We build a framework using tools from Bayesian data analysis to evaluate models explaining the periodic variations in spin-down and beamwidth of PSR B1828-11. The available data consist of the time-averaged spin-down rate, which displays a distinctive double-peaked modulation, and measurements of the beamwidth. Two concepts exist in the literature that are capable of explaining these variations; we formulate predictive models from these and quantitatively compare them. The first concept is phenomenological and stipulates that the magnetosphere undergoes periodic switching between two metastable states as first suggested by Lyne et al. The second concept, precession, was first considered as a candidate for the modulation of B1828-11 by Stairs et al. We quantitatively compare models built from these concepts using a Bayesian odds ratio. Because the phenomenological switching model itself was informed by these data in the first place, it is difficult to specify appropriate parameterspace priors that can be trusted for an unbiased model comparison. Therefore, we first perform a parameter estimation using the spin-down data, and then use the resulting posterior distributions as priors for model comparison on the beamwidth data. We find that a precession model with a simple circular Gaussian beam geometry fails to appropriately describe the data, while allowing for a more general beam geometry provides a good fit to the data. The resulting odds between the precession model (with a general beam geometry) and the switching model are estimated as 102.7±0.5 in favour of the precession model.
- Methods: data analysis
- Pulsars: individual: PSR B1828-11
- Stars: neutron