Magnetic Field Data Calibration with Transformer Model Using Physical Constraints: A Scalable Method for Satellite Missions, Illustrated by Tianwen-1

TL;DR

This paper presents a Transformer-based neural network approach constrained by physical laws to rapidly calibrate magnetic field data from satellite missions, significantly improving accuracy and efficiency over traditional methods.

Contribution

It introduces a novel, physics-informed Transformer model for magnetic data calibration that is faster and more accurate than existing manual or traditional techniques.

Findings

Calibration time reduced from weeks to hours

Improved physical consistency of magnetic data

Fast predictions within seconds

Abstract

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.