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Fast Calculation of Magnetic Coordinates Using Artificial Neural Network in Jupiter’s Magnetosphere

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Abstract

In the modeling approach of Jupiter’s radiation belt, the accurate calculation of magnetic coordinates from geographic coordinates is the basis. In the previous studies, the L-shell parameters are always calculated based on the assumption of a dipole field though the accuracy of this method is low. We present a new L-shell calculation method based on the magnetic field lines tracing method and ANN (Artificial Neural Network). In this method, a compromise between calculation accuracy and speed is achieved. This method consists of a classifier and a predictor. The Classifier is a BP (Back Propagation) ANN based on AdaBoost algorithm and the Predictor is a BP ANN optimized by GA (Genetic Algorithm). The Classifier is used to identify whether the coordinates are within Jupiter’s inner magnetosphere. If so, the Predictor is used to calculate the L-shell parameters. The error rates of the Classifier and the Predictor are 3 and 7%, relatively. In an example of the Juno’s orbit, the calculation speed of this ANN-based method is about 3 orders higher than that based on the magnetic field lines tracing method.

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ACKNOWLEDGMENTS

The authors would like to thank the Planetary Data System (PDS) of NASA for providing the data of Juno spacecraft. The authors would also like to thank the NASA Goddard space flight center for providing the JRM09 model. This work was supported by the National Natural Science Foundation of China under Contracts 11675013.

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Authors and Affiliations

  1. Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, China

    Jian-zhao Wang & Zhuo-xi Huo

  2. Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing, China

    Ji-nan Ma, Xiao-yu Jia, Dai Tian & Ao-song Zhou

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  1. Jian-zhao Wang
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  2. Ji-nan Ma
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Correspondence to Jian-zhao Wang.

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Wang, Jz., Ma, Jn., Jia, Xy. et al. Fast Calculation of Magnetic Coordinates Using Artificial Neural Network in Jupiter’s Magnetosphere. Sol Syst Res 55, 218–226 (2021). https://doi.org/10.1134/S0038094621030072

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  • DOI: https://doi.org/10.1134/S0038094621030072

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