Pith. sign in

REVIEW 4 minor 71 references

The polarimetric response of the Nan\c{c}ay Radio Telescope and its impact on precision pulsar timing

T0 review · 0 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The Nançay Radio Telescope's response is direction-independent, and a template-based calibration recovers accurate polarimetry for pre-2019 pulsar data, cutting timing noise.

desk verdict A careful, transparent calibration paper that convincingly recovers the pre-2019 Nançay archive for precision timing, with the main caveats being an unquantified reference-pulsar stability test and an indirect null result on direction dependence. read the letter →

arxiv 2505.04990 v2 pith:UCFAECZA submitted 2025-05-08 astro-ph.HE astro-ph.IM

Lucas Guillemot , Willem van Straten , Ismaël Cognard , Aurélien Chalumeau , Gilles Theureau , Éric Gérard This is my paper · ORCID
classification astro-ph.HEastro-ph.IM
keywords polarizationcalibrationpulsartimingmeasurementequationtemplatematchingmatrixNançayRadioTelescopemillisecondpulsarspolarimetryGaussianprocessinterpolation
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 establishes that the Nançay Radio Telescope's polarimetric response is effectively independent of where in the sky it points: the apparent hour-angle dependence seen in earlier work came from a sign error in the parallactic-angle calculation, and a constant calibration model performs best once that is fixed. It then shows how to extend the improved polarization calibration to NUPPI observations taken between 2011 and 2019, before the rotating-feed-horn calibration scheme existed, using measurement equation template matching on a bright, polarization-stable reference pulsar. The method describes each calibration parameter's frequency dependence as a scaled and offset version of a segment-specific archetype function, with the scale and offset factors interpolated in time by Gaussian processes. Applied to twelve millisecond pulsars, this calibration yields more homogeneous polarized profiles, higher signal-to-noise ratios in most cases, and timing data with lower white and red noise; the best results come from combining it with matrix template matching for time-of-arrival extraction. If right, it makes the first eight years of NUPPI data usable at modern precision, which matters for pulsar timing array searches for gravitational waves.

What carries the argument

The central object is the calibration archetype, used inside the METM (measurement equation template matching) procedure that derives instrumental calibration by comparing observations of a reference pulsar with a well-calibrated polarized template. Within each manually identified time segment in which the NRT's response was stable, the frequency variation of every calibration parameter is represented by a single archetype function, and each individual observation's parameter values are assumed to be a scaled and possibly offset copy of that function (Eqs. 7 to 9). The time evolution of the scale and offset factors is then modeled with Gaussian processes, yielding predicted calibration solutions at arbitrary epochs. The direction-dependence test uses a modified measurement-equation model in which differential gain and phase are two-dimensional polynomials of declination and hour angle, fit jointly to several pulsars' rotating-horn observations; the winning model is the one with constant parameters.

What would settle it

Re-derive the pre-2019 calibration without the archetype constraint, fitting each epoch's calibration parameters freely, and compare the resulting millisecond-pulsar timing residuals with the archetype-based ones; materially lower white or red noise in the free fits would show the archetype assumption is biasing the solutions. Independently, compare METM-predicted Stokes $Q$ and $U$ for J0953+0755 at an early epoch against a well-calibrated observation of a different bright pulsar from the same epoch; systematic growth of the residuals would falsify the assumed decade-long profile stability.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is twofold. First, the NRT's polarimetric response does not appear to vary measurably with hour angle or declination: a joint analysis of rotating-feed-horn observations of seven pulsars spanning declinations from roughly $-28^\circ$ to $+56^\circ$, with differential gain and phase modeled as polynomials in hour angle and declination, selects the constant $0/0$ model, and applying higher-order solutions to normal-mode MSP observations degrades signal-to-noise ratios. Second, a calibration procedure built on measurement equation template matching recovers accurate polarization calibration for pre-November 2019 data, where no rotating-horn observations exist. Using the bright pulsar J0953+0755 as a reference, the authors define time segments of stable instrumental response, construct archetype functions for the frequency dependence of each calibration parameter, and model the time evolution of scale and offset factors with Gaussian processes. On twelve millisecond pulsars this raises median signal-to-noise ratios (for example $1.035$ for J1730$-$2304 and $1.130$ for J1744$-$1134), lowers time-of-arrival uncertainties, and reduces both white noise and red noise in timing residuals. Combined with matrix template matching for TOA extraction, the calibration gives the lowest median weighted-rms residuals among the four dataset types tested, with the median dropping from $1.146$ to $1.078~\mu\mathrm{s}$ for standard FDM extraction and from $0.842$ to $0.818~\mu\mathrm{s}$ for MTM extraction.

