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Energetic Particle Perpendicular Diffusion: Simulations and Theory in Noisy Reduced Magnetohydrodynamic Turbulence

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arxiv 2205.09225 v1 pith:XXKRXWZZ submitted 2022-05-18 physics.plasm-ph astro-ph.HEphysics.space-ph

classification physics.plasm-phastro-ph.HEphysics.space-ph
keywords theoryparallelmagneticparticlediffusionperpendicularsimulationsturbulence
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abstract

The transport of energetic charged particles (e.g., cosmic rays) in turbulent magnetic fields is usually characterized in terms of the diffusion parallel and perpendicular to a large-scale (or mean) magnetic field. The nonlinear guiding center theory (NLGC) has been a prominent perpendicular diffusion theory. A recent version of this theory, based on random ballistic spreading of magnetic field lines and a backtracking correction (RBD/BC), has shown good agreement with test particle simulations for a two-component magnetic turbulence model. The aim of the present study is to test the generality of the improved theory by applying it to the noisy reduced magnetohydrodynamic (NRMHD) turbulence model, determining perpendicular diffusion coefficients that are compared with those from the field line random walk (FLRW) and unified nonlinear (UNLT) theories and our test particle simulations. The synthetic NRMHD turbulence model creates special conditions for energetic particle transport, with no magnetic fluctuations at higher parallel wavenumbers so there is no resonant parallel scattering if the particle Larmor radius $R_{\rm L}$ is even slightly smaller than the minimum resonant scale. This leads to non-monotonic variation in the parallel mean free path $\lambda_\parallel$ with $R_{\rm L}$. Among the theories considered, only RBD/BC matches simulations within a factor of two over the range of parameters considered. This accuracy is obtained even though the theory depends on $\lambda_\parallel$ and has no explicit dependence on $R_{\rm L}$. In addition, the UNLT theory often provides accurate results and even the FLRW limit provides a very simple and reasonable approximation in many cases.

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