[P-CG19] Planetary Magnetosphere, Ionosphere, and Atmosphere

[PCG19-P04] The 2-4 day periodicity in Jupiter’s auroral variations discovered by unsupervised clustering

*Seiya Yano^1,5, Tomoki Kimura¹, Hajime Kita², Fuminori Tsuchiya³, Atsushi Yamazaki⁴, Go Murakami⁴, Kazuo Yoshioka⁵ (1.Tokyo University of Science, 2.Tohoku Institute of Technology, 3.Tohoku University, 4.Institute of Space and Astronautical Science, 5.The University of Tokyo)

Keywords:Jupiter, aurora, magnetosphere, unsupervised learning, clustering

Since planetary aurora is excited by the material and energy exchange between the planetary magnetosphere and upper atmosphere, it is an important clue for understanding the dynamics of the space environment and atmosphere of the planet. Jupiter’s aurora is mainly generated when volcanic ejecta from the moon Io is supplied into the Jovian magnetosphere, circulates in it while ionized, and then falls onto the Jovian upper atmosphere. Thus, spatiotemporal variations in Jupiter’s auroral intensity tell us about Io’s volcanic activity and material and energy transport dynamics in Jupiter’s magnetosphere. Many studies on Jupiter’s auroral time variability have been conducted for long years, and some characteristic periodicities in the auroral variability have been discovered (e.g., Gladstone et al., 2002; Radioti et al., 2008). Most studies, however, were based on the datasets for limited periods, and no study has comprehensively analyzed continuous data with uniform quality for periodicity detection. Moreover, most evaluations have been conducted based on visual inspections by humans. In the present study, we systematically detected several periodicities in the auroral variability by applying artificial intelligence consisting of the Lomb-Scargle periodogram and Spectral Clustering (one of the unsupervised machine learning for clustering) to a large-scale continuous dataset monitored with the planetary spectrographic satellite HISAKI, of which covers 228 days (2014-2015) with 10-minute resolution. We detected significant periodicities of 2.1, 2.9, 3.1, and 3.8 days in the auroral variations observed after February 2015, when Io’s volcano erupted and the volcanic materials were supplied to the magnetosphere. The ~3-day periodicity occurred around the period when Io’s mass loading rate almost peaked. On the other hand, the ~2-day periodicity appeared just before the mass loading rate peak and remained even until when the mass loading rate dropped to the quiet level. The observed periodic variabilities appear to correspond to the onset period of the transient aurora (Kimura et al., 2015; 17; 18; Tao et al. 2020), which has been suggested to be related to the centrifugally-driven magnetic tail reconnection, Vasyliūnas reconnection, in the magnetotail (Vasyliūnas, 1983). The variabilities detected in this study are also consistent with the timescale of magnetotail thickness variability theoretically estimated from the Vasyliūnas reconnection model (Kronberg et al., 2007). Our result suggests for the first time that temporal variations in the mass loading rate may control the periodicity of global changes in magnetospheric configurations such as Vasyliūnas reconnection. More detailed analyses of these periodic components will provide an accurate picture of magnetospheric reconnection and plasmoid ejection.