Load-bearing premise

The procedure assumes that inside each manually chosen time segment the frequency shape of every calibration parameter stayed constant up to a per-epoch scaling and offset, and that the polarization profile of the reference pulsar J0953+0755 was intrinsically stable over the whole decade.

Editorial extensions

If this is right

  • The 2011-2019 NUPPI archive can be calibrated to the same standard as post-2019 data, so pulsar timing array analyses no longer need to treat the earlier epoch as a separate, noisier regime.
  • Combining the new calibration with matrix template matching for TOA extraction gives the lowest median weighted-rms residuals and the lowest additional white noise among the four dataset combinations tested, so future NRT-based timing analyses should adopt both together.
  • Because the polarimetric response is independent of hour angle and declination, a single calibration solution per stable epoch is sufficient for normal-mode observations; no pointing-dependent correction is needed beyond the known variation of absolute gain with declination.
  • More homogeneous and higher-signal-to-noise polarimetric profiles should improve rotation-measure determinations and wide-band template matching on NRT data.

Reading between the lines

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

  • The same archetype-plus-Gaussian-process recipe should transfer to the older BON backend data, which the paper flags as noisy and possibly poorly calibrated; success there would extend high-quality NRT timing back toward 2004.
  • The contrast between an earlier result where the simplest feed model won and the results here and at another telescope where model-based calibration won suggests the best calibration method is set by each telescope's reference-source and feed stability rather than by a universal rule; a portable comparison protocol could test this across observatories.
  • Because the procedure only requires a bright, frequently observed, polarization-stable pulsar and regular noise-diode measurements, other transit telescopes with narrow parallactic-angle coverage could adopt it directly.
  • Correcting the parallactic-angle sign changes the interpretation of the earlier apparent hour-angle dependence and may require revisiting published NRT position angles from analyses that used the uncorrected convention.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 4 minor

Summary. This paper presents two main results. First, using three sessions of special rotating-feed observations of bright pulsars at different declinations, the authors test whether the polarimetric response of the Nançay Radio Telescope (NRT) depends on hour angle or declination. From a model-comparison analysis with a modified pcm tool and validation on normal-mode millisecond pulsar (MSP) observations, they conclude that the response does not appear to vary with these parameters. Second, to calibrate pre-November 2019 NUPPI data, they develop a new calibration scheme based on the Measurement Equation Template Matching (METM) technique, using PSR J0953+0755 as the reference pulsar. Within manually determined time segments, the frequency dependence of each calibration parameter is represented by an archetype function, and the time variation of per-observation scale and offset parameters is modeled with Gaussian processes. Applying this calibration to 12 MSPs, they find more homogeneous polarimetric profiles, higher signal-to-noise ratios in most cases, and improved timing quality as quantified by reduced weighted RMS residuals and lower white- and red-noise parameters (median Wrms drops from 1.146 to 1.078 microseconds for FDM and from 0.842 to 0.818 microseconds for MTM).

