Using ultra-low frequency waves and their characteristics to diagnose key physics of substorm onset

Substorm onset is marked in the ionosphere by the sudden brightening of an existing auroral arc or the creation of a new auroral arc. Also present is the formation of auroral beads, proposed to play a key role in the detonation of the substorm, as well as the development of the large-scale substorm current wedge (SCW), invoked to carry the current diversion. Both these phenomena, auroral beads and the SCW, have been intimately related to ultra-low frequency (ULF) waves of specific frequencies as observed by ground-based magnetometers. We present a case study of the absolute and relative timing of Pi1 and Pi2 ULF wave bands with regard to a small substorm expansion phase onset. We find that there is both a location and frequency dependence for the onset of ULF waves. A clear epicentre is observed in specific wave frequencies concurrent with the brightening of the substorm onset arc and the presence of "auroral beads". At higher and lower wave frequencies, different epicentre patterns are revealed, which we conclude demonstrate different characteristics of the onset process; at higher frequencies, this epicentre may demonstrate phase mixing, and at intermediate and lower frequencies these epicentres are characteristic of auroral beads and cold plasma approximation of the "Tamao travel time" from near-earth neutral line reconnection and formation of the SCW.

Separation of stationary and non-stationary sources with a generalized eigenvalue problem

Non-stationary effects are ubiquitous in real world data. In many settings, the observed signals are a mixture of underlying stationary and non-stationary sources that cannot be measured directly. For example, in EEG analysis, electrodes on the scalp record the activity from several sources located inside the brain, which one could only measure invasively. Discerning stationary and non-stationary contributions is an important step towards uncovering the mechanisms of the data generating system. To that end, in Stationary Subspace Analysis (SSA), the observed signal is modeled as a linear superposition of stationary and non-stationary sources, where the aim is to separate the two groups in the mixture. In this paper, we propose the first SSA algorithm that has a closed form solution. The novel method, Analytic SSA (ASSA), is more than 100 times faster than the state-of-the-art, numerically stable, and guaranteed to be optimal when the covariance between stationary and non-stationary sources is time-constant. In numerical simulations on wide range of settings, we show that our method yields superior results, even for signals with time-varying group-wise covariance. In an application to geophysical data analysis, ASSA extracts meaningful components that shed new light on the Pi 2 pulsations of the geomagnetic field.