive filter (Eq. 1 spectral model; Triggering via matched filtering and ACF, Eq. 3-4; CF localization, Eq. 5; max-SNR sub-band selection, Eq. 7; SNR enhancement ratio, Eq. 8), then validate it on FAST data and on 2000 injected mock pulses with specified parameter distributions (Section 5.3). The central quantitative claims are not equivalent to the filter definition: the 79 newly detected pulses come from reprocessing an external FAST dataset whose baseline of 75 pulses is from the independently published Niu et al. (2022a) catalog, and the 90% completeness threshold (fluence SNR 7.5 -> 6.0) is obtained from MCMC injections whose parameter ranges are not fitted to the reported result. The Section 3.1 'best location' residual analysis is partly self-referential because 'best' is defined as the maximum-SNR time-frequency range, the same objective BASSET optimizes; however, that diagnostic is presented as a technical accuracy check, not as the main evidence for sensitivity. The boxcar/Gaussian assumption in Eq. 1 is an explicit modeling choice adopted from Pleunis et al. (2021), and the paper itself labels it an oversimplification (Section 2.3) and notes that the Triggering step fails for faint noise-contaminated pulses (Section 3.3) and that drift/scintillation blur the morphology (Section 3.1); these are stated limitations, not input-output restatements. Self-citations such as Niu et al. (2022a) supply an external dataset and energy-calibration methodology, not an unverified theorem forcing BASSET's design. The genuine caveat is statistical rather than circular: SNRB in Eq. (7) is a maximum over many trial sub-bands, central frequencies, and adjacent time intervals, and Section 5.3 applies a fixed SNR=5 threshold without an explicit trials correction, so the quantitative sensitivity gain and the 79-pulse count may be affected by selection bias. This is a correctness/sensitivity risk, not a circular step, because no claimed prediction reduces to its own input by construction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The algorithm relies on a small set of ad hoc tuning parameters (WACF threshold, trial bandwidth grid, time-frequency search step) and several domain assumptions about pulse spectral shape and noise statistics. The MCMC simulation inputs are not free parameters of the algorithm but do set the scale of the completeness measurement.

free parameters (4)
  • WACF rejection threshold
    Candidates with ACF width less than this preset threshold are removed in Section 2.3, but the value is never given, so the sensitivity depends on an unspecified hand-chosen number.
  • Matched filter trail lengths L0...Lm
    The set of boxcar trial bandwidths used in Triggering (Eq. 2) is not specified; the bandwidth grid controls which pulses are caught.
  • Time-frequency search intervals = 5 ms bandpass sampling, 1 ms time-range step
    The 5 ms cadence for matched filtering on the bandpass and the 1 ms interval for time-range search in Eq. (6) are chosen by hand and not justified.
  • MCMC injection distribution parameters = Width lognormal mean 3 ms, STD 2 ms; bandwidth normal mean 150 MHz, STD 100 MHz; SNR uniform 1-11; center frequency…
    These are simulation inputs, not fit parameters, but the measured completeness threshold depends on them.
assumptions (5)
  • domain assumption Pulse spectra are well described by a boxcar or Gaussian function
    Adopted from Pleunis et al. (2021) and used as the basis of the matched filter in Eq. (1); complex pulses with drifting or scintillation violate it, as the paper itself notes in Section 3.1.
  • domain assumption Excluding zero-detection frequency channels removes only noise and preserves all pulse flux
    This is the core premise of the SNR enhancement in Section 2.3, Eqs. (7) and (8); if the excluded bands contain pulse wings or correlated noise, the gain is overestimated.
  • ad hoc to paper The ACF width WACF is a valid estimator of the candidate's bandwidth
    The paper states that 'WACF can serve as an estimate of the candidates' bandwidth' in Section 2.3 without derivation, and the rejection rule depends on this heuristic.
  • domain assumption The system bandpass spectrum is stable and can be removed in advance
    Assumed in Section 2.2 for bandpass optimization; a time-varying bandpass would bias the adaptive filter.
  • domain assumption Noise is Gaussian and independent across frequency channels
    Needed for the SNR definition in Eq. (7) and for the MCMC injections; real RFI is non-Gaussian and partly handled by masking, but the assumption is still load-bearing.

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Cite this review

Pith. "Pith review of BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit -- Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs." pith.science (2026). https://pith.science/paper/ZBC3R5GG

@misc{pith2026250105875,
  author       = {Pith},
  title        = {Pith review of: BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit -- Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZBC3R5GG}},
  note         = {Machine review of arXiv:2501.05875}
}
read the original abstract

The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo (MCMC) simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrow-band pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrow-band pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale.

Figures

Figures reproduced from arXiv: 2501.05875 by the authors.