Significance. If the calibration improvement is real, it will enable consistent use of the NRT's 2011-2019 NUPPI data in pulsar timing array analyses and polarimetric studies, extending the improvements previously demonstrated for post-November 2019 data. The paper's main strengths are the validation of the calibration on MSPs that were not used to derive the calibration solutions, the multi-metric assessment (profile homogeneity, S/N, TOA uncertainties, and noise parameters), and the careful null test of direction dependence. The work also provides a reproducible analysis path through public PSRCHIVE extensions. The conclusions are consistent with those of Rogers et al. (2024) and appropriately nuanced in their wording, e.g., that the response 'does not appear to vary' with direction. The manuscript is well written and the figures are informative.

minor comments (4)
  1. [Sect. 3, choice of METM reference pulsar] The choice of PSR J0953+0755 as the METM reference is justified by the smoothness of the derived parameters compared with J1136+1551, but a quantitative stability test would strengthen the paper. For example, deriving independent calibration solutions from disjoint subsets of J0953+0755 observations (e.g., early versus late epochs) and comparing them would directly address the reference-profile-stability failure mode discussed for Dey et al. (2024).
  2. [Sect. 4.1 and Table 3] For three MSPs (J0613-0200, J1022+1001, J2124-3358) the median S/N ratio R1 is below 1, so the statement that S/N increases in 'almost all' tested MSPs is correct but could be more explicit. It would be helpful to state which pulsars show degradation and whether the effect is statistically significant relative to the scatter in the ratios.
  3. [Sect. 4.2 and Table 4] The reported improvements in Wrms are modest (a few percent) and per-pulsar changes in Table C.1 are often comparable to the quoted uncertainties. A paired statistical test across the 12 pulsars (e.g., a Wilcoxon signed-rank test on the Wrms ratios or on the EQUAD values) would better quantify the significance of the global improvement and would address the concern that some changes may be within noise.
  4. [Sect. 2, qAIC metric] The quasi-AIC metric is introduced with appropriate caveats about the asymmetric sampling of declination versus hour angle, and the conclusion of no direction dependence ultimately rests on the S/N comparison in Fig. 5. It would be informative to report the effective number of observations that probe extreme hour angles for each declination, or to show a direct comparison of calibration solutions (e.g., differential gain) evaluated at the extreme hour angles, to make the null result more transparent.

Circularity Check

1 steps flagged · score 2.0 of 10

No construction-level circularity: METM calibration is validated on MSP data not used in the fit; only a minor internal self-consistency check is overstated.

  1. other [Section 3, paragraph following Eqs. (7)-(9) and discussion of Fig. 8]
    "The scaled and offset calibration parameters match the corresponding archetypes well, validating the construction of the archetypes and the measurement of the a and b parameters for each observation."

    The archetypes were refined by forming weighted averages of the calibration parameters within each time segment after correcting for the same a and b scale/offset factors, and then a and b were re-fit against those same archetypes. The agreement displayed in Fig. 8 is therefore a measure of the internal consistency of the fit, not an independent test: the archetype is, by construction, the average of the scaled/offset data to which it is compared. This 'validation' is not load-bearing for the paper's central claim; the independent evidence is the Sect. 4 application to MSPs whose data were not used to build the archetypes or the Gaussian processes.

full rationale

The central claim, that the METM/archetype/GP calibration procedure significantly improves pre-November 2019 NUPPI data, is not circular at the construction level. The calibration model (time-segmented archetypes, scale/offset parameters, and Gaussian-process fits) is derived exclusively from J0953+0755 observations, with J1136+1551 used only to locate time-segment boundaries; the 12 MSPs used to demonstrate profile homogeneity, higher S/N, lower TOA uncertainties, and reduced red/white noise are not inputs to those fits. The improvement is therefore evaluated out-of-sample on data that were not used to derive the calibration solutions. The only genuinely circular sentence is the Fig. 8 statement that scaled/offset parameters 'match' the archetypes; this is a self-consistency check because the archetypes are weighted averages of those same data. It is a minor overstatement and is not the basis of the headline result. The paper's main vulnerability is the unverified stability of the J0953+0755 reference polarized profile: if that profile drifted, the smooth METM solutions would imprint a common, stable-but-wrong transformation on all pulsars, making profiles mutually homogeneous without being accurate. The paper cites Dey et al. (2024) for exactly this failure mode and states that future work will add more reference pulsars, but it provides no quantitative stability test for J0953+0755. This is a correctness and robustness concern, not a circular reduction: no equation reduces the claimed improvement to the fitted inputs by construction, and the MSP-based validation is independent of the calibration fit. Score 2 reflects the single minor self-consistent-validation overstatement plus the paper's reliance on the authors' prior calibration framework.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim depends on the stability of the reference pulsar and on the archetype scaling model, both of which are assumptions about the instrument and calibrator, not on new physical entities. The fitted parameters are the calibration outputs themselves. No free parameters are hidden in the final validation, since the MSP datasets used for validation were not used for calibration.

