Statistical Characteristics of Multi‐Scale Auroral Arc Width Based on Machine Learning

Abstract

AbstractArc width is important for understanding the generation mechanism of auroral arcs. However, the continuity or discreteness of the distribution of small and meso‐large scale auroral arc widths has not been determined in previous studies. This study employs machine learning techniques to investigate the distribution of arc widths across multiple scales using multi‐field‐of‐view (multi‐FOV) auroral observations. Based on the 180°, 47°, and 19° auroral observations at the Antarctic Zhongshan Station from February to October 2012, the statistical results demonstrate that the auroral arc width spectrum is continuously distributed across small, meso, and large scales, suggesting that the mechanisms responsible for their generation are capable of producing arcs at all scales. Furthermore, the arc width distribution at each FOV can be well fitted with a log‐normal function. We also find that the main widths observed at different FOVs depend on the spatial resolution of the instruments. Our work provides new observational evidence for the generation mechanism of auroral arcs.