Autocorrelations in pulsar glitch waiting times and sizes

Carlin, J. B.; Melatos, A.

doi:10.1093/mnras/stz2014

Cited by 20 publications

(29 citation statements)

References 45 publications

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“…While we do not model the microphysics in detail, the Brownian meta-model encompasses such a trigger mechanism. We show in Section 4 and Carlin & Melatos (2019b) that the glitch statistics of PSR J0835−4510 are consistent with the predictions of both the Brownian and SDP meta-models.…”

Section: Discussionsupporting

confidence: 75%

Long-term statistics of pulsar glitches triggered by a Brownian stress accumulation process

Carlin

Melatos

2020

Monthly Notices of the Royal Astronomical Society

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A microphysics-agnostic meta-model of rotational glitches in rotation-powered pulsars is developed, wherein the globally averaged internal stress accumulates as a Brownian process between glitches, and a glitch is triggered once a critical threshold is surmounted. Precise, falsifiable predictions are made regarding long-term event statistics in individual pulsars. For example, the Spearman cross-correlation coefficient between the size of a glitch and the waiting time until the next glitch should exceed 0.25 in all pulsars. Among the six pulsars with the most recorded glitches, PSR J0537−6910 and PSR J0835−4510 are consistent with the predictions of the meta-model, while PSR J1740−3015 and PSR J0631+1036 are not. PSR J0534+2200 and PSR J1341−6220 are only consistent with the meta-model, if there exists an undetected population of small glitches with small waiting times, which we do not resolve. The results are compared with a state-dependent Poisson process, another microphysics-agnostic meta-model in the literature. The results are also applied briefly to recent pulse-to-pulse observations of PSRJ0835−4510, which appear to reveal evidence for a negative fluctuation in rotation frequency just prior to the 2016 glitch.

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Section: Discussionsupporting

confidence: 75%

Long-term statistics of pulsar glitches triggered by a Brownian stress accumulation process

Carlin

Melatos

2020

Monthly Notices of the Royal Astronomical Society

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“…In this paper, we approach the challenge of epoch prediction by modeling glitch activity as a state-dependent Poisson process (Fulgenzi et al 2017;Melatos et al 2018;Carlin & Melatos 2019b,a), in which the glitch rate is variable and depends instantaneously on the 'stress' in the system (elastic stress in the starquake picture, crust-superfluid differential rotation in the vortex avalanche picture). The model naturally predicts two categories of glitch activity (Fulgenzi et al 2017;Carlin & Melatos 2019b) and a paucity of size-waiting-time auto-and cross-correlations Carlin & Melatos 2019a) in line with observations. It also allows us to reconstruct the stress history of a pulsar from the observed sequence of glitch sizes and epochs in terms of seven model parameters and hence derive a maximum likelihood estimate of the stress value today, which in turn predicts a Poisson waiting time to the next glitch.…”

Section: Introductionsupporting

confidence: 72%

Pulsar Glitch Activity as a State-dependent Poisson Process: Parameter Estimation and Epoch Prediction

Melatos

Drummond

2019

ApJ

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Rotational glitches in some rotation-powered pulsars display power-law size and exponential waiting time distributions. These statistics are consistent with a state-dependent Poisson process, where the glitch rate is an increasing function of a global stress variable (e.g. crust-superfluid angular velocity lag), diverges at a threshold stress, increases smoothly while the star spins down, and decreases step-wise at each glitch. A minimal, seven-parameter, maximum likelihood model is calculated for PSR J1740−3015, PSR J0534+2200, and PSR J0631+1036, the three objects with the largest samples whose glitch activity is Poisson-like. The estimated parameters have theoretically reasonable values and contain useful information about the glitch microphysics. It is shown that the maximum likelihood, state-dependent Poisson model is a marginally (23-27 per cent) better post factum "predictor" of historical glitch epochs than a homogeneous Poisson process for PSR J1740−3015 and PSR J0631+1036 and a comparable predictor for PSR J0534+2200. Monte Carlo simulations imply that 50 glitches are needed to test reliably whether one model outperforms the other. It is predicted that the next glitch will occur at Modified Julian Date (MJD) 57784 ± 256.8, 60713 ± 1935, and 57406 ± 1444 for the above three objects respectively. The analysis does not apply to quasiperiodic glitchers like PSR J0537−6910 and PSR J0835−4510, which are not described accurately by the state-dependent Poisson model in its original form.

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“…The most commonly used one is that of performing an ordinary linear regression on the cumulative glitch data, which is justified if one considers the activity parameter as an intrinsic characteristic of a glitching pulsar, and thus inferable from a limited observation of its glitching behaviour (which, in general, may not be stationary). While this last statement might be true, it is also true that a strong autocorrelation or correlation between glitch sizes and waiting times has very rarely been observed [46,65]. Moreover, fitting the cumulative data may also affect the uncertainty calculated from a ordinary linear regression, as the hypothesis of the homoscedasticity of the data cannot be satisfied.…”

Section: Discussionmentioning

confidence: 99%

“…The independence of the variables is loosely justified by observing the small correlation and autocorrelation in glitch sizes and waiting times [46,65], while being identically distributed is a working assumption. The above equation boils down to:…”

Section: Discussionmentioning

confidence: 99%

Statistical Estimates of the Pulsar Glitch Activity

et al. 2021

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A common way to calculate the glitch activity of a pulsar is an ordinary linear regression of the observed cumulative glitch history. This method however is likely to underestimate the errors on the activity, as it implicitly assumes a (long-term) linear dependence between glitch sizes and waiting times, as well as equal variance, i.e., homoscedasticity, in the fit residuals, both assumptions that are not well justified from pulsar data. In this paper, we review the extrapolation of the glitch activity parameter and explore two alternatives: the relaxation of the homoscedasticity hypothesis in the linear fit and the use of the bootstrap technique. We find a larger uncertainty in the activity with respect to that obtained by ordinary linear regression, especially for those objects in which it can be significantly affected by a single glitch. We discuss how this affects the theoretical upper bound on the moment of inertia associated with the region of a neutron star containing the superfluid reservoir of angular momentum released in a stationary sequence of glitches. We find that this upper bound is less tight if one considers the uncertainty on the activity estimated with the bootstrap method and allows for models in which the superfluid reservoir is entirely in the crust.

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Autocorrelations in pulsar glitch waiting times and sizes

Cited by 20 publications

References 45 publications

Long-term statistics of pulsar glitches triggered by a Brownian stress accumulation process

Long-term statistics of pulsar glitches triggered by a Brownian stress accumulation process

Pulsar Glitch Activity as a State-dependent Poisson Process: Parameter Estimation and Epoch Prediction

Statistical Estimates of the Pulsar Glitch Activity

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