free parameters (3)
  • Per-observation scale/offset parameters (a, b) = Values determined per observation per calibration parameter (not tabulated)
    Fitted in Eqs. 7-9 to match each METM-derived calibration parameter to the archetype frequency function within a time segment.
  • Gaussian process hyperparameters (amplitude, lengthscale) = Best-fit values not tabulated
    Modeled the time variation of the scale and offset parameters (Section 3, Fig. 9).
  • Time segment boundaries = Chosen by visual inspection (dashed lines in Fig. 6)
    Delimit intervals in which the NRT response was consistent; a subjective modeling choice.
assumptions (5)
  • domain assumption The NRT feeds have equal ellipticities (epsilon0 = epsilon1) and the first feed's orientation is zero.
    Made to break the degeneracy in the MEM solutions (Section 2, following Guillemot et al. 2023 and van Straten 2004).
  • domain assumption The reference noise diode is coupled after the frontend, so pcm's -Q option applies.
    Used in the MEM/METM fits to model the reference signal correctly (Section 2).
  • domain assumption The intrinsic polarized profile of PSR J0953+0755 is stable over the NUPPI dataset, so METM variations reflect the instrument.
    Required for using METM to derive calibration solutions from that pulsar (Section 3).
  • ad hoc to paper Within a time segment, each calibration parameter's frequency dependence is a scaled/offset version of a common archetype function.
    Central modeling assumption of the new calibration method (Eqs. 7-9, Section 3).
  • domain assumption The measurement equation model (MEM) accurately represents the NRT polarimetric response.
    Used in all pcm fits to interpret the rotating-horn and noise diode observations (Section 2).

how reviews work

0 comments
Cite this review

Pith. "Pith review of The polarimetric response of the Nan\c{c}ay Radio Telescope and its impact on precision pulsar timing." pith.science (2026). https://pith.science/paper/UCFAECZA

@misc{pith2026250504990,
  author       = {Pith},
  title        = {Pith review of: The polarimetric response of the Nan\ccay Radio Telescope and its impact on precision pulsar timing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UCFAECZA}},
  note         = {Machine review of arXiv:2505.04990}
}
abstract

In \citet{Guillemot2023} we presented a new method for calibrating pulsar observations conducted with the Nan\c{c}ay decimetric Radio Telescope (NRT), which significantly improved NRT polarimetric measurements and pulsar timing quality for data taken after this method was developed, in November 2019. Results hinted at a dependence of the polarimetric response of the NRT on the observed direction. We investigated this potential dependence, since unaccounted variations of the instrumental response could degrade polarimetric measurements. Additionally, we aimed to develop a method for properly calibrating NRT pulsar observations conducted before November 2019. We conducted three series of observations of bright pulsars over wide declination ranges, in a special observation mode in which the feed horn rotates by $\sim$ 180$^\circ$ degrees across the observation, enabling us to determine the full polarimetric response of the NRT while modeling potential variations of calibration parameters with hour angle and declination. In addition, we used the METM technique to improve the calibration of pre-November 2019 data. From the analysis of the series of observations of bright pulsars with horn rotation, we found that the polarimetric response of the NRT does not appear to vary with hour angle or declination. On the other hand, the new METM-based calibration method appears to significantly improve the calibration of pre-November 2019 data. By analyzing NRT data on a selection of millisecond pulsars we found that the new polarimetric profiles are more homogeneous, they generally have larger signal-to-noise ratios, and found that the TOA data for these MSPs are more accurate and contain lower levels of noise, especially when combining the new calibration method with the \textit{Matrix Template Matching} (MTM) method for extracting TOAs from pulsar observations.

