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REVIEW 2 major objections 5 minor 81 references

PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures

T0 review · 2 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This paper argues that dispersive noise in pulsar timing can be modeled directly from epoch-wise dispersion-measure time series, with no time-of-arrival delays used anywhere, and that the recovered noise parameters match injections in simul

desk verdict A sensible DM-domain GP noise fit with a real payoff if corrected, but Eq. (13) inverts the DM-to-delay scaling and the claimed advantage over ToA-based SPNA is not actually demonstrated. read the letter →

arxiv 2607.12609 v3 pith:UP3ET2K3 submitted 2026-07-14 astro-ph.IM astro-ph.HE

Churchil Dwivedi , Abhimanyu Susobhanan This is my paper
classification astro-ph.IMastro-ph.HE
keywords pulsartimingdispersionmeasureGaussianprocesssingle-pulsarnoiseDMsolarwindwidebandnarrowband
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 tries to establish that dispersive single-pulsar noise—the stochastic and deterministic variations in the dispersion measure (DM) caused by the interstellar medium and the solar wind—can be characterized accurately using epoch-wise DM estimates alone, without ever forming time-of-arrival (ToA) delays. The method models the DM series as a constant plus polynomial deterministic terms, Fourier-domain Gaussian processes for DM noise and solar wind noise, and a white-noise term; a Gaussian likelihood with a diagonal covariance leads to analytically marginalised posteriors for the noise hyper-parameters. In simulated narrowband and wideband datasets with injected noise, the recovered amplitudes and spectral indices agree with injections at about the one-sigma level, and the reconstructed time-domain realisations track the injected processes. If the paper is right, it gives an independent, achromatic-red-noise-insensitive channel for validating conventional ToA-based noise analyses, which matters for pulsar timing array sensitivity.

What carries the argument

The central object is the epoch-wise DM time series and its decomposition into deterministic and stochastic parts. The load-bearing mechanism is a Fourier-domain Gaussian process on DMs: a Fourier basis matrix φ and amplitude covariance Φ build the effective noise covariance Ξ = ζ + φΦφ^T, where ζ is the diagonal white-noise covariance; a power-law spectral density with dispersive scaling ν^-2 controls the stochastic amplitudes. This lets the likelihood be analytically marginalised over the noise amplitudes, leaving only hyper-parameters and deterministic coefficients to be sampled.

What would settle it

Take DM time series produced by a global fit (for example a piecewise-constant or spline solution) that is known to create inter-epoch correlations, run PSRDISP on them, and check whether the recovered noise amplitude and spectral index shift outside the nominal posterior uncertainties. Alternatively, inject a scattering delay proportional to ν^-4 into simulated sub-banded arrival times before extracting DMs, and test whether the dispersive-noise parameters become biased.

Watch

Extended reading notes

Core claim

The central claim is that dispersive processes leave a direct imprint on the DM time series itself, so the noise covariance can be constructed from DM fluctuations (δDM) rather than from delays. The total DM is decomposed as DM0 plus polynomial DM and solar-wind terms, Fourier-basis stochastic DM noise and solar-wind noise, and white measurement noise. The likelihood is multivariate Gaussian with a diagonal white-noise covariance, and the Fourier amplitudes are integrated out analytically via a reduced-rank covariance. Because no ToA delays enter the model at any point, the treatment is almost unaffected by achromatic red noise and applies equally to narrowband-derived or wideband-measured D

Load-bearing premise

The method assumes epoch-wise dispersion measures are statistically independent with a diagonal covariance matrix, and that the DMs are not biased by scatter broadening; if either fails, the recovered noise parameters are no longer reliable.

Editorial extensions

If this is right

  • Provides an independent, ToA-free cross-check on conventional single-pulsar noise analyses.
  • Characterisation of dispersive noise becomes largely insensitive to achromatic red noise and white ToA noise.
  • The same machinery applies to both narrowband and wideband DM estimates, as long as epoch-wise DMs are independent.
  • The recovered DM-noise and solar-wind-noise parameters can be used to tune or validate the noise budget of a pulsar timing array.
  • In simulations, recovered noise amplitudes and spectral indices are consistent with injections, and time-domain realisations track the injected noise process.

