Regimes of ion dynamics in current sheets: The machine learning approach

Area of Science:

• Space Physics
• Plasma Physics
• Astrophysics

Background:

• Current sheets are localized 1D structures vital for magnetic energy storage and plasma separation in space environments.
• They are key sites for magnetic reconnection, leading to plasma heating and particle acceleration.
• Theoretical models often assume conserved ion adiabatic invariants for 1D current sheets like rotational discontinuities.

Purpose of the Study:

• To investigate the conditions under which ion adiabatic invariants are conserved in observed 1D current sheet configurations.
• To determine the parametrical domains of adiabatic invariant conservation using a machine learning approach.
• To interpret the findings in the context of current sheet dynamics and equilibria.

Main Methods:

• Application of a machine learning approach, AI Poincaré.
• Analysis of three distinct current sheet configurations found in Earth's magnetopause, magnetotail, and near-Earth solar wind.
• Determination of parametrical domains for conserved adiabatic invariants.

Main Results:

• The study found that domains where adiabatic invariants are conserved are narrow for all three current sheet configurations.
• These conserved domains do not encompass the full range of observed current sheet parameters.
• The magnetic field in these current sheets is supported by transient ion currents dependent on invariant conservation.

Conclusions:

• The narrow parametrical domains for adiabatic invariant conservation challenge static equilibrium models of current sheets.
• Results suggest that 1D current sheets are likely dynamical plasma structures rather than static equilibria.
• This implies a need for revised theoretical frameworks to account for the dynamic nature of current sheets.