Figures

Figures reproduced from arXiv: 2505.04990 by the authors.

Figure 1
Figure 1. Left: Measured and modeled Stokes parameters as a function of time and position angle Φ′ = Φ + α (where Φ denotes the parallactic angle and α is the orientation of the horn) for PSR J0742−2822, observed on MJD 59368. The pulsar was observed for ∼1 h with the NUPPI backend at the central frequency of 1484 MHz. Across the observation, the feed horn was made to rotate by 180◦ . The data points represent measured Stokes… view at source ↗
Figure 2
Figure 2. Left: Best-fit calibration parameters as a function of channel index, as determined from the analysis of observations of a selection of pulsars (conducted between MJDs 59368 and 59373; see [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Same as [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Best-fit calibration parameters as a function of channel index, from two analyses. The blue dots show the results from a combined analysis of the seven pulsar observations with horn rotation conducted between MJDs 59368 and 59373 listed in [PITH_FULL_IMAGE:figures/ful…
Figure 5
Figure 5. Figure 5: Comparison of S/N values of normal mode 1.4 GHz observations of millisecond pulsars with NUPPI, calibrated using the ideal feed assumption (IFA) calibration model, and using improved calibration models obtained from 0/0, 0/1, and 1/1 analyses. The middle panel includes…
Figure 6
Figure 6. Figure 6: Gain, differential gain and differential phase parameters as a function of time, as determined from the METM analyses of 1.4 GHz NUPPI data on PSR J1136+1551. The best-fit parameters displayed as blue dots are those for the 64th 4 MHz channel recorded by NUPPI, which h…
Figure 7
Figure 7. Figure 7: Top: Gain parameter G as determined from the METM analysis of individual observations selected within each of the time intervals de￾limited in [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Calibration parameters as a function of frequency as determined from METM analyses of PSR J0953+0755 observations, in two different time intervals. The best-fit calibration parameters were corrected for the scale and offset factors determined from the analysis describe…
Figure 9
Figure 9. Figure 9: Scale and offset parameters (blue points; a and b terms from Eq. 9) as a function of time, as determined from fits of the variations of the Stokes U of the reference noise diode with frequency, for PSR J0953+0755. The dashed red lines represent fits of the scale and of…
Figure 10
Figure 10. Figure 10: NUPPI polarimetric pulse profiles for PSR J1730−2304 at 1.4 GHz. The left panel shows the profiles obtained when calibrating the NUPPI data with the ideal feed assumption (IFA) method, and the right panel shows polarimetric profiles obtained with the calibration metho…
Figure 11
Figure 11. Figure 11: Ratios of S/N and TOA uncertainty values for PSR J1730−2304 as a function of time. Signal-to-noise ratio (S/N) and TOA uncertainty values (σ) were derived from NUPPI observations at 1.4 GHz, cali￾brated using the IFA method and the calibration method presented in Sect…
Figure 13
Figure 13. Figure 13: Best-fit EFAC (Ef) and EQUAD (Eq) values for different pul￾sars and data calibration methods (see Section 4.2 for descriptions of the EFAC and EQUAD parameters). log10 Eq are found for METM+MTM in most cases, and the val￾ues of log10 Eq for METM+MTM are compatible wit…
Figure 14
Figure 14. Figure 14: Best-fit achromatic red noise power-law amplitudes and spec￾tral indices for different pulsars and data calibration methods. J 0 1 2 5 2 3 2 7 J 0 6 1 3 0 2 0 0 J1022+1001 J 1 0 2 4 0 7 1 9 J 1 6 0 0 3 0 5 3 J 1 6 4 3 1 2 2 4 J 1 7 3 0 2 3 0 4 J 1 7 4 4 1 1 3 4 J1857+…
Figure 15
Figure 15. Figure 15: Best-fit DM power-law amplitudes and spectral indices for dif￾ferent pulsars and data calibration methods. sented in Sect. 3 or the usage of the MTM method for determin￾ing TOAs attenuated at least partially. From this analysis we conclude that the enhanced polariza￾t…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

