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Auroral bright spots have been observed at Earth, Jupiter, and Saturn in regions that map to the magnetopause boundary layer. It has been suggested that the bright spots are associated with the Kelvin-Helmholtz instability. We utilize a quasistatic magnetosphere-ionosphere coupling model driven by a vortex in the boundary layer to determine how the field-aligned current structure depends on ionospheric and boundary layer parameters. We compare vortex induced currents with shear-flow induced currents. We find that the strength of the maximum currents are comparable, but the structure is significantly different. For a vortex, the current and electron precipitation maximize when the vortex size mapped to the ionosphere is approximately 1.5 L, where is the auroral scale length, ∑P is the Pedersen conductivity, and κ is the Knight parameter. For a vortex, the current width provides a direct measure of the size, D, of the boundary layer structure, while the current width of shear-flow aurora is generally determined by the larger of Δ or L. For comparison with observations, an event is considered where auroral bright spots in the ionosphere are detected by DMSP SSUSI FUV when Kelvin-Helmholtz structures are observed on the dusk flank by THEMIS. [ABSTRACT FROM AUTHOR]

The waiting time between stellar flares describes the fundamental dynamics of the stars and is driven by physical processes such as flux emergence. While it is rather straightforward to identify large flares, identification of weaker flares can be challenging because of the presence of noise. A common practice is to limit flare identification to those events stronger than a threshold value that significantly exceeds the noise level (k-sigma). However, the selection of the k value is normally made based on an empirical rule (typically 3), which can lead to a biased threshold level. This study presents a thresholding method that addresses the optimum method to identify a stellar flare event. The method involves an information theoretical approach to determining whether flare data are significantly distinguishable from a nonstationary Poisson process. If the low amplitude enhancements were simply random noise, there should be no significant relationship between subsequent waiting times based on noise events. However, if there is a significant relationship between the low amplitude enhancements, then many of those events are likely flares. The aim is to search for an optimal threshold level for the detection of flares that produce the highest discriminating significance. To demonstrate the technique, we analyze the stellar intensity data produced by a solar-type star (KIC 11551430). [ABSTRACT FROM AUTHOR]

Because the dynamics of stellar flares are modulated by magnetic activity cycles, they provide a window to the underlying dynamics. Using mutual information, we can determine whether flares are affected by earlier flares. From the stellar intensity data of KIC 11551430 collected by the Kepler satellite, we designate flare events based on a sudden elevation in the intensity of the light curve above a threshold condition. The waiting times are calculated as the time difference between flare events, and surrogate datasets are constructed by scaling waiting time sequences from other stars, creating a stationary Poisson ensemble of waiting times. When the mutual information of the time-lagged waiting time distributions is plotted for both the original and surrogate data, an increase in the mutual information is present for the original data as compared to the surrogates, indicating that there is short-term memory stored within the stellar flare-producing process. These results are compared with the solar waiting time distribution, which is analyzed in a similar manner. [ABSTRACT FROM AUTHOR]

Recent simulations of tail dynamics using the Auburn Global Hybrid Code in 3-D (ANGIE3D) suggest that tail flows are closely related to the dynamics of Alfven waves propagating from the magnetotail to the ionosphere. To understand the dynamical coupling process described by the simulation, we consider the simulated time series of plasma sheet structures associated with tail flows and the Poynting flux into the ionosphere. We present 2-D and 3-D plots showing how the ionospheric Poynting flux responds to activity in the plasma sheet over time and the location at which Alfven waves are generated. We utilize transfer entropy (Schrieber, 2001) to identify causal, non-linear relationships among reconnection events, tail flows, and Poynting flux into the ionosphere. Results suggest that flows in the plasma sheet are the primary driver of the Poynting flux, which is consistent with expectations. This work demonstrates how system science tools can be used in conjunction with complex simulations to describe the underlying system dynamics and provide a framework for understanding the interrelated components, functions, and causalities in the system. [ABSTRACT FROM AUTHOR]

The solar activity cycle impacts processes ranging from the sun throughout the heliosphere. Many processes can be identified as "events" such as solar flares, geomagnetic storms, and geomagnetic substorms. The waiting times between these events are governed by a combination of external driving by the activity cycle and internal dynamics. If the events occur randomly, the process can be described as a Poisson process. However, because the magnetic activity cycle generally modulates the rate of the events, the process is better described as a nonstationary Poisson process. For longer waiting times, Aschwanden (2010) showed that nonstationary Poisson processes with piecewise continuous exponential occurrence rate functions generate waiting time distributions that are power-law-like. Such tails are characteristic of many processes throughout the heliosphere including solar flares, geomagnetic storms, and substorms. In our study, we explore the tail of various waiting times datasets with nonstationary Poisson distribution with a sinusoidal dependence. Analytically, we find that the distribution of large waiting times of such processes can be described using a power law slope of -2.5. We show that this result applies more broadly to any nonstationary Poisson process driven periodically such as solar/stellar flares, geomagnetic storms, and substorms. We also discuss how the power law specifically relates to the behavior of driver near its minima. [ABSTRACT FROM AUTHOR]