Regimes of charged particle dynamics in current sheets: the machine learning approach
This addresses the understanding of plasma physics in space environments like Earth's magnetosphere, but it is incremental as it applies a new method to existing theoretical models.
The study applied a novel machine learning method, AI Poincaré, to analyze charged particle dynamics in current sheets, finding that domains where adiabatic invariants are conserved are narrow and do not cover the full observed parametric range, suggesting these structures are dynamic rather than static equilibria.
Current sheets are spatially localized almost-1D structures with intense plasma currents. They play a key role in storing the magnetic field energy and they separate different plasma populations in planetary magnetospheres, the solar wind, and the solar corona. Current sheets are primary regions for the magnetic field line reconnection responsible for plasma heating and charged particle acceleration. One of the most interesting and widely observed type of 1D current sheets is the rotational discontinuity, that can be force-free or include plasma compression. Theoretical models of such 1D current sheets are based on the assumption of adiabatic motion of ions, i.e. ion adiabatic invariants are conserved. We focus on three current sheet configurations, widely observed in the Earth magnetopause and magnetotail and in the near-Earth solar wind. Magnetic field in such current sheets is supported by currents carried by transient ions, which exist only when there is a sufficient number of invariants. In this paper, we apply a novel machine learning approach, AI Poincar'e, to determine parametrical domains where adiabatic invariants are conserved. For all three current sheet configurations, these domains are quite narrow and do not cover the entire parametrical range of observed current sheets. We discuss possible interpretation of obtained results indicating that 1D current sheets are dynamical rather than static plasma equilibria.