71 extracted references · 48 canonical work pages

  1. [1]

    M., et al

    Agazie , G., Anumarlapudi , A., Archibald , A. M., et al. 2023, , 951, L8

    2023

  2. [2]

    1973, Information Theory and an Extension of the Maximum Likelihood Principle (New York, NY: Springer New York), 199--213

    Akaike, H. 1973, Information Theory and an Extension of the Maximum Likelihood Principle (New York, NY: Springer New York), 199--213

    1973

  3. [3]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, , 935, 167

    2022

  4. [4]

    A., Lorimer , D

    Bailes , M., Harrison , P. A., Lorimer , D. R., et al. 1994, , 425, L41

    work page 1994

  5. [5]

    F., et al

    Bailes , M., Johnston , S., Bell , J. F., et al. 1997, , 481, 386

    work page 1997

  6. [6]

    R., Salter , C

    Bonsignori-Facondi , S. R., Salter , C. J., & Sutton , J. M. 1973, , 27, 67

    work page 1973

  7. [7]

    Britton , M. C. 2000, , 532, 1240

    work page 2000

  8. [8]

    J., Shrauner , J

    Camilo , F., Nice , D. J., Shrauner , J. A., & Taylor , J. H. 1996, , 469, 819

    work page 1996

Show all 71 references
  1. [9]

    2022, , 509, 5538

    Chalumeau , A., Babak , S., Petiteau , A., et al. 2022, , 509, 5538

    2022

  2. [10]

    2013, in SF2A-2013: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed

    Cognard , I., Theureau , G., Guillemot , L., et al. 2013, in SF2A-2013: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed. L. Cambresy , F. Martins , E. Nuss , & A. Palacios , 327--330

    2013

  3. [11]

    D., Lovelace , R

    Craft , H. D., Lovelace , R. V. E., & Sutton , J. M. 1968, , 2100, 1

    1968

  4. [12]

    H., & Hulse , R

    Damashek , M., Taylor , J. H., & Hulse , R. A. 1978, , 225, L31

    1978

  5. [13]

    Davies , J. G. & Large , M. I. 1970, , 149, 301

    1970

  6. [14]

    C., Cognard , I., Lespagnol , P., & Theureau , G

    Desvignes , G., Barott , W. C., Cognard , I., Lespagnol , P., & Theureau , G. 2011, in American Institute of Physics Conference Series, Vol. 1357, Radio Pulsars: An Astrophysical Key to Unlock the Secrets of the Universe, ed. M. Burgay , N. D'Amico , P. Esposito , A. Pellizzon...

    2011

  7. [15]

    A., Wahl , H

    Dey , L., McLaughlin , M. A., Wahl , H. M., et al. 2024, arXiv e-prints, arXiv:2406.13463

    2024 arXiv

  8. [16]

    & van Haasteren, R

    Ellis, J. & van Haasteren, R. 2017, jellis18/PTMCMCSampler: Official Release

    2017

  9. [17]

    A., Vallisneri , M., Taylor , S

    Ellis , J. A., Vallisneri , M., Taylor , S. R., & Baker , P. T. 2020, ENTERPRISE: Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE

    2020

  10. [18]

    2023 a , , 678, A48

    EPTA Collaboration , Antoniadis , J., Babak , S., et al. 2023 a , , 678, A48

    2023

  11. [19]

