2024/12/16 by Beibei Li, Yutian Chi, Li, Beibei +3
Biochemistry, Genetics and Molecular Biology · Engineering · Physics and Astronomy · #Earth and Planetary Astrophysics (astro-ph.EP) #FOS: Computer and information sciences #FOS: Physical sciences #Geomagnetism and Paleomagnetism Studies #Inertial Sensor and Navigation #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Solar and Space Plasma Dynamics #Space Physics (physics.space-ph)
paper · pdf · doi:10.48550/arxiv.2501.00020
openalex publication_date 2024/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study introduces a novel approach that integrates the magnetic field data correction from the Tianwen-1 Mars mission with a neural network architecture constrained by physical principles derived from Maxwell's equation equations. By employing a Transformer based model capable of efficiently handling sequential data, the method corrects measurement anomalies caused by satellite dynamics, instrument interference, and environmental noise. As a result, it significantly improves both the accuracy and the physical consistency of the calibrated data. Compared to traditional methods that require long data segments and manual intervention often taking weeks or even months to complete this new approach can finish calibration in just minutes to hours, and predictions are made within seconds. This innovation not only accelerates the process of space weather modeling and planetary magnetospheric studies but also provides a robust framework for future planetary exploration and solar wind interaction research.