This Letter reports on the results of numerical simulations which may provide a possible explanation for the strahl broadening during quiet solar conditions. The relevant processes involved in the broadening are due to kinetic quasi-linear wave-particle interaction. Making use of static analytical electron distribution in an inhomogeneous field, it is found that self-generated electrostatic waves at the plasma frequency, i.e., Langmuir waves, are capable of scattering the strahl component, resulting in energy and pitch-angle diffusion that broadens its velocity distribution significantly. The present theoretical results provide an alternative or complementary explanation to the usual whistler diffusion scenario, suggesting that self-induced electrostatic waves at the plasma frequency might play a key role in broadening the solar wind strahl during quiet solar conditions.

The solar wind electrons observed at 1 AU are characterized by velocity distribution functions (VDF) that deviate from the Maxwellian form in a high energy regime. Such a feature is often modeled by a kappa distribution. In the present paper a self-consistent theory of quiet-time solar wind electrons that contain a power-law tail component, f ∝ v-α is discussed. These electrons are assumed to be in dynamic equilibrium with enhanced electrostatic fluctuations with peak frequency near the plasma frequency (i.e., the Langmuir turbulence). In order to verify the theoretical prediction, the solar wind electrons in the high-energy range known as the super-halo distribution detected by WIND and STEREO spacecraft are compared against the theoretical model where it was found that the theoretical power-law index is intermittent with regard to the observed range of indices, thus indicating that the turbulent equilibrium model of suprathermal solar wind electrons may be valid.

Here we develop the theory of seasonal FIEGARCH processes, denoted by SFIEGARCH, establishing conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We analyze their asymptotic dependence structure by means of the autocovariance and autocorrelation functions. We also present some properties regarding their spectral representation. All properties are illustrated through graphical examples and an application of SFIEGARCH models to describe the volatility of the S&P500 US stock index log-return time series in the period from December 13, 2004 to October 10, 2009 is provided.