Figure 1
Figure 1. The framework of BASSET. The SNR of the narrow-band pulses is improved on the time series using BASSET. The frequency channels affected by RFI are masked and highlighted. 2.2. Bandpass optimization The pulse spectrum in the masked data ( [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The performance of BASSET from application to the three FAST-detected single-component pulses. The pulses in panels a and b are from FRB 20121102A (Li et al. 2021), while the pulse in panel c is from FRB 20201124A. The time￾frequency regions searched by BASSET are highlighted with red boxes. The frequency channels affected by RFI are masked and highlighted. The blue dashed line represents the time series processed b… view at source ↗
Figure 3
Figure 3. The accuracy of Searching time-frequency range is quantified using a 2D KDE of the residuals. Panels a, b, and c show the results of the sample subsets with different bandwidths and SNR. Panel d shows the result of the entire sample set. The percentages of samples with total residuals within 0.2 and 0.3 are shown in red. We use SNRmax to estimate the uncertainty (ϵ) of the best location: ϵ = 1 √ SNRmax . (12) The re… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: The performance of BASSET when applied to a series of morphologically complex pulses from FRB 20201124A. Panel a shows the SNRB/SNRA. In panel b, The blue dashed line represents the time series processed by the standard pipeline, and the orange solid line represents th…
Figure 5
Figure 5. Figure 5: BASSET performance in three typical RFI mode environments. Panel a: Calibration noise diode. The injected mock pulse has a width of 5 ms and a bandwidth of 300 MHz centered at 1250 MHz, both with a Gaussian shape, and an fluence SNR of 60. Panel b: The stationary narro…
Figure 6
Figure 6. Figure 6: A gallery of the newly detected pulses from FRB 20190520B data reprocessing. The SNRA is obtained by searching the time series processed by the standard pipeline (blue dashed line), using single pulse search.py from PRESTO. SNRB is obtained by searching the time series…
Figure 7
Figure 7. Figure 7: The bandwidth and SNR distribution of the newly detected pulses. Panel a shows the bandwidth histogram of the newly detected pulses by BASSET, compared to the previous detection by Niu et al. (2022a). Panel b shows the bandwidth-SNR statistics. The newly detected pulse…
Figure 8
Figure 8. Figure 8: The fluence-width distribution of the FRB 20190520B. The red dots and histograms are newly detected 79 pulses using BASSET, while the grey dots and bars are taken from Niu et al. (2022a). The specific fluence Fν is expressed in units of Jy · ms, measured over the full …
Figure 9
Figure 9. Figure 9: The luminosity function of the FRB 20190520B. The red line and bars represent the 79 newly detected pulses using BASSET, while the grey line and bars are taken from Niu et al. (2022a) [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Comparison between the BASSET and the standard pipeline. The result is based on 2000 mock pulses. Panel a presents the distribution of the SNR improvement ratio (SNRA/SNRB). Panel b shows the recovery rate as a function of the injected SNR, where SNR90 represents the …
Figure 11
Figure 11. Figure 11: The dynamic spectra of the newly detected pulses from FRB 20190520B. The frequency channels affected by RFI are masked and highlighted [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]

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Works this paper leans on

37 extracted references · 27 canonical work pages

  1. [1]

    F., et al

    Aggarwal, K., Agarwal, D., Lewis, E. F., et al. 2021, The Astrophysical Journal, 922, 115

    work page 2021

  2. [2]

    2018, The Astrophysical Journal, 863, 48

    Amiri, M., Bandura, K., Berger, P., et al. 2018, The Astrophysical Journal, 863, 48

    work page 2018

  3. [3]

    2023, Science, 380, 599 Astropy Collaboration, Robitaille, T

    Anna-Thomas, R., Connor, L., Dai, S., et al. 2023, Science, 380, 599 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    doi:10.1051/0004-6361/201322068 2023

  4. [4]

    W., Shannon, R., Macquart, J.-P., et al

    Bannister, K. W., Shannon, R., Macquart, J.-P., et al. 2017, The Astrophysical Journal Letters, 841, L12

    work page 2017

  5. [5]

    2017, Monthly Notices of the Royal Astronomical Society, 468, 3746

    Caleb, M., Flynn, C., Bailes, M., et al. 2017, Monthly Notices of the Royal Astronomical Society, 468, 3746

    work page 2017

  6. [6]

    2016, Monthly Notices of the Royal Astronomical Society: Letters, 460, L30

    Champion, D., Petroff, E., Kramer, M., et al. 2016, Monthly Notices of the Royal Astronomical Society: Letters, 460, L30

    work page 2016

  7. [7]

    M., & Chatterjee, S

    Cordes, J. M., & Chatterjee, S. 2019, Annual Review of Astronomy and Astrophysics, 57, 417

    work page 2019

  8. [8]

    P., et al

    Czesla, S., Schr¨ oter, S., Schneider, C. P., et al. 2019, PyA: Python astronomy-related packages. http://ascl.net/1906.010

    2019

Show all 37 references
  1. [9]

    2019, The Astrophysical Journal Letters, 877, L19

    Gourdji, K., Michilli, D., Spitler, L., et al. 2019, The Astrophysical Journal Letters, 877, L19

    2019

  2. [10]

    2022, Research in Astronomy and Astrophysics, 22, 124003

    Jiang, J.-C., Wang, W.-Y., Xu, H., et al. 2022, Research in Astronomy and Astrophysics, 22, 124003

