Regimes of charged particle dynamics in current sheets: the machine learning approach. (arXiv:2211.03787v1 [physics.plasm-ph])

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.



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