Reading between the lines

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

  • If it holds on real data, PSRDISP could flag occasions where ToA-based analyses misattribute chromatic noise because of spectral leakage, since the two methods should agree when models are correct.
  • A straightforward extension is to model the solar wind as a spatially correlated process across an array of pulsars; the present framework already has the Fourier-basis structure to accommodate this.
  • A testable use is to run both this DM-only method and a joint ToA+DM wideband fit on the same dataset; agreement would corroborate both, and disagreement would localise the source of bias.
  • The main limitation the authors leave open—scattering-induced biases in DM estimates—suggests the method could be extended by jointly fitting a scattering term with a known frequency scaling, providing a path to separate scattering from true dispersive noise.
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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

2 major / 5 minor

Summary. This paper presents PSRDISP, a Bayesian Gaussian-process framework for modeling dispersive noise processes—DM noise, solar-wind noise, and deterministic DM/SW trends—directly from epoch-wise dispersion-measure estimates rather than from times of arrival. The method uses a reduced-rank Fourier-domain GP with a power-law PSD, analytically marginalizes over Fourier amplitudes and deterministic DM polynomial parameters, and samples the remaining hyperparameters with emcee. The authors validate the method on simulated narrowband and wideband datasets, reporting good recovery of injected DMN and SWN amplitudes and spectral indices, and reconstructing time-domain realizations. They argue that working in the DM domain makes the method largely immune to achromatic red noise and provides an independent check on ToA-based single-pulsar noise analyses.

Significance. The approach is timely for PTA-era noise characterization and, if correct, would offer a complementary data domain for estimating chromatic noise. The paper's strengths are the use of the standard van Haasteren–Levin marginalization, the explicit likelihood derivation, the inclusion of both narrowband and wideband simulated validation, and the candid statement of key limitations (independence of DM estimates, scattering biases). The claimed advantage over ToA-based analyses, however, is not quantitatively demonstrated, and one equation in the central derivation is dimensionally inconsistent. These issues must be addressed before the comparability and reproducibility claims can be accepted.

major comments (2)
  1. [§2, Eqs. (12)–(13) and (21)–(22)] The transformation in Eq. (13) is dimensionally inconsistent with Eq. (4). Since Δt = D·DM/ν², converting a DM-domain Fourier amplitude (units pc cm⁻³) to the conventional time-delay amplitude requires multiplication by D/ν_ref² ≈ 2.12×10⁻³ s/(pc cm⁻³), not by ν_ref²/D ≈ 472 (pc cm⁻³)/s. As written, Eq. (13) would change the variance entering Φ (Eq. 21) by (ν_ref²/D)² ≈ 2.23×10⁵, i.e., ≈2.67 dex in log10 A. The recovery in Fig. 3 (log10 A_DMN = −13.52 vs injected −13.5) cannot be reproduced from the text as written. This is load-bearing for the paper's central comparability claim. Please correct the factor and clarify whether Eqs. (21)–(22) define the PSD for DM-domain or delay-domain amplitudes.
  2. [§4 and Appendix A] The headline advantage over ToA-based SPNA—reduced achromatic-red-noise contamination—is asserted but not demonstrated. In the wideband simulation, ARN is injected into the ToAs only; the DM data used by PSRDISP are unaffected by ARN by construction. The paper does not report a comparison with ENTERPRISE on the same simulated ToAs, with and without ARN, to quantify leakage into DMN/SWN recovery. Without such a benchmark, the 'almost unaffected' claim and the method's value as an independent check remain plausible but unquantified. Please add this comparison or soften the claim.
minor comments (5)
  1. [Figs. 3 and A.3] The posterior corner plots omit the minus sign on log10 A_DMN and log10 A_SWN; e.g., 'log10 Adm = 13.52' should read '−13.52', consistent with Table 1 and the text.
  2. [§3 and Fig. 2] The units of DM are written as 'pc/cm³' in several places; the standard notation is pc cm⁻³. Also, check the units of D in Eq. (4) against the factor in Eq. (13).
  3. [§3] The text says γ_DMN is recovered at ~2σ, but the quoted value 3.31±0.20 versus the injected 3.0 corresponds to ~1.6σ. Please reconcile the statement with the quoted uncertainty.
  4. [Appendix A] The sentence 'we used DMEFAC=EFAC=1.2' and the later statement that DISPEFAC is close to the injected DMEFAC should be clarified: DISPEFAC is defined in Eq. (15) for DM-domain white noise, while DMEFAC/EFAC are ToA-domain parameters. Explain the expected relationship.
  5. [Software/Data Availability] The Data Availability section says the simulated datasets are shared as supplementary material, but no statement is made about availability of the PSRDISP analysis code. Please add a software availability statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PSRDISP fits a standard marginalised GP to simulated DM data; the recovery is a genuine simulation benchmark, not a derivation that reduces to its inputs.