    2022

  3. [11]

    Kumar, P., Zackay, B., & Law, C. J. 2024, The Astrophysical Journal, 960, 128

    2024

  4. [12]

    2018, IEEE Microwave Magazine, 19, 112

    Li, D., Wang, P., Qian, L., et al. 2018, IEEE Microwave Magazine, 19, 112

    2018

  5. [13]

    2021, Nature, 598, 267

    Li, D., Wang, P., Zhu, W., et al. 2021, Nature, 598, 267

    2021

  6. [14]

    2022, arXiv preprint arXiv:2208.13677

    Lin, H.-H., Main, R., Pen, U.-L., et al. 2022, arXiv preprint arXiv:2208.13677

    2022 arXiv

  7. [15]

    2020, Nature, 581, 391

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

    2020

  8. [16]

    2022, Monthly Notices of the Royal Astronomical Society, 509, 2209

    Marthi, V., Bethapudi, S., Main, R., et al. 2022, Monthly Notices of the Royal Astronomical Society, 509, 2209

    2022

  9. [17]

    2023, The Astrophysical Journal, 950, 12

    Mckinven, R., Gaensler, B., Michilli, D., et al. 2023, The Astrophysical Journal, 950, 12

    2023

  10. [18]

    2024, arXiv preprint arXiv:2401.13834

    Men, Y., & Barr, E. 2024, arXiv preprint arXiv:2401.13834

    2024 arXiv

  11. [19]

    2018, Nature, 553, 182

    Michilli, D., Seymour, A., Hessels, J., et al. 2018, Nature, 553, 182

    2018

  12. [20]

    2011, International Journal of Modern Physics D, 20, 989

    Nan, R., Li, D., Jin, C., et al. 2011, International Journal of Modern Physics D, 20, 989

    2011

  13. [21]

    2022b, Research in Astronomy and Astrophysics, 22, 124004 O’Neil, K

    Niu, J.-R., Zhu, W.-W., Zhang, B., et al. 2022b, Research in Astronomy and Astrophysics, 22, 124004 O’Neil, K. 2002, arXiv preprint astro-ph/0203001

    2002 arXiv

  14. [22]

    2019, The Astronomy and Astrophysics Review, 27, 4

    Petroff, E., Hessels, J., & Lorimer, D. 2019, The Astronomy and Astrophysics Review, 27, 4

    2019

  15. [23]

    C., Kaspi, V

    Pleunis, Z., Good, D. C., Kaspi, V. M., et al. 2021, The Astrophysical Journal, 923, 1

    2021

  16. [24]

    Ransom, S. M. 2001, New search techniques for binary pulsars (Harvard University)

    2001

  17. [25]

    Rhodes, B. C. 2011, Astrophysics Source Code Library, ascl

    2011

  18. [26]

    2012, The Astrophysical Journal, 748, 73

    Spitler, L., Cordes, J., Chatterjee, S., & Stone, J. 2012, The Astrophysical Journal, 748, 73

    2012

  19. [27]

    M., Hessels, J., et al

    Spitler, L., Cordes, J. M., Hessels, J., et al. 2014, The Astrophysical Journal, 790, 101

    2014

  20. [28]

    2016, Nature, 531, 202

    Spitler, L., Scholz, P., Hessels, J., et al. 2016, Nature, 531, 202

    2016

  21. [29]

    e., Stappers, B., Bailes, M., et al

    Thornton, D. e., Stappers, B., Bailes, M., et al. 2013, Science, 341, 53

    2013

  22. [30]

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

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

    2021

  23. [31]

    2022, Nature, 609, 685

    Xu, H., Niu, J., Chen, P., et al. 2022, Nature, 609, 685

    2022

  24. [32]

    2023, Universe, 9, 330

    Xu, J., Feng, Y., Li, D., et al. 2023, Universe, 9, 330

    2023

  25. [33]

    Zackay, B., & Ofek, E. O. 2017, The Astrophysical Journal, 835, 11

    2017

  26. [34]

    2018, The Astrophysical Journal Letters, 867, L21 —

    Zhang, B. 2018, The Astrophysical Journal Letters, 867, L21 —. 2020, Nature, 587, 45 —. 2023, Reviews of Modern Physics, 95, 035005 22 Cao et al

    2018

  27. [35]

    2022, Research in Astronomy and Astrophysics, 22, 124002

    Zhang, Y.-K., Wang, P., Feng, Y., et al. 2022, Research in Astronomy and Astrophysics, 22, 124002

    2022

  28. [36]

    2023, The Astrophysical Journal, 955, 142

    Zhang, Y.-K., Li, D., Zhang, B., et al. 2023, The Astrophysical Journal, 955, 142

    2023

  29. [37]

    2022, Research in Astronomy and Astrophysics, 22, 124001

    Zhou, D., Han, J., Zhang, B., et al. 2022, Research in Astronomy and Astrophysics, 22, 124001

    2022

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