    2023 b , , 678, A50

    EPTA Collaboration , InPTA Collaboration , Antoniadis , J., et al. 2023 b , , 678, A50

    2023

  12. [20]

    B., et al

    FERMI-LAT Collaboration , Ajello , M., Atwood , W. B., et al. 2022, Science, 376, 521

    2022

  13. [21]

    2015, , 453, 1489

    Foster , G., Karastergiou , A., Paulin , R., et al. 2015, , 453, 1489

    2015

  14. [22]

    S., Wolszczan , A., & Camilo , F

    Foster , R. S., Wolszczan , A., & Camilo , F. 1993, , 410, L91

    1993

  15. [23]

    2023, , 678, A79

    Guillemot , L., Cognard , I., van Straten , W., Theureau , G., & G \'e rard , E. 2023, , 678, A79

    2023

  16. [24]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    2020

  17. [25]

    B., Edwards , R

    Hobbs , G. B., Edwards , R. T., & Manchester , R. N. 2006, , 369, 655

    2006

  18. [26]

    W., van Straten , W., & Manchester , R

    Hotan , A. W., van Straten , W., & Manchester , R. N. 2004, , 21, 302

    2004

  19. [27]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90

    2007

  20. [28]

    A., Bailes , M., Ord , S

    Jacoby , B. A., Bailes , M., Ord , S. M., Knight , H. S., & Hotan , A. W. 2007, , 656, 408

    2007

  21. [29]

    A., Bailes , M., van Kerkwijk , M

    Jacoby , B. A., Bailes , M., van Kerkwijk , M. H., et al. 2003, , 599, L99

    2003

  22. [30]

    F., et al

    Jankowski , F., van Straten , W., Keane , E. F., et al. 2018, , 473, 4436

    2018

  23. [31]

    J., Cordes , J

    Jennings , R. J., Cordes , J. M., Chatterjee , S., et al. 2022, arXiv e-prints, arXiv:2210.12266

    2022 arXiv

  24. [32]

    2002, , 19, 277

    Johnston , S. 2002, , 19, 277

    2002

  25. [33]

    1998, International Journal of Computer Vision, 26, 171

    Kanatani, K. 1998, International Journal of Computer Vision, 26, 171

    1998

  26. [34]

    H., Manchester , R

    Kramer , M., Stairs , I. H., Manchester , R. N., et al. 2021, Physical Review X, 11, 041050

    2021

  27. [35]

    Kraus , J. D. 1960, , 65, 54

    1960

  28. [36]

    Kraus , J. D. 1966, Radio astronomy

    1966

  29. [37]

    Lang , K. R. 1969, , 158, L175

    1969

  30. [38]

    2016, , 458, 868

    Lazarus , P., Karuppusamy , R., Graikou , E., et al. 2016, , 458, 868

    2016

  31. [39]

    M., Coles , W

    Lentati , L., Shannon , R. M., Coles , W. A., et al. 2016, , 458, 2161

    2016

  32. [40]

    Lorimer , D. R. & Kramer , M. 2004, Handbook of Pulsar Astronomy , Vol. 4

    2004

  33. [41]

    R., Nicastro , L., Lyne , A

    Lorimer , D. R., Nicastro , L., Lyne , A. G., et al. 1995, , 439, 933

    1995

  34. [42]

    N., Hobbs , G., Bailes , M., et al

    Manchester , R. N., Hobbs , G., Bailes , M., et al. 2013, , 30, e017

    2013

  35. [43]

    N., Hobbs , G

    Manchester , R. N., Hobbs , G. B., Teoh , A., & Hobbs , M. 2005, , 129, 1993

    2005

  36. [44]

    N., Lyne , A

    Manchester , R. N., Lyne , A. G., Taylor , J. H., et al. 1978, , 185, 409

    1978

  37. [45]

    N., Taylor , J

    Manchester , R. N., Taylor , J. H., & Huguenin , G. R. 1972, Nature Physical Science, 240, 74

