Dissecting magnetar variability with Bayesian hierarchical models

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.

New Methods for Timing Analysis of Transient Events, Applied to Fermi/GBM Magnetar Bursts

In order to discern the physical nature of many gamma-ray sources in the sky, we must look not only in spectral and spatial dimensions, but also understand their temporal variability. However, timing analysis of sources with a highly transient nature, such as magnetar bursts, is difficult: standard Fourier techniques developed for long-term variability generally observed, for example, from AGN often do not apply. Here, we present newly developed timing methods applicable to transient events of all kinds, and show their successful application to magnetar bursts observed with Fermi/GBM. Magnetars are a prime subject for timing studies, thanks to the detection of quasi-periodicities in magnetar Giant Flares and their potential to help shed light on the structure of neutron stars. Using state-of-the art statistical techniques, we search for quasi-periodicities (QPOs) in a sample of bursts from Soft Gamma Repeater SGR J0501+4516 observed with Fermi/GBM and provide upper limits for potential QPO detections. Additionally, for the first time, we characterise the broadband variability behaviour of magnetar bursts and highlight how this new information could provide us with another way to probe these mysterious objects.

Quasi-Periodic Oscillations and broadband variability in short magnetar bursts

The discovery of quasi-periodic oscillations (QPOs) in magnetar giant flares has opened up prospects for neutron star asteroseismology. However, with only three giant flares ever recorded, and only two with data of sufficient quality to search for QPOs, such analysis is seriously data limited. We set out a procedure for doing QPO searches in the far more numerous, short, less energetic magnetar bursts. The short, transient nature of these bursts requires the implementation of sophisticated statistical techniques to make reliable inferences. Using Bayesian statistics, we model the periodogram as a combination of red noise at low frequencies and white noise at high frequencies, which we show is a conservative approach to the problem. We use empirical models to make inferences about the potential signature of periodic and quasi-periodic oscillations at these frequencies. We compare our method with previously used techniques and find that although it is on the whole more conservative, it is also more reliable in ruling out false positives. We illustrate our Bayesian method by applying it to a sample of 27 bursts from the magnetar SGR J0501+4516 observed by the Fermi Gamma-ray Burst Monitor, and we find no evidence for the presence of QPOs in any of the bursts in the unbinned spectra, but do find a candidate detection in the binned spectra of one burst. However, whether this signal is due to a genuine quasi-periodic process, or can be attributed to unmodeled effects in the noise is at this point a matter of interpretation.