Plasma-Sheet Bubble Identification Using Muitivariate Time Series Classification

Abstract: Plasma-sheet bubbles play a major role in the process of magnetotail particle injections. They are defined as fast flows with reduced plasma density or pressure accompanied by magnetic field dipolarization. Typically, we can detect these bubbles from in-situ observations, but subjective uncertainty needs human verification. In this study, we combine three different methods including MINImally RandOm Convolutional KErnel Transform (MINIROCKET), 1D and 2D convolution neural network (CNN) to identify bubbles. The imbalanced training dataset consists of bubble and non-bubble events with a ratio of 1:40 from year 2007 to 2020. The results indicate that the accuracy of the all three models is around 99%, and the precision and recall rates of all three models are above 80% in both the validation and test datasets. The three methods are combined with the intersection set as the minimum set of predictions and the union set as the maximum set. The methods greatly reduce the number of false positives. To identify bubbles in the observations of year 2021, our neural network model is found to be comparably good to the traditional criterial and manual inspections. Using joint machine learning forecasting methods, we can easily and automatically identify bubbles without a priori knowledge like a domain expert.

Keywords: plasma-sheet bubble, multivariate time series classification, sample imbalanced, image identification