Statistics of Reconnection-Driven Turbulence
Grzegorz Kowal, Diego A. Falceta-Gon\c{c}alves, Alex Lazarian, and Ethan T. Vishniac

TL;DR
This paper demonstrates through simulations that reconnection-driven turbulence can self-generate and produce Kolmogorov-like turbulence, with reconnection rates that are weakly resolution-dependent, challenging the need for external turbulence drivers.
Contribution
It introduces a realistic 3D model of reconnection-driven turbulence initiated by stochastic noise, showing self-generation of turbulence and detailed statistical properties.
Findings
Reconnection produces Kolmogorov-like turbulence consistent with Goldreich-Sridhar model.
Anisotropy scaling $l_\parallel \propto l_\perp^{2/3}$ is observed in broad turbulent regions.
Reconnection rates are weakly dependent on resolution, indicating intrinsic fast reconnection without external turbulence.
Abstract
Magnetic reconnection is a process that changes magnetic field topology in highly conducting fluids. Within the standard Sweet-Parker model, this process would be too slow to explain observations (e.g. solar flares). In reality, the process must be ubiquitous as astrophysical fluids are magnetized and motions of fluid elements necessarily entail crossing of magnetic frozen-in field lines and magnetic reconnection. In the presence of turbulence, the reconnection is independent of microscopic plasma properties, and may be much faster than previously thought, as proposed in Lazarian & Vishniac (1999) and tested in Kowal et al. (2009, 2012). However, the considered turbulence in the Lazarian-Vishniac model was imposed externally. In this work we consider reconnection-driven magnetized turbulence in realistic three-dimensional geometry initiated by stochastic noise. We demonstrate through…
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