Multidimensional Iterative Filtering: a new approach for investigating plasma turbulence in numerical simulations

Abstract

Turbulent space and astrophysical plasmas exhibit a complex dynamics, which involves nonlinear coupling across different temporal and spatial scales. There is growing evidence that impulsive events, such as magnetic reconnection instabilities, lead to a spatially localized enhancement of energy dissipation, thus speeding up the energy transfer at small scales. Capturing such a diverse dynamics is challenging. Here, we employ the Multidimensional Iterative Filtering (MIF) method, a novel technique for the analysis of nonstationary multidimensional signals. Unlike other traditional methods (e.g., based on Fourier or wavelet decomposition), MIF does not require any previous assumption on the functional form of the signal to be identified. Using MIF, we carry out a multiscale analysis of Hall-magnetohydrodynamic (HMHD) and hybrid particle-in-cell (HPIC) numerical simulations of decaying plasma turbulence. The results assess the ability of MIF to spatially identify and separate the different scales (the MHD inertial range, the sub-ion kinetic, and the dissipation scales) of the plasma dynamics. Furthermore, MIF decomposition allows to detect localized current structures and to characterize their contribution to the statistical and spectral properties of turbulence. Overall, MIF arises as a very promising technique for the study of turbulent plasma environments.