Abstract
Abstract. The auroral oval boundary represents an important physical process with implications for the ionosphere and magnetosphere. An automatic auroral oval boundary prediction method based on deep learning in this paper is applied to study the variation of the auroral oval boundary associated with different space physical parameters. We construct an auroral oval boundary dataset to train our proposed model, which consists of 184 416 auroral oval boundary points extracted from 3842 images captured by the Ultraviolet Imager (UVI) of the Polar satellite and its corresponding 18 space physical parameters selected from the OMNI dataset from December 1996 to March 1997. Furthermore, several statistical experiments and correlation analysis experiments are performed based on our dataset to explore the relationship between space physical parameters and the location of the auroral oval boundary. The experiment results show that the prediction model based on the deep learning method can estimate the auroral oval boundary efficiently, and different space physical parameters have different effects on the auroral oval boundary, especially the interplanetary magnetic field (IMF), geomagnetic indexes, and solar wind parameters.
Highlights
An auroral oval is a circular belt of auroral emission around magnetic poles (Loomis, 1890; Akasofu, 1964)
The experiment results show that the prediction model based on the deep learning method can estimate the auroral oval boundary efficiently, and different space physical parameters have different effects on the auroral oval boundary, especially the interplanetary magnetic field (IMF), geomagnetic indexes, and solar wind parameters
We establish a model to measure the relationship between space physical parameters from the OMNI dataset on the NASA website and poleward and equatorward auroral oval boundaries based on a deep learning network
Summary
An auroral oval is a circular belt of auroral emission around magnetic poles (Loomis, 1890; Akasofu, 1964). The auroral oval poleward and equatorward boundaries are related to geophysical parameters, which can indicate for the coupling process among the solar wind, ionosphere, and magnetosphere, for example, the polar cap ionosphere, which is considered an area of the opening magnetic field inside the auroral oval poleward boundary. Since 2010, there have been more and more new methods to construct a connection between the position of the auroral oval boundary and space physical parameters with the development of machine learning. A new automatic auroral oval boundary prediction model is proposed based on a deep learning method. The experiment results show that the model proposed in this paper can predict the auroral oval boundary accurately by using space physical parameters and the location of the auroral oval boundary at the previous moment.
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