full rationale

The paper's derivation chain is a standard Fourier-domain Gaussian-process likelihood applied to epoch-wise DMs (Eqs. 7-22). The fitted parameters (A_DMN, gamma_DMN, SWN parameters, etc.) are estimated from simulated DM data whose injected values are external targets; the injected values are not used as inputs to the likelihood, so the recovery in Figs. 3 and A.3 is not tautological. The only sense in which the validation is 'self-consistency' is that the simulated data were drawn from the same power-law Fourier-GP family used for fitting; this limits the strength of the benchmark but is not circularity. The paper explicitly warns against the actual circular use in Section 4: 'care should be taken as to not lead to circular analysis on grounds discussed in van Haasteren (2024), for instance, by using the results of this technique as prior distributions for other SPNA approaches on the same dataset.' Self-citations (Susobhanan & van Haasteren 2025; Susobhanan et al. 2024, 2026; Susarla et al. 2024) are used for comparison and implementation context, not as load-bearing evidence for the central likelihood, and no uniqueness theorem is imported from the authors' prior work. The apparent inversion in Eq. (13) relative to Eq. (4) is a dimensional/reproducibility discrepancy, not a reduction of the output to the input, so I do not score it as circularity.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The model introduces no new physics; its free parameters are standard pulsar-timing noise hyperparameters plus a new DISPEFAC white-noise scale. The central validation rests on the assumptions that DM estimates are independent, purely dispersive, and follow the same power-law Fourier-GP model family used for fitting.

free parameters (8)
  • DISPEFAC = 1.10 (narrowband), 1.22 (wideband)
    White-noise scaling applied to DM uncertainties, Eq. 15; new parameter introduced in this work.
  • DMN spectral index gamma_DMN = 3.31 (injected 3.0)
    Power-law index of the DMN PSD in Eq. 22.
  • log10 A_DMN = -13.52 (injected -13.5)
    Amplitude of the DMN power spectral density.
  • SWN spectral index gamma_SWN = 1.96 (injected 2.5, wideband)
    Power-law index of the SWN PSD, following Susarla et al. 2024.
  • log10 A_SWN = -6.69 (injected -6.8, wideband)
    Amplitude of the SWN power spectral density.
  • NE_SW = 1.17 (injected 2.0, narrowband); 7.24 (injected 2.0, wideband)
    Constant solar wind electron density at 1 AU; deterministic SW coefficient.
  • NE_SW1, NE_SW2 = 1.00 / 0.71 (injected 1.0 / 0.75, narrowband); 0.04 / 0.73 (injected 0 / 1.0, wideband)
    First and second order time derivatives of solar wind electron density.
  • Nharm (recovery) = 100 (injection used 1000)
    Truncation of the Fourier basis in reduced-rank GP; chosen by hand, affects spectral coverage.
assumptions (6)
  • standard math Cold plasma dispersion relation: delay = D * DM / nu^2
    Eq. 4; assumes homogeneous IISM, nu_p << nu, no magnetic field or temperature effects. Used throughout to relate DM to timing delays.
  • domain assumption Epoch-wise DM measurements are independent with diagonal covariance
    Stated in Section 2: 'we assume that the DM measurements for different epochs are independent'; makes zeta diagonal and excludes DMs from global DMX/DMMODEL fits.
  • domain assumption DMN and SWN are power-law Fourier Gaussian processes
    Eqs. 12, 14, 22; standard pulsar-timing modeling assumption but adopted here rather than derived.
  • domain assumption Spherically symmetric solar wind model with geometric factor G
    Eqs. 9-11 from Edwards et al. 2006 / Hazboun et al. 2022; used for deterministic SW contribution.
  • domain assumption No scattering-induced effects on DMs
    Stated in Section 1: 'we restrict ourselves to the treatment of purely dispersive processes in the present work.' Violated if scatter-broadening biases DM estimates.
  • standard math Analytic marginalization of Fourier amplitudes with Gaussian priors
    Eqs. 17-18 follow van Haasteren & Levin 2013; infinite-variance priors for DM0/DM1/DM2 follow ENTERPRISE convention.

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Cite this review

Pith. "Pith review of PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures." pith.science (2026). https://pith.science/paper/UP3ET2K3

@misc{pith2026260712609,
  author       = {Pith},
  title        = {Pith review of: PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UP3ET2K3}},
  note         = {Machine review of arXiv:2607.12609}
}
read the original abstract

We present PSRDISP, a novel approach to modeling deterministic and stochastic dispersive processes in pulsar timing datasets using high-precision epoch-wise dispersion measure (DM) estimates, with a Gaussian Process-based approach. Unlike the conventional single-pulsar noise analysis methodology, which is applied to frequency-resolved times of arrival (ToAs) of pulses, this technique is applied to epoch-wise DMs which are derived from these ToAs. It can also be applied to wideband DMs measured simultaneously with wideband ToAs. Therefore, this framework provides a paradigm-agnostic approach to characterise single-pulsar dispersive processes. This method is expected to minimise the impact of achromatic red noise processes while characterising these dispersive effects. We substantiate the discussed technique with representative examples using simulated narrowband and wideband datasets with realistic noise injections. We found the recovery to be in close agreement with the injections, and agnostic to the estimation technique. Our method applies to pulsar timing experiments where precise, epoch-wise DM estimates are possible, such as the Indian Pulsar Timing Array. This technique can serve as a powerful diagnostic tool for validating single-pulsar noise analyses, which is crucial for precision pulsar timing experiments, such as Pulsar Timing Arrays.

