A study of microglitches in Hartebeesthoek radio pulsar

Onuchukwu, C. C.; Chukwude, A. E.

doi:10.1007/s10509-016-2835-8

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…As such, the glitch size distribution is seen to be power law, while the waiting times distribution is exponential. Following Melatos et al (2008), Onuchukwu & Chukwude (2016) presented a similar result using a dataset of microglitches recorded Hartebeesthoek radio telescope. Also, using the CDF plot, Eya et al (2017) demonstrated that the cumulative spin-up sizes in 12 pulsars follows a normal distribution.…”

Section: Introductionmentioning

confidence: 67%

On the distributions of pulsar glitch sizes and the inter-glitch time intervals

Eya

Urama

Chukwude

2019

Res. Astron. Astrophys.

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The glitch size, ∆ν/ν, inter-glitch time interval, t i and frequency of glitches in pulsars are key parameters in discussing glitch phenomena. In this paper, the glitch sizes and inter-glitch time intervals were statistically analysed in a sample of 168 pulsars with a total of 483 glitches. The glitches were broadly divided into two groups. Those with ∆ν/ν < 10 −7 are regarded as small size glitches, while those with ∆ν/ν ≥ 10 −7 are regarded as relatively large size glitches. In the ensemble of glitches, the distribution of ∆ν/ν is seen to be bimodal as usual. The distribution of inter-glitch time intervals is unimodal and the inter-glitch time intervals between small and large size glitches are not significantly different from each other. This observation shows that inter-glitch time intervals are size independent. In addition, the distribution of the ratio ∆ν/ν : t i in both small and large size glitches has the same pattern. This observation suggests that a parameter which depends on time, which could be the spin-down rate of a pulsar plays a similar role in the processes that regulate both small and large size glitches. Equally this could be an indication that a single physical mechanism, which could produce varying glitch sizes at similar time-intervals could be responsible for both classes of glitch sizes.

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Section: Introductionmentioning

confidence: 67%

On the distributions of pulsar glitch sizes and the inter-glitch time intervals

Eya

Urama

Chukwude

2019

Res. Astron. Astrophys.

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“…The secular, electromagnetic braking of some rotation-powered pulsars is interrupted by random, impulsive, spin-up events called glitches (Lyne & Graham-Smith 2012). Two categories of glitch activity have been identified (Melatos et al 2008;Espinoza et al 2011;Onuchukwu & Chukwude 2016;Howitt et al 2018;Carlin & Melatos 2019b;Fuentes et al 2019): Poisson-like, in which the waiting time probability density function (PDF) is exponential, and the size PDF is a power law over 4 dex; and quasiperiodic, where both the size and waiting time PDFs are approximately Gaussian and centered on characteristic values. Most glitching pulsars with statistically significant glitch samples fall into one of the categories above, although there is some cross-over.…”

Section: Introductionmentioning

confidence: 99%

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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“…In 7 It is possible that the glitches observed to date, with ∆f p 10 −10 f , represent the "tip of the iceberg", and there exists a (e.g. power-law) population of microglitches below the resolution limit of current experiments (Melatos et al 2008;Onuchukwu & Chukwude 2016). Indeed it has been argued that microglitches collectively add up to produce timing noise (D'Alessandro et al 1995).…”

Section: Grid Resolution and Doimentioning

confidence: 99%

Pulsar glitch detection with a hidden Markov model

Melatos,

Dunn,

Suvorova

et al. 2020

Preprint

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Pulsar timing experiments typically generate a phase-connected timing solution from a sequence of times-of-arrival (TOAs) by absolute pulse numbering, i.e. by fitting an integer number of pulses between TOAs in order to minimize the residuals with respect to a parametrized phase model. In this observing mode, rotational glitches are discovered, when the residuals of the no-glitch phase model diverge after some epoch, and glitch parameters are refined by Bayesian followup. Here an alternative, complementary approach is presented which tracks the pulse frequency f and its time derivative ḟ with a hidden Markov model (HMM), whose dynamics include stochastic spin wandering (timing noise) and impulsive jumps in f and ḟ (glitches). The HMM tracks spin wandering explicitly, as a specific realization of a discrete-time Markov chain. It discovers glitches by comparing the Bayes factor for glitch and no-glitch models. It ingests standard TOAs for convenience and, being fully automated, allows performance bounds to be calculated quickly via Monte Carlo simulations. Practical, user-oriented plots are presented of the false alarm probability and detection threshold (e.g. minimum resolvable glitch size) versus observational scheduling parameters (e.g. TOA uncertainty, mean delay between TOAs) and glitch parameters (e.g. transient and permanent jump sizes, exponential recovery time-scale). The HMM is also applied to ∼ 1 yr of real data bracketing the 2016 December 12 glitch in PSR J0835−4510 as a proof of principle. It detects the known glitch and confirms that no other glitch exists in the same data with size 10 −7 f .

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A study of microglitches in Hartebeesthoek radio pulsar

Cited by 11 publications

References 34 publications

On the distributions of pulsar glitch sizes and the inter-glitch time intervals

On the distributions of pulsar glitch sizes and the inter-glitch time intervals

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

Pulsar glitch detection with a hidden Markov model

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