Data Mining Reconstruction of Magnetotail Reconnection and Implications for Its First-Principle Modeling

Abstract

Magnetic reconnection is a fundamental process providing topological changes of the magnetic field, reconfiguration of space plasmas and release of energy in key space weather phenomena, solar flares, coronal mass ejections and magnetospheric substorms. Its multiscale nature is difficult to study in observations because of their sparsity. Here we show how the lazy learning method, known as K nearest neighbors, helps mine data in historical space magnetometer records to provide empirical reconstructions of reconnection in the Earth’s magnetotail where the energy of solar wind-magnetosphere interaction is stored and released during substorms. Data mining reveals two reconnection regions (X-lines) with different properties. In the mid tail (∼30RE from Earth, where RE is the Earth’s radius) reconnection is steady, whereas closer to Earth (∼20RE) it is transient. It is found that a similar combination of the steady and transient reconnection processes can be reproduced in kinetic particle-in-cell simulations of the magnetotail current sheet.