Figures

Figures reproduced from arXiv: 2607.12609 by the authors.

Figure 1
Figure 1. The simulated narrowband ToA residuals with 16 sub-bands in the 300 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 1
Figure 1. The simulated narrowband ToA residuals with 16 sub-bands in the 300 [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. The DM time series extracted from the simulated narrowband ToAs (as crimson points) with 16 sub-bands using the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figures from the paper (5 more)
Figure 2
Figure 2. Figure 2: The DM time series extracted from the simulated narrowband ToAs (as crimson points) with 16 sub-bands using the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png]
Figure 3
Figure 3. Figure 3: The posterior distributions of the deterministic SW process and the DM [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 3
Figure 3. Figure 3: The posterior distributions of the deterministic SW process and the DM [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]
Figure 4
Figure 4. Figure 4: The extracted DM time series from narrowband ToAs along with the deterministic SW process estimated from [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 4
Figure 4. Figure 4: The extracted DM time series from narrowband ToAs along with the deterministic SW process estimated from [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Works this paper leans on

81 extracted references · 39 linked inside Pith

  1. [1]

    Monthly Notices of the Royal Astronomical Society , volume =

    Hobbs, G and Guo, L and Caballero, R N and Coles, W and Lee, K J and others , title =. Monthly Notices of the Royal Astronomical Society , volume =. 2019 , month =. doi:10.1093/mnras/stz3071 , url =

    doi:10.1093/mnras/stz3071 2019

  2. [2]

    Handbook of Pulsar Astronomy

  3. [4]

    , keywords =

    Comparing Recent Pulsar Timing Array Results on the Nanohertz Stochastic Gravitational-wave Background. , keywords =. doi:10.3847/1538-4357/ad36be , archivePrefix =. 2309.00693 , primaryClass =

    arXiv

  4. [5]

    2026 , issn =

    Effects of coronal mass ejection on PSR J1022+1001 and possible mode change of PSR J2145--0750 in the InPTA DR2 , journal =. 2026 , issn =. doi:https://doi.org/10.1016/j.jheap.2025.100547 , author =

    arXiv 2026

  5. [6]

    , keywords =

    Dispersion measure variability for 36 millisecond pulsars at 150 MHz with LOFAR. , keywords =. doi:10.1051/0004-6361/202039517 , archivePrefix =. 2011.13742 , primaryClass =

    arXiv 2011

  6. [7]

    , keywords =

    Pulsar timing and general relativity. , keywords =. doi:10.1146/annurev.aa.24.090186.002541 , adsurl =

    arXiv

  7. [8]

    Physical Review X , keywords =

    Strong-Field Gravity Tests with the Double Pulsar. Physical Review X , keywords =. doi:10.1103/PhysRevX.11.041050 , archivePrefix =. 2112.06795 , primaryClass =

    arXiv

  8. [9]

    , keywords =

    Constructing a Pulsar Timing Array. , keywords =. doi:10.1086/169195 , adsurl =

    doi:10.1086/169195

Show all 81 references
  1. [10]

    , keywords =

    The NANOGrav Nine-year Data Set: Observations, Arrival Time Measurements, and Analysis of 37 Millisecond Pulsars. , keywords =. doi:10.1088/0004-637X/813/1/65 , archivePrefix =. 1505.07540 , primaryClass =

    arXiv

  2. [11]

    Keith, M. J. and Coles, W. and Shannon, R. M. and Hobbs, G. B. and Manchester, R. N. and Bailes, M. and Bhat, N. D. R. and Burke-Spolaor, S. and Champion, D. J. and Chaudhary, A. and Hotan, A. W. and Khoo, J. and Kocz, J. and Osłowski, S. and Ravi, V. and Reynolds, J. E. and S...

