Bridging in situ satellite measurements and simulations of magnetic reconnection using recurrent neural networks
Author(s)
Type
Journal Article
Abstract
Magnetic reconnection is inherently structured, with distinct spatial regions such as inflows, outflows, and separatrices playing key roles in energy conversion and particle transport. While in situ spacecraft measurements provide detailed local information, determining where a spacecraft lies within the global reconnection geometry remains a major challenge. Proxy-based methods are often ambiguous, while full reconstructions require strong assumptions and are difficult to apply systematically across events. Here, we present a method that bridges these approaches by using machine learning to infer global structural context from local measurements. We first apply k-means clustering to a 2.5-D particle-in-cell simulation to identify six characteristic symmetric reconnection regions. A recurrent neural network (RNN) is then trained on spacecraft-like trajectories through the simulation to classify time series data into these regions. When applied to Magnetospheric Multiscale (MMS) observations of magnetotail reconnection, this method successfully identifies regional transitions, including inflow, outflow, and separatrix crossings, in agreement with previous reconstructions where available. The approach provides a practical, scalable, and automated framework for determining spatial context in reconnection events without requiring full geometric reconstruction, enabling large-scale and efficient statistical studies of reconnection dynamics across multiple events.
Date Issued
2025-10-01
Date Acceptance
2025-09-18
Citation
Journal of Geophysical Research: Space Physics, 2025, 130 (10)
ISSN
2169-9380
Publisher
American Geophysical Union (AGU)
Journal / Book Title
Journal of Geophysical Research: Space Physics
Volume
130
Issue
10
Copyright Statement
© 2025. The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
e2025JA034383
Date Publish Online
2025-09-28