English

Effective resistivity in relativistic reconnection: a prescription based on fully kinetic simulations

High Energy Astrophysical Phenomena 2025-01-10 v1

Abstract

A variety of high-energy astrophysical phenomena are powered by the release -- via magnetic reconnection -- of the energy stored in oppositely directed fields. Single-fluid resistive magnetohydrodynamic (MHD) simulations with uniform resistivity yield dissipation rates that are much lower (by nearly one order of magnitude) than equivalent kinetic calculations. Reconnection-driven phenomena could be accordingly modeled in resistive MHD employing a non-uniform, ``effective'' resistivity informed by kinetic calculations. In this work, we analyze a suite of fully kinetic particle-in-cell (PIC) simulations of relativistic pair-plasma reconnection -- where the magnetic energy is greater than the rest mass energy -- for different strengths of the guide field orthogonal to the alternating component. We extract an empirical prescription for the effective resistivity, ηeff=αB0Jp/(Jp+1+(entc)p+1)\eta_{\mathrm{eff}} = \alpha B_0 \mathbf{|J|}^p / \left(|\mathbf{J}|^{p+1}+\left(e n_t c\right)^{p+1}\right), where B0B_0 is the reconnecting magnetic field strength, J\bf J is the current density, ntn_t the lab-frame total number density, ee the elementary charge, and cc the speed of light. The guide field dependence is encoded in α\alpha and pp, which we fit to PIC data. This resistivity formulation -- which relies only on single-fluid MHD quantities -- successfully reproduces the spatial structure and strength of nonideal electric fields, and thus provides a promising strategy for enhancing the reconnection rate in resistive MHD simulations.

Keywords

Cite

@article{arxiv.2501.04800,
  title  = {Effective resistivity in relativistic reconnection: a prescription based on fully kinetic simulations},
  author = {Abigail Moran and Lorenzo Sironi and Aviad Levis and Bart Ripperda and Elias R. Most and Sebastiaan Selvi},
  journal= {arXiv preprint arXiv:2501.04800},
  year   = {2025}
}