    2012 doi

  3. [12]

    , keywords =

    TEMPONEST: a Bayesian approach to pulsar timing analysis. , keywords =. doi:10.1093/mnras/stt2122 , archivePrefix =. 1310.2120 , primaryClass =

    arXiv

  4. [13]

    The timing model and precision estimates

    TEMPO2, a new pulsar timing package - II. The timing model and precision estimates. , keywords =. doi:10.1111/j.1365-2966.2006.10870.x , archivePrefix =. astro-ph/0607664 , primaryClass =

    2006

  5. [14]

    , keywords =

    PINT: A Modern Software Package for Pulsar Timing. , keywords =. doi:10.3847/1538-4357/abe62f , archivePrefix =. 2012.00074 , primaryClass =

    2012 arXiv

  6. [15]

    , keywords =

    PINT: Maximum-likelihood Estimation of Pulsar Timing Noise Parameters. , keywords =. doi:10.3847/1538-4357/ad59f7 , archivePrefix =. 2405.01977 , primaryClass =

    arXiv

  7. [16]

    Dataset and timing analysis

    The Indian Pulsar Timing Array data release 2: I. Dataset and timing analysis. , keywords =. doi:10.1017/pasa.2025.10066 , archivePrefix =. 2506.16769 , primaryClass =

    2025

  8. [17]

    , keywords =

    The Indian Pulsar Timing Array: First data release. , keywords =. doi:10.1017/pasa.2022.46 , archivePrefix =. 2206.09289 , primaryClass =

    2022 arXiv

  9. [18]

    , keywords =

    Noise analysis of the Indian Pulsar Timing Array data release I. , keywords =. doi:10.1103/PhysRevD.108.023008 , archivePrefix =. 2303.12105 , primaryClass =

    arXiv

  10. [19]

    , keywords =

    On the usefulness of existing solar wind models for pulsar timing corrections. , keywords =. doi:10.1093/mnras/stz1278 , archivePrefix =. 1905.02989 , primaryClass =

    1905 arXiv

  11. [20]

    , keywords =

    The impact of solar wind variability on pulsar timing. , keywords =. doi:10.1051/0004-6361/202039846 , archivePrefix =. 2012.11726 , primaryClass =

    2012 arXiv

  12. [21]

    , keywords =

    Bayesian Solar Wind Modeling with Pulsar Timing Arrays. , keywords =. doi:10.3847/1538-4357/ac5829 , archivePrefix =. 2111.09361 , primaryClass =

    arXiv

  13. [22]

    , keywords =

    Exploring the time variability of the solar wind using LOFAR pulsar data. , keywords =. doi:10.1051/0004-6361/202450680 , archivePrefix =. 2409.09838 , primaryClass =

    arXiv

  14. [23]

    , keywords =

    Measuring scattering variations in pulsar timing observations: a test of the fidelity of current methods. , keywords =. doi:10.1093/mnras/staf1930 , archivePrefix =. 2511.03185 , primaryClass =

  15. [24]

    , keywords =

    Improving DM estimates using low-frequency scatter-broadening estimates. , keywords =. doi:10.1093/mnras/stae2405 , archivePrefix =. 2309.16765 , primaryClass =

    arXiv

  16. [25]

    arXiv e-prints , keywords =

    Recovering Intrinsic Pulsar Profiles and Scattering Parameters with a CLEAN-Based Algorithm for High-Precision Timing. arXiv e-prints , keywords =. doi:10.48550/arXiv.2511.22299 , archivePrefix =. 2511.22299 , primaryClass =

    doi

  17. [26]

    , keywords =

    Pulsar timing noise and the minimum observation time to detect gravitational waves with pulsar timing arrays. , keywords =. doi:10.1093/mnras/stv540 , archivePrefix =. 1503.03298 , primaryClass =

    arXiv

  18. [27]

    , keywords =

    Pulse Jitter and Single-pulse Variability in Millisecond Pulsars. , keywords =. doi:10.3847/1538-4357/ad217b , archivePrefix =. 2401.12426 , primaryClass =

    arXiv

  19. [28]

    , keywords =

    An insight into chromatic behaviour of jitter in pulsars and its modelling: a case study of PSR J0437-4715. , keywords =. doi:10.1093/mnras/stae041 , archivePrefix =. 2401.03660 , primaryClass =

    arXiv

  20. [29]

    , keywords =

    Pulsar timing methods for evaluating dispersion measure time series. , keywords =. doi:10.1051/0004-6361/202450740 , archivePrefix =. 2410.22170 , primaryClass =

    arXiv

  21. [30]

    , keywords =

    The NANOGrav 15 yr Data Set: Chromatic Gaussian Process Noise Models for Six Pulsars. , keywords =. doi:10.3847/1538-4357/ad5291 , archivePrefix =. 2405.14941 , primaryClass =

    arXiv

  22. [31]

    Common-red-signal analysis with 24-yr high-precision timing of the European Pulsar Timing Array: inferences in the stochastic gravitational-wave background search , volume =

    S Chen and R N Caballero and Y J Guo and A Chalumeau and K Liu and G Shaifullah and K J Lee and S Babak and G Desvignes and A Parthasarathy and H Hu and E van der Wateren and J Antoniadis and A-S Bak Nielsen and C G Bassa and A Berthereau and M Burgay and D J Champion and I Co...

