Edinburgh Research Explorer

Dissecting Magnetar Variability with Bayesian Hierarchical Models

Research output: Contribution to journalArticle

  • Daniela Huppenkothen
  • Brendon J. Brewer
  • David W. Hogg
  • Iain Murray
  • Marcus Frean
  • Chris Elenbaas
  • Anna L. Watts
  • Yuri Levin
  • Alexander J. Van Der Horst
  • Chryssa Kouveliotou

Related Edinburgh Organisations

Original languageEnglish
Number of pages21
JournalAstrophysical Journal
Issue number1
Publication statusPublished - 1 Sep 2015


Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behaviour, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favoured models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo (MCMC) sampling augmented with reversible jumps between models with different numbers of parameters, we characterise the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organised criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.

ID: 20196128