Global Structure of Magnetotail Reconnection Revealed by Mining Space Magnetometer Data

Abstract

X-lines are one of the most fundamental structures in magnetized plasmas, particularly in space, where they link global or even astronomical scale processes to those on the single particle orbit scale, thereby allowing those microscale processes to shape the universe (Ji et al., 2022). Dungey (1961) suggested that the interaction between Earth's magnetic dipole and the solar wind causes reconnection of magnetic field lines on both the day and nightsides of Earth's magnetosphere. The shape of these reconnecting field lines resembles the letter "X" and extends tens of Earth radii (R E = 6,371.2 km) in the dawn-dusk direction thus forming X-lines. An X-line divides space into four sectors. In one pair of opposing sectors, the magnetic field and plasma converge toward the center of the X while in the other pair, they are rapidly ejected from it. This reconnection process transforms energy stored in the magnetic field into particle kinetic and thermal energy, making it an efficient energy converter and particle accelerator (Ji et al., 2022). X-lines couple kinetic processes on proton and even electron gyroradius scales (≲0.01R E ) (Torbert et al., 2018) to space weather phenomena on global scales: such as solar flares, coronal mass ejections, and magnetospheric storms and substorms (∼10R E ) (Camporeale, 2019). This range of scales is so immense that its modeling has become one of the major challenges for nascent exascale computing (Ji et al., 2022).

Data Mining Inspired Localized Resistivity in Global MHD Simulations of the Magnetosphere

“…Note that both near‐mid tail X‐lines (Run DM_Reconst) and midtail X‐lines (Run Mid) are observed in the DM analysis of substorms (Stephens et al., 2022). In our future simulations we plan to use the DM output with 5‐min cadence to incorporate the whole empirical story of X‐lines into Gamera simulations.…”

“…This is especially impressive since one of the misses occurred during a time of weak magnetospheric activity and the other was during a gap in solar wind data. While (G. K. Stephens et al., 2023) has not yet been published, it has been peer reviewed and they have provided a detailed uncertainty evaluation that makes their comparison with MMS data extremely convincing. Further, although the present work was inspired by the success of the DM applications, expressed in (G. K. Stephens et al., 2023), the present results and conclusions do not really depend on that paper and its acceptance.…”

“…While (G. K. Stephens et al., 2023) has not yet been published, it has been peer reviewed and they have provided a detailed uncertainty evaluation that makes their comparison with MMS data extremely convincing. Further, although the present work was inspired by the success of the DM applications, expressed in (G. K. Stephens et al., 2023), the present results and conclusions do not really depend on that paper and its acceptance. By identifying the location of x‐lines in the reconstruction and placing localized resistivity in these regions in the MHD simulation, we can effectively take non‐MHD physics into account and “encourage” the simulation to form an x‐line where and when we expect there to be reconnection from the DM results.…”

“…Recently, data mining (DM) has been harnessed to reconstruct the state of the magnetosphere for a given point in time (G. Stephens et al., 2019; G. K. Stephens et al., 2023). In order to test the accuracy of this method the authors examined B z , GSM = 0 contours in the magnetotail current sheet and compared them to Magnetospheric Multiscale Mission (MMS) observations of ion diffusion regions (IDRs).…”

Magnetosphere Distortions During the “Satellite Killer” Storm of February 3–4, 2022, as Derived From a Hybrid Empirical Model and Archived Data Mining

“…Instead, the magnetosphere's dynamics is derived from a sequence of relatively small subsets, each of which is compiled (mined) from the grand set, based on the proximity of its data points to the modeled event trajectory in the hyperspace of magnetospheric state parameters and their temporal trends. Unifying that “nearest‐neighbor” (NN) approach with sufficiently flexible high‐resolution models made it possible to resolve previously unaccessible details of the spatial structure and time evolution of the magnetosphere during storms (e.g., Sitnov et al., 2018; N. A. Tsyganenko, Andreeva, & Sitnov, 2021) and even substorms (Stephens et al., 2019, 2022; N. A. Tsyganenko, Andreeva, Sitnov, Stephens, et al., 2021).…”