    1972

  38. [46]

    Matthews, A. G. d. G., van der Wilk , M., Nickson, T., et al. 2017, Journal of Machine Learning Research, 18, 1

    2017

  39. [47]

    2018, RMextract: Ionospheric Faraday Rotation calculator , Astrophysics Source Code Library, record ascl:1806.024

    Mevius , M. 2018, RMextract: Ionospheric Faraday Rotation calculator , Astrophysics Source Code Library, record ascl:1806.024

    2018

  40. [48]

    D., Cooper , S., et al

    Morello , V., Barr , E. D., Cooper , S., et al. 2019, , 483, 3673

    2019

  41. [49]

    C., Keith , M

    Ni t u , I. C., Keith , M. J., Champion , D. J., et al. 2024, , 534, 1753

    2024

  42. [50]

    & Freire , P

    \"O zel , F. & Freire , P. 2016, , 54, 401

    2016

  43. [51]

    V., Barr , E

    Padmanabh , P. V., Barr , E. D., Champion , D. J., et al. 2021, , 500, 1178

    2021

  44. [52]

    S., Folkner , W

    Park , R. S., Folkner , W. M., Williams , J. G., & Boggs , D. H. 2021, , 161, 105

    2021

  45. [53]

    T., Demorest , P

    Pennucci , T. T., Demorest , P. B., & Ransom , S. M. 2014, , 790, 93

    2014

  46. [54]

    Perera , B. B. P., DeCesar , M. E., Demorest , P. B., et al. 2019, , 490, 4666

    2019

  47. [55]

    Pilkington , J. D. H., Hewish , A., Bell , S. J., & Cole , T. W. 1968, , 218, 126

    1968

  48. [56]

    J., Zic , A., Shannon , R

    Reardon , D. J., Zic , A., Shannon , R. M., et al. 2023, , 951, L6

    2023

  49. [57]

    1989, Stochastic Complexity in Statistical Inquiry, Computer Science Series (World Scientific Publishing Company Pte Limited)

    Rissanen, J. 1989, Stochastic Complexity in Statistical Inquiry, Computer Science Series (World Scientific Publishing Company Pte Limited)

    1989

  50. [58]

    F., van Straten , W., Gulyaev , S., et al

    Rogers , A. F., van Straten , W., Gulyaev , S., et al. 2024, , 973, 94

    2024

  51. [59]

    1978, The Annals of Statistics, 6, 461

    Schwarz, G. 1978, The Annals of Statistics, 6, 461

    1978

  52. [60]

    J., Rawley , L

    Segelstein , D. J., Rawley , L. A., Stinebring , D. R., Fruchter , A. S., & Taylor , J. H. 1986, , 322, 714

    1986

  53. [61]

    P., Joshi , B

    Singha , J., Surnis , M. P., Joshi , B. C., et al. 2021, , 507, L57

    2021

  54. [62]

    R., Cordes , J

    Stinebring , D. R., Cordes , J. M., Rankin , J. M., Weisberg , J. M., & Boriakoff , V. 1984, , 55, 247

    1984

  55. [63]

    2005, , 430, 373

    Theureau , G., Coudreau , N., Hallet , N., et al. 2005, , 430, 373

    2005

  56. [64]

    2004, , 152, 129

    van Straten , W. 2004, , 152, 129

    2004

  57. [65]

    2006, , 642, 1004

    van Straten , W. 2006, , 642, 1004

    2006

  58. [66]

    2013, , 204, 13

    van Straten , W. 2013, , 204, 13

    2013

  59. [67]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  60. [68]

    2025, , 693, A143

    Voisin , G., Cognard , I., Saillenfest , M., et al. 2025, , 693, A143

    2025

  61. [69]

    X., Burgay , M., et al

    Xu , H., Huang , Y. X., Burgay , M., et al. 2021, The Astronomer's Telegram, 14642, 1

    2021

  62. [70]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sent...

  63. [71]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.