  23. [32]

    Baker and Harsha Blumer and Bence Bécsy and Adam Brazier and Paul R

    Zaven Arzoumanian and Paul T. Baker and Harsha Blumer and Bence Bécsy and Adam Brazier and Paul R. Brook and Sarah Burke-Spolaor and Shami Chatterjee and Siyuan Chen and James M. Cordes and Neil J. Cornish and Fronefield Crawford and H. Thankful Cromartie and Megan E. DeCesar ...

  24. [33]

    Boris Goncharov and R. M. Shannon and D. J. Reardon and G. Hobbs and A. Zic and M. Bailes and M. Curyło and S. Dai and M. Kerr and M. E. Lower and R. N. Manchester and R. Mandow and H. Middleton and M. T. Miles and A. Parthasarathy and E. Thrane and N. Thyagarajan and X. Xue a...

  25. [34]

    The International Pulsar Timing Array second data release: Search for an isotropic gravitational wave background , volume =

    J Antoniadis and Z Arzoumanian and S Babak and M Bailes and A-S Bak Nielsen and P T Baker and C G Bassa and B Bécsy and A Berthereau and M Bonetti and A Brazier and P R Brook and M Burgay and S Burke-Spolaor and R N Caballero and J A Casey-Clyde and A Chalumeau and D J Champio...

  26. [35]

    , keywords =

    New advances in the Gaussian-process approach to pulsar-timing data analysis. , keywords =. doi:10.1103/PhysRevD.90.104012 , archivePrefix =. 1407.1838 , primaryClass =

    arXiv

  27. [36]

    Ellis and Michele Vallisneri and Stephen R

    Justin A. Ellis and Michele Vallisneri and Stephen R. Taylor and Paul T. Baker , title =. doi:10.5281/zenodo.4059815 , url =

    doi

  28. [37]

    NANOGrav 15-year gravitational-wave background methods , author =. Phys. Rev. D , volume =. 2024 , month =. doi:10.1103/PhysRevD.109.103012 , url =

    2024 doi

  29. [38]

    Journal of Astrophysics and Astronomy , keywords =

    Precision pulsar timing with the ORT and the GMRT and its applications in pulsar astrophysics. Journal of Astrophysics and Astronomy , keywords =. doi:10.1007/s12036-018-9549-y , adsurl =

    doi

  30. [39]

    Bhal Chandra Joshi and Achamveedu Gopakumar and Arul Pandian and Thiagaraj Prabu and Lankeswar Dey and Manjari Bagchi and Shantanu Desai and Pratik Tarafdar and Prerna Rana and Yogesh Maan and Neelam Dhanda BATRA and Raghav Girgaonkar and Nikita Agarwal and Paramasivan Arumuga...

  31. [40]

    Hasasia: A Python package for Pulsar Timing Array Sensitivity Curves , url =

    Hazboun, Jeffrey and Romano, Joseph and Smith, Tristan , pages =. Hasasia: A Python package for Pulsar Timing Array Sensitivity Curves , url =. Journal of Open Source Software , doi =. 2019 , month =

    2019

  32. [41]

    J. P. W. Verbiest and L. Lentati and G. Hobbs and R. van Haasteren and P. B. Demorest and G. H. Janssen and J.-B. Wang and G. Desvignes and R. N. Caballero and M. J. Keith and D. J. Champion and Z. Arzoumanian and S. Babak and C. G. Bassa and N. D. R. Bhat and A. Brazier and P...

  33. [42]

    The International Pulsar Timing Array: second data release , volume =

    B B P Perera and M E DeCesar and P B Demorest and M Kerr and L Lentati and D J Nice and S Osłowski and S M Ransom and M J Keith and Z Arzoumanian and M Bailes and P T Baker and C G Bassa and N D R Bhat and A Brazier and M Burgay and S Burke-Spolaor and R N Caballero and D J Ch...

  34. [43]

    , keywords =

    The JPL Planetary and Lunar Ephemerides DE440 and DE441. , keywords =. doi:10.3847/1538-3881/abd414 , adsurl =

    doi

  35. [44]

    , keywords =

    Gaussian process representation of dispersion measure noise in pulsar wideband data sets. , keywords =. doi:10.1093/mnras/staf1422 , archivePrefix =. 2505.05274 , primaryClass =

    arXiv

  36. [45]

    emcee: The MCMC Hammer

  37. [46]

    , year = 1953, month = jun, volume =

    Equation of State Calculations by Fast Computing Machines. , year = 1953, month = jun, volume =. doi:10.1063/1.1699114 , adsurl =

    1953 doi

  38. [47]

    Biometrika , year = 1970, month = apr, volume =

    Monte Carlo Sampling Methods using Markov Chains and their Applications. Biometrika , year = 1970, month = apr, volume =. doi:10.1093/biomet/57.1.97 , adsurl =

    1970 doi

  39. [48]

    Physical Chemistry Chemical Physics (Incorporating Faraday Transactions) , keywords =

    Parallel tempering: Theory, applications, and new perspectives. Physical Chemistry Chemical Physics (Incorporating Faraday Transactions) , keywords =. doi:10.1039/B509983H , archivePrefix =. physics/0508111 , primaryClass =

    arXiv

  40. [49]

    The second data release from the European Pulsar Timing Array. I. The dataset and timing analysis. , keywords =. doi:10.1051/0004-6361/202346841 , archivePrefix =. 2306.16224 , primaryClass =

    arXiv

  41. [50]

    , keywords =

    Bayesian pulsar timing and noise analysis with Vela.jl: The wideband paradigm. , keywords =. doi:10.1103/n3ck-lfdy , archivePrefix =. 2505.10141 , primaryClass =

    arXiv

  42. [51]

    , keywords =

    High precision measurements of interstellar dispersion measure with the upgraded GMRT. , keywords =. doi:10.1051/0004-6361/202140340 , archivePrefix =. 2101.05334 , primaryClass =

    arXiv

  43. [52]

    doi:10.5281/zenodo.1037579 , url =

    Justin Ellis and Rutger van Haasteren , title =. doi:10.5281/zenodo.1037579 , url =

    doi

  44. [53]

    , keywords =

    Pulsar Timing Arrays Require Hierarchical Models. , keywords =. doi:10.3847/1538-4365/ad530f , archivePrefix =. 2406.05081 , primaryClass =

    arXiv

  45. [54]

    , title =

    Van Rossum, Guido and Drake, Fred L. , title =. 2009 , isbn =

    2009

  46. [55]

    and Baker, Paul T

    Taylor, Stephen R. and Baker, Paul T. and Hazboun, Jeffrey S. and Simon, Joseph and Vigeland, Sarah J. , title =. 2021 , howpublished =

    2021

  47. [56]

    , keywords =

    The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package. , keywords =. doi:10.3847/1538-4357/ac7c74 , adsurl =

    doi

  48. [57]

    Oliphant and Matt Haberland and Tyler Reddy and David Cournapeau and Evgeni Burovski and Pearu Peterson and Warren Weckesser and Jonathan Bright and Stéfan J

    Pauli Virtanen and Ralf Gommers and Travis E. Oliphant and Matt Haberland and Tyler Reddy and David Cournapeau and Evgeni Burovski and Pearu Peterson and Warren Weckesser and Jonathan Bright and Stéfan J. van der Walt and Matthew Brett and Joshua Wilson and K. Jarrod Millman a...

  49. [58]

    Hunter, J. D. , Title =. Computing in Science & Engineering , Volume =

  50. [59]

    corner.py: Scatterplot matrices in Python , volume =

    Daniel Foreman-Mackey , doi =. corner.py: Scatterplot matrices in Python , volume =. The Journal of Open Source Software , month =

  51. [60]

    Harris and K

    Charles R. Harris and K. Jarrod Millman and Stéfan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti Picus and Stephan Hoyer and Marten H. van Kerkwijk ...

  52. [61]

    , keywords =

    The NANOGrav 15 yr Data Set: Evidence for a Gravitational-wave Background. , keywords =. doi:10.3847/2041-8213/acdac6 , adsurl =

    doi

  53. [62]

    , keywords =

    Search for an Isotropic Gravitational-wave Background with the Parkes Pulsar Timing Array. , keywords =. doi:10.3847/2041-8213/acdd02 , adsurl =

    doi

  54. [63]

    , keywords =

    The MeerKAT Pulsar Timing Array: the first search for gravitational waves with the MeerKAT radio telescope. , keywords =. doi:10.1093/mnras/stae2571 , archivePrefix =. 2412.01153 , primaryClass =

    arXiv

  55. [64]

    Research in Astronomy and Astrophysics , keywords =

    Searching for the Nano-Hertz Stochastic Gravitational Wave Background with the Chinese Pulsar Timing Array Data Release I. Research in Astronomy and Astrophysics , keywords =. doi:10.1088/1674-4527/acdfa5 , archivePrefix =. 2306.16216 , primaryClass =

    arXiv

  56. [65]

    The second data release from the European Pulsar Timing Array. II. Customised pulsar noise models for spatially correlated gravitational waves. , keywords =. doi:10.1051/0004-6361/202346842 , archivePrefix =. 2306.16225 , primaryClass =

    arXiv

  57. [66]

    The second data release from the European Pulsar Timing Array. III. Search for gravitational wave signals. , keywords =. doi:10.1051/0004-6361/202346844 , adsurl =

    doi

  58. [67]

    , keywords =

    Elementary Wideband Timing of Radio Pulsars. , keywords =. doi:10.1088/0004-637X/790/2/93 , archivePrefix =. 1402.1672 , primaryClass =

    arXiv

  59. [68]

    arXiv e-prints , keywords =

    Dispersion measure: Confusion, Constants & Clarity. arXiv e-prints , keywords =. doi:10.48550/arXiv.2007.02886 , archivePrefix =. 2007.02886 , primaryClass =

    2007 doi arXiv

  60. [69]

    K. Nobleson and Churchil Dwivedi and Shantanu Desai and Bhal Chandra Joshi and Himanshu Grover and Debabrata Deb and Vaishnavi Vyasraj and Kunjal Vara and Hemanga Tahbildar and Abhimanyu Susobhanan and Mayuresh Surnis and Aman Srivastava and Shubhit Sardana and Keitaro Takahas...

  61. [70]

    , keywords =

    The MeerKAT Pulsar Timing Array: the 4.5-yr data release and the noise and stochastic signals of the millisecond pulsar population. , keywords =. doi:10.1093/mnras/stae2572 , archivePrefix =. 2412.01148 , primaryClass =

    arXiv

  62. [71]

    arXiv e-prints , keywords =

    CHIME-o-Grav: Wideband Timing of Four Millisecond Pulsars from the NANOGrav 15-yr dataset. arXiv e-prints , keywords =. doi:10.48550/arXiv.2510.16668 , archivePrefix =. 2510.16668 , primaryClass =

    doi

  63. [72]

    , keywords =

    Understanding and analysing time-correlated stochastic signals in pulsar timing. , keywords =. doi:10.1093/mnras/sts097 , archivePrefix =. 1202.5932 , primaryClass =

    arXiv

  64. [73]

    , keywords =

    Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715. , keywords =. doi:10.1017/pasa.2024.30 , archivePrefix =. 2312.01875 , primaryClass =

    2024 arXiv

  65. [74]

    , keywords =

    Assessing the Role of Spin Noise in the Precision Timing of Millisecond Pulsars. , keywords =. doi:10.1088/0004-637X/725/2/1607 , archivePrefix =. 1010.4794 , primaryClass =

    arXiv

  66. [75]

    , keywords =

    Frequency-dependent Template Profiles for High-precision Pulsar Timing. , keywords =. doi:10.3847/1538-4357/aaf6ef , archivePrefix =. 1812.02006 , primaryClass =

    arXiv

  67. [76]

    , keywords =

    The NANOGrav 12.5 yr Data Set: Wideband Timing of 47 Millisecond Pulsars. , keywords =. doi:10.3847/1538-4365/abc6a1 , archivePrefix =. 2005.06495 , primaryClass =

    2005 arXiv

  68. [77]

    , keywords =

    Bayesian Pulsar Timing and Noise Analysis with Vela.jl: An Overview. , keywords =. doi:10.3847/1538-4357/adaaec , archivePrefix =. 2412.15858 , primaryClass =

    arXiv

  69. [78]

    , keywords =

    Revisiting wideband pulsar timing measurements. , keywords =. doi:10.1093/mnras/stag444 , archivePrefix =. 2512.01288 , primaryClass =

  70. [79]

    Communications in Applied Mathematics and Computational Science , keywords =

    Ensemble samplers with affine invariance. Communications in Applied Mathematics and Computational Science , keywords =. doi:10.2140/camcos.2010.5.65 , adsurl =

    2010 doi

  71. [80]

    and Oliver, Daniel J

    Larsen, Bjorn and Baier, Jeremy G. and Oliver, Daniel J. and Wayt, Kalista and Chang, Yu-Ting and Hazboun, Jeffrey S. and Mingarelli, Chiara M. F. and Simon, Joseph and Miles, Matthew T. and Agazie, Gabriella and Anumarlapudi, Akash and Archibald, Anne M. and Arzoumanian, Zave...

    2026

  72. [81]

    , keywords =

    A study of spatial correlations in pulsar timing array data. , keywords =. doi:10.1093/mnras/stv2143 , archivePrefix =. 1510.02363 , primaryClass =

    arXiv

  73. [82]

    , keywords =

    First detection of frequency-dependent, time-variable dispersion measures. , keywords =. doi:10.1051/0004-6361/201834059 , archivePrefix =. 1902.03814 , primaryClass =

    1902 arXiv

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