Linking long- and short-term emission variability in pulsars

Brook, Paul R.; Karastergiou, A.; Johnston, S.

doi:10.1093/mnras/stz2092

Cited by 7 publications

(5 citation statements)

References 23 publications

(44 reference statements)

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“…Variations in the integrated profile have been detected to correlate with rotational changes in pulsars (e.g Brook et al 2019). Apart from intermittent pulsars, there are ten radio pulsars which show connections between spin-down rate variation and change of emission, which suggest that pulsar rotation is correlated with emission for some pulsars.…”

Section: Discussionmentioning

confidence: 99%

Timing and emission variation of PSR J1733−3716

Lin

Yuan

et al. 2020

Astrophys Space Sci

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We present analysis of the timing noise in PSR J1733−3716, which combines data from Parkes 64-m radio telescope and nearly 15 years of timing data obtained from the Nanshan 25-m radio telescope. The variations in the spin frequency and frequency derivative are determined. The fluctuation in the spin frequency is obvious with an amplitude of 1.94(7)×10 −9 Hz. Variations of the integrated profile at 1369 MHz are detected with the changes occur in the relative peak intensity from the right profile component. From analysis of the single pulse data at 1382 MHz, we detect weak emission states that account for 63% of the whole data, and its duration distribution can be fitted with a power law. The pulsar also exhibits strong emission states, during which the emission shows multiple modes. This includes the normal mode, left mode and the right mode, with the time scales spanning between one and seventeen pulse periods. Such short term variability in pulses contributes to the variation of the integrated profile. Examination of the correlations between the spin parameters and the integrated profiles shows likelihood of a random distribution, which reveals that there is probably no obvious relationship between spin

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Section: Discussionmentioning

confidence: 99%

Timing and emission variation of PSR J1733−3716

Lin

Yuan

et al. 2020

Astrophys Space Sci

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“…GPs have also been used to model pulse shape variations in decades-long pulsar observations (Brook et al 2016(Brook et al , 2019Shaw et al 2022). In this case, the GP is used to model the pulses themselves and to track long-term changes in pulse periodicity and shape.…”

Section: Compact Objects Gravitational Waves and Transientsmentioning

confidence: 99%

Gaussian Process Regression for Astronomical Time Series

Aigrain

Foreman-Mackey

2023

Annual Review of Astronomy and Astrophysics

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The past two decades have seen a major expansion in the availability, size, and precision of time-domain data sets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity, and comparative robustness, Gaussian processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such data sets. In this review, we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modeling choices involved in GP regression. We then review applications of GPs to time-domain data sets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code; discuss the problem of computational cost and scalability; and give a snapshot of the current ecosystem of open source GP software packages. In summary: ▪ GP regression is a conceptually simple but statistically principled and powerful tool for the analysis of astronomical time series. ▪ It is already widely used in some subfields, such as exoplanets, and gaining traction in many others, such as optical transients. ▪ Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time domain astronomy for many years to come. Expected final online publication date for the Annual Review of Astronomy and Astrophysics, Volume 61 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…GPs have also been used to model pulse shape variations in decades-long pulsar observations (Brook et al 2016(Brook et al , 2019Shaw et al 2022). In this case, the GP is used to model the pulses themselves, and to track long-term changes in pulse periodicity and shape.…”

Section: Compact Objects Gravitational Waves and Transientsmentioning

confidence: 99%

Gaussian Process regression for astronomical time-series

Aigrain¹,

Foreman-Mackey²

2022

Preprint

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The last two decades have seen a major expansion in the availability, size, and precision of time-domain datasets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity and comparative robustness, Gaussian Processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such datasets. In this review we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modelling choices involved in GP regression. We then review applications of GPs to time-domain datasets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code, discuss the problem of computational cost and scalability, and give a snapshot of the current ecosystem of open source GP software packages. Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time domain astronomy for many years to come.

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Linking long- and short-term emission variability in pulsars

Cited by 7 publications

References 23 publications

Timing and emission variation of PSR J1733−3716

Timing and emission variation of PSR J1733−3716

Gaussian Process Regression for Astronomical Time Series

Gaussian Process regression for astronomical time-series

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