Bayesian Insights into post-Glitch Dynamics: Model comparison and parameter constraint from decades long observation data of the Crab pulsar

TL;DR

This paper uses Bayesian analysis to compare models explaining the Crab pulsar's glitch-related persistent shifts, finding that logarithmic and power-law models best fit the long-term observational data, challenging traditional starquake explanations.

Contribution

It introduces a Bayesian framework to compare multiple models for pulsar glitch data, highlighting the potential of non-linear models to better explain observed phenomena.

Findings

Logarithmic model best fits the data.

Power-law models closely compete with the logarithmic model.

Linear starquake model is less consistent with large glitches.

Abstract

The Crab Pulsar has exhibited numerous glitches accompanied by persistent shifts in its spin-down rate. The explanation of the observed persistent shifts remain a challenge. We perform a detailed Bayesian analysis to compare four data-fitting models, ranging from a simple linear model to more complex power-law and logarithmic models, using a dataset of observed glitches and persistent shifts. Our results show the large observed events are difficult to explain by the usually assumed linear model due to starquakes. A particularly notable finding is that the logarithmic model provides the best fit to the observation data but the two power-law models show a close tie to it. Detail differences of these models may be further clarified by the understanding